Optimal Control of Unknown Discrete-Time Nonlinear Systems with Constrained Inputs Using GDHP Technique

摘要：

The adaptive dynamic programming (ADP) approach is employed to design an optimal controller for unknown discrete-time nonlinear systems with control constraints. First, a neural network is constructed to identify the unknown dynamical system with stability proof. Then, the iterative ADP algorithm is developed to solve the optimal control problem with convergence analysis. Moreover, two other neural networks are introduced to approximate the cost function and its derivative and the control law, under the framework of globalized dual heuristic programming technique. Finally, two simulation examples are included to verify the theoretical results.

关键词： Adaptive dynamic programming Approximate dynamic programming Control constraints Neural networks Optimal control System identification

作者: LIU Derong WANG Ding LI Hongliang

作者单位: State Key Laboratory of Management and Control for Complex Systems Institute of Automation, Chinese State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese A

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 2926-2931

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）