Adaptive Dynamic Programming Neural Networks for Control

Abstract

This chapter proposes a self-learning control method applicable to Euler-Lagrange systems. In contrast to existing work on intelligent control of Euler-Lagrange systems, the stability of the closed loop system with the proposed method is proven in theory. On the other hand, different from model-based design strategies, such as backstepping-based design and variable structure-based design, the proposed method does not require information on the model parameters and therefore is a model independent method. The chapter formulates the problem from an optimal control perspective. In this framework, the goal is to find the input sequence to minimize the cost function defined on infinite horizon under the constraint of the system dynamics. The solution is found by solving a Bellman equation according to the principle of optimality. The chapter utilizes an adaptive dynamic programming strategy to numerically solve the input sequence in real time.

Cite this paper

@misc{li2019adaptive,
  title = {{Adaptive Dynamic Programming Neural Networks for Control}},
  author = {Li, Shuai and Jin, Long and Mirza, Mohammed Aquil},
  pages = {17--25},
  year = {2019},
  doi = {10.1002/9781119557005.ch2}
}

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