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Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems with Disturbances

  • Ruizhuo Song
  • Qinglai Wei
  • Qing Li
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 166)

Abstract

An optimal control method is developed for unknown continuous time systems with unknown disturbances in this chapter. The integral reinforcement learning (IRL) algorithm is presented to obtain the iterative control. Off-policy learning is used to allow the dynamics to be completely unknown. Neural networks (NN) are used to construct critic and action networks. It is shown that if there are unknown disturbances, off-policy IRL may not converge or may be biased. For reducing the influence of unknown disturbances, a disturbances compensation controller is added. It is proven that the weight errors are uniformly ultimately bounded (UUB) based on Lyapunov techniques. Convergence of the Hamiltonian function is also proven. The simulation study demonstrates the effectiveness of the proposed optimal control method for unknown systems with disturbances.

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Copyright information

© Science Press, Beijing and Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.University of Science and Technology BeijingBeijingChina
  2. 2.Institute of AutomationChinese Academy of SciencesBeijingChina

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