Neural Adaptive Self-Triggered Control for Uncertain Nonlinear Systems With Input Hysteresis.

Area of Science:

• Control Systems Engineering
• Artificial Neural Networks
• Nonlinear Dynamics

Background:

• Uncertain nonlinear systems present significant control challenges.
• Input hysteresis complicates accurate system modeling and control.
• Existing event-triggered control requires continuous monitoring, hindering practical application.

Purpose of the Study:

• To develop a neural adaptive self-triggered tracking control strategy.
• To address control issues in uncertain nonlinear systems with input hysteresis.
• To improve upon traditional event-triggered control mechanisms.

Main Methods:

• Utilizing radial basis function neural networks (RBFNNs) for system approximation.
• Employing adaptive backstepping techniques for controller design.
• Implementing a self-triggered mechanism to determine the next control update instant based on current system information.

Main Results:

• The proposed controller effectively compensates for input hysteresis effects.
• Tracking errors are bounded by an explicit function of design parameters.
• All signals within the closed-loop system remain bounded, ensuring stability.

Conclusions:

• The developed adaptive self-triggered control is effective for uncertain nonlinear systems with hysteresis.
• The self-triggered approach offers practical advantages over continuous monitoring methods.
• Simulation examples validate the controller's performance and robustness.