Prescribed-Time Optimal Consensus for Switched Stochastic Multiagent Systems: Reinforcement Learning Strategy

This paper focuses on the event-triggered-based prescribed-time optimal consensus control issue for switched stochastic nonlinear multi-agent systems under switching topologies. Notably, the system stability may be affected owing to the change in information transmission channels between agents. To surmount this obstacle, this paper presents a reconstruction mechanism to rebuild the consensus error at the switching topology instant. Combining optimal control theory and reinforcement learning strategy, the identifier neural network is utilized to approximate the unknown function, with its corresponding updating law being independent of the switching duration of system dynamics. In addition, an event-triggered mechanism is adopted to enhance the efficiency of resource utilization. With the assistance of the Lyapunov stability principle, sufficient conditions are established to ensure that all signals in the closed-loop system are cooperatively semi-globally uniformly ultimately bounded in probability and the consensus error is capable of converging to the specified interval in a prescribed time. At last, a simulation example is carried out to validate the feasibility of the presented control scheme.