Model-free adaptive dynamic programming for optimal control of discrete-time affine nonlinear system
Xia ZP(夏中谱); Dongbin Zhao
2014-08
会议名称International Federation of Automatic Control
会议录名称Proceedings of International Federation of Automatic Control 2014
会议日期2014-08
会议地点South Africa
摘要
In this paper, a model-free and effective approach is proposed to solve infinite horizon optimal control problem for affine nonlinear systems based on adaptive dynamic programming technique. The developed approach, referred to as the actor-critic structure, employs two multilayer perceptron neural networks to approximate the state-action value function and the control policy, respectively. It uses data collected arbitrarily from any reasonable sampling distribution for policy iteration. In the policy evaluation phase, a novel objective function is defined for updating the critic network, and thus makes the critic network converge to the Bellman equation directly rather than iteratively. In the policy improvement phase, the action network is updated to minimize the outputs of the critic network. The two phases alternate until no more improvement of the control policy is observed, such that the optimal control policy is achieved. Two simulation examples are provided to show the effectiveness of the approach.
关键词Model-free Adaptive Dynamic Programming Reinforcement Learning Policy Iteration Multilayer Perceptron Neural Network.
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11460
专题复杂系统管理与控制国家重点实验室_智能化团队
通讯作者Dongbin Zhao
作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Xia ZP,Dongbin Zhao. Model-free adaptive dynamic programming for optimal control of discrete-time affine nonlinear system[C],2014.
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