Low-Complexity Tracking Control of Unknown Strict-Feedback Systems With Quantitative Performance Guarantees.

Area of Science:

• Control Systems Engineering
• Nonlinear Control Theory
• Robotics and Automation

Background:

• Strict-feedback systems often face challenges with unknown nonlinearities and unmatched disturbances.
• Achieving precise trajectory tracking with quantitative performance specifications (peak value, overshoot, settling time, accuracy) is a significant control problem.
• Existing methods may require complex components like function approximators or parameter identifiers.

Purpose of the Study:

• To develop a novel robust prescribed performance control (PPC) approach for strict-feedback systems.
• To address the challenge of achieving complete performance specifications for trajectory tracking under unknown nonlinearities and disturbances.
• To ensure natural satisfaction of initial conditions while quantitatively regulating tracking error metrics.

Main Methods:

• An error transformation incorporating a shifting function and a new-type barrier function is introduced.
• A class of performance functions is utilized to define settling times and steady-state bounds for intermediate errors.
• Asymmetric performance boundaries are designed to enhance the flexibility of tracking error specifications.

Main Results:

• A novel robust prescribed performance control (PPC) approach is proposed.
• The method achieves quantitative performance guarantees for trajectory tracking.
• The approach avoids the need for function approximation, parameter identification, disturbance estimation, derivative calculation, or command filtering.

Conclusions:

• The proposed PPC method effectively handles unknown nonlinearities and unmatched disturbances in strict-feedback systems.
• It provides a robust and simplified solution for achieving precise trajectory tracking with guaranteed performance.
• Simulation studies confirm the theoretical findings and the efficacy of the developed control strategy.