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A novel optimal tracking control scheme for a class of discrete-time nonlinear systems using generalised policy iteration adaptive dynamic programming algorithm

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  • Qiao Lin
  • Qinglai Wei
  • Derong Liu

Abstract

In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm, called generalised policy iteration ADP algorithm, is developed to solve optimal tracking control problems for discrete-time nonlinear systems. The idea is to use two iteration procedures, including an i-iteration and a j-iteration, to obtain the iterative tracking control laws and the iterative value functions. By system transformation, we first convert the optimal tracking control problem into an optimal regulation problem. Then the generalised policy iteration ADP algorithm, which is a general idea of interacting policy and value iteration algorithms, is introduced to deal with the optimal regulation problem. The convergence and optimality properties of the generalised policy iteration algorithm are analysed. Three neural networks are used to implement the developed algorithm. Finally, simulation examples are given to illustrate the performance of the present algorithm.

Suggested Citation

  • Qiao Lin & Qinglai Wei & Derong Liu, 2017. "A novel optimal tracking control scheme for a class of discrete-time nonlinear systems using generalised policy iteration adaptive dynamic programming algorithm," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(3), pages 525-534, February.
    • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:3:p:525-534
    DOI: 10.1080/00207721.2016.1188177
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    Cited by:

    1. Wang, Wei & Xie, Xiangpeng & Feng, Changyang, 2022. "Model-free finite-horizon optimal tracking control of discrete-time linear systems," Applied Mathematics and Computation, Elsevier, vol. 433(C).

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