Neural Adaptive Event-Triggered Control for Nonlinear Uncertain Stochastic Systems With Unknown Hysteresis

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Stochastic Systems

Background:

• Hysteresis systems are prone to instability due to stochastic disturbances.
• Ensuring tracking performance in uncertain nonlinear hysteresis systems with actuator failures is challenging.
• Limited communication resources complicate control design for stochastic nonlinear systems.

Purpose of the Study:

• To develop an adaptive neural network (NN) control method for uncertain nonlinear hysteresis systems.
• To address challenges posed by stochastic disturbances, actuator failures, and limited communication resources.
• To ensure bounded system states and tracking errors in probability.

Main Methods:

• Proposed novel properties of the Nussbaum function.
• Designed an auxiliary virtual controller using a neural network (NN) universal approximator.
• Implemented an event-triggered controller combined with back-stepping technology and an optimization algorithm.

Main Results:

• The proposed adaptive NN control method effectively manages uncertain nonlinear hysteresis systems.
• The event-triggered strategy ensures that system states and tracking errors remain bounded in probability.
• Demonstrated the method's effectiveness through numerical examples.

Conclusions:

• The developed adaptive NN control strategy with an event-triggered mechanism is effective for uncertain nonlinear hysteresis systems.
• The approach successfully handles stochastic disturbances, actuator failures, and communication constraints.
• This method offers a robust solution for maintaining system performance under complex operating conditions.