Neuroadaptive Control With Enhanced Stability and Reliability

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Machine Learning

Background:

• Neural network (NN) performance in control systems depends on NN unit reliability.
• Maintaining compact set conditions for NN training signals is vital for universal approximation capabilities but often neglected.
• Existing NN control studies frequently overlook the importance of compact input sets for sustained NN functionality.

Purpose of the Study:

• To develop a method ensuring NN training signals remain within a fixed region during operation.
• To safeguard the functionality of NN-driven control units by meeting the universal approximation theorem's compactness condition.
• To enhance the robustness and reliability of NN-based control schemes, even under NN underperformance.

Main Methods:

• Introduced a constraint transformation-based design method to ensure excitation signals originate from a fixed region.
• Employed a decaying damping rate for asymptotic convergence of tracking error to zero.
• Developed a fail-secure control strategy based on worst-case NN behavior to handle underperformance.

Main Results:

• The proposed method ensures the compactness condition for NN training signals, preserving NN capabilities.
• Tracking errors asymptotically converge to zero, surpassing the ultimately uniformly bounded (UUB) limitation.
• Numerical simulations confirm significant improvements in the robustness and performance of NN-driven control systems.

Conclusions:

• The constraint transformation method effectively maintains NN training signal compactness, enhancing control system reliability.
• The fail-secure mechanism provides robust operation, ensuring system stability even with suboptimal NN performance.
• The study demonstrates a substantial advancement in creating dependable and high-performing NN-driven control systems.