Output feedback control of nonlinear systems using RBF neural networks

Area of Science:

• Control Theory
• Nonlinear Systems
• Artificial Intelligence

Background:

• Nonlinear plants present significant challenges for traditional control methods.
• Adaptive control strategies are crucial for systems with uncertainties and time-varying dynamics.
• Radial Basis Function (RBF) neural networks offer powerful function approximation capabilities for complex systems.

Purpose of the Study:

• To develop an adaptive output feedback control scheme for continuous-time nonlinear plants.
• To utilize RBF neural networks for adaptive compensation of plant nonlinearities.
• To ensure semi-global uniform ultimate boundedness of the system output.

Main Methods:

• Employing an RBF neural network for adaptive nonlinearity compensation.
• Utilizing a Lyapunov-based design for network weight adaptation.
• Incorporating parameter projection, control saturation, and a high-gain observer.

Main Results:

• Demonstrated the effectiveness of the proposed adaptive control scheme through simulations.
• Achieved semi-global uniform ultimate boundedness for the output tracking error.
• Showcased the benefits of combining adaptive and robust control for improved performance.

Conclusions:

• The proposed adaptive output feedback control scheme effectively handles nonlinear plant dynamics.
• The integration of RBF neural networks and Lyapunov-based adaptation provides a robust solution.
• Combining adaptive and robust control enhances tolerance to approximation errors, enabling simpler network designs.