Work

Invariant Adaptive Dynamic Programming for Discrete-Time Optimal Control

Year: 2019

Type: article

Abstract: For systems that can only be locally stabilized, control laws and their effective regions are both important. In this paper, invariant policy iteration is proposed to solve the optimal control of discrete-time systems. At each iteration, a given policy is evaluated in its invariantly admissible region, and a new policy and a new region are updated for the next iteration. Theoretical analysis shows the method is regionally convergent to the optimal value and the optimal policy. Combined with sum-of-squares polynomials, the method is able to achieve the near-optimal control of a class of discrete-time systems. An invariant adaptive dynamic programming algorithm is developed to extend the method to scenarios where system dynamics is not available. Online data are utilized to learn the near-optimal policy and the invariantly admissible region. Simulated experiments verify the effectiveness of our method.

Source: IEEE Transactions on Systems Man and Cybernetics Systems

DOI: 10.1109/tsmc.2019.2911900

Authors: Yuanheng Zhu, Dongbin Zhao, Haibo He

Institutions: Chinese Academy of Sciences, Institute of Automation, University of Chinese Academy of Sciences, University of Rhode Island

Language: English

Topic: Adaptive Dynamic Programming Control

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Award: G1220341689