Intelligent robust tracking control for a class of uncertain strict-feedback nonlinear systems

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Artificial Intelligence

Background:

• Designing robust tracking controllers for nonlinear systems with uncertainties and disturbances is challenging.
• Existing methods may lack computational simplicity or ease of implementation.
• Strict-feedback nonlinear systems require specialized control strategies.

Purpose of the Study:

• To develop adaptive fuzzy-based or neural-network-based dynamic feedback tracking controllers.
• To ensure boundedness of all states and signals in the closed-loop system.
• To minimize trajectory tracking errors in the presence of uncertainties.

Main Methods:

• Utilizing adaptive approximators with linearly parameterized models.
• Implementing a partitioned procedure for fuzzy/neural network basis functions.
• Extending the approach to nonlinearly parameterized adaptive approximators.
• Designing intelligent robust tracking control schemes.

Main Results:

• The proposed controllers ensure all closed-loop system states and signals remain bounded.
• Trajectory tracking errors are minimized effectively.
• The developed control schemes exhibit computational simplicity and are easy to implement.
• Simulation examples validate the effectiveness of the proposed algorithms.

Conclusions:

• The adaptive fuzzy/neural network controllers provide robust tracking for strict-feedback nonlinear systems.
• The methods offer a computationally simple and easily implementable solution.
• The approach effectively handles plant uncertainties and external disturbances.