Prescribed Performance Model-Free Adaptive Integral Sliding Mode Control for Discrete-Time Nonlinear Systems.

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Adaptive Control Theory

Background:

• Prescribed Performance Control (PPC) addresses tracking error constraints in control systems.
• Existing PPC methods often require detailed knowledge of system dynamics.
• Discrete-time nonlinear systems present unique challenges for control design.

Purpose of the Study:

• To develop a data-driven prescribed performance control (PPC) strategy for discrete-time nonlinear systems.
• To design a controller that enforces strict constraints on tracking error, convergence rate, and overshoot.
• To eliminate the need for prior knowledge of the system's structure or dynamics.

Main Methods:

• Utilizing equivalent dynamic linearization techniques.
• Constructing a novel transformed error strategy.
• Designing an adaptive integral sliding mode controller.

Main Results:

• The designed controller ensures tracking errors converge within a predefined neighborhood.
• The control scheme guarantees convergence rates below a specified threshold and limits overshoot.
• The approach achieves superior tracking performance through parameter regulation.
• The controller operates effectively without requiring plant structure or system dynamics information.

Conclusions:

• The proposed data-driven PPC method offers a robust solution for controlling discrete-time nonlinear systems with performance constraints.
• This approach enhances practical applicability by not relying on system identification.
• The controller's efficiency is validated through simulation examples.