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

Authors

  • Shivdeep Kaur * Assistant Professor, Department of Mathematics, Mata Gujri College, Fatehgarh Sahib, Punjab, 140406 & Research Scholar in Mathematics, Desh Bhagat University, Mandi Gobindgarh, Punjab, India. https://orcid.org/0009-0003-2485-465X

https://doi.org/10.48313/maa.vi.40

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.

Keywords:

Adaptive fuzzy control, Stochastic multi-agent systems, Consensus, Event-triggered control, Prescribed performance, Multi-objective optimization

References

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Published

2024-03-10

Issue

Vol. 1 No. 1 (2024): Metaheuristic Algorithms with Applications

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How to Cite

Kaur, S. (2024). Adaptive Fuzzy Control of Stochastic Systems: Developments in Event-Triggered, Finite-Time and Optimization-Based Approaches. Metaheuristic Algorithms With Applications, 1(1), 36-50. https://doi.org/10.48313/maa.vi.40

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