Adaptive Fuzzy Control of Stochastic Systems: Developments In Event-Triggered, Finite-Time and Optimization-Based Approaches

Abstract

This review highlights recent advances in adaptive fuzzy control and optimization for stochastic multi-agent and nonlinear systems, focusing on consensus, event-triggered strategies, prescribed performance, and multi-objective optimization. Existing studies demonstrate robust methods for global consensus, finite-/fixed-time convergence, and resilience against uncertainties, network delays, and cyber-attacks. Event-triggered control effectively reduces communication overhead, while prescribed performance ensures predefined transient and steady-state behaviors. Fuzzy-guided optimization further enhances multi-objective problem-solving under uncertainty. Key gaps remain in computational efficiency, scalability, fault-tolerance, and integration of consensus, event-triggering, and optimization into unified frameworks. Addressing these challenges can enable more efficient, scalable, and resilient fuzzy control strategies for complex stochastic systems.