Event-Triggered Adaptive Neural Network Sensor Failure Compensation for Switched Interconnected Nonlinear Systems...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• System Dynamics

Background:

• Investigating control issues in nonlinear switched large-scale systems presents challenges due to unknown parameters, interconnections, and sensor faults.
• Existing methods are insufficient for addressing complex scenarios involving arbitrary switchings and sensor failures simultaneously.

Purpose of the Study:

• To develop a robust decentralized adaptive neural network (NN) event-triggered control scheme for nonlinear switched large-scale systems with sensor failures.
• To address limitations in prior research concerning unknown control coefficients, output interactions, and arbitrary system switchings.

Main Methods:

• A novel observer is designed for state estimation, and neural networks (NNs) are employed for identifying system uncertainties and interconnections.
• A fault compensation mechanism and a Nussbaum-type function are introduced to handle sensor faults and unknown control coefficients, respectively.
• An event-triggered decentralized control scheme based on the common Lyapunov function (CLF) method is proposed, incorporating a novel switching threshold strategy.

Main Results:

• The proposed control strategy guarantees boundedness of all closed-loop signals even in the presence of sensor failures.
• The developed method effectively compensates for sensor faults and unknown control coefficients in nonlinear switched large-scale systems.
• The strategy avoids Zeno behavior, ensuring practical implementation, as confirmed by simulation results.

Conclusions:

• The proposed event-triggered decentralized adaptive NN control is effective for nonlinear switched large-scale systems with sensor failures.
• The approach offers a robust solution for complex systems with uncertainties and communication constraints.
• Simulation outcomes validate the efficacy and practical applicability of the developed control strategy.