A Comprehensive Survey on Advanced Control Techniques for T-S Fuzzy Systems Subject to Control Input and System Output Requirements

Abstract

This paper provides a comprehensive survey on advanced control techniques for Takagi-Sugeno (T-S) fuzzy systems that are subject to input and output performance constraints. The focus is on addressing practical applications, such as actuator saturation and output limits, which are often encountered in industries like aerospace, automotive, and robotics. The paper discusses key control methods such as model predictive control, anti-windup compensators, and Linear Matrix Inequality (LMI)-based control, emphasizing their effectiveness in handling input and output constraints. These techniques ensure system stability, robustness, and performance even under strict physical limitations. The survey also highlights the importance of T-S fuzzy systems, which provide a flexible framework for modeling and controlling nonlinear systems by breaking them down into simpler linear models. Additionally, recent developments in robust and adaptive control strategies are explored, particularly in handling time delays, disturbances, and uncertainties. These methods are crucial for real-time applications where the system must remain stable and safe despite unmeasured states or external disturbances. By reviewing these advanced techniques, the paper aims to identify research gaps and future directions, particularly in scalable solutions and integrating data-driven approaches with T-S fuzzy control frameworks.