DMET-Based Fuzzy Optimized Consensus Control for Nonlinear MASs With Quantized Reference

This article investigates the dynamic memory event-triggered (DMET) fuzzy optimized consensus control for nonlinear multiagent systems with quantized reference signal. To alleviate the communication burden, a dual communication channels DMET scheme is proposed, which encompasses event-driven communication for interactions among followers and communication between controllers and actuators. In comparison to the traditional dynamic event-triggered scheme, the devised DMET scheme incorporates historical information of the dynamic variable, resulting in longer triggering time intervals. Note that the problem of nondifferentiability in backstepping method is generated by the event-triggered communication and quantization. To address this challenge, a smooth signal generator is introduced to reconstruct the step signals into the differentiable new one. Meanwhile, a reinforcement learning approach is employed to optimize the controllers, which utilizes an identifier–critic–actor architecture with fuzzy logic system approximations at each step of backstepping method. The effectiveness of the proposed control method is demonstrated through simulations, confirming its capabilities in achieving optimized consensus control while mitigating communication loads.