Reinforcement Learning‐Based Adaptive Optimal Containment Control for Constrained Nonlinear Multiagent Systems

Abstract

This paper proposes an adaptive optimal containment control method for nonlinear strict‐feedback multiagent systems with state constraints. First, a neural network‐based reinforcement learning algorithm is developed within an optimized backstepping framework. Unlike the traditional actor‐critic network structure, the paper introduces an observer‐actor‐critic architecture, where observers are used to estimate unmeasurable states, improving the reliability and accuracy of the system. Then, logarithmic barrier Lyapunov functions are combined with an optimal cost function to handle the state constraints. Using the Lyapunov stability theory, it is rigorously proven that all closed‐loop signals are uniformly, ultimately bounded, and that all system states remain within the constraint set. Finally, the proposed scheme is demonstrated to be valid using numerical and practical simulation examples.