This paper investigates the cooperative adaptive optimal output regulation problem of continuous-time linear multi-agent systems.As the multi-agent system dynamics are uncertain,solving regulator equations and the cor...This paper investigates the cooperative adaptive optimal output regulation problem of continuous-time linear multi-agent systems.As the multi-agent system dynamics are uncertain,solving regulator equations and the corresponding algebraic Riccati equations is challenging,especially for high-order systems.In this paper,a novel method is proposed to approximate the solution of regulator equations,i.e.,gradient descent method.It is worth noting that this method obtains gradients through online data rather than model information.A data-driven distributed adaptive suboptimal controller is developed by adaptive dynamic programming,so that each follower can achieve asymptotic tracking and disturbance rejection.Finally,the effectiveness of the proposed control method is validated by simulations.展开更多
As human beings,people coordinate movements and interact with the environment through sensory information and motor adaptation in the daily lives.Many characteristics of these interactions can be studied using optimiz...As human beings,people coordinate movements and interact with the environment through sensory information and motor adaptation in the daily lives.Many characteristics of these interactions can be studied using optimization-based models,which assume that the precise knowledge of both the sensorimotor system and its interactive environment is available for the central nervous system(CNS).However,both static and dynamic uncertainties occur inevitably in the daily movements.When these uncertainties are taken into consideration,the previously developed models based on optimization theory may fail to explain how the CNS can still coordinate human movements which are also robust with respect to the uncertainties.In order to address this problem,this paper presents a novel computational mechanism for sensorimotor control from a perspective of robust adaptive dynamic programming(RADP).Sharing some essential features of reinforcement learning,which was originally observed from mammals,the RADP model for sensorimotor control suggests that,instead of identifying the system dynamics of both the motor system and the environment,the CNS computes iteratively a robust optimal control policy using the real-time sensory data.An online learning algorithm is provided in this paper,with rigorous convergence and stability analysis.Then,it is applied to simulate several experiments reported from the past literature.By comparing the proposed numerical results with these experimentally observed data,the authors show that the proposed model can reproduce movement trajectories which are consistent with experimental observations.In addition,the RADP theory provides a unified framework that connects optimality and robustness properties in the sensorimotor system.展开更多
Nonlinearity is ubiquitous in engineering and natural systems.The development of nonlinear control can be traced back to decades ago.To date,the research has reached the stage that emphasizes developing methodologies ...Nonlinearity is ubiquitous in engineering and natural systems.The development of nonlinear control can be traced back to decades ago.To date,the research has reached the stage that emphasizes developing methodologies that can handle the complexity characterized by uncertainty,展开更多
This paper reports latest developments in event-triggered and self-triggered control of uncertain nonholonomic systems in the perturbed chained form.In order to tackle the effects of drift uncertain nonlinearities,non...This paper reports latest developments in event-triggered and self-triggered control of uncertain nonholonomic systems in the perturbed chained form.In order to tackle the effects of drift uncertain nonlinearities,nonholonomic constraints and nonsmooth aperiodic sampling in eventbased control,a novel systematic design scheme is proposed by integrating set-valued maps with stateseparation and state-scaling techniques.The stability analysis of the closed-loop event-triggered control system is based on the cyclic-small-gain techniques that overcome the limitation of Lyapunov theory in the construction of Lyapunov functions for nonsmooth dynamical systems and enjoy inherent robustness properties due to the use of gain-based characterization of robust stability.More specifically,the closed-loop event-triggered control system is transformed into an interconnection of multiple input-tostate stable systems,to which the cyclic-small-gain theorem is applied for robust stability analysis.New self-triggered mechanisms are also developed as natural extensions of the event-triggered control result.The proposed event-based control design approach is new and original even when the system model is reduced to the ideal unperturbed chained form.Interestingly,the proposed methodology is also applicable to a broader class of nonholonomic systems subject to state and input-dependent uncertainties.The efficacy of the obtained event-triggered controllers is validated by a benchmark example of mobile robots subject to parametric uncertainties and a measurement noise such as bias in the orientation.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No.62373090the U.S.National Science Foundation under Grant No.CNS-2227153.
文摘This paper investigates the cooperative adaptive optimal output regulation problem of continuous-time linear multi-agent systems.As the multi-agent system dynamics are uncertain,solving regulator equations and the corresponding algebraic Riccati equations is challenging,especially for high-order systems.In this paper,a novel method is proposed to approximate the solution of regulator equations,i.e.,gradient descent method.It is worth noting that this method obtains gradients through online data rather than model information.A data-driven distributed adaptive suboptimal controller is developed by adaptive dynamic programming,so that each follower can achieve asymptotic tracking and disturbance rejection.Finally,the effectiveness of the proposed control method is validated by simulations.
基金supported in part by the US National Science Foundation Grant Nos.ECCS-1101401 and ECCS-1230040
文摘As human beings,people coordinate movements and interact with the environment through sensory information and motor adaptation in the daily lives.Many characteristics of these interactions can be studied using optimization-based models,which assume that the precise knowledge of both the sensorimotor system and its interactive environment is available for the central nervous system(CNS).However,both static and dynamic uncertainties occur inevitably in the daily movements.When these uncertainties are taken into consideration,the previously developed models based on optimization theory may fail to explain how the CNS can still coordinate human movements which are also robust with respect to the uncertainties.In order to address this problem,this paper presents a novel computational mechanism for sensorimotor control from a perspective of robust adaptive dynamic programming(RADP).Sharing some essential features of reinforcement learning,which was originally observed from mammals,the RADP model for sensorimotor control suggests that,instead of identifying the system dynamics of both the motor system and the environment,the CNS computes iteratively a robust optimal control policy using the real-time sensory data.An online learning algorithm is provided in this paper,with rigorous convergence and stability analysis.Then,it is applied to simulate several experiments reported from the past literature.By comparing the proposed numerical results with these experimentally observed data,the authors show that the proposed model can reproduce movement trajectories which are consistent with experimental observations.In addition,the RADP theory provides a unified framework that connects optimality and robustness properties in the sensorimotor system.
基金supported by the National Science Foundation under Grant No.ECCS-1501044the National Natural Science Foundation under Grant Nos.61374042,61522305,61633007 and 61533007the State Key Laboratory of Intelligent Control and Decision of Complex Systems at BIT
文摘Nonlinearity is ubiquitous in engineering and natural systems.The development of nonlinear control can be traced back to decades ago.To date,the research has reached the stage that emphasizes developing methodologies that can handle the complexity characterized by uncertainty,
基金the National Natural Science Foundation of China Grant Nos.61633007and U1911401the National Natural Science Foundation of China under Grant No.EPCN1903781。
文摘This paper reports latest developments in event-triggered and self-triggered control of uncertain nonholonomic systems in the perturbed chained form.In order to tackle the effects of drift uncertain nonlinearities,nonholonomic constraints and nonsmooth aperiodic sampling in eventbased control,a novel systematic design scheme is proposed by integrating set-valued maps with stateseparation and state-scaling techniques.The stability analysis of the closed-loop event-triggered control system is based on the cyclic-small-gain techniques that overcome the limitation of Lyapunov theory in the construction of Lyapunov functions for nonsmooth dynamical systems and enjoy inherent robustness properties due to the use of gain-based characterization of robust stability.More specifically,the closed-loop event-triggered control system is transformed into an interconnection of multiple input-tostate stable systems,to which the cyclic-small-gain theorem is applied for robust stability analysis.New self-triggered mechanisms are also developed as natural extensions of the event-triggered control result.The proposed event-based control design approach is new and original even when the system model is reduced to the ideal unperturbed chained form.Interestingly,the proposed methodology is also applicable to a broader class of nonholonomic systems subject to state and input-dependent uncertainties.The efficacy of the obtained event-triggered controllers is validated by a benchmark example of mobile robots subject to parametric uncertainties and a measurement noise such as bias in the orientation.
