Adaptive Iterative Learning Fault-Tolerant Control for State Constrained Nonlinear Systems With Randomly Varying...

Area of Science:

• Control Engineering
• Nonlinear Systems Theory
• Fault-Tolerant Control

Background:

• State-constrained nonlinear systems are challenging to control, especially with actuator faults.
• Iterative learning control (ILC) is effective for systems with repetitive tasks but struggles with varying iteration lengths.
• Ensuring system stability and performance under uncertainty and constraints is a critical research area.

Purpose of the Study:

• To develop an adaptive iterative learning fault-tolerant control (AILFTC) algorithm.
• To address state constraints and randomly varying iteration lengths in nonlinear systems.
• To handle unknown nonlinearities and actuator faults.

Main Methods:

• Designing modified parameter updating laws using a novel tracking error for variable iteration lengths.
• Employing radial basis function neural networks to approximate time-iteration-dependent nonlinearities.
• Utilizing a barrier Lyapunov function to enforce state constraints.
• Developing a barrier composite energy function for convergence analysis.

Main Results:

• The proposed AILFTC algorithm achieves tracking error convergence along the iteration axis.
• The control strategy effectively handles state constraints and actuator faults.
• The method is extended to high-order nonlinear systems.
• Simulations on a single-link manipulator demonstrate the algorithm's effectiveness.

Conclusions:

• The presented adaptive iterative learning fault-tolerant control algorithm is effective for state-constrained nonlinear systems with varying iteration lengths and actuator faults.
• The use of neural networks and barrier functions provides a robust framework for handling system uncertainties and constraints.
• The theoretical results are validated through simulation, showing practical applicability.