Optimal Asymptotic Tracking Control for Nonzero-Sum Differential Game Systems with Unknown Drift Dynamics via Integral Reinforcement Learning

Chonglin Jing, Chaoli Wang (), Hongkai Song, Yibo Shi and Longyan Hao
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Chonglin Jing: Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Chaoli Wang: Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Hongkai Song: Equipment Assets Management Office, Shanghai Jian Qiao University, Shanghai 201306, China
Yibo Shi: Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Longyan Hao: Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Mathematics, 2024, vol. 12, issue 16, 1-21

Abstract: This paper employs an integral reinforcement learning (IRL) method to investigate the optimal tracking control problem (OTCP) for nonlinear nonzero-sum (NZS) differential game systems with unknown drift dynamics. Unlike existing methods, which can only bound the tracking error, the proposed approach ensures that the tracking error asymptotically converges to zero. This study begins by constructing an augmented system using the tracking error and reference signal, transforming the original OTCP into solving the coupled Hamilton–Jacobi (HJ) equation of the augmented system. Because the HJ equation contains unknown drift dynamics and cannot be directly solved, the IRL method is utilized to convert the HJ equation into an equivalent equation without unknown drift dynamics. To solve this equation, a critic neural network (NN) is employed to approximate the complex value function based on the tracking error and reference information data. For the unknown NN weights, the least squares (LS) method is used to design an estimation law, and the convergence of the weight estimation error is subsequently proven. The approximate solution of optimal control converges to the Nash equilibrium, and the tracking error asymptotically converges to zero in the closed system. Finally, we validate the effectiveness of the proposed method in this paper based on MATLAB using the ode45 method and least squares method to execute Algorithm 2.

Keywords: nonzero-sum games; optimal asymptotic tracking control; integral reinforcement learning; neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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