Event-triggered adaptive iterative learning prescribed performance control of high-order strict feedback nonlinear...

Area of Science:

• Control Engineering
• Nonlinear Systems Theory
• Robotics

Background:

• Existing adaptive iterative learning control methods often use first-order systems and ignore network communication limits.
• High-order strict feedback nonlinear systems present challenges due to uncertainties and complex dynamics.

Purpose of the Study:

• To develop an event-triggered adaptive iterative learning prescribed performance control (PPC) for uncertain high-order strict feedback nonlinear systems.
• To address limitations of existing methods by incorporating network communication constraints and improving control performance.

Main Methods:

• Designed an event-triggered adaptive iterative learning PPC algorithm using nonlinear mapping and dynamic surface control.
• Employed an improved parameter estimation error reconstruction mechanism and a projection operator.
• Constructed a preset error trajectory within a performance envelope to manage initial conditions and tracking errors.

Main Results:

• Proved uniform ultimate boundedness of tracking error and parameter estimation errors.
• Demonstrated that tracking error satisfies prescribed transient performance in each iteration.
• Validated the approach via simulations on a single-link flexible-joint robot.

Conclusions:

• The proposed event-triggered adaptive iterative learning PPC is effective for uncertain high-order nonlinear systems.
• The method enhances parameter estimation and avoids control law singularity.
• Successfully addresses network communication constraints while ensuring prescribed performance.