Event-Based Adaptive NN Tracking Control of Nonlinear Discrete-Time Systems

Area of Science:

• Control Systems Engineering
• Nonlinear System Dynamics
• Artificial Intelligence in Control

Background:

• Traditional control schemes often rely on periodic data transmission, leading to high communication loads.
• Event-triggered control strategies offer a promising alternative to reduce communication overhead in networked control systems.
• Neural networks (NNs) have shown effectiveness in adaptive control for complex nonlinear systems.

Purpose of the Study:

• To simultaneously design a neural network (NN)-based adaptive control law and an event-triggering condition.
• To address strict feedback nonlinear discrete-time systems.
• To formally prove the stability and tracking performance of the developed event-triggered control system.

Main Methods:

• Development of a hybrid framework incorporating Lyapunov theory for stability analysis.
• Formulation of an event-triggered algorithm for updating control input and NN weights based on system state.
• Implementation of a strategy where feedback signals are transmitted only when an event-trigger error threshold is exceeded.

Main Results:

• Formal proof of closed-loop stability and tracking performance under the proposed event-triggering strategy.
• Significant reduction in communication load compared to traditional periodic control methods.
• Validation of the approach's effectiveness through a simulation example.

Conclusions:

• The proposed simultaneous design of NN-based adaptive control and event-triggering conditions is effective for nonlinear discrete-time systems.
• The event-triggering mechanism substantially reduces communication requirements without compromising system performance.
• This method offers a practical solution for implementing advanced control in resource-constrained networked environments.