IRL-Based Optimal Consensus Control of MASs With Predefined Time Convergence Performance
针对非线性多智能体系统，提出一种结合预设时间收敛性能的最优一致性控制方法，利用积分强化学习避免系统动力学建模，并采用单评判网络降低计算复杂度。
This study addresses the adaptive optimal consensus control problem for nonlinear multiagent systems (MASs). To enhance the convergence speed of the consensus error, a predefined time performance technique is integrated into the optimal control framework. Unlike the conventional Hamilton–Jacobi–Bellman equation (HJBE), a time-varying HJBE is formulated to solve the optimal control problem for MASs. To further improve efficiency, an integral reinforcement learning (IRL) algorithm is developed, which eliminates the need for precise system dynamics during controller design. In addition, a single critic network is employed to simultaneously evaluate system performance and execute control actions, effectively reducing computational complexity. The experience replay technique is incorporated into the update law for the critic network weights, thus alleviating the requirement for persistent excitation. Finally, a simulation example is presented to validate the feasibility and effectiveness of the proposed method.