Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,co...Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,counter-terrorism,firefighting,surveillance,and cargo transportation.While performing these tasks,quadrotors must operate in noisy environments.Therefore,a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance.Many researchers have focused only on white Gaussian noise in their studies,whereas researchers need to consider the effects of all colored noises during the operation of the quadrotor.This study aims to design a robust controller that is resistant to all colored noises.Firstly,a nonlinear quadrotormodel was created with MATLAB.Then,a backstepping controller resistant to colored noises was designed.Thedesigned backstepping controller was tested under Gaussian white,pink,brown,blue,and purple noises.PID and Lyapunov-based controller designswere also carried out,and their time responses(rise time,overshoot,settling time)were compared with those of the backstepping controller.In the simulations,time was in seconds,altitude was in meters,and roll,pitch,and yaw references were in radians.Rise and settling time values were in seconds,and overshoot value was in percent.When the obtained values are examined,simulations prove that the proposed backstepping controller has the least overshoot and the shortest settling time under all noise types.展开更多
Dear Editor,H_(∞)This letter develops a new framework for the robust stability and performance conditions as well as the relevant controller synthesis with respect to uncertain robot manipulators.There often exist mo...Dear Editor,H_(∞)This letter develops a new framework for the robust stability and performance conditions as well as the relevant controller synthesis with respect to uncertain robot manipulators.There often exist model uncertainties between the nominal model and the real robot manipulator and disturbances. Hence, dealing with their effects plays a crucial role in leading to high tracking performances, as discussed in [1]–[5].展开更多
While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case.Integrating the Internet of Things devices in the se...While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case.Integrating the Internet of Things devices in the sensing area of transportation infrastructure has resulted in ubiquitous cyber-physical systems and increasing interdependen-cies between the physical and cyber networks.As a result,the robustness of transportation networks relies on the uninterrupted serviceability of physical and cyber networks.Current studies on interdependent networks overlook the civil engineering aspect of cyber-physical systems.Firstly,they rely on the assumption of a uniform and strong level of interdependency.That is,once a node within a network fails its counterpart fails immedi-ately.Current studies overlook the impact of earthquake and other natural hazards on the operation of modern transportation infrastructure,that now serve as a cyber-physical system.The last is responsible not only for the physical operation(e.g.,flow of vehicles)but also for the continuous data transmission and subsequently the cy-ber operation of the entire transportation network.Therefore,the robustness of modern transportation networks should be modelled from a new cyber-physical perspective that includes civil engineering aspects.In this paper,we propose a new robustness assessment approach for modern transportation networks and their underlying in-terdependent physical and cyber network,subjected to earthquake events.The novelty relies on the modelling of interdependent networks,in the form of a graph,based on their interdependency levels.We associate the service-ability level of the coupled physical and cyber network with the damage states induced by earthquake events.Robustness is then measured as a degradation of the cyber-physical serviceability level.The application of the approach is demonstrated by studying an illustrative transportation network using seismic data from real-world transportation infrastructure.Furthermore,we propose the integration of a robustness improvement indicator based on physical and cyber attributes to enhance the cyber-physical serviceability level.Results indicate an improvement in robustness level(i.e.,41%)by adopting the proposed robustness improvement indicator.The usefulness of our approach is highlighted by comparing it with other methods that consider strong interdepen-dencies and key node protection strategies.The approach is of interest to stakeholders who are attempting to incorporate cyber-physical systems into civil engineering systems.展开更多
Numerous uncertainties in practical production and operation can seriously affect the drive performance of permanent magnet synchronous machines(PMSMs).Various robust control methods have been developed to mitigate or...Numerous uncertainties in practical production and operation can seriously affect the drive performance of permanent magnet synchronous machines(PMSMs).Various robust control methods have been developed to mitigate or eliminate the effects of these uncertainties.However,the robustness to uncertainties of electrical drive systems has not been clearly defined.No systemic procedures have been proposed to evaluate a control system's robustness(how robust it is).This paper proposes a systemic method for evaluating control systems'robustness to uncertainties.The concept and fundamental theory of robust control are illustrated by considering a simple uncertain feedback control system.The effects of uncertainties on the control performance and stability are analyzed and discussed.The concept of design for six-sigma(a robust design method)is employed to numerically evaluate the robustness levels of control systems.To show the effectiveness of the proposed robustness evaluation method,case studies are conducted for second-order systems,DC motor drive systems,and PMSM drive systems.Besides the conventional predictive control of PMSM drive,three different robust predictive control methods are evaluated in terms of two different parametric uncertainty ranges and three application requirements against parametric uncertainties.展开更多
This paper tackles uncertainties between planning and actual models.It extends the concept of RCI(robust control invariant)tubes,originally a parameterized representation of closed-loop control robustness in tradition...This paper tackles uncertainties between planning and actual models.It extends the concept of RCI(robust control invariant)tubes,originally a parameterized representation of closed-loop control robustness in traditional feedback control,to the domain of motion planning for autonomous vehicles.Thus,closed-loop system uncertainty can be preemptively addressed during vehicle motion planning.This involves selecting collision-free trajectories to minimize the volume of robust invariant tubes.Furthermore,constraints on state and control variables are translated into constraints on the RCI tubes of the closed-loop system,ensuring that motion planning produces a safe and optimal trajectory while maintaining flexibility,rather than solely optimizing for the open-loop nominal model.Additionally,to expedite the solving process,we were inspired by L2gain to parameterize the RCI tubes and developed a parameterized explicit iterative expression for propagating ellipsoidal uncertainty sets within closedloop systems.Furthermore,we applied the pseudospectral orthogonal collocation method to parameterize the optimization problem of transcribing trajectories using high-order Lagrangian polynomials.Finally,under various operating conditions,we incorporate both the kinematic and dynamic models of the vehicle and also conduct simulations and analyses of uncertainties such as heading angle measurement,chassis response,and steering hysteresis.Our proposed robust motion planning framework has been validated to effectively address nearly all bounded uncertainties while anticipating potential tracking errors in control during the planning phase.This ensures fast,closed-loop safety and robustness in vehicle motion planning.展开更多
Superhydrophobic glass has inspiring development prospects in endoscopes,solar panels and other engineering and medical fields.However,the surface topography required to achieve superhydrophobicity will inevitably aff...Superhydrophobic glass has inspiring development prospects in endoscopes,solar panels and other engineering and medical fields.However,the surface topography required to achieve superhydrophobicity will inevitably affect the surface transparency and limit the application of glass materials.To resolve the contradiction between the surface transparency and the robust superhydrophobicity,an efficient and low-cost laser-chemical surface functionalization process was utilized to fabricate superhydrophobic glass surface.The results show that the air can be effectively trapped in surface micro/nanostructure induced by laser texturing,thus reducing the solid-liquid contact area and interfacial tension.The deposition of hydrophobic carbon-containing groups on the surface can be accelerated by chemical treatment,and the surface energy is significantly reduced.The glass surface exhibits marvelous robust superhydrophobicity with a contact angle of 155.8°and a roll-off angle of 7.2°under the combination of hierarchical micro/nanostructure and low surface energy.Moreover,the surface transparency of the prepared superhydrophobic glass was only 5.42%lower than that of the untreated surface.This superhydrophobic glass with high transparency still maintains excellent superhydrophobicity after durability and stability tests.The facile fabrication of superhydrophobic glass with high transparency and robustness provides a strong reference for further expanding the application value of glass materials.展开更多
Over-the-air computation(AirComp)enables federated learning(FL)to rapidly aggregate local models at the central server using waveform superposition property of wireless channel.In this paper,a robust transmission sche...Over-the-air computation(AirComp)enables federated learning(FL)to rapidly aggregate local models at the central server using waveform superposition property of wireless channel.In this paper,a robust transmission scheme for an AirCompbased FL system with imperfect channel state information(CSI)is proposed.To model CSI uncertainty,an expectation-based error model is utilized.The main objective is to maximize the number of selected devices that meet mean-squared error(MSE)requirements for model broadcast and model aggregation.The problem is formulated as a combinatorial optimization problem and is solved in two steps.First,the priority order of devices is determined by a sparsity-inducing procedure.Then,a feasibility detection scheme is used to select the maximum number of devices to guarantee that the MSE requirements are met.An alternating optimization(AO)scheme is used to transform the resulting nonconvex problem into two convex subproblems.Numerical results illustrate the effectiveness and robustness of the proposed scheme.展开更多
Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing,FL offers enhanced capabilities for machin...Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data aggregation.To address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing scenarios.Specifically,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node dropping.Then,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication overhead.To minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive score.Theoretical analysis demonstrates that EPRFL offers robust security and low latency.Extensive experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.展开更多
The electromagnetic levitation system(EMLS)serves as the most important part of any magnetic levitation system.However,its characteristics are defined by its highly nonlinear dynamics and instability.Furthermore,the u...The electromagnetic levitation system(EMLS)serves as the most important part of any magnetic levitation system.However,its characteristics are defined by its highly nonlinear dynamics and instability.Furthermore,the uncertainties in the dynamics of an electromagnetic levitation system make the controller design more difficult.Therefore,it is necessary to design a robust control law that will ensure the system’s stability in the presence of these uncertainties.In this framework,the dynamics of an electromagnetic levitation system are addressed in terms of matched and unmatched uncertainties.The robust control problem is translated into the optimal control problem,where the uncertainties of the electromagnetic levitation system are directly reflected in the cost function.The optimal control method is used to solve the robust control problem.The solution to the optimal control problem for the electromagnetic levitation system is indeed a solution to the robust control problem of the electromagnetic levitation system under matched and unmatched uncertainties.The simulation and experimental results demonstrate the performance of the designed control scheme.The performance indices such as integral absolute error(IAE),integral square error(ISE),integral time absolute error(ITAE),and integral time square error(ITSE)are compared for both uncertainties to showcase the robustness of the designed control scheme.展开更多
Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy...Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.展开更多
Deep neural networks are known to be vulnerable to adversarial attacks.Unfortunately,the underlying mechanisms remain insufficiently understood,leading to empirical defenses that often fail against new attacks.In this...Deep neural networks are known to be vulnerable to adversarial attacks.Unfortunately,the underlying mechanisms remain insufficiently understood,leading to empirical defenses that often fail against new attacks.In this paper,we explain adversarial attacks from the perspective of robust features,and propose a novel Generative Adversarial Network(GAN)-based Robust Feature Disentanglement framework(GRFD)for adversarial defense.The core of GRFD is an adversarial disentanglement structure comprising a generator and a discriminator.For the generator,we introduce a novel Latent Variable Constrained Variational Auto-Encoder(LVCVAE),which enhances the typical beta-VAE with a constrained rectification module to enforce explicit clustering of latent variables.To supervise the disentanglement of robust features,we design a Robust Supervisory Model(RSM)as the discriminator,sharing architectural alignment with the target model.The key innovation of RSM is our proposed Feature Robustness Metric(FRM),which serves as part of the training loss and synthesizes the classification ability of features as well as their resistance to perturbations.Extensive experiments on three benchmark datasets demonstrate the superiority of GRFD:it achieves 93.69%adversarial accuracy on MNIST,77.21%on CIFAR10,and 58.91%on CIFAR100 with minimal degradation in clean accuracy.Codes are available at:(accessed on 23 July 2025).展开更多
Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics.However,metric learning method...Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics.However,metric learning methods often suffer from high sensitivity,leading to unstable recommendation results when facing adversarial samples generated through malicious user behavior.Adversarial training is considered to be an effective method for improving the robustness of tag recommendation systems and addressing adversarial samples.However,it still faces the challenge of overfitting.Although curriculum learning-based adversarial training somewhat mitigates this issue,challenges still exist,such as the lack of a quantitative standard for attack intensity and catastrophic forgetting.To address these challenges,we propose a Self-Paced Adversarial Metric Learning(SPAML)method.First,we employ a metric learning model to capture the deep distance relationships between normal samples.Then,we incorporate a self-paced adversarial training model,which dynamically adjusts the weights of adversarial samples,allowing the model to progressively learn from simpler to more complex adversarial samples.Finally,we jointly optimize the metric learning loss and self-paced adversarial training loss in an adversarial manner,enhancing the robustness and performance of tag recommendation tasks.Extensive experiments on the MovieLens and LastFm datasets demonstrate that SPAML achieves F1@3 and NDCG@3 scores of 22%and 32.7%on the MovieLens dataset,and 19.4%and 29%on the LastFm dataset,respectively,outperforming the most competitive baselines.Specifically,F1@3 improves by 4.7%and 6.8%,and NDCG@3 improves by 5.0%and 6.9%,respectively.展开更多
This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine map.The map demonstrates remarkable chaotic dynamics over a wide range of parameters.We employ nonlinea...This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine map.The map demonstrates remarkable chaotic dynamics over a wide range of parameters.We employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption process.Our findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective encryption.The encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit planes.These planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant ones.The subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance security.An auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted image.Experimental results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical images.As a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted images.Additionally,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,respectively.Furthermore,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.展开更多
Graph Neural Networks(GNNs)have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models,such as Large Language Models(LLMs),t...Graph Neural Networks(GNNs)have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models,such as Large Language Models(LLMs),to enhance structural reasoning,knowledge retrieval,and memory management.The expansion of their application scope imposes higher requirements on the robustness of GNNs.However,as GNNs are applied to more dynamic and heterogeneous environments,they become increasingly vulnerable to real-world perturbations.In particular,graph data frequently encounters joint adversarial perturbations that simultaneously affect both structures and features,which are significantly more challenging than isolated attacks.These disruptions,caused by incomplete data,malicious attacks,or inherent noise,pose substantial threats to the stable and reliable performance of traditional GNN models.To address this issue,this study proposes the Dual-Shield Graph Neural Network(DSGNN),a defense model that simultaneously mitigates structural and feature perturbations.DSGNN utilizes two parallel GNN channels to independently process structural noise and feature noise,and introduces an adaptive fusion mechanism that integrates information from both pathways to generate robust node representations.Theoretical analysis demonstrates that DSGNN achieves a tighter robustness boundary under joint perturbations compared to conventional single-channel methods.Experimental evaluations across Cora,CiteSeer,and Industry datasets show that DSGNN achieves the highest average classification accuracy under various adversarial settings,reaching 81.24%,71.94%,and 81.66%,respectively,outperforming GNNGuard,GCN-Jaccard,GCN-SVD,RGCN,and NoisyGNN.These results underscore the importance of multi-view perturbation decoupling in constructing resilient GNN models for real-world applications.展开更多
Emotion recognition under uncontrolled and noisy environments presents persistent challenges in the design of emotionally responsive systems.The current study introduces an audio-visual recognition framework designed ...Emotion recognition under uncontrolled and noisy environments presents persistent challenges in the design of emotionally responsive systems.The current study introduces an audio-visual recognition framework designed to address performance degradation caused by environmental interference,such as background noise,overlapping speech,and visual obstructions.The proposed framework employs a structured fusion approach,combining early-stage feature-level integration with decision-level coordination guided by temporal attention mechanisms.Audio data are transformed into mel-spectrogram representations,and visual data are represented as raw frame sequences.Spatial and temporal features are extracted through convolutional and transformer-based encoders,allowing the framework to capture complementary and hierarchical information fromboth sources.Across-modal attentionmodule enables selective emphasis on relevant signals while suppressing modality-specific noise.Performance is validated on a modified version of the AFEW dataset,in which controlled noise is introduced to emulate realistic conditions.The framework achieves higher classification accuracy than comparative baselines,confirming increased robustness under conditions of cross-modal disruption.This result demonstrates the suitability of the proposed method for deployment in practical emotion-aware technologies operating outside controlled environments.The study also contributes a systematic approach to fusion design and supports further exploration in the direction of resilientmultimodal emotion analysis frameworks.The source code is publicly available at https://github.com/asmoon002/AVER(accessed on 18 August 2025).展开更多
In the Visual Place Recognition(VPR)task,existing research has leveraged large-scale pre-trained models to improve the performance of place recognition.However,when there are significant environmental differences betw...In the Visual Place Recognition(VPR)task,existing research has leveraged large-scale pre-trained models to improve the performance of place recognition.However,when there are significant environmental differences between query images and reference images,a large number of ineffective local features will interfere with the extraction of key landmark features,leading to the retrieval of visually similar but geographically different images.To address this perceptual aliasing problem caused by environmental condition changes,we propose a novel Visual Place Recognition method with Cross-Environment Robust Feature Enhancement(CerfeVPR).This method uses the GAN network to generate similar images of the original images under different environmental conditions,thereby enhancing the learning of robust features of the original images.This enables the global descriptor to effectively ignore appearance changes caused by environmental factors such as seasons and lighting,showing better place recognition accuracy than other methods.Meanwhile,we introduce a large kernel convolution adapter to fine tune the pre-trained model,obtaining a better image feature representation for subsequent robust feature learning.Then,we process the information of different local regions in the general features through a 3-layer pyramid scene parsing network and fuse it with a tag that retains global information to construct a multi-dimensional image feature representation.Based on this,we use the fused features of similar images to drive the robust feature learning of the original images and complete the feature matching between query images and retrieved images.Experiments on multiple commonly used datasets show that our method exhibits excellent performance.On average,CerfeVPR achieves the highest results,with all Recall@N values exceeding 90%.In particular,on the highly challenging Nordland dataset,the R@1 metric is improved by 4.6%,significantly outperforming other methods,which fully verifies the superiority of CerfeVPR in visual place recognition under complex environments.展开更多
For nonlinear state estimation driven by non-Gaussian noise,the estimator is required to be updated iteratively.Since the iterative update approximates a linear process,it fails to capture the nonlinearity of observat...For nonlinear state estimation driven by non-Gaussian noise,the estimator is required to be updated iteratively.Since the iterative update approximates a linear process,it fails to capture the nonlinearity of observation models,and this further degrades filtering accuracy and consistency.Given the flaws of nonlinear iteration,this work incorporates a recursive strategy into generalized M-estimation rather than the iterative strategy.The proposed algorithm extends nonlinear recursion to nonlinear systems using the statistical linear regression method.The recursion allows for the gradual release of observation information and consequently enables the update to proceed along the nonlinear direction.Considering the correlated state and observation noise induced by recursions,a separately reweighting strategy is adopted to build a robust nonlinear system.Analogous to the nonlinear recursion,a robust nonlinear recursive update strategy is proposed,where the associated covariances and the observation noise statistics are updated recursively to ensure the consistency of observation noise statistics,thereby completing the nonlinear solution of the robust system.Compared with the iterative update strategies under non-Gaussian observation noise,the recursive update strategy can facilitate the estimator to achieve higher filtering accuracy,stronger robustness,and better consistency.Therefore,the proposed strategy is more suitable for the robust nonlinear filtering framework.展开更多
The paper develops a robust control approach for nonaffine nonlinear continuous systems with input constraints and unknown uncertainties. Firstly, this paper constructs an affine augmented system(AAS) within a pre-com...The paper develops a robust control approach for nonaffine nonlinear continuous systems with input constraints and unknown uncertainties. Firstly, this paper constructs an affine augmented system(AAS) within a pre-compensation technique for converting the original nonaffine dynamics into affine dynamics. Secondly, the paper derives a stability criterion linking the original nonaffine system and the auxiliary system, demonstrating that the obtained optimal policies from the auxiliary system can achieve the robust controller of the nonaffine system. Thirdly, an online adaptive dynamic programming(ADP) algorithm is designed for approximating the optimal solution of the Hamilton–Jacobi–Bellman(HJB) equation.Moreover, the gradient descent approach and projection approach are employed for updating the actor-critic neural network(NN) weights, with the algorithm's convergence being proven. Then, the uniformly ultimately bounded stability of state is guaranteed. Finally, in simulation, some examples are offered for validating the effectiveness of this presented approach.展开更多
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
文摘Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,counter-terrorism,firefighting,surveillance,and cargo transportation.While performing these tasks,quadrotors must operate in noisy environments.Therefore,a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance.Many researchers have focused only on white Gaussian noise in their studies,whereas researchers need to consider the effects of all colored noises during the operation of the quadrotor.This study aims to design a robust controller that is resistant to all colored noises.Firstly,a nonlinear quadrotormodel was created with MATLAB.Then,a backstepping controller resistant to colored noises was designed.Thedesigned backstepping controller was tested under Gaussian white,pink,brown,blue,and purple noises.PID and Lyapunov-based controller designswere also carried out,and their time responses(rise time,overshoot,settling time)were compared with those of the backstepping controller.In the simulations,time was in seconds,altitude was in meters,and roll,pitch,and yaw references were in radians.Rise and settling time values were in seconds,and overshoot value was in percent.When the obtained values are examined,simulations prove that the proposed backstepping controller has the least overshoot and the shortest settling time under all noise types.
基金support by “R&D Program for Forest Science Technology(RS-2024-0040 3460)” provided by Korea Forest Service(Korea Forestry Promotion Institute)
文摘Dear Editor,H_(∞)This letter develops a new framework for the robust stability and performance conditions as well as the relevant controller synthesis with respect to uncertain robot manipulators.There often exist model uncertainties between the nominal model and the real robot manipulator and disturbances. Hence, dealing with their effects plays a crucial role in leading to high tracking performances, as discussed in [1]–[5].
文摘While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case.Integrating the Internet of Things devices in the sensing area of transportation infrastructure has resulted in ubiquitous cyber-physical systems and increasing interdependen-cies between the physical and cyber networks.As a result,the robustness of transportation networks relies on the uninterrupted serviceability of physical and cyber networks.Current studies on interdependent networks overlook the civil engineering aspect of cyber-physical systems.Firstly,they rely on the assumption of a uniform and strong level of interdependency.That is,once a node within a network fails its counterpart fails immedi-ately.Current studies overlook the impact of earthquake and other natural hazards on the operation of modern transportation infrastructure,that now serve as a cyber-physical system.The last is responsible not only for the physical operation(e.g.,flow of vehicles)but also for the continuous data transmission and subsequently the cy-ber operation of the entire transportation network.Therefore,the robustness of modern transportation networks should be modelled from a new cyber-physical perspective that includes civil engineering aspects.In this paper,we propose a new robustness assessment approach for modern transportation networks and their underlying in-terdependent physical and cyber network,subjected to earthquake events.The novelty relies on the modelling of interdependent networks,in the form of a graph,based on their interdependency levels.We associate the service-ability level of the coupled physical and cyber network with the damage states induced by earthquake events.Robustness is then measured as a degradation of the cyber-physical serviceability level.The application of the approach is demonstrated by studying an illustrative transportation network using seismic data from real-world transportation infrastructure.Furthermore,we propose the integration of a robustness improvement indicator based on physical and cyber attributes to enhance the cyber-physical serviceability level.Results indicate an improvement in robustness level(i.e.,41%)by adopting the proposed robustness improvement indicator.The usefulness of our approach is highlighted by comparing it with other methods that consider strong interdepen-dencies and key node protection strategies.The approach is of interest to stakeholders who are attempting to incorporate cyber-physical systems into civil engineering systems.
文摘Numerous uncertainties in practical production and operation can seriously affect the drive performance of permanent magnet synchronous machines(PMSMs).Various robust control methods have been developed to mitigate or eliminate the effects of these uncertainties.However,the robustness to uncertainties of electrical drive systems has not been clearly defined.No systemic procedures have been proposed to evaluate a control system's robustness(how robust it is).This paper proposes a systemic method for evaluating control systems'robustness to uncertainties.The concept and fundamental theory of robust control are illustrated by considering a simple uncertain feedback control system.The effects of uncertainties on the control performance and stability are analyzed and discussed.The concept of design for six-sigma(a robust design method)is employed to numerically evaluate the robustness levels of control systems.To show the effectiveness of the proposed robustness evaluation method,case studies are conducted for second-order systems,DC motor drive systems,and PMSM drive systems.Besides the conventional predictive control of PMSM drive,three different robust predictive control methods are evaluated in terms of two different parametric uncertainty ranges and three application requirements against parametric uncertainties.
基金Supported by National Natural Science Foundation of China(Grant Nos.52025121,52394263)National Key R&D Plan of China(Grant No.2023YFD2000301)+2 种基金Foundation of State Key Laboratory of Automobile Safety and Energy Saving of China(Grant No.KFZ2201)the Jiangsu Provincial Scientific Research Center of Applied Mathematics under(Grant No.BK20233002)Special Fund of Jiangsu Province for the Transformation of Scientific and Technological Achievements under(Grant No.BA2021023)。
文摘This paper tackles uncertainties between planning and actual models.It extends the concept of RCI(robust control invariant)tubes,originally a parameterized representation of closed-loop control robustness in traditional feedback control,to the domain of motion planning for autonomous vehicles.Thus,closed-loop system uncertainty can be preemptively addressed during vehicle motion planning.This involves selecting collision-free trajectories to minimize the volume of robust invariant tubes.Furthermore,constraints on state and control variables are translated into constraints on the RCI tubes of the closed-loop system,ensuring that motion planning produces a safe and optimal trajectory while maintaining flexibility,rather than solely optimizing for the open-loop nominal model.Additionally,to expedite the solving process,we were inspired by L2gain to parameterize the RCI tubes and developed a parameterized explicit iterative expression for propagating ellipsoidal uncertainty sets within closedloop systems.Furthermore,we applied the pseudospectral orthogonal collocation method to parameterize the optimization problem of transcribing trajectories using high-order Lagrangian polynomials.Finally,under various operating conditions,we incorporate both the kinematic and dynamic models of the vehicle and also conduct simulations and analyses of uncertainties such as heading angle measurement,chassis response,and steering hysteresis.Our proposed robust motion planning framework has been validated to effectively address nearly all bounded uncertainties while anticipating potential tracking errors in control during the planning phase.This ensures fast,closed-loop safety and robustness in vehicle motion planning.
基金Projects(52105175,52305149)supported by the National Natural Science Foundation of ChinaProject(2242024RCB0035)supported by the Zhishan Young Scholar Program of Southeast University,China+5 种基金Project(BK20210235)supported by the Natural Science Foundation of Jiangsu Province,ChinaProject(2023MK042)supported by the State Administration for Market Regulation,ChinaProject(KJ2023003)supported by the Jiangsu Administration for Market Regulation,ChinaProjects(KJ(Y)202429,KJ(YJ)2023001)supported by the Jiangsu Province Special Equipment Safety Supervision Inspection Institute,ChinaProject(JSSCBS20210121)supported by the Jiangsu Provincial Innovative and Entrepreneurial Doctor Program,ChinaProject(1102002310)supported by the Technology Innovation Project for Returnees in Nanjing,China。
文摘Superhydrophobic glass has inspiring development prospects in endoscopes,solar panels and other engineering and medical fields.However,the surface topography required to achieve superhydrophobicity will inevitably affect the surface transparency and limit the application of glass materials.To resolve the contradiction between the surface transparency and the robust superhydrophobicity,an efficient and low-cost laser-chemical surface functionalization process was utilized to fabricate superhydrophobic glass surface.The results show that the air can be effectively trapped in surface micro/nanostructure induced by laser texturing,thus reducing the solid-liquid contact area and interfacial tension.The deposition of hydrophobic carbon-containing groups on the surface can be accelerated by chemical treatment,and the surface energy is significantly reduced.The glass surface exhibits marvelous robust superhydrophobicity with a contact angle of 155.8°and a roll-off angle of 7.2°under the combination of hierarchical micro/nanostructure and low surface energy.Moreover,the surface transparency of the prepared superhydrophobic glass was only 5.42%lower than that of the untreated surface.This superhydrophobic glass with high transparency still maintains excellent superhydrophobicity after durability and stability tests.The facile fabrication of superhydrophobic glass with high transparency and robustness provides a strong reference for further expanding the application value of glass materials.
文摘Over-the-air computation(AirComp)enables federated learning(FL)to rapidly aggregate local models at the central server using waveform superposition property of wireless channel.In this paper,a robust transmission scheme for an AirCompbased FL system with imperfect channel state information(CSI)is proposed.To model CSI uncertainty,an expectation-based error model is utilized.The main objective is to maximize the number of selected devices that meet mean-squared error(MSE)requirements for model broadcast and model aggregation.The problem is formulated as a combinatorial optimization problem and is solved in two steps.First,the priority order of devices is determined by a sparsity-inducing procedure.Then,a feasibility detection scheme is used to select the maximum number of devices to guarantee that the MSE requirements are met.An alternating optimization(AO)scheme is used to transform the resulting nonconvex problem into two convex subproblems.Numerical results illustrate the effectiveness and robustness of the proposed scheme.
基金supported in part by the National Natural Science Foundation of China(62462053)the Science and Technology Foundation of Qinghai Province(2023-ZJ-731)+1 种基金the Open Project of the Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Area(2023-KF-12)the Open Research Fund of Guangdong Key Laboratory of Blockchain Security,Guangzhou University。
文摘Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data aggregation.To address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing scenarios.Specifically,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node dropping.Then,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication overhead.To minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive score.Theoretical analysis demonstrates that EPRFL offers robust security and low latency.Extensive experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.
文摘The electromagnetic levitation system(EMLS)serves as the most important part of any magnetic levitation system.However,its characteristics are defined by its highly nonlinear dynamics and instability.Furthermore,the uncertainties in the dynamics of an electromagnetic levitation system make the controller design more difficult.Therefore,it is necessary to design a robust control law that will ensure the system’s stability in the presence of these uncertainties.In this framework,the dynamics of an electromagnetic levitation system are addressed in terms of matched and unmatched uncertainties.The robust control problem is translated into the optimal control problem,where the uncertainties of the electromagnetic levitation system are directly reflected in the cost function.The optimal control method is used to solve the robust control problem.The solution to the optimal control problem for the electromagnetic levitation system is indeed a solution to the robust control problem of the electromagnetic levitation system under matched and unmatched uncertainties.The simulation and experimental results demonstrate the performance of the designed control scheme.The performance indices such as integral absolute error(IAE),integral square error(ISE),integral time absolute error(ITAE),and integral time square error(ITSE)are compared for both uncertainties to showcase the robustness of the designed control scheme.
基金supported by the National Natural Science Foundation of China(Grant No.82151302)the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-B-113)+1 种基金the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-A-019)the CAMS Innovation Fund for Medical Sciences(Grant No.2021-12M-1-014).
文摘Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.
基金funded by the National Natural Science Foundation of China Project"Research on Intelligent Detection Techniques of Encrypted Malicious Traffic for Large-Scale Networks"(Grant No.62176264).
文摘Deep neural networks are known to be vulnerable to adversarial attacks.Unfortunately,the underlying mechanisms remain insufficiently understood,leading to empirical defenses that often fail against new attacks.In this paper,we explain adversarial attacks from the perspective of robust features,and propose a novel Generative Adversarial Network(GAN)-based Robust Feature Disentanglement framework(GRFD)for adversarial defense.The core of GRFD is an adversarial disentanglement structure comprising a generator and a discriminator.For the generator,we introduce a novel Latent Variable Constrained Variational Auto-Encoder(LVCVAE),which enhances the typical beta-VAE with a constrained rectification module to enforce explicit clustering of latent variables.To supervise the disentanglement of robust features,we design a Robust Supervisory Model(RSM)as the discriminator,sharing architectural alignment with the target model.The key innovation of RSM is our proposed Feature Robustness Metric(FRM),which serves as part of the training loss and synthesizes the classification ability of features as well as their resistance to perturbations.Extensive experiments on three benchmark datasets demonstrate the superiority of GRFD:it achieves 93.69%adversarial accuracy on MNIST,77.21%on CIFAR10,and 58.91%on CIFAR100 with minimal degradation in clean accuracy.Codes are available at:(accessed on 23 July 2025).
基金supported by the Key Research and Development Program of Zhejiang Province(No.2024C01071)the Natural Science Foundation of Zhejiang Province(No.LQ15F030006).
文摘Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics.However,metric learning methods often suffer from high sensitivity,leading to unstable recommendation results when facing adversarial samples generated through malicious user behavior.Adversarial training is considered to be an effective method for improving the robustness of tag recommendation systems and addressing adversarial samples.However,it still faces the challenge of overfitting.Although curriculum learning-based adversarial training somewhat mitigates this issue,challenges still exist,such as the lack of a quantitative standard for attack intensity and catastrophic forgetting.To address these challenges,we propose a Self-Paced Adversarial Metric Learning(SPAML)method.First,we employ a metric learning model to capture the deep distance relationships between normal samples.Then,we incorporate a self-paced adversarial training model,which dynamically adjusts the weights of adversarial samples,allowing the model to progressively learn from simpler to more complex adversarial samples.Finally,we jointly optimize the metric learning loss and self-paced adversarial training loss in an adversarial manner,enhancing the robustness and performance of tag recommendation tasks.Extensive experiments on the MovieLens and LastFm datasets demonstrate that SPAML achieves F1@3 and NDCG@3 scores of 22%and 32.7%on the MovieLens dataset,and 19.4%and 29%on the LastFm dataset,respectively,outperforming the most competitive baselines.Specifically,F1@3 improves by 4.7%and 6.8%,and NDCG@3 improves by 5.0%and 6.9%,respectively.
文摘This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine map.The map demonstrates remarkable chaotic dynamics over a wide range of parameters.We employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption process.Our findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective encryption.The encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit planes.These planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant ones.The subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance security.An auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted image.Experimental results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical images.As a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted images.Additionally,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,respectively.Furthermore,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.
基金funded by the Key Research and Development Program of Zhejiang Province No.2023C01141the Science and Technology Innovation Community Project of the Yangtze River Delta No.23002410100suported by the Open Research Fund of the State Key Laboratory of Blockchain and Data Security,Zhejiang University.
文摘Graph Neural Networks(GNNs)have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models,such as Large Language Models(LLMs),to enhance structural reasoning,knowledge retrieval,and memory management.The expansion of their application scope imposes higher requirements on the robustness of GNNs.However,as GNNs are applied to more dynamic and heterogeneous environments,they become increasingly vulnerable to real-world perturbations.In particular,graph data frequently encounters joint adversarial perturbations that simultaneously affect both structures and features,which are significantly more challenging than isolated attacks.These disruptions,caused by incomplete data,malicious attacks,or inherent noise,pose substantial threats to the stable and reliable performance of traditional GNN models.To address this issue,this study proposes the Dual-Shield Graph Neural Network(DSGNN),a defense model that simultaneously mitigates structural and feature perturbations.DSGNN utilizes two parallel GNN channels to independently process structural noise and feature noise,and introduces an adaptive fusion mechanism that integrates information from both pathways to generate robust node representations.Theoretical analysis demonstrates that DSGNN achieves a tighter robustness boundary under joint perturbations compared to conventional single-channel methods.Experimental evaluations across Cora,CiteSeer,and Industry datasets show that DSGNN achieves the highest average classification accuracy under various adversarial settings,reaching 81.24%,71.94%,and 81.66%,respectively,outperforming GNNGuard,GCN-Jaccard,GCN-SVD,RGCN,and NoisyGNN.These results underscore the importance of multi-view perturbation decoupling in constructing resilient GNN models for real-world applications.
基金funded by the Institute of Information&CommunicationsTechnology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT),grant number 2021-0-01341.
文摘Emotion recognition under uncontrolled and noisy environments presents persistent challenges in the design of emotionally responsive systems.The current study introduces an audio-visual recognition framework designed to address performance degradation caused by environmental interference,such as background noise,overlapping speech,and visual obstructions.The proposed framework employs a structured fusion approach,combining early-stage feature-level integration with decision-level coordination guided by temporal attention mechanisms.Audio data are transformed into mel-spectrogram representations,and visual data are represented as raw frame sequences.Spatial and temporal features are extracted through convolutional and transformer-based encoders,allowing the framework to capture complementary and hierarchical information fromboth sources.Across-modal attentionmodule enables selective emphasis on relevant signals while suppressing modality-specific noise.Performance is validated on a modified version of the AFEW dataset,in which controlled noise is introduced to emulate realistic conditions.The framework achieves higher classification accuracy than comparative baselines,confirming increased robustness under conditions of cross-modal disruption.This result demonstrates the suitability of the proposed method for deployment in practical emotion-aware technologies operating outside controlled environments.The study also contributes a systematic approach to fusion design and supports further exploration in the direction of resilientmultimodal emotion analysis frameworks.The source code is publicly available at https://github.com/asmoon002/AVER(accessed on 18 August 2025).
基金supported by Postgraduate Scientific Research Innovation Project of Hunan Province CX20230915National Natural Science Foundation of China under Grant 62472440.
文摘In the Visual Place Recognition(VPR)task,existing research has leveraged large-scale pre-trained models to improve the performance of place recognition.However,when there are significant environmental differences between query images and reference images,a large number of ineffective local features will interfere with the extraction of key landmark features,leading to the retrieval of visually similar but geographically different images.To address this perceptual aliasing problem caused by environmental condition changes,we propose a novel Visual Place Recognition method with Cross-Environment Robust Feature Enhancement(CerfeVPR).This method uses the GAN network to generate similar images of the original images under different environmental conditions,thereby enhancing the learning of robust features of the original images.This enables the global descriptor to effectively ignore appearance changes caused by environmental factors such as seasons and lighting,showing better place recognition accuracy than other methods.Meanwhile,we introduce a large kernel convolution adapter to fine tune the pre-trained model,obtaining a better image feature representation for subsequent robust feature learning.Then,we process the information of different local regions in the general features through a 3-layer pyramid scene parsing network and fuse it with a tag that retains global information to construct a multi-dimensional image feature representation.Based on this,we use the fused features of similar images to drive the robust feature learning of the original images and complete the feature matching between query images and retrieved images.Experiments on multiple commonly used datasets show that our method exhibits excellent performance.On average,CerfeVPR achieves the highest results,with all Recall@N values exceeding 90%.In particular,on the highly challenging Nordland dataset,the R@1 metric is improved by 4.6%,significantly outperforming other methods,which fully verifies the superiority of CerfeVPR in visual place recognition under complex environments.
基金co-supported by the National Natural Science Foundation of China(No.62303246,No.62103204)the China Postdoctoral Science Foundation(No.2023M731788)。
文摘For nonlinear state estimation driven by non-Gaussian noise,the estimator is required to be updated iteratively.Since the iterative update approximates a linear process,it fails to capture the nonlinearity of observation models,and this further degrades filtering accuracy and consistency.Given the flaws of nonlinear iteration,this work incorporates a recursive strategy into generalized M-estimation rather than the iterative strategy.The proposed algorithm extends nonlinear recursion to nonlinear systems using the statistical linear regression method.The recursion allows for the gradual release of observation information and consequently enables the update to proceed along the nonlinear direction.Considering the correlated state and observation noise induced by recursions,a separately reweighting strategy is adopted to build a robust nonlinear system.Analogous to the nonlinear recursion,a robust nonlinear recursive update strategy is proposed,where the associated covariances and the observation noise statistics are updated recursively to ensure the consistency of observation noise statistics,thereby completing the nonlinear solution of the robust system.Compared with the iterative update strategies under non-Gaussian observation noise,the recursive update strategy can facilitate the estimator to achieve higher filtering accuracy,stronger robustness,and better consistency.Therefore,the proposed strategy is more suitable for the robust nonlinear filtering framework.
基金Project supported by the National Natural Science Foundation of China (Grant No. 62103408)Beijing Nova Program (Grant No. 20240484516)the Fundamental Research Funds for the Central Universities (Grant No. KG16314701)。
文摘The paper develops a robust control approach for nonaffine nonlinear continuous systems with input constraints and unknown uncertainties. Firstly, this paper constructs an affine augmented system(AAS) within a pre-compensation technique for converting the original nonaffine dynamics into affine dynamics. Secondly, the paper derives a stability criterion linking the original nonaffine system and the auxiliary system, demonstrating that the obtained optimal policies from the auxiliary system can achieve the robust controller of the nonaffine system. Thirdly, an online adaptive dynamic programming(ADP) algorithm is designed for approximating the optimal solution of the Hamilton–Jacobi–Bellman(HJB) equation.Moreover, the gradient descent approach and projection approach are employed for updating the actor-critic neural network(NN) weights, with the algorithm's convergence being proven. Then, the uniformly ultimately bounded stability of state is guaranteed. Finally, in simulation, some examples are offered for validating the effectiveness of this presented approach.
