This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequentia...This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations.The feasibility of the formulated optimization problem is guaranteed under derived conditions.The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure.Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.展开更多
The purpose of this work is to propose a scheme to stabilize the predictive control systems in the practical stability sense. In the paper, the authors dealt with a general discrete predictive control system x j+1|t =...The purpose of this work is to propose a scheme to stabilize the predictive control systems in the practical stability sense. In the paper, the authors dealt with a general discrete predictive control system x j+1|t =f(x j|t , u j|t ) by using the Lyapunov direct method combining with receding horizon control technique, and presented a new condition to guarantee the practical stabilization of the systems. With the proposed results, one can design the optimal controllers easily to practically stabilize the predictive control systems.展开更多
In this paper,a data-driven conflict-aware safe reinforcement learning(CAS-RL)algorithm is presented for control of autonomous systems.Existing safe RL results with predefined performance functions and safe sets can o...In this paper,a data-driven conflict-aware safe reinforcement learning(CAS-RL)algorithm is presented for control of autonomous systems.Existing safe RL results with predefined performance functions and safe sets can only provide safety and performance guarantees for a single environment or circumstance.By contrast,the presented CAS-RL algorithm provides safety and performance guarantees across a variety of circumstances that the system might encounter.This is achieved by utilizing a bilevel learning control architecture:A higher metacognitive layer leverages a data-driven receding-horizon attentional controller(RHAC)to adapt relative attention to different system’s safety and performance requirements,and,a lower-layer RL controller designs control actuation signals for the system.The presented RHAC makes its meta decisions based on the reaction curve of the lower-layer RL controller using a metamodel or knowledge.More specifically,it leverages a prediction meta-model(PMM)which spans the space of all future meta trajectories using a given finite number of past meta trajectories.RHAC will adapt the system’s aspiration towards performance metrics(e.g.,performance weights)as well as safety boundaries to resolve conflicts that arise as mission scenarios develop.This will guarantee safety and feasibility(i.e.,performance boundness)of the lower-layer RL-based control solution.It is shown that the interplay between the RHAC and the lower-layer RL controller is a bilevel optimization problem for which the leader(RHAC)operates at a lower rate than the follower(RL-based controller)and its solution guarantees feasibility and safety of the control solution.The effectiveness of the proposed framework is verified through a simulation example.展开更多
This paper investigates the problem of receding horizon state estimation for networked control systems (NCSs) with random network-induced delays less than one sample period, which are formulated as multirate control...This paper investigates the problem of receding horizon state estimation for networked control systems (NCSs) with random network-induced delays less than one sample period, which are formulated as multirate control systems. Based on a batch of recent past slow rate measurements in a finite horizon window, the initial state estimation in this window is solved by minimizing a receding-horizon objective function, and then the fast rate state estimations are calculated by the prediction of dynamic equation to compensate for the network-induced time delays. Furthermore, convergence results and unbiasedness properties are analyzed. An upper bound of estimation error is presented under the assumption of bounded disturbances acting on the system and measurement equations. A simulation example shows the effectiveness of the proposed method.展开更多
Good access to traffic information provides enormous potential for automotive powertrain control.We propose a logical control approach for the gearshift strategy,aimed at improving the fuel efficiency of vehicles.The ...Good access to traffic information provides enormous potential for automotive powertrain control.We propose a logical control approach for the gearshift strategy,aimed at improving the fuel efficiency of vehicles.The driver power demand in a specific position usually exhibits stochastic features and can be statistically analyzed in accordance with historical driving data and instant traffic conditions;therefore,it offers opportunities for the design of a gearshift control scheme.Due to the discrete characteristics of a gearshift,the control design of the gearshift strategy can be formulated under a logic system framework.To this end,vehicle dynamics are discretized with several logic states,and then modeled as a logic system with the Markov process model.The fuel optimization problem is constructed as a receding-horizon optimal control problem under the logic system framework,and a dynamic programming algorithm with algebraic operations is applied to determine the optimal strategy online.Simulation results demonstrate that the proposed control design has better potential for fuel efficiency improvement than the conventional method.展开更多
基金supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China(11202318,11203721)the Australian Research Council(DP200100700)。
文摘This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations.The feasibility of the formulated optimization problem is guaranteed under derived conditions.The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure.Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.
文摘The purpose of this work is to propose a scheme to stabilize the predictive control systems in the practical stability sense. In the paper, the authors dealt with a general discrete predictive control system x j+1|t =f(x j|t , u j|t ) by using the Lyapunov direct method combining with receding horizon control technique, and presented a new condition to guarantee the practical stabilization of the systems. With the proposed results, one can design the optimal controllers easily to practically stabilize the predictive control systems.
文摘In this paper,a data-driven conflict-aware safe reinforcement learning(CAS-RL)algorithm is presented for control of autonomous systems.Existing safe RL results with predefined performance functions and safe sets can only provide safety and performance guarantees for a single environment or circumstance.By contrast,the presented CAS-RL algorithm provides safety and performance guarantees across a variety of circumstances that the system might encounter.This is achieved by utilizing a bilevel learning control architecture:A higher metacognitive layer leverages a data-driven receding-horizon attentional controller(RHAC)to adapt relative attention to different system’s safety and performance requirements,and,a lower-layer RL controller designs control actuation signals for the system.The presented RHAC makes its meta decisions based on the reaction curve of the lower-layer RL controller using a metamodel or knowledge.More specifically,it leverages a prediction meta-model(PMM)which spans the space of all future meta trajectories using a given finite number of past meta trajectories.RHAC will adapt the system’s aspiration towards performance metrics(e.g.,performance weights)as well as safety boundaries to resolve conflicts that arise as mission scenarios develop.This will guarantee safety and feasibility(i.e.,performance boundness)of the lower-layer RL-based control solution.It is shown that the interplay between the RHAC and the lower-layer RL controller is a bilevel optimization problem for which the leader(RHAC)operates at a lower rate than the follower(RL-based controller)and its solution guarantees feasibility and safety of the control solution.The effectiveness of the proposed framework is verified through a simulation example.
基金Supported by the National Natural Science Foundation of China (Grant Nos. 60774015, 60674018, 60825302)the National High-Tech Research& Development Program of China (Grant No. 2006AA04Z173)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20060248001)Shanghai Natural Science Foundation (Grant No. 07JC14016)
文摘This paper investigates the problem of receding horizon state estimation for networked control systems (NCSs) with random network-induced delays less than one sample period, which are formulated as multirate control systems. Based on a batch of recent past slow rate measurements in a finite horizon window, the initial state estimation in this window is solved by minimizing a receding-horizon objective function, and then the fast rate state estimations are calculated by the prediction of dynamic equation to compensate for the network-induced time delays. Furthermore, convergence results and unbiasedness properties are analyzed. An upper bound of estimation error is presented under the assumption of bounded disturbances acting on the system and measurement equations. A simulation example shows the effectiveness of the proposed method.
基金Project supported by the National Natural Science Foundation of China(Nos.61803079,61703179,and 61890924)the Foundation of the Education Department of Jilin Province,China(No.JJKH20190189KJ)the Foundation of State Key Laboratory of Automotive Simulation and Control,China(No.20170102)。
文摘Good access to traffic information provides enormous potential for automotive powertrain control.We propose a logical control approach for the gearshift strategy,aimed at improving the fuel efficiency of vehicles.The driver power demand in a specific position usually exhibits stochastic features and can be statistically analyzed in accordance with historical driving data and instant traffic conditions;therefore,it offers opportunities for the design of a gearshift control scheme.Due to the discrete characteristics of a gearshift,the control design of the gearshift strategy can be formulated under a logic system framework.To this end,vehicle dynamics are discretized with several logic states,and then modeled as a logic system with the Markov process model.The fuel optimization problem is constructed as a receding-horizon optimal control problem under the logic system framework,and a dynamic programming algorithm with algebraic operations is applied to determine the optimal strategy online.Simulation results demonstrate that the proposed control design has better potential for fuel efficiency improvement than the conventional method.