Tracking Control of Self-Restructuring Systems: A Low-Complexity Neuroadaptive PID Approach With Guaranteed...

Area of Science:

• Control Systems Engineering
• Adaptive Control Theory
• Neural Network Applications

Background:

• Traditional control systems often assume fixed structures, which are insufficient for dynamic systems.
• Self-restructuring systems, common in biology and engineering, present challenges due to their complex and uncertain dynamics.
• Model-based control for such systems is often impractical due to high costs and complexity.

Purpose of the Study:

• To develop a low-complexity, non-model-based control strategy for self-restructuring systems.
• To address the tracking control problem in systems with varying structures.
• To ensure system stability and performance specifications are met despite dynamic uncertainties.

Main Methods:

• Exploration of a non-model-based, low-complexity proportional-integral-derivative (PID) control approach.
• Integration of neural-network (NN)-based self-tuning adaptive gains into the PID controller.
• Analytical derivation of the tuning strategy based on system stability and performance criteria.
• Application of matrix decomposition techniques to handle both square and nonsquare systems.

Main Results:

• The proposed adaptive PID controller effectively manages systems with varying structures.
• The neural-network-based tuning ensures transient behavior and steady-state performance.
• The method is validated for both square and nonsquare system configurations.
• Simulations confirm the benefits and feasibility of the adaptive control strategy.

Conclusions:

• A novel adaptive PID control method offers a practical solution for self-restructuring systems.
• The NN-based adaptive gains provide robust tracking control under system uncertainties.
• The approach is versatile, applicable to diverse system structures, and validated through simulations.