Output-Constrained Prescribed Performance Control of MIMO Nonlinear Systems With a Priori Unknown References.

Area of Science:

• Robotics and Control Systems
• Nonlinear System Dynamics
• Adaptive Control Theory

Background:

• Prescribed Performance Control (PPC) is crucial for nonlinear systems.
• Existing methods struggle with unknown dynamics and advance reference signals.
• Output constraints pose significant challenges in control system design.

Purpose of the Study:

• To develop a robust decoupling PPC strategy for multi-input multi-output block-triangular nonlinear systems.
• To handle systems with unknown dynamics, unknown references, and output constraints.
• To ensure system outputs track references within specified performance bounds.

Main Methods:

• A novel robust decoupling PPC strategy is introduced.
• An online boundary generation scheme is devised.
• A smoothly constraint switching rule is incorporated.
• The controller is designed to be independent of function approximation, parameter identification, or disturbance estimation.

Main Results:

• The developed controller guarantees system outputs stay within constraint bands.
• Precise tracking of references with predetermined overshoot, settling time, and accuracy is achieved.
• The approach demonstrates efficacy and superiority on a 2-DOF serial flexible link robot.

Conclusions:

• The proposed robust decoupling PPC strategy effectively manages nonlinear systems with unknown dynamics and output constraints.
• The controller offers a low-complexity, high-performance solution for challenging control problems.
• This method overcomes limitations of existing approaches by not requiring parameter identification or disturbance estimation.