A novel prescribed performance controller for strict-feedback nonlinear systems with input constraints.

Area of Science:

• Control Engineering
• Nonlinear Systems Analysis
• Robotics

Background:

• Input saturation is a common constraint in nonlinear control systems.
• Achieving both transient and steady-state performance is challenging under these constraints.
• Existing methods may require approximations or complex derivative calculations.

Purpose of the Study:

• To develop a prescribed performance control (PPC) strategy for strict-feedback nonlinear systems with input saturation.
• To achieve desired steady-state and transient performance within a pre-designed time frame.
• To enhance robustness against uncertain nonlinearities and disturbances.

Main Methods:

• Utilized an improved tuning function for performance regulation.
• Introduced a novel error transformation function with inherent robustness.
• Avoided approximation techniques and analytical derivative calculations.

Main Results:

• The proposed PPC scheme successfully achieved prescribed performance.
• Demonstrated superior transient and steady-state performance compared to existing methods.
• The control strategy exhibited robustness in the presence of uncertain nonlinearities and disturbances.
• Validated through simulation experiments.

Conclusions:

• The developed PPC method offers an effective solution for controlling nonlinear systems with input saturation.
• The novel error transformation function provides inherent robustness and simplifies the control design.
• The approach achieves improved performance with lower complexity.