Finite-horizon optimal control for unknown systems with saturating control inputs

被引：0

作者：

Cui X.-H. ^{[1
,2
]}

Luo Y.-H. ^{[1
]}

Zhang H.-G. ^{[1
]}

Zu P.-F. ^{[2
]}

机构：

[1] School of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning

[2] Institute of Mathematical Sciences, Mudanjiang Normal College, Mudanjiang, 157011, Heilongjiang

来源：

Zhang, Hua-Guang (hgzhang@ieee.org) | 2016年 / South China University of Technology卷 / 33期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Adaptive dynamic programming; Finite-horizon; Neural network; Optimal control;

D O I：

10.7641/CTA.2016.41195

中图分类号：

学科分类号：

摘要：

An adaptive dynamic programming (ADP)-based online integral reinforcement learning algorithm is designed for finite-horizon optimal control of nonlinear continuous-time systems with saturating control inputs and partially unknown dynamics. Moreover, the convergence of the algorithm is proved. Firstly, the control constraints are handled through nonquadratic function. Secondly, a single neural network (NN) with constant weights and time-dependent activation functions is designed in order to approximate the unknown and continuous value function. Compared with the traditional dual neural networks, the burden of computation by the single NN is lessened. Meanwhile, the NN weights are updated by the least square method with considering both the residual error and terminal error. Furthermore, the convergence of iterative value function on the base of NN is proved. Lastly, two simulation examples show the effectiveness of the proposed algorithm. © 2016, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.

引用

页码：631 / 637

页数：6

共 20 条

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