Command Filtered Neuroadaptive Fault-Tolerant Control for Nonlinear Systems With Input Saturation and Unknown Control...

Area of Science:

• Control Systems Engineering
• Nonlinear System Analysis
• Artificial Intelligence in Control

Background:

• Nonlinear systems with input saturation present significant control challenges.
• Nonaffine faults and unknown control direction further complicate tracking control.
• Existing methods struggle with complexity explosion and filter error impacts.

Purpose of the Study:

• To develop a robust fault-tolerant control strategy for nonlinear systems with input saturation, nonaffine faults, and unknown control direction.
• To address the 'explosion of complexity' and filter error issues in command filtered control.
• To ensure boundedness and convergence of all signals in the closed-loop system.

Main Methods:

• A fault-tolerant command filtered control (CFC) approach is combined with error compensation.
• Adaptive radial basis function neural networks (NNs) are employed to approximate unknown nonlinear functions and nonaffine faults.
• Nussbaum gain technology is utilized to handle the unknown control direction.

Main Results:

• The proposed controller guarantees that all signals within the closed-loop system remain bounded and converge.
• An explicit upper bound for the absolute value of the system tracking error is derived.
• Simulation results demonstrate the effectiveness and superiority of the proposed fault-tolerant control method.

Conclusions:

• The developed adaptive neural network-based CFC method provides an effective solution for robust tracking control of nonlinear systems with challenging uncertainties.
• The integration of CFC, error compensation, NNs, and Nussbaum gain offers a comprehensive approach to fault tolerance.
• The controller's performance is validated through comparative simulations, confirming its practical applicability.