Quantized iterative learning control for impulsive differential inclusion systems with data dropouts.

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Networked Systems

Background:

• Impulsive differential inclusion systems are complex and susceptible to data dropouts.
• Existing control methods struggle with asynchronous data and quantization effects.

Purpose of the Study:

• To develop a quantized iterative learning control strategy for impulsive systems with random data dropouts.
• To achieve zero-error tracking performance despite communication limitations.

Main Methods:

• Utilized a Steiner-type selector to transform set-valued mappings into single-valued mappings.
• Designed an intermittent update learning algorithm to handle data asynchronism from two-sided dropouts.
• Introduced a scaling sequence for zero-error tracking and determined an upper bound for quantization levels.

Main Results:

• The proposed quantization method reduces network communication load by increasing computation.
• The learning algorithm effectively manages data dropouts without requiring specific probability distributions.
• Achieved bounded quantization error and zero-error tracking performance.

Conclusions:

• The developed quantized iterative learning control is effective for impulsive systems with data dropouts.
• The method offers a practical solution for networked control systems facing communication uncertainties.
• Validated through numerical simulations on a switched reluctance motor system.