Data-Driven Distributed Optimal Control Using Neighbourhood Optimization for Nonlinear Interconnected Systems

Behzad Farzanegan (), Mohammad Bagher Menhaj () and Amir Abolfazl Suratgar ()
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Behzad Farzanegan: Amirkabir University of Technology
Mohammad Bagher Menhaj: Amirkabir University of Technology
Amir Abolfazl Suratgar: Amirkabir University of Technology

Journal of Optimization Theory and Applications, 2024, vol. 203, issue 1, No 36, 1054-1082

Abstract: Abstract In this paper, a novel data-driven distributed optimal control (DDOC) scheme via neighbourhood optimization is proposed for affine nonlinear interconnected systems with unknown dynamics. In this scheme, each subsystem merely broadcasts its local information to its neighbours, and receives information only from them. The proposed distributed method is established based on a hybrid structure mainly consisting of both algebraic and analytical parts, in which two neural networks (NN) are simultaneously tuned in an online manner by using neighbourhood information for each subsystem. To tackle the unknown dynamics, a new distributed data-driven model is established to reconstruct subsystem dynamics and estimate system states. Moreover, by using the Hamilton–Jacobi–Bellman (HJB) based formulation, a distributed NN-based optimal control is designed to minimize both the performance of the corresponding subsystem and its neighbours, in the presence of unknown dynamics. Through Lyapunov’s direct method, the uniformly ultimately boundedness (UUB) of the overall closed-loop system is guaranteed, and the provided results of the stability are exploited to find updating rules for the NN-based observer and controller. Finally, two illustrative examples are investigated to certify the efficacy of the new distributed optimal control (DOC).

Keywords: Data-driven; Distributed systems; Neighbourhood optimization; Neural networks; Networked control systems; Reinforcement learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10957-024-02543-x

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