A Novel Adaptive Gain Strategy for Stochastic Learning Control.

Area of Science:

• Control Theory
• Stochastic Systems
• Machine Learning

Background:

• High-precision tracking and rapid convergence conflict in stochastic system learning control.
• Decreasing gain sequences ensure convergence but slow down speed.

Purpose of the Study:

• Propose a novel multistage learning control strategy to resolve the conflict between tracking precision and convergence speed.
• Improve the optimization of stage lengths for enhanced performance.

Main Methods:

• A multistage learning control strategy with constant learning gain per stage.
• Gain reduction occurs between stages, with switching iterations determined by performance indices and noise drift.
• An optimization mechanism for stage lengths is incorporated.

Main Results:

• Strict establishment of asymptotic convergence for the generated input sequence.
• Demonstration of improved tracking precision and convergence speed compared to traditional methods.
• Validation of theoretical results through numerical simulations.

Conclusions:

• The proposed multistage strategy effectively resolves the conflict in stochastic system learning control.
• The method ensures asymptotic convergence while optimizing for faster convergence speed.
• This approach offers a significant advancement in learning control for stochastic systems.