Time-Varying HJBE-Based Adaptive Safe Critic Control Design for Stochastic Asymmetric Constrained Multiagent Systems
针对存在非对称状态和输入约束的随机多智能体系统，提出一种自适应安全评判控制方法，通过统一变换函数处理约束、结合时变HJBE与积分强化学习实现无模型最优控制，并用经验回放提升学习效率。
In this article, we investigate the problem of adaptive safe critic control design for stochastic multiagent systems (MASs) subject to asymmetric state and input constraints. To systematically address asymmetric state constraints, a unified transformation function (UTF) is proposed to convert the constrained consensus control problem into the stability analysis of an unconstrained error system. In addition, a nonquadratic cost function is incorporated to address input limitations effectively. Building upon these developments, a time-varying Hamilton-Jacobi-Bellman equation (HJBE) is formulated by integrating the Bellman optimality principle with Itô's lemma, thereby accommodating stochastic disturbances and enhancing controller robustness. To improve data utilization and eliminate reliance on explicit drift dynamics, an integral reinforcement learning (IRL) algorithm is developed within this framework. Furthermore, a time-varying single-critic network is designed to approximate the solution to the HJBE and generate optimal control policies, thereby considerably reducing computational complexity. To further enhance learning efficiency and relax the persistent excitation (PE) condition, the experience replay (ER) technique is incorporated into the update process of the critic weight. Finally, two simulation examples are provided to verify the feasibility and effectiveness of the proposed approach.