In this paper,the authors design a reinforcement learning algorithm to solve the adaptive linear-quadratic stochastic n-players non-zero sum differential game with completely unknown dynamics.For each player,a critic ...In this paper,the authors design a reinforcement learning algorithm to solve the adaptive linear-quadratic stochastic n-players non-zero sum differential game with completely unknown dynamics.For each player,a critic network is used to estimate the Q-function,and an actor network is used to estimate the control input.A model-free online Q-learning algorithm is obtained for solving this kind of problems.It is proved that under some mild conditions the system state and weight estimation errors can be uniformly ultimately bounded.A simulation with five players is given to verify the effectiveness of the algorithm.展开更多
In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the robot.The presented HRI controller design i...In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the robot.The presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller design.The task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the inner-loop.Data-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance parameters.In the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity measurement.On this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task space.The simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant Nos.62122043,62192753in part by Natural Science Foundation of Shandong Province for Distinguished Young Scholars under Grant No.ZR2022JQ31in part by the Innovative Research Groups of the National Natural Science Foundation of China under Grant No.61821004.
文摘In this paper,the authors design a reinforcement learning algorithm to solve the adaptive linear-quadratic stochastic n-players non-zero sum differential game with completely unknown dynamics.For each player,a critic network is used to estimate the Q-function,and an actor network is used to estimate the control input.A model-free online Q-learning algorithm is obtained for solving this kind of problems.It is proved that under some mild conditions the system state and weight estimation errors can be uniformly ultimately bounded.A simulation with five players is given to verify the effectiveness of the algorithm.
基金This work was supported in part by the National Natural Science Foundation of China(61903028)the Youth Innovation Promotion Association,Chinese Academy of Sciences(2020137)+1 种基金the Lifelong Learning Machines Program from DARPA/Microsystems Technology Officethe Army Research Laboratory(W911NF-18-2-0260).
文摘In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the robot.The presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller design.The task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the inner-loop.Data-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance parameters.In the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity measurement.On this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task space.The simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.
