Iterative Learning Model-Free Control for Networked Systems With Dual-Direction Data Dropouts and Actuator Faults

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Networked Systems

Background:

• Networked nonlinear discrete systems are susceptible to actuator faults and data dropouts, complicating control.
• Existing control strategies often struggle with dual-direction data loss and unknown system dynamics.

Purpose of the Study:

• To develop a novel adaptive fault-tolerant iterative learning model-free control strategy.
• To address tracking problems in nonlinear discrete systems with actuator faults and dual-direction data dropouts.

Main Methods:

• Compact form dynamic linearization to transform the nonlinear system into a data-driven model.
• A new mathematical relationship to model dual-direction data dropouts.
• An adaptive fault-tolerant iterative learning control scheme using randomly received data.
• A varying parameter approach to enhance learning and convergence rates.

Main Results:

• The developed control strategy effectively handles actuator faults and data dropouts.
• The varying parameter approach significantly improves the learning and convergence rates.
• The closed-loop system stability is rigorously proven in the sense of uniform ultimate boundedness.

Conclusions:

• The proposed adaptive fault-tolerant iterative learning model-free control strategy is effective for networked nonlinear discrete systems.
• The method provides robust tracking performance despite actuator faults and data dropouts.
• Numerical simulations confirm the practical applicability and effectiveness of the designed control scheme.