Observer-Based Adaptive Fuzzy Fault-Tolerant Optimal Control for SISO Nonlinear Systems

Area of Science:

• Control Engineering
• Artificial Intelligence
• Nonlinear System Analysis

Background:

• Nonlinear systems with strict feedback forms present challenges in control design due to unknown nonaffine nonlinear faults and unmeasured states.
• Traditional control methods struggle to address both fault tolerance and optimality simultaneously in such complex systems.
• Adaptive fuzzy systems offer a promising approach for approximating unknown system dynamics and control objectives.

Purpose of the Study:

• To develop an adaptive fuzzy output feedback fault-tolerant optimal control scheme for nonlinear systems.
• To address the challenges posed by unknown nonaffine nonlinear faults and unmeasured states.
• To ensure system stability and achieve optimal control performance.

Main Methods:

• Fuzzy logic systems are employed to approximate the unknown nonlinear functions and the system's cost function.
• An adaptive state observer is designed to estimate unmeasured states.
• Adaptive backstepping control, combined with adaptive critic and fault-tolerant control techniques, is utilized.
• Filtered signals are introduced to handle nonaffine nonlinear faults within the control design.

Main Results:

• A novel adaptive fuzzy fault-tolerant optimal control scheme is successfully developed.
• The stability of the closed-loop system is rigorously proven using Lyapunov stability theory.
• Simulation results demonstrate the effectiveness and robustness of the proposed control strategy.

Conclusions:

• The proposed adaptive fuzzy fault-tolerant optimal control scheme effectively manages nonlinear systems with unknown faults and unmeasured states.
• The integration of adaptive observers, fuzzy logic, and adaptive critic techniques provides a robust solution.
• The validated stability and performance highlight the practical applicability of the developed control strategy.