未知非线性离散系统的自适应动态规划最优控制

2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL) Pub Date : 2011-04-11 DOI:10.1109/ADPRL.2011.5967357

Derong Liu, Ding Wang, Dongbin Zhao

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引用次数: 14

摘要

针对成本函数中含有折扣因子的未知非线性离散系统，提出了一种智能最优控制方案。提出了一种基于全球化对偶启发式规划(GDHP)的迭代自适应动态规划(ADP)算法，通过收敛性分析获得最优控制器。为了便于迭代算法的实现，采用了三个神经网络作为参数结构，在每次迭代时分别逼近代价函数、最优控制律和未知非线性系统。通过两个仿真实例验证了所提出的最优控制方法的有效性。

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Adaptive dynamic programming for optimal control of unknown nonlinear discrete-time systems

An intelligent optimal control scheme for unknown nonlinear discrete-time systems with discount factor in the cost function is proposed in this paper. An iterative adaptive dynamic programming (ADP) algorithm via globalized dual heuristic programming (GDHP) technique is developed to obtain the optimal controller with convergence analysis. Three neural networks are used as parametric structures to facilitate the implementation of the iterative algorithm, which will approximate at each iteration the cost function, the optimal control law, and the unknown nonlinear system, respectively. Two simulation examples are provided to verify the effectiveness of the presented optimal control approach.

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2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

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