Iterative learning control for differential inclusion systems with random fading channels by varying average...

Area of Science:

• Control Systems Engineering
• Networked Systems
• Robotics

Background:

• Network transmission inevitably causes fading, degrading system tracking performance.
• Fading channels impact both input and output sides of control systems.
• Iterative learning control (ILC) is crucial for systems requiring repetitive task precision.

Purpose of the Study:

• To investigate iterative learning control for differential inclusion systems affected by random fading channels.
• To analyze the influence of fading on tracking performance.
• To develop a control strategy that mitigates the effects of fading channels.

Main Methods:

• Utilizing a Steiner-type selector to convert set-valued mappings into single-valued mappings.
• Constructing a variable local average operator to counteract fading effects.
• Proving the convergence of the developed learning control algorithm.

Main Results:

• A novel variable local average operator effectively offsets fading channel impacts.
• The learning control algorithm's convergence is mathematically proven.
• Adjustable parameters in the operator allow a trade-off between learning rate and fading offset.

Conclusions:

• The proposed iterative learning control method enhances tracking performance in systems with fading channels.
• The variable local average operator provides a tunable mechanism for managing fading effects.
• Numerical simulations on switched reluctance motors validate the theoretical findings.