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 pose significant control challenges.
• Input hysteresis complicates precise tracking control.
• Existing event-triggered control requires continuous monitoring.

Purpose of the Study:

• To develop an adaptive self-triggered tracking control for uncertain nonlinear systems with input hysteresis.
• To overcome limitations of continuous monitoring in event-triggered systems.
• To ensure effective compensation of hysteresis and bounded tracking errors.

Main Methods:

• Integration of radial basis function neural networks (RBFNNs) with adaptive backstepping.
• Development of a self-triggered mechanism determining the next trigger instant based on current information.
• Controller design to compensate for hysteresis effects.

Main Results:

• Effective compensation of input hysteresis demonstrated.
• Tracking errors bounded by an explicit function of design parameters.
• All other closed-loop system signals proven to remain bounded.
• Validation through two illustrative examples.

Conclusions:

• The proposed adaptive self-triggered control is effective for uncertain nonlinear systems with hysteresis.
• The method offers practical advantages over traditional event-triggered control by avoiding continuous monitoring.
• Guaranteed stability and performance bounds are achieved.