Neural-Network Approximation-Based Adaptive Periodic Event-Triggered Output-Feedback Control of Switched Nonlinear...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Switched nonlinear systems (SNSs) present significant control challenges due to their complex dynamics.
• Efficient resource utilization in control systems is crucial, especially in networked environments.
• Existing adaptive neural-network (NN) control methods often require full state information or impose stringent conditions on system nonlinearities.

Purpose of the Study:

• To develop an adaptive neural-network (NN) periodic event-triggered control (PETC) strategy for switched nonlinear systems (SNSs).
• To design a controller that utilizes only sampled system output, reducing communication load.
• To overcome limitations of previous NN control approaches by removing restrictions on system nonlinearities.

Main Methods:

• Construction of a novel adaptive law and a state observer using only sampled system output.
• Development of an output-feedback adaptive NN PETC strategy with an event-triggering mechanism (ETM).
• Analysis of closed-loop system (CLS) stability under arbitrary switchings using Lyapunov methods.

Main Results:

• The proposed adaptive NN PETC strategy effectively reduces communication resource usage.
• The controller does not require prior knowledge or restrictions on the nonlinear functions of the SNSs.
• Stability analysis proves that all states of the closed-loop system (CLS) are semiglobally uniformly ultimately bounded (SGUUB) under arbitrary switchings.

Conclusions:

• The developed output-feedback adaptive NN PETC strategy is effective for switched nonlinear systems.
• The approach enhances communication efficiency by employing event-triggered control.
• The method is validated through application to a continuous stirred tank reactor (CSTR) system and numerical simulations.