Background Both medication and non-medication therapies are effective approaches to control blood pressure (BP) in hypertension patients.However,the association of joint changes in antihypertensive medication use and ...Background Both medication and non-medication therapies are effective approaches to control blood pressure (BP) in hypertension patients.However,the association of joint changes in antihypertensive medication use and healthy lifestyle index (HLI)with BP control among hypertension patients is seldom reported,which needs to provide more evidence by prospective intervention studies.We examined the association of antihypertensive medication use and HLI with BP control among employees with hypertension in China based on a workplace-based multicomponent intervention program.Methods Between January 2013 and December 2014,a cluster randomized clinical trial of a workplace-based multicomponent intervention program was conducted in 60 workplaces across 20 urban areas in China.Workplaces were randomly divided into intervention (n=40) and control (n=20) groups.Basic information on employees at each workplace was collected by trained professionals,including sociodemographic characteristics,medical history,family history,lifestyle behaviors,medication status and physical measurements.After baseline,the intervention group received a 2-year intervention to achieve BP control,which included:(1) a workplace wellness program for all employees;(2) a guidelines-oriented hypertension management protocol.HLI including nonsmoking,nondrinking,adequate physical activity,weight within reference range and balanced diet,were coded on a 5-point scale (range:0-5,with higher score indicating a healthier lifestyle).Antihypertensive medication use was defined as taking drug within the last 2 weeks.Changes in HLI,antihypertensive medication use and BP control from baseline to 24 months were measured after the intervention.Results Overall,4655 employees were included (age:46.3±7.6 years,men:3547 (82.3%)).After 24 months of the intervention,there was a significant improvement in lifestyle[smoking (OR=0.65,95%CI:0.43-0.99;P=0.045),drinking (OR=0.52,95%CI:0.40-0.68;P<0.001),regular exercise (OR=3.10,95%CI:2.53-3.78;P<0.001),excessive intake of fatty food (OR=0.17,95%CI:0.06-0.52;P=0.002),restrictive use of salt (OR=0.26,95%CI:0.12-0.56;P=0.001)].Compare to employees with a deteriorating lifestyle after the intervention,those with an improved lifestyle had a higher BP control.In the intervention group,compared with employees not using antihypertensive medication,those who consistent used (OR=2.34;95%CI:1.16-4.72;P=0.017) or changed from not using to using antihypertensive medication (OR=2.24;95%CI:1.08-4.62;P=0.030) had higher BP control.Compared with those having lower HLI,participants with a same (OR=1.38;95%CI:0.99-1.93;P=0.056) or high (OR=1.79;95%CI:1.27~2.53;P<0.001) HLI had higher BP control.Those who used antihypertensive medication and had a high HLI had the highest BP control (OR=1.88;95%CI:1.32-2.67,P<0.001).Subgroup analysis also showed the consistent effect as the above.Conclusion These findings suggest that adherence to antihypertensive medication treatment and healthy lifestyle were associated with a significant improvement in BP control among employees with hypertension.展开更多
Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and ...Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.展开更多
In this paper,a novel adaptive Fault-Tolerant Control(FTC)strategy is proposed for non-minimum phase Hypersonic Vehicles(HSVs)that are affected by actuator faults and parameter uncertainties.The strategy is based on t...In this paper,a novel adaptive Fault-Tolerant Control(FTC)strategy is proposed for non-minimum phase Hypersonic Vehicles(HSVs)that are affected by actuator faults and parameter uncertainties.The strategy is based on the output redefinition method and Adaptive Dynamic Programming(ADP).The intelligent FTC scheme consists of two main parts:a basic fault-tolerant and stable controller and an ADP-based supplementary controller.In the basic FTC part,an output redefinition approach is designed to make zero-dynamics stable with respect to the new output.Then,Ideal Internal Dynamic(IID)is obtained using an optimal bounded inversion approach,and a tracking controller is designed for the new output to realize output tracking of the nonminimum phase HSV system.For the ADP-based compensation control part,an ActionDependent Heuristic Dynamic Programming(ADHDP)adopting an actor-critic learning structure is utilized to further optimize the tracking performance of the HSV control system.Finally,simulation results are provided to verify the effectiveness and efficiency of the proposed FTC algorithm.展开更多
The objective of this paper is to present a robust safety-critical control system based on the active disturbance rejection control approach, designed to guarantee safety even in the presence of model inaccuracies, un...The objective of this paper is to present a robust safety-critical control system based on the active disturbance rejection control approach, designed to guarantee safety even in the presence of model inaccuracies, unknown dynamics, and external disturbances. The proposed method combines control barrier functions and control Lyapunov functions with a nonlinear extended state observer to produce a robust and safe control strategy for dynamic systems subject to uncertainties and disturbances. This control strategy employs an optimization-based control, supported by the disturbance estimation from a nonlinear extended state observer. Using a quadratic programming algorithm, the controller computes an optimal, stable, and safe control action at each sampling instant. The effectiveness of the proposed approach is demonstrated through numerical simulations of a safety-critical interconnected adaptive cruise control system.展开更多
This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent s...This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent systems(MASs).First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique's introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an eventtriggered mechanism is adopted to save communication resources while ensuring the system's stability. The coupled HamiltonJacobi(HJ) equation's solution is approximated using a critic neural network(NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded(UUB). Finally,numerical simulations demonstrate the effectiveness of the developed ETRPOC method.展开更多
Tailoring thermal history during additive manufacturing(AM)offers a feasible approach to customise the microstructure and properties of materials without changing alloy compositions or post-heat treatment,which is gen...Tailoring thermal history during additive manufacturing(AM)offers a feasible approach to customise the microstructure and properties of materials without changing alloy compositions or post-heat treatment,which is generally overlooked as it is hard to achieve in commercial materials.Herein,a customised Fe-Ni-Ti-Al maraging steel with rapid precipitation kinetics offers the opportunity to leverage thermal history during AM for achieving large-range tunable strength-ductility combinations.The Fe-Ni-Ti-Al steel was processed by laser-directed energy deposition(LDED)with different deposition strategies to tailor the thermal history.As the phase transformation and in-situ formation of multi-scale secondary phases of the Fe-Ni-Ti-Al steel are sensitive to the thermal histories,the deposited steel achieved a large range of tuneable mechanical properties.Specifically,the interlayer paused deposited sample exhibits superior tensile strength(∼1.54 GPa)and moderate elongation(∼8.1%),which is attributed to the formation of unique hierarchical structures and the in-situ precipitation of high-densityη-Ni_(3)(Ti,Al)during LDED.In contrast,the substrate heating deposited sample has an excellent elongation of 19.3%together with a high tensile strength of 1.24 GPa.The achievable mechanical property range via tailoring thermal history in the LDED-built Fe-Ni-Ti-Al steel is significantly larger than most commercial materials.The findings highlight the material customisation along with AM’s unique thermal history to achieve versatile mechanical performances of deposited materials,which could inspire more property or function manipulations of materials by AM process control or innovation.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
This paper studies motor joint control of a 4-degree-of-freedom(DoF)robotic manipulator using learning-based Adaptive Dynamic Programming(ADP)approach.The manipulator’s dynamics are modelled as an open-loop 4-link se...This paper studies motor joint control of a 4-degree-of-freedom(DoF)robotic manipulator using learning-based Adaptive Dynamic Programming(ADP)approach.The manipulator’s dynamics are modelled as an open-loop 4-link serial kinematic chain with 4 Degrees of Freedom(DoF).Decentralised optimal controllers are designed for each link using ADP approach based on a set of cost matrices and data collected from exploration trajectories.The proposed control strategy employs an off-line,off-policy iterative approach to derive four optimal control policies,one for each joint,under exploration strategies.The objective of the controller is to control the position of each joint.Simulation and experimental results show that four independent optimal controllers are found,each under similar exploration strategies,and the proposed ADP approach successfully yields optimal linear control policies despite the presence of these complexities.The experimental results conducted on the Quanser Qarm robotic platform demonstrate the effectiveness of the proposed ADP controllers in handling significant dynamic nonlinearities,such as actuation limitations,output saturation,and filter delays.展开更多
This paper presents an optimized shared control algorithm for human–AI interaction, implemented through a digital twin framework where the physical system and human operator act as the real agent while an AI-driven d...This paper presents an optimized shared control algorithm for human–AI interaction, implemented through a digital twin framework where the physical system and human operator act as the real agent while an AI-driven digital system functions as the virtual agent. In this digital twin architecture, the real agent acquires an optimal control strategy through observed actions, while the AI virtual agent mirrors the real agent to establish a digital replica system and corresponding control policy. Both the real and virtual optimal controllers are approximated using reinforcement learning(RL) techniques. Specifically, critic neural networks(NNs) are employed to learn the virtual and real optimal value functions, while actor NNs are trained to derive their respective optimal controllers. A novel shared mechanism is introduced to integrate both virtual and real value functions into a unified learning framework, yielding an optimal shared controller. This controller adaptively adjusts the confidence ratio between virtual and real agents, enhancing the system's efficiency and flexibility in handling complex control tasks. The stability of the closed-loop system is rigorously analyzed using the Lyapunov method. The effectiveness of the proposed AI–human interactive system is validated through two numerical examples: a representative nonlinear system and an unmanned aerial vehicle(UAV) control system.展开更多
A mixed adaptive dynamic programming(ADP)scheme based on zero-sum game theory is developed to address optimal control problems of autonomous underwater vehicle(AUV)systems subject to disturbances and safe constraints....A mixed adaptive dynamic programming(ADP)scheme based on zero-sum game theory is developed to address optimal control problems of autonomous underwater vehicle(AUV)systems subject to disturbances and safe constraints.By combining prior dynamic knowledge and actual sampled data,the proposed approach effectively mitigates the defect caused by the inaccurate dynamic model and significantly improves the training speed of the ADP algorithm.Initially,the dataset is enriched with sufficient reference data collected based on a nominal model without considering modelling bias.Also,the control object interacts with the real environment and continuously gathers adequate sampled data in the dataset.To comprehensively leverage the advantages of model-based and model-free methods during training,an adaptive tuning factor is introduced based on the dataset that possesses model-referenced information and conforms to the distribution of the real-world environment,which balances the influence of model-based control law and data-driven policy gradient on the direction of policy improvement.As a result,the proposed approach accelerates the learning speed compared to data-driven methods,concurrently also enhancing the tracking performance in comparison to model-based control methods.Moreover,the optimal control problem under disturbances is formulated as a zero-sum game,and the actor-critic-disturbance framework is introduced to approximate the optimal control input,cost function,and disturbance policy,respectively.Furthermore,the convergence property of the proposed algorithm based on the value iteration method is analysed.Finally,an example of AUV path following based on the improved line-of-sight guidance is presented to demonstrate the effectiveness of the proposed method.展开更多
Optimal impulse control and impulse games provide the cutting-edge frameworks for modeling systems where control actions occur at discrete time points,and optimizing objectives under discontinuous interventions.This r...Optimal impulse control and impulse games provide the cutting-edge frameworks for modeling systems where control actions occur at discrete time points,and optimizing objectives under discontinuous interventions.This review synthesizes the theoretical advancements,computational approaches,emerging challenges,and possible research directions in the field.Firstly,we briefly review the fundamental theory of continuous-time optimal control,including Pontryagin's maximum principle(PMP)and dynamic programming principle(DPP).Secondly,we present the foundational results in optimal impulse control,including necessary conditions and sufficient conditions.Thirdly,we systematize impulse game methodologies,from Nash equilibrium existence theory to the connection between Nash equilibrium and systems stability.Fourthly,we summarize the numerical algorithms including the intelligent computation approaches.Finally,we examine the new trends and challenges in theory and applications as well as computational considerations.展开更多
To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the mod...To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the model predictive static programming method is developed by incorporating neighboring terms and trust region,enabling rapid generation of precise optimal solutions.Next,an adaptive fish swarm optimization technique is employed to identify a sub-optimal solution,while a momentum gradient descent method with learning rate decay ensures the convergence to the global optimal solution.To validate the feasibility and accuracy of the proposed method,a near-space vehicle example is analyzed and simulated during its glide phase.The simulation results demonstrate that the proposed method aligns with theoretical derivations and outperforms existing methods in terms of convergence speed and accuracy.Therefore,the proposed method offers significant practical value for solving the fast trajectory optimization problem in near-space vehicle applications.展开更多
Mitochondria,the most crucial energy-generating organelles in eukaryotic cells,play a pivotal role in regulating energy metabolism.However,their significance extends beyond this,as they are also indispensable in vital...Mitochondria,the most crucial energy-generating organelles in eukaryotic cells,play a pivotal role in regulating energy metabolism.However,their significance extends beyond this,as they are also indispensable in vital life processes such as cell proliferation,differentiation,immune responses,and redox balance.In response to various physiological signals or external stimuli,a sophisticated mitochondrial quality control(MQC)mechanism has evolved,encompassing key processes like mitochondrial biogenesis,mitochondrial dynamics,and mitophagy,which have garnered increasing attention from researchers to unveil their specific molecular mechanisms.In this review,we present a comprehensive summary of the primary mechanisms and functions of key regulators involved in major components of MQC.Furthermore,the critical physiological functions regulated by MQC and its diverse roles in the progression of various systemic diseases have been described in detail.We also discuss agonists or antagonists targeting MQC,aiming to explore potential therapeutic and research prospects by enhancing MQC to stabilize mitochondrial function.展开更多
This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems.Unlike existing optimal state feedback control,the control input of the optimal parallel control is int...This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems.Unlike existing optimal state feedback control,the control input of the optimal parallel control is introduced into the feedback system.However,due to the introduction of control input into the feedback system,the optimal state feedback control methods can not be applied directly.To address this problem,an augmented system and an augmented performance index function are proposed firstly.Thus,the general nonlinear system is transformed into an affine nonlinear system.The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically.It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function.Moreover,an adaptive dynamic programming(ADP)technique is utilized to implement the optimal parallel tracking control using a critic neural network(NN)to approximate the value function online.The stability analysis of the closed-loop system is performed using the Lyapunov theory,and the tracking error and NN weights errors are uniformly ultimately bounded(UUB).Also,the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals.Finally,the effectiveness of the developed optimal parallel control method is verified in two cases.展开更多
This paper presents a new design approach to achieve decentralized optimal control of high-dimension complex singular systems with dynamic uncertainties. Based on robust adaptive dynamic programming(robust ADP) method...This paper presents a new design approach to achieve decentralized optimal control of high-dimension complex singular systems with dynamic uncertainties. Based on robust adaptive dynamic programming(robust ADP) method, controllers for solving the singular systems optimal control problem are designed. The proposed algorithm can work well when the system model is not exactly known but the input and output data can be measured. The policy iteration of each controller only uses their own states and input information for learning,and do not need to know the whole system dynamics. Simulation results on the New England 10-machine 39-bus test system show the effectiveness of the designed controller.展开更多
In the machining process of large-scale complex curved surface,workers will encounter problems such as empty stroke of tool,collision interference,and overcut or undercut of the workpieces.This paper presents a method...In the machining process of large-scale complex curved surface,workers will encounter problems such as empty stroke of tool,collision interference,and overcut or undercut of the workpieces.This paper presents a method for generating the optimized tool path,compiling and checking the numerical control(NC)program.Taking the bogie frame as an example,the tool paths of all machining surface are optimized by the dynamic programming algorithm,Creo software is utilized to compile the optimized computerized numerical control(CNC)machining program,and VERICUT software is employed to simulate the machining process,optimize the amount of cutting and inspect the machining quality.The method saves the machining time,guarantees the correctness of NC program,and the overall machining efficiency is improved.The method lays a good theoretical and practical foundation for integration of the similar platform.展开更多
基金supported by grant 2011BAI11B01 from the Projects in the Chinese National Science and Technology Pillar Program during the 12th Five-year Plan Periodby grant 2017-I2M-1-004 from the Chinese Academy of Medical Science Innovation Fund for Medical Sciencesby the Major science and technology special plan project of Yunnan Province (202302AA310045)。
文摘Background Both medication and non-medication therapies are effective approaches to control blood pressure (BP) in hypertension patients.However,the association of joint changes in antihypertensive medication use and healthy lifestyle index (HLI)with BP control among hypertension patients is seldom reported,which needs to provide more evidence by prospective intervention studies.We examined the association of antihypertensive medication use and HLI with BP control among employees with hypertension in China based on a workplace-based multicomponent intervention program.Methods Between January 2013 and December 2014,a cluster randomized clinical trial of a workplace-based multicomponent intervention program was conducted in 60 workplaces across 20 urban areas in China.Workplaces were randomly divided into intervention (n=40) and control (n=20) groups.Basic information on employees at each workplace was collected by trained professionals,including sociodemographic characteristics,medical history,family history,lifestyle behaviors,medication status and physical measurements.After baseline,the intervention group received a 2-year intervention to achieve BP control,which included:(1) a workplace wellness program for all employees;(2) a guidelines-oriented hypertension management protocol.HLI including nonsmoking,nondrinking,adequate physical activity,weight within reference range and balanced diet,were coded on a 5-point scale (range:0-5,with higher score indicating a healthier lifestyle).Antihypertensive medication use was defined as taking drug within the last 2 weeks.Changes in HLI,antihypertensive medication use and BP control from baseline to 24 months were measured after the intervention.Results Overall,4655 employees were included (age:46.3±7.6 years,men:3547 (82.3%)).After 24 months of the intervention,there was a significant improvement in lifestyle[smoking (OR=0.65,95%CI:0.43-0.99;P=0.045),drinking (OR=0.52,95%CI:0.40-0.68;P<0.001),regular exercise (OR=3.10,95%CI:2.53-3.78;P<0.001),excessive intake of fatty food (OR=0.17,95%CI:0.06-0.52;P=0.002),restrictive use of salt (OR=0.26,95%CI:0.12-0.56;P=0.001)].Compare to employees with a deteriorating lifestyle after the intervention,those with an improved lifestyle had a higher BP control.In the intervention group,compared with employees not using antihypertensive medication,those who consistent used (OR=2.34;95%CI:1.16-4.72;P=0.017) or changed from not using to using antihypertensive medication (OR=2.24;95%CI:1.08-4.62;P=0.030) had higher BP control.Compared with those having lower HLI,participants with a same (OR=1.38;95%CI:0.99-1.93;P=0.056) or high (OR=1.79;95%CI:1.27~2.53;P<0.001) HLI had higher BP control.Those who used antihypertensive medication and had a high HLI had the highest BP control (OR=1.88;95%CI:1.32-2.67,P<0.001).Subgroup analysis also showed the consistent effect as the above.Conclusion These findings suggest that adherence to antihypertensive medication treatment and healthy lifestyle were associated with a significant improvement in BP control among employees with hypertension.
基金supported in part by the National Natural Science Foundation of China(62222301, 62073085, 62073158, 61890930-5, 62021003)the National Key Research and Development Program of China (2021ZD0112302, 2021ZD0112301, 2018YFC1900800-5)Beijing Natural Science Foundation (JQ19013)。
文摘Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.
基金supported in part by the Science Center Program of National Natural Science Foundation of China(62373189,62188101,62020106003)the Research Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures,China。
文摘In this paper,a novel adaptive Fault-Tolerant Control(FTC)strategy is proposed for non-minimum phase Hypersonic Vehicles(HSVs)that are affected by actuator faults and parameter uncertainties.The strategy is based on the output redefinition method and Adaptive Dynamic Programming(ADP).The intelligent FTC scheme consists of two main parts:a basic fault-tolerant and stable controller and an ADP-based supplementary controller.In the basic FTC part,an output redefinition approach is designed to make zero-dynamics stable with respect to the new output.Then,Ideal Internal Dynamic(IID)is obtained using an optimal bounded inversion approach,and a tracking controller is designed for the new output to realize output tracking of the nonminimum phase HSV system.For the ADP-based compensation control part,an ActionDependent Heuristic Dynamic Programming(ADHDP)adopting an actor-critic learning structure is utilized to further optimize the tracking performance of the HSV control system.Finally,simulation results are provided to verify the effectiveness and efficiency of the proposed FTC algorithm.
基金supported by the Fondo para el Primer Proyecto of the Comitépara el Desarrollo de la Investigación(CODI)at the Universidad de Antioquia(Grant Number PRV2024-78509)。
文摘The objective of this paper is to present a robust safety-critical control system based on the active disturbance rejection control approach, designed to guarantee safety even in the presence of model inaccuracies, unknown dynamics, and external disturbances. The proposed method combines control barrier functions and control Lyapunov functions with a nonlinear extended state observer to produce a robust and safe control strategy for dynamic systems subject to uncertainties and disturbances. This control strategy employs an optimization-based control, supported by the disturbance estimation from a nonlinear extended state observer. Using a quadratic programming algorithm, the controller computes an optimal, stable, and safe control action at each sampling instant. The effectiveness of the proposed approach is demonstrated through numerical simulations of a safety-critical interconnected adaptive cruise control system.
基金supported in part by the National Key Research and Development Program of China(2021YFE0206100)the National Natural Science Foundation of China(62425310,62073321)+2 种基金the National Defense Basic Scientific Research Program(JCKY2019203C029,JCKY2020130C025)the Science and Technology Development FundMacao SAR(FDCT-22-009-MISE,0060/2021/A2,0015/2020/AMJ)
文摘This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent systems(MASs).First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique's introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an eventtriggered mechanism is adopted to save communication resources while ensuring the system's stability. The coupled HamiltonJacobi(HJ) equation's solution is approximated using a critic neural network(NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded(UUB). Finally,numerical simulations demonstrate the effectiveness of the developed ETRPOC method.
基金financially supported by the Career Development Fund(Grant No.C210112051)under the Agency for Science,Technology and Research(A*STAR)of Singapore2022 MTC Young Individual Research Grants(Grant No:M22K3c0097)under Singapore Research,Innovation and Enterprise(RIE)2025 Plan,led by C Tan。
文摘Tailoring thermal history during additive manufacturing(AM)offers a feasible approach to customise the microstructure and properties of materials without changing alloy compositions or post-heat treatment,which is generally overlooked as it is hard to achieve in commercial materials.Herein,a customised Fe-Ni-Ti-Al maraging steel with rapid precipitation kinetics offers the opportunity to leverage thermal history during AM for achieving large-range tunable strength-ductility combinations.The Fe-Ni-Ti-Al steel was processed by laser-directed energy deposition(LDED)with different deposition strategies to tailor the thermal history.As the phase transformation and in-situ formation of multi-scale secondary phases of the Fe-Ni-Ti-Al steel are sensitive to the thermal histories,the deposited steel achieved a large range of tuneable mechanical properties.Specifically,the interlayer paused deposited sample exhibits superior tensile strength(∼1.54 GPa)and moderate elongation(∼8.1%),which is attributed to the formation of unique hierarchical structures and the in-situ precipitation of high-densityη-Ni_(3)(Ti,Al)during LDED.In contrast,the substrate heating deposited sample has an excellent elongation of 19.3%together with a high tensile strength of 1.24 GPa.The achievable mechanical property range via tailoring thermal history in the LDED-built Fe-Ni-Ti-Al steel is significantly larger than most commercial materials.The findings highlight the material customisation along with AM’s unique thermal history to achieve versatile mechanical performances of deposited materials,which could inspire more property or function manipulations of materials by AM process control or innovation.
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.
基金supported by the DEEPCOBOT project under Grant 306640/O70 funded by the Research Council of Norway.
文摘This paper studies motor joint control of a 4-degree-of-freedom(DoF)robotic manipulator using learning-based Adaptive Dynamic Programming(ADP)approach.The manipulator’s dynamics are modelled as an open-loop 4-link serial kinematic chain with 4 Degrees of Freedom(DoF).Decentralised optimal controllers are designed for each link using ADP approach based on a set of cost matrices and data collected from exploration trajectories.The proposed control strategy employs an off-line,off-policy iterative approach to derive four optimal control policies,one for each joint,under exploration strategies.The objective of the controller is to control the position of each joint.Simulation and experimental results show that four independent optimal controllers are found,each under similar exploration strategies,and the proposed ADP approach successfully yields optimal linear control policies despite the presence of these complexities.The experimental results conducted on the Quanser Qarm robotic platform demonstrate the effectiveness of the proposed ADP controllers in handling significant dynamic nonlinearities,such as actuation limitations,output saturation,and filter delays.
基金supported by China Postdoctoral Science Foundation(Project ID:2024M762602)the National Natural Science Foundation of China under Grant No.62306232Natural Science Basic Research Program of Shaanxi Province under Grant No.2023-JC-QN-0662.
文摘This paper presents an optimized shared control algorithm for human–AI interaction, implemented through a digital twin framework where the physical system and human operator act as the real agent while an AI-driven digital system functions as the virtual agent. In this digital twin architecture, the real agent acquires an optimal control strategy through observed actions, while the AI virtual agent mirrors the real agent to establish a digital replica system and corresponding control policy. Both the real and virtual optimal controllers are approximated using reinforcement learning(RL) techniques. Specifically, critic neural networks(NNs) are employed to learn the virtual and real optimal value functions, while actor NNs are trained to derive their respective optimal controllers. A novel shared mechanism is introduced to integrate both virtual and real value functions into a unified learning framework, yielding an optimal shared controller. This controller adaptively adjusts the confidence ratio between virtual and real agents, enhancing the system's efficiency and flexibility in handling complex control tasks. The stability of the closed-loop system is rigorously analyzed using the Lyapunov method. The effectiveness of the proposed AI–human interactive system is validated through two numerical examples: a representative nonlinear system and an unmanned aerial vehicle(UAV) control system.
基金National Key Research and Development Program of China,Grant/Award Number:2021YFC2801700Defense Industrial Technology Development Program,Grant/Award Numbers:JCKY2021110B024,JCKY2022110C072+6 种基金Science and Technology Innovation 2030-“New Generation Artificial Intelligence”Major Project,Grant/Award Number:2022ZD0116305Natural Science Foundation of Hefei,China,Grant/Award Number:202321National Natural Science Foundation of China,Grant/Award Numbers:U2013601,U20A20225Yangtze River Delta S&T Innovation Community Joint Research Project,Grant/Award Number:2022CSJGG0900Anhui Province Natural Science Funds for Distinguished Young Scholar,Grant/Award Number:2308085J02State Key Laboratory of Intelligent Green Vehicle and Mobility,Grant/Award Number:KFY2417State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle,Grant/Award Number:32215010。
文摘A mixed adaptive dynamic programming(ADP)scheme based on zero-sum game theory is developed to address optimal control problems of autonomous underwater vehicle(AUV)systems subject to disturbances and safe constraints.By combining prior dynamic knowledge and actual sampled data,the proposed approach effectively mitigates the defect caused by the inaccurate dynamic model and significantly improves the training speed of the ADP algorithm.Initially,the dataset is enriched with sufficient reference data collected based on a nominal model without considering modelling bias.Also,the control object interacts with the real environment and continuously gathers adequate sampled data in the dataset.To comprehensively leverage the advantages of model-based and model-free methods during training,an adaptive tuning factor is introduced based on the dataset that possesses model-referenced information and conforms to the distribution of the real-world environment,which balances the influence of model-based control law and data-driven policy gradient on the direction of policy improvement.As a result,the proposed approach accelerates the learning speed compared to data-driven methods,concurrently also enhancing the tracking performance in comparison to model-based control methods.Moreover,the optimal control problem under disturbances is formulated as a zero-sum game,and the actor-critic-disturbance framework is introduced to approximate the optimal control input,cost function,and disturbance policy,respectively.Furthermore,the convergence property of the proposed algorithm based on the value iteration method is analysed.Finally,an example of AUV path following based on the improved line-of-sight guidance is presented to demonstrate the effectiveness of the proposed method.
文摘Optimal impulse control and impulse games provide the cutting-edge frameworks for modeling systems where control actions occur at discrete time points,and optimizing objectives under discontinuous interventions.This review synthesizes the theoretical advancements,computational approaches,emerging challenges,and possible research directions in the field.Firstly,we briefly review the fundamental theory of continuous-time optimal control,including Pontryagin's maximum principle(PMP)and dynamic programming principle(DPP).Secondly,we present the foundational results in optimal impulse control,including necessary conditions and sufficient conditions.Thirdly,we systematize impulse game methodologies,from Nash equilibrium existence theory to the connection between Nash equilibrium and systems stability.Fourthly,we summarize the numerical algorithms including the intelligent computation approaches.Finally,we examine the new trends and challenges in theory and applications as well as computational considerations.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(No.52425212)National Key Research and Development Program of China(No.2021YFA0717100)National Natural Science Foundation of China(Nos.12072270,U2013206,and 52442214).
文摘To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the model predictive static programming method is developed by incorporating neighboring terms and trust region,enabling rapid generation of precise optimal solutions.Next,an adaptive fish swarm optimization technique is employed to identify a sub-optimal solution,while a momentum gradient descent method with learning rate decay ensures the convergence to the global optimal solution.To validate the feasibility and accuracy of the proposed method,a near-space vehicle example is analyzed and simulated during its glide phase.The simulation results demonstrate that the proposed method aligns with theoretical derivations and outperforms existing methods in terms of convergence speed and accuracy.Therefore,the proposed method offers significant practical value for solving the fast trajectory optimization problem in near-space vehicle applications.
基金supported by the National Natural Science Foundation of China(8210082163,81800343)the Fundamental Research Fund for the Central Universities(2042021kf0081)+1 种基金the Science Fund for Creative Research Groups of the Natural Science Foundation of Hubei Province(2020CFA027)the Doctor of Excellence Program of the First Hospital of Jilin University(JDYY-DEP-2023043).
文摘Mitochondria,the most crucial energy-generating organelles in eukaryotic cells,play a pivotal role in regulating energy metabolism.However,their significance extends beyond this,as they are also indispensable in vital life processes such as cell proliferation,differentiation,immune responses,and redox balance.In response to various physiological signals or external stimuli,a sophisticated mitochondrial quality control(MQC)mechanism has evolved,encompassing key processes like mitochondrial biogenesis,mitochondrial dynamics,and mitophagy,which have garnered increasing attention from researchers to unveil their specific molecular mechanisms.In this review,we present a comprehensive summary of the primary mechanisms and functions of key regulators involved in major components of MQC.Furthermore,the critical physiological functions regulated by MQC and its diverse roles in the progression of various systemic diseases have been described in detail.We also discuss agonists or antagonists targeting MQC,aiming to explore potential therapeutic and research prospects by enhancing MQC to stabilize mitochondrial function.
基金supported in part by the National Key Reseanch and Development Program of China(2018AAA0101502,2018YFB1702300)in part by the National Natural Science Foundation of China(61722312,61533019,U1811463,61533017)in part by the Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles。
文摘This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems.Unlike existing optimal state feedback control,the control input of the optimal parallel control is introduced into the feedback system.However,due to the introduction of control input into the feedback system,the optimal state feedback control methods can not be applied directly.To address this problem,an augmented system and an augmented performance index function are proposed firstly.Thus,the general nonlinear system is transformed into an affine nonlinear system.The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically.It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function.Moreover,an adaptive dynamic programming(ADP)technique is utilized to implement the optimal parallel tracking control using a critic neural network(NN)to approximate the value function online.The stability analysis of the closed-loop system is performed using the Lyapunov theory,and the tracking error and NN weights errors are uniformly ultimately bounded(UUB).Also,the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals.Finally,the effectiveness of the developed optimal parallel control method is verified in two cases.
基金supported in part by the National Natural Science Foundation of China(61473070,61433004,61627809)SAPI Fundamental Research Funds(2018ZCX22)
文摘This paper presents a new design approach to achieve decentralized optimal control of high-dimension complex singular systems with dynamic uncertainties. Based on robust adaptive dynamic programming(robust ADP) method, controllers for solving the singular systems optimal control problem are designed. The proposed algorithm can work well when the system model is not exactly known but the input and output data can be measured. The policy iteration of each controller only uses their own states and input information for learning,and do not need to know the whole system dynamics. Simulation results on the New England 10-machine 39-bus test system show the effectiveness of the designed controller.
基金supported by the Collaborative Innovation Center of Ma jor Machine Manufacturing in Liaoning
文摘In the machining process of large-scale complex curved surface,workers will encounter problems such as empty stroke of tool,collision interference,and overcut or undercut of the workpieces.This paper presents a method for generating the optimized tool path,compiling and checking the numerical control(NC)program.Taking the bogie frame as an example,the tool paths of all machining surface are optimized by the dynamic programming algorithm,Creo software is utilized to compile the optimized computerized numerical control(CNC)machining program,and VERICUT software is employed to simulate the machining process,optimize the amount of cutting and inspect the machining quality.The method saves the machining time,guarantees the correctness of NC program,and the overall machining efficiency is improved.The method lays a good theoretical and practical foundation for integration of the similar platform.
