Prescribed-Time Fuzzy Optimal Containment Control for Multiagent Systems With Deferred Output Constraints: An Output Mask Method
Xiaona Song, Peng Sun, Shuai Song, Choon Ki Ahn
Abstract
This article studies the adaptive prescribed-time fuzzy optimal containment control issue for multiagent systems (MASs) with deferred output constraints based on the reinforcement learning (RL) algorithm. Given that agents require confidential state messages, an output mask scheme is delicately synthesized to ensure that other agents cannot identify the true state message, potentially adding to the sophistication of the containment control process of MAS. Then, an adaptive prescribed-time fuzzy optimal containment control strategy is developed that counts on the masked state of neighboring agents. In addition, an auxiliary error via the shifting function is incorporated into the nonlinear mapping function to manage error constraints, not only avoiding the feasibility criteria but also realizing the unified control. Notably, an emerging intermediate variable is executed to tackle the issue of unknown control gains acting on the RL-based recursive design procedure. Moreover, the drawback of semiglobal boundedness of the error surface induced by dynamic surface control can be avoided with the aid of the novel Lyapunov-like energy candidate. With the assistance of the practical prescribed-time stability, it can be guaranteed that the original state value of each agent remains undisclosed, and the output of the followers can be centered on a convex hull made up of leaders within a prescribed time. Herein, the efficacy of the suggested tactic is exemplified through two illustrative examples.