Adaptive Fuzzy Event-Triggered Control for High-Order Nonlinear Systems With Prescribed Performance

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Fuzzy Logic Systems

Background:

• High-order uncertain nonlinear systems present significant control challenges.
• Prescribed performance requirements necessitate advanced control strategies.
• Event-triggered control aims to reduce communication load.

Purpose of the Study:

• To design a novel adaptive fuzzy event-triggered tracking control approach.
• To address high-order uncertain nonlinear systems with prescribed performance.
• To simplify control design and reduce communication burden.

Main Methods:

• Utilizing a high-order tan-type barrier Lyapunov function (BLF) for output tracking error analysis.
• Employing fuzzy systems for identification of unknown nonlinear functions.
• Designing a single gain function to manage system uncertainties, avoiding parameter estimation.

Main Results:

• The proposed controller ensures tracking error remains within a predefined region.
• A simplified control structure is achieved, avoiding complex calculations.
• Communication load between the controller and actuator is significantly reduced.

Conclusions:

• The adaptive fuzzy event-triggered control is effective for high-order uncertain nonlinear systems.
• The approach successfully meets prescribed performance requirements.
• The method offers a practical solution with reduced computational and communication overhead.