This paper concerns the observer-based adaptive control problem of uncertain time-delay switched systems with stuck actuator faults. Under the case where the original controller cannot stabilize the faulty system, mul...This paper concerns the observer-based adaptive control problem of uncertain time-delay switched systems with stuck actuator faults. Under the case where the original controller cannot stabilize the faulty system, multiple adaptive controllers are designed and a suitable switching logic is incorporated to ensure the closed-loop system stability and state tracking. New delay-independent sufficient conditions for asymptotic stability are obtained in terms of linear matrix inequalities based on piecewise Lyapunov stability theory. On the other hand, adaptive laws for on-line updating of some of the controller parameters are also designed to compensate the effect of stuck failures. Finally, simulation results for reference [1] model show that the design is feasible and efficient.展开更多
In the field of flexible polishing,the accuracy of contact force control directly affects processing quality and material removal uniformity.However,the complex dynamic contact model and inherent strong hysteresis of ...In the field of flexible polishing,the accuracy of contact force control directly affects processing quality and material removal uniformity.However,the complex dynamic contact model and inherent strong hysteresis of pneumatic systems can significantly impact the force control accuracy of pneumatic polishing system end-effectors.To enhance responsiveness and control precision during the flexible polishing process,this study proposes an observer-based fuzzy adaptive control(OBFAC)scheme.To ensure control accuracy under an uncertain dynamic contact model,a fuzzy state observer is designed to estimate unmeasured states,while fuzzy logic approximates the uncertain nonlinear functions in the model to improve control performance.Additionally,the integral barrier Lyapunov function is employed to ensure that all states remain within predefined constraints.The stability of the proposed control scheme is analyzed using the Lyapunov function,and a pneumatic polishing experimental platform is constructed to conduct polishing contact force control experiments under multiple scenarios.Experimental results demonstrate that the proposed OBFAC scheme achieves superior tracking control performance compared to existing control schemes.展开更多
To solve the attitude trajectory tracking problem for hypersonic vehicles in the presence of system constraints and unknown disturbances,this paper designed a nonlinear robust model predictive control(RMPC)scheme,whic...To solve the attitude trajectory tracking problem for hypersonic vehicles in the presence of system constraints and unknown disturbances,this paper designed a nonlinear robust model predictive control(RMPC)scheme,which can produce near-optimal tracking commands.Unlike the existing designs,the proposed scheme is less conservative and successfully prioritizes the solution optimality.The established RMPC follows a dualloop structure.Specifically,in the outer feedback loop,the reference attitude angle profiles are optimally tracked,while in the inner feedback loop,the control moment commands are produced by optimally tracking the desired angular rate trajectories.Besides,an adaptive disturbance observer(ADO)is designed and embedded in the inner and outer RMPC controllers to alleviate the negative effects caused by unknown external disturbances.The recursive feasibility of the optimization process,together with the input-to-state stability of the proposed RMPC,is theoretically guaranteed by introducing a tightened control constraint and terminal region.The derived property reveals that our proposal can steer the tracking error within a small region of convergence.Finally,the effectiveness of the proposed scheme is demonstrated by performing simulation studies.展开更多
Dear Editor,This letter deals with the autonomous underwater vehicle(AUV)three dimensional(3D)trajectory tracking control chronically suffering from poor accuracy and efficiency in complex hydrodynamics.A state-of-the...Dear Editor,This letter deals with the autonomous underwater vehicle(AUV)three dimensional(3D)trajectory tracking control chronically suffering from poor accuracy and efficiency in complex hydrodynamics.A state-of-the-art predictive adaptive controller(PAC)is proposed with a distinct dual closed-loop structure.展开更多
This paper is concerned with adaptive consensus tracking control of nonlinear multi-agent systems with actuator faults and unknown nonidentical control directions under double semi-Markovian switching topologies.Consi...This paper is concerned with adaptive consensus tracking control of nonlinear multi-agent systems with actuator faults and unknown nonidentical control directions under double semi-Markovian switching topologies.Considering the complex working environment and the stability differences in communication links between leaders and followers,a double semi-Markov process is first introduced to describe the random switching of communication topologies in the leader-follower structure.In order to address challenges from the unknown nonidentical control directions and partial loss of effectiveness actuator faults,a completely independent parameter is introduced into the Nussbaum function to overcome the inherent obstacle of mutual cancellation and avoid the rapid growth rate.Considering only the state information of agents is transmitted among the agents,an adaptive distributed fault-tolerant consensus tracking control is proposed based on the double semi-Markovian switching topologies using the designed Nussbaum function.Furthermore,the stability of the closed-loop nonlinear multi-agent systems is analyzed using contradiction argument and Lyapunov theorem,from which the asymptotic consensus tracking in mean square sense can be obtained.A numerical simulation example is provided to verify the effectiveness of the proposed algorithm.展开更多
In this paper,a practical method named linear active disturbance rejection control(LADRC)with adaptive tuning is proposed for attitude control of small-scale unmanned helicopter.The proposed method accounts for both e...In this paper,a practical method named linear active disturbance rejection control(LADRC)with adaptive tuning is proposed for attitude control of small-scale unmanned helicopter.The proposed method accounts for both external disturbances and internal dynamic uncertainties,as well as parameter deviations arising from parameter uncertainty,while maintaining a relatively small number of adjustable parameters.Furthermore,it addresses the limitation that conventional active disturbance rejection control methods cannot be rigorously analyzed for stability.The total disturbance of unmanned helicopter is estimated and compensated by designed LADRC.The introduction of adaptive control realizes online parameter tuning,which eliminates parameter deviation and further improves control precision.Moreover,it also provides a novel idea to prove the stability of controller,so that it can be analyzed by Lyapunov function.Finally,the anti-disturbance performance and effectiveness of proposed method are verified by numerical simulation.展开更多
This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temp...This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.展开更多
This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems(VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data.To reduce the ad...This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems(VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data.To reduce the adverse effects of quantization errors on system performance,a coupling sliding mode surface is established for each following vehicle.The radial basis function(RBF) neural networks are employed to approximate the unknown external disturbances.Then,a novel platoon control law is proposed for cooperative tracking in which each following vehicle only uses the uniformly quantized data of the neighboring vehicles.And the designed controllers in this paper are fully distributed due to the fact that the selection of each vehicle's controller parameters is independent of the entire communication topology.The string stability of VCPSs in the entire control process is ensured rather than only ensuring the string stability after the sliding mode surface converges to zero.Compared with the existing controller design methods and quantization mechanisms,the neural adaptive sliding-mode platoon controller proposed in this paper is superior in performances including tracking errors,driving comfort and fuel economy.Numerical simulations illustrate the effectiveness and superiority of the designed control strategy.展开更多
Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft conti...Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft continuum morphologies which are difficult to explicitly model.This paper proposes a model-free adaptive optimal control algorithm(ADAPT)for CRs.In our strategy,we consider CRs as a class of nonlinear continuous-time dynamical systems in the state space,wherein the position of the end-effector is considered as the state and the input torque is mapped as the control input.Then,the optimized Hamilton-Jacobi-Bellman(HJB) equation is derived by optimal control principles,and subsequently solved by the proposed ADAPT algorithm without requiring knowledge of the original system dynamics.Under some mild assumptions,the global stability and convergence of the closed-loop control approach are guaranteed.Several simulation experiments are conducted on a magnetic CR(MCR) to demonstrate the practicality and effectiveness of the ADAPT algorithm.展开更多
In this paper,a security defense issue is investigated for networked control systems susceptible to stochastic denial of service(DoS) attacks by using the sliding mode control method.To utilize network communication r...In this paper,a security defense issue is investigated for networked control systems susceptible to stochastic denial of service(DoS) attacks by using the sliding mode control method.To utilize network communication resources more effectively,a novel adaptive event-triggered(AET) mechanism is introduced,whose triggering coefficient can be adaptively adjusted according to the evolution trend of system states.Differing from existing event-triggered(ET) mechanisms,the proposed one demonstrates exceptional relevance and flexibility.It is closely related to attack probability,and its triggering coefficient dynamically adjusts depending on the presence or absence of an attack.To leverage attacker information more effectively,a switching-like sliding mode security controller is designed,which can autonomously select different controller gains based on the sliding function representing the attack situation.Sufficient conditions for the existence of the switching-like sliding mode secure controller are presented to ensure the stochastic stability of the system and the reachability of the sliding surface.Compared with existing time-invariant control strategies within the triggered interval,more resilient defense performance can be expected since the correlation with attack information is established in both the proposed AET scheme and the control strategy.Finally,a simulation example is conducted to verify the effectiveness and feasibility of the proposed security control method.展开更多
The increasing demand for artillery firepower,coupled with the growing size of gun barrels,imposes significant challenges on servo system performance.To address these challenges while ensuring fast and stable response...The increasing demand for artillery firepower,coupled with the growing size of gun barrels,imposes significant challenges on servo system performance.To address these challenges while ensuring fast and stable response,this paper proposes an adaptive robust controller based on an asymmetric barrier Lyapunov function(ABLF).The controller design incorporates both load and driver states through a backstepping synthesis.The overshoot and lag of barrel position errors are constrained within asymmetric boundaries,accounting for complex rotational uncertainties via an adaptive law and linear extended state observers(LESO).Simulations and experiments under typical artillery operating conditions validate the effectiveness and dynamic tracking performance of the proposed control strategy in comparison with other methods.展开更多
An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control...An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control method based on the observed states. Both the designed observer and controller are independent of time delays. Using an appropriate Lyapunov-Krasovskii functional, the uncertainty of the unknown time delay is compensated, and then the fuzzy logic system in Mamdani type is utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, the constructed observer-based controller and the closed-loop system are proved to be asymptotically stable. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector, which is to be updated on-line. Simulation results are presented to demonstrate the effectiveness of the proposed approach.展开更多
This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-d...This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements.Meanwhile,this control scheme can also provide more reasonable control signals when Do S attacks occur.To save network resources,an adaptive memory event-triggered mechanism(AMETM)is also proposed and Zeno behavior is excluded.It is worth mentioning that the AMETM's updates do not require global information.Then,the observer and controller gains are obtained by using the linear matrix inequality(LMI)technique.Finally,simulation examples show the effectiveness of the proposed control scheme.展开更多
An observer-based adaptive iterative learning control(AILC)scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays.The linear matrix inequality(LMI)met...An observer-based adaptive iterative learning control(AILC)scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays.The linear matrix inequality(LMI)method is employed to design the nonlinear observer.The designed controller contains a proportional-integral-derivative(PID)feedback term in time domain.The learning law of unknown constant parameter is differential-difference-type,and the learning law of unknown time-varying parameter is difference-type.It is assumed that the unknown delay-dependent uncertainty is nonlinearly parameterized.By constructing a Lyapunov-Krasovskii-like composite energy function(CEF),we prove the boundedness of all closed-loop signals and the convergence of tracking error.A simulation example is provided to illustrate the effectiveness of the control algorithm proposed in this paper.展开更多
A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints.The fuzzy logic system is used to design the approximator,which deals with...A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints.The fuzzy logic system is used to design the approximator,which deals with uncertain and continuous functions in the process of backstepping design.The use of an integral barrier Lyapunov function not only ensures that all states are within the bounds of the constraint,but also mixes the states and errors to directly constrain the state,reducing the conservativeness of the constraint satisfaction condition.Considering that the states in most nonlinear systems are immeasurable,a fuzzy adaptive states observer is constructed to estimate the unknown states.Combined with adaptive backstepping technique,an adaptive fuzzy output feedback control method is proposed.The proposed control method ensures that all signals in the closed-loop system are bounded,and that the tracking error converges to a bounded tight set without violating the full state constraint.The simulation results prove the effectiveness of the proposed control scheme.展开更多
In this paper an adaptive iterative learning control scheme is presented for the output tracking of a class of nonlinear systems. An observer is designed to estimate the tracking errors. A mixed time domain and s-doma...In this paper an adaptive iterative learning control scheme is presented for the output tracking of a class of nonlinear systems. An observer is designed to estimate the tracking errors. A mixed time domain and s-domain representation is constructed to derive an error model with relative degree one for our purpose. And time-varying radial basis function neural network is employed to deal with system uncertainty. A new signal is constructed by using a first-order filter, which removes the requirement of strict positive real(SPR) condition and identical initial condition of iterative learning control. Based on property of hyperbolic tangent function,the system tracing error is proved to converge to the origin as the iteration tends to infinity by constructing Lyapunov-like composite energy function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.展开更多
In this paper,we propose a fuzzy logic-based coded event-triggered control with self-adjustable prescribed performance(FL-CEC-SPP)to address the trade-off between control performance and communication efficiency in re...In this paper,we propose a fuzzy logic-based coded event-triggered control with self-adjustable prescribed performance(FL-CEC-SPP)to address the trade-off between control performance and communication efficiency in resource-constrained networked control systems.The method integrates a fuzzy-coded event-triggered controller into a coded control framework to dynamically adjust the triggering threshold,thereby reducing unnecessary transmissions while maintaining system stability.A self-adjustable prescribed performance constraint is also incorporated to ensure that the tracking error remains within predefined bounds under arbitrary initial conditions.Theoretical analyses and simulation comparisons show that the method proposed in this paper maintains good tracking performance and stability while reducing the communication burden,and has wide applications in resource-constrained network control systems.展开更多
This paper explores the adaptive exponentially designated-time stabilization issue via event-triggered feedback for a kind of uncertain high-order nonlinear systems.The motivation mainly comes from the following two c...This paper explores the adaptive exponentially designated-time stabilization issue via event-triggered feedback for a kind of uncertain high-order nonlinear systems.The motivation mainly comes from the following two challenges:the undesired singularity problem arising from infinite control gains at the prescribed-time instant,the effective trade-off between the control amplitude and the triggering duration.The goal is to build an event-triggered mechanism comprising a skillful triggered rule alongside a time-dependent threshold.Utilizing the designed control strategy,the solutions'existence and the prevention of Zeno phenomenon are successfully guaranteed by using a new transformation equipped with a time-varying function and redesigning the continuous state-feedback dominance approach with an array of integral functions involving embedded sign functions.Better than existing prescribed-time methods,our approach not only ensures that state variables converge to a small compact set before a designated time and stay there henceforth,and converge to the origin exponentially,but also ensures that the controller continuously works on the whole-time horizon.Two illustrative examples are given to show the effectiveness of the devised scheme.展开更多
This study constructs a dual-capacitor neuron circuit(connected via a memristor)integrated with a phototube and a thermistor to simulate the ability of biological neurons to simultaneously perceive light and thermal s...This study constructs a dual-capacitor neuron circuit(connected via a memristor)integrated with a phototube and a thermistor to simulate the ability of biological neurons to simultaneously perceive light and thermal stimuli.The circuit model converts photothermal signals into electrical signals,and its dynamic behavior is described using dimensionless equations derived from Kirchhoff's laws.Based on Helmholtz's theorem,a pseudo-Hamiltonian energy function is introduced to characterize the system's energy metabolism.Furthermore,an adaptive control function is proposed to elucidate temperature-dependent firing mechanisms,in which temperature dynamics are regulated by pseudo-Hamiltonian energy.Numerical simulations using the fourth-order Runge-Kutta method,combined with bifurcation diagrams,Lyapunov exponent spectra,and phase portraits,reveal that parameters such as capacitance ratio,phototube voltage amplitude/frequency,temperature,and thermistor reference resistance significantly modulate neuronal firing patterns,inducing transitions between periodic and chaotic states.Periodic states typically exhibit higher average pseudo-Hamiltonian energy than chaotic states.Two-parameter analysis demonstrates that phototube voltage amplitude and temperature jointly govern firing modes,with chaotic behavior emerging within specific parameter ranges.Adaptive control studies show that gain/attenuation factors,energy thresholds,ceiling temperatures,and initial temperatures regulate the timing and magnitude of system temperature saturation.During both heating and cooling phases,temperature dynamics are tightly coupled with pseudoHamiltonian energy and neuronal firing activity.These findings validate the circuit's ability to simulate photothermal perception and adaptive temperature regulation,contributing to a deeper understanding of neuronal encoding mechanisms and multimodal sensory processing.展开更多
基金supported by the National Basic Research Program of China (No.2007CB714006)
文摘This paper concerns the observer-based adaptive control problem of uncertain time-delay switched systems with stuck actuator faults. Under the case where the original controller cannot stabilize the faulty system, multiple adaptive controllers are designed and a suitable switching logic is incorporated to ensure the closed-loop system stability and state tracking. New delay-independent sufficient conditions for asymptotic stability are obtained in terms of linear matrix inequalities based on piecewise Lyapunov stability theory. On the other hand, adaptive laws for on-line updating of some of the controller parameters are also designed to compensate the effect of stuck failures. Finally, simulation results for reference [1] model show that the design is feasible and efficient.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFB3403402)National Natural Science Foundation of China Basic Research Programme for PhD Students(Grant No.524B2049)。
文摘In the field of flexible polishing,the accuracy of contact force control directly affects processing quality and material removal uniformity.However,the complex dynamic contact model and inherent strong hysteresis of pneumatic systems can significantly impact the force control accuracy of pneumatic polishing system end-effectors.To enhance responsiveness and control precision during the flexible polishing process,this study proposes an observer-based fuzzy adaptive control(OBFAC)scheme.To ensure control accuracy under an uncertain dynamic contact model,a fuzzy state observer is designed to estimate unmeasured states,while fuzzy logic approximates the uncertain nonlinear functions in the model to improve control performance.Additionally,the integral barrier Lyapunov function is employed to ensure that all states remain within predefined constraints.The stability of the proposed control scheme is analyzed using the Lyapunov function,and a pneumatic polishing experimental platform is constructed to conduct polishing contact force control experiments under multiple scenarios.Experimental results demonstrate that the proposed OBFAC scheme achieves superior tracking control performance compared to existing control schemes.
文摘To solve the attitude trajectory tracking problem for hypersonic vehicles in the presence of system constraints and unknown disturbances,this paper designed a nonlinear robust model predictive control(RMPC)scheme,which can produce near-optimal tracking commands.Unlike the existing designs,the proposed scheme is less conservative and successfully prioritizes the solution optimality.The established RMPC follows a dualloop structure.Specifically,in the outer feedback loop,the reference attitude angle profiles are optimally tracked,while in the inner feedback loop,the control moment commands are produced by optimally tracking the desired angular rate trajectories.Besides,an adaptive disturbance observer(ADO)is designed and embedded in the inner and outer RMPC controllers to alleviate the negative effects caused by unknown external disturbances.The recursive feasibility of the optimization process,together with the input-to-state stability of the proposed RMPC,is theoretically guaranteed by introducing a tightened control constraint and terminal region.The derived property reveals that our proposal can steer the tracking error within a small region of convergence.Finally,the effectiveness of the proposed scheme is demonstrated by performing simulation studies.
基金supported by the National Natural Science Foundation of China(U24B20183)the Pioneer Leading Goose+X Science and Technology Program of Zhejiang Province(2025C02018)。
文摘Dear Editor,This letter deals with the autonomous underwater vehicle(AUV)three dimensional(3D)trajectory tracking control chronically suffering from poor accuracy and efficiency in complex hydrodynamics.A state-of-the-art predictive adaptive controller(PAC)is proposed with a distinct dual closed-loop structure.
基金supported by the National Natural Science Foundation of China(62333011,62020106003)the Natural Science Foundation of Jiangsu Province of China(BK20222012)+1 种基金the Fundamental Research Funds for the Central Universities(NE2024005)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_0594)。
文摘This paper is concerned with adaptive consensus tracking control of nonlinear multi-agent systems with actuator faults and unknown nonidentical control directions under double semi-Markovian switching topologies.Considering the complex working environment and the stability differences in communication links between leaders and followers,a double semi-Markov process is first introduced to describe the random switching of communication topologies in the leader-follower structure.In order to address challenges from the unknown nonidentical control directions and partial loss of effectiveness actuator faults,a completely independent parameter is introduced into the Nussbaum function to overcome the inherent obstacle of mutual cancellation and avoid the rapid growth rate.Considering only the state information of agents is transmitted among the agents,an adaptive distributed fault-tolerant consensus tracking control is proposed based on the double semi-Markovian switching topologies using the designed Nussbaum function.Furthermore,the stability of the closed-loop nonlinear multi-agent systems is analyzed using contradiction argument and Lyapunov theorem,from which the asymptotic consensus tracking in mean square sense can be obtained.A numerical simulation example is provided to verify the effectiveness of the proposed algorithm.
基金supported by the Aeronautical Science Foundation of China(Nos.20220058052002,20240007052001)。
文摘In this paper,a practical method named linear active disturbance rejection control(LADRC)with adaptive tuning is proposed for attitude control of small-scale unmanned helicopter.The proposed method accounts for both external disturbances and internal dynamic uncertainties,as well as parameter deviations arising from parameter uncertainty,while maintaining a relatively small number of adjustable parameters.Furthermore,it addresses the limitation that conventional active disturbance rejection control methods cannot be rigorously analyzed for stability.The total disturbance of unmanned helicopter is estimated and compensated by designed LADRC.The introduction of adaptive control realizes online parameter tuning,which eliminates parameter deviation and further improves control precision.Moreover,it also provides a novel idea to prove the stability of controller,so that it can be analyzed by Lyapunov function.Finally,the anti-disturbance performance and effectiveness of proposed method are verified by numerical simulation.
文摘This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.
基金supported by the National Natural Science Foundation of China(62173079,62473203)Liaoning Provincial Science and Technology Plan Joint Program(2024-MSLH-019)+1 种基金the Education Department of Liaoning Province(LJKMZ20221840)Interdisciplinary project of Dalian University(DLUXK-2024-YB-004)。
文摘This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems(VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data.To reduce the adverse effects of quantization errors on system performance,a coupling sliding mode surface is established for each following vehicle.The radial basis function(RBF) neural networks are employed to approximate the unknown external disturbances.Then,a novel platoon control law is proposed for cooperative tracking in which each following vehicle only uses the uniformly quantized data of the neighboring vehicles.And the designed controllers in this paper are fully distributed due to the fact that the selection of each vehicle's controller parameters is independent of the entire communication topology.The string stability of VCPSs in the entire control process is ensured rather than only ensuring the string stability after the sliding mode surface converges to zero.Compared with the existing controller design methods and quantization mechanisms,the neural adaptive sliding-mode platoon controller proposed in this paper is superior in performances including tracking errors,driving comfort and fuel economy.Numerical simulations illustrate the effectiveness and superiority of the designed control strategy.
基金supported in part by the Innovation and Technology Commission of Hong Kong,China(ITS/136/20,ITS/234/21,MHP/096/22,ITS/235/22)Multi-Scale Medical Robotics Center,InnoHK,China(8312051)+1 种基金Research Grants Council(RGC) of Hong Kong,China(CUHK 14217822,CUHK14207823,AoE/E-407/24-N)The Chinese University of Hong Kong(CUHK) Direct Grant。
文摘Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft continuum morphologies which are difficult to explicitly model.This paper proposes a model-free adaptive optimal control algorithm(ADAPT)for CRs.In our strategy,we consider CRs as a class of nonlinear continuous-time dynamical systems in the state space,wherein the position of the end-effector is considered as the state and the input torque is mapped as the control input.Then,the optimized Hamilton-Jacobi-Bellman(HJB) equation is derived by optimal control principles,and subsequently solved by the proposed ADAPT algorithm without requiring knowledge of the original system dynamics.Under some mild assumptions,the global stability and convergence of the closed-loop control approach are guaranteed.Several simulation experiments are conducted on a magnetic CR(MCR) to demonstrate the practicality and effectiveness of the ADAPT algorithm.
基金supported in part by Shanghai Natural Science Foundation(24ZR1454700)the National Natural Science Foundation of China(62503331,62533016,62573279,62173231,62203288)Shanghai Pujiang Program(23PJD033)。
文摘In this paper,a security defense issue is investigated for networked control systems susceptible to stochastic denial of service(DoS) attacks by using the sliding mode control method.To utilize network communication resources more effectively,a novel adaptive event-triggered(AET) mechanism is introduced,whose triggering coefficient can be adaptively adjusted according to the evolution trend of system states.Differing from existing event-triggered(ET) mechanisms,the proposed one demonstrates exceptional relevance and flexibility.It is closely related to attack probability,and its triggering coefficient dynamically adjusts depending on the presence or absence of an attack.To leverage attacker information more effectively,a switching-like sliding mode security controller is designed,which can autonomously select different controller gains based on the sliding function representing the attack situation.Sufficient conditions for the existence of the switching-like sliding mode secure controller are presented to ensure the stochastic stability of the system and the reachability of the sliding surface.Compared with existing time-invariant control strategies within the triggered interval,more resilient defense performance can be expected since the correlation with attack information is established in both the proposed AET scheme and the control strategy.Finally,a simulation example is conducted to verify the effectiveness and feasibility of the proposed security control method.
文摘The increasing demand for artillery firepower,coupled with the growing size of gun barrels,imposes significant challenges on servo system performance.To address these challenges while ensuring fast and stable response,this paper proposes an adaptive robust controller based on an asymmetric barrier Lyapunov function(ABLF).The controller design incorporates both load and driver states through a backstepping synthesis.The overshoot and lag of barrel position errors are constrained within asymmetric boundaries,accounting for complex rotational uncertainties via an adaptive law and linear extended state observers(LESO).Simulations and experiments under typical artillery operating conditions validate the effectiveness and dynamic tracking performance of the proposed control strategy in comparison with other methods.
文摘An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control method based on the observed states. Both the designed observer and controller are independent of time delays. Using an appropriate Lyapunov-Krasovskii functional, the uncertainty of the unknown time delay is compensated, and then the fuzzy logic system in Mamdani type is utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, the constructed observer-based controller and the closed-loop system are proved to be asymptotically stable. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector, which is to be updated on-line. Simulation results are presented to demonstrate the effectiveness of the proposed approach.
基金supported by the National Natural Science Foundation of China(61773056)the Scientific and Technological Innovation Foundation of Shunde Graduate School,University of Science and Technology Beijing(USTB)(BK19AE018)+2 种基金the Fundamental Research Funds for the Central Universities of USTB(FRF-TP-20-09B,230201606500061,FRF-DF-20-35,FRF-BD-19-002A)supported by Zhejiang Natural Science Foundation(LD21F030001)supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(Ministry of Science and Information and Communications Technology)(NRF-2020R1A2C1005449)。
文摘This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements.Meanwhile,this control scheme can also provide more reasonable control signals when Do S attacks occur.To save network resources,an adaptive memory event-triggered mechanism(AMETM)is also proposed and Zeno behavior is excluded.It is worth mentioning that the AMETM's updates do not require global information.Then,the observer and controller gains are obtained by using the linear matrix inequality(LMI)technique.Finally,simulation examples show the effectiveness of the proposed control scheme.
基金supported by National Natural Science Foundation of China(No.60804021,No.60702063)
文摘An observer-based adaptive iterative learning control(AILC)scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays.The linear matrix inequality(LMI)method is employed to design the nonlinear observer.The designed controller contains a proportional-integral-derivative(PID)feedback term in time domain.The learning law of unknown constant parameter is differential-difference-type,and the learning law of unknown time-varying parameter is difference-type.It is assumed that the unknown delay-dependent uncertainty is nonlinearly parameterized.By constructing a Lyapunov-Krasovskii-like composite energy function(CEF),we prove the boundedness of all closed-loop signals and the convergence of tracking error.A simulation example is provided to illustrate the effectiveness of the control algorithm proposed in this paper.
基金supported in part by the National Natural Science Foundation of China(6202530361973147)the LiaoNing Revitalization Talents Program(XLYC1907050)。
文摘A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints.The fuzzy logic system is used to design the approximator,which deals with uncertain and continuous functions in the process of backstepping design.The use of an integral barrier Lyapunov function not only ensures that all states are within the bounds of the constraint,but also mixes the states and errors to directly constrain the state,reducing the conservativeness of the constraint satisfaction condition.Considering that the states in most nonlinear systems are immeasurable,a fuzzy adaptive states observer is constructed to estimate the unknown states.Combined with adaptive backstepping technique,an adaptive fuzzy output feedback control method is proposed.The proposed control method ensures that all signals in the closed-loop system are bounded,and that the tracking error converges to a bounded tight set without violating the full state constraint.The simulation results prove the effectiveness of the proposed control scheme.
基金supported by the Natural Sciences and Engineering Research Council of Canada(N00892)in part by National Natural Science Foundation of China(51405436,51375452,61573174)
基金the National Natural Science Foundation of China(No.61273058)
文摘In this paper an adaptive iterative learning control scheme is presented for the output tracking of a class of nonlinear systems. An observer is designed to estimate the tracking errors. A mixed time domain and s-domain representation is constructed to derive an error model with relative degree one for our purpose. And time-varying radial basis function neural network is employed to deal with system uncertainty. A new signal is constructed by using a first-order filter, which removes the requirement of strict positive real(SPR) condition and identical initial condition of iterative learning control. Based on property of hyperbolic tangent function,the system tracing error is proved to converge to the origin as the iteration tends to infinity by constructing Lyapunov-like composite energy function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.
基金supported by Singapore RIE2025 Manufacturing,Trade and Connectivity Industry Alignment Fund-Pre-Positioning(IAF-PP)under Grant M24N2a0039 through WP2-Intelligent Switching Controlthe National Research Foundation Singapore under its AI Singapore Programme under Grant AISG4-GC-2023-007-1B.
文摘In this paper,we propose a fuzzy logic-based coded event-triggered control with self-adjustable prescribed performance(FL-CEC-SPP)to address the trade-off between control performance and communication efficiency in resource-constrained networked control systems.The method integrates a fuzzy-coded event-triggered controller into a coded control framework to dynamically adjust the triggering threshold,thereby reducing unnecessary transmissions while maintaining system stability.A self-adjustable prescribed performance constraint is also incorporated to ensure that the tracking error remains within predefined bounds under arbitrary initial conditions.Theoretical analyses and simulation comparisons show that the method proposed in this paper maintains good tracking performance and stability while reducing the communication burden,and has wide applications in resource-constrained network control systems.
基金supported in part by the National Natural Science Foundation of China(62173208)Taishan Scholar Project of Shandong Province of China(tsqn202103061)the National Science and Technology Council(NSTC),Taiwan,China(NSTC 113-2221-E-006-145-MY2)。
文摘This paper explores the adaptive exponentially designated-time stabilization issue via event-triggered feedback for a kind of uncertain high-order nonlinear systems.The motivation mainly comes from the following two challenges:the undesired singularity problem arising from infinite control gains at the prescribed-time instant,the effective trade-off between the control amplitude and the triggering duration.The goal is to build an event-triggered mechanism comprising a skillful triggered rule alongside a time-dependent threshold.Utilizing the designed control strategy,the solutions'existence and the prevention of Zeno phenomenon are successfully guaranteed by using a new transformation equipped with a time-varying function and redesigning the continuous state-feedback dominance approach with an array of integral functions involving embedded sign functions.Better than existing prescribed-time methods,our approach not only ensures that state variables converge to a small compact set before a designated time and stay there henceforth,and converge to the origin exponentially,but also ensures that the controller continuously works on the whole-time horizon.Two illustrative examples are given to show the effectiveness of the devised scheme.
基金supported by the Natural Science Founda tion of Chongqing(Grant No.CSTB2024NSCQ-MSX0944)。
文摘This study constructs a dual-capacitor neuron circuit(connected via a memristor)integrated with a phototube and a thermistor to simulate the ability of biological neurons to simultaneously perceive light and thermal stimuli.The circuit model converts photothermal signals into electrical signals,and its dynamic behavior is described using dimensionless equations derived from Kirchhoff's laws.Based on Helmholtz's theorem,a pseudo-Hamiltonian energy function is introduced to characterize the system's energy metabolism.Furthermore,an adaptive control function is proposed to elucidate temperature-dependent firing mechanisms,in which temperature dynamics are regulated by pseudo-Hamiltonian energy.Numerical simulations using the fourth-order Runge-Kutta method,combined with bifurcation diagrams,Lyapunov exponent spectra,and phase portraits,reveal that parameters such as capacitance ratio,phototube voltage amplitude/frequency,temperature,and thermistor reference resistance significantly modulate neuronal firing patterns,inducing transitions between periodic and chaotic states.Periodic states typically exhibit higher average pseudo-Hamiltonian energy than chaotic states.Two-parameter analysis demonstrates that phototube voltage amplitude and temperature jointly govern firing modes,with chaotic behavior emerging within specific parameter ranges.Adaptive control studies show that gain/attenuation factors,energy thresholds,ceiling temperatures,and initial temperatures regulate the timing and magnitude of system temperature saturation.During both heating and cooling phases,temperature dynamics are tightly coupled with pseudoHamiltonian energy and neuronal firing activity.These findings validate the circuit's ability to simulate photothermal perception and adaptive temperature regulation,contributing to a deeper understanding of neuronal encoding mechanisms and multimodal sensory processing.
