ADP-Based Fault-Tolerant Control with Stability Guarantee for Nonlinear Systems.

Area of Science:

• Control Theory
• Nonlinear Systems
• Artificial Intelligence

Background:

• Actuator faults in nonlinear systems can lead to performance degradation and instability.
• Existing fault tolerant control (FTC) methods may require stringent initial conditions.
• Adaptive dynamic programming (ADP) offers a data-driven approach for optimal control design.

Purpose of the Study:

• To develop a stability-guaranteed adaptive dynamic programming (ADP)-based fault tolerant control (FTC) strategy for nonlinear systems with actuator faults.
• To design a fault observer for identifying unknown actuator faults.
• To ensure system stability without relying on initial admissible control assumptions.

Main Methods:

• Design of a fault observer to estimate unknown actuator faults.
• Utilization of a critic neural network (NN) to approximate the optimal control of the nominal system.
• Development of a stability-aware weight update mechanism based on the Lyapunov stability theorem.
• Integration of fault estimation and nominal optimal control for fault compensation.

Main Results:

• The proposed ADP-based FTC effectively eliminates the influence of actuator faults.
• Uniform ultimate boundedness is proven for observer errors, NN weight estimation errors, and the closed-loop system using Lyapunov's direct method.
• Simulation examples validate the effectiveness and stability guarantees of the developed control strategy.

Conclusions:

• The developed stability-guaranteed ADP-based FTC provides robust fault tolerance for nonlinear systems with actuator faults.
• The method relaxes initial control constraints, enhancing applicability.
• The approach demonstrates strong theoretical underpinnings and practical validity through simulations.