Constrained Online Optimal Control for Continuous-Time Nonlinear Systems Using Neuro-Dynamic Programming

摘要：

　　This paper develops an online adaptive optimal control scheme to solve the infinite-horizon optimal control problem of continuous-time nonlinear systems with control constraints.A novel architecture is presented to approximate the Hamilton-Jacobi-Bellman equation.That is,only a critic neural network is used to derive the optimal control instead of typical action– critic dual networks employed in neuro-dynamic programming methods.Meanwhile,unlike existing tuning laws for the critic,the newly developed critic update rule not only ensures convergence of the critic to the optimal control but also guarantees the closed-loop system to be uniformly ultimately bounded.In addition,no initial stabilizing control is required.Finally,an example is provided to verify the effectiveness of the present approach.

关键词： Constrained input Neuro-dynamic programming Nonlinear systems Online control Optimal control

作者: YANG Xiong LIU Derong WANG Ding MA Hongwen

作者单位: The State Key Laboratory of Management and Control for Complex Systems Institute of Automation,Chinese Academy of Sciences Beijing 100190,China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 8717-8722

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）