Observer-Based Adaptive NN Control for a Class of Uncertain Nonlinear Systems With Nonsymmetric Input Saturation

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Artificial Intelligence

Background:

• Addressing control challenges in uncertain nonlinear systems is crucial for practical applications.
• Nonsymmetric input saturation and immeasurable states present significant hurdles in control design.
• Existing methods often struggle to provide explicit bounds on tracking errors under saturation.

Purpose of the Study:

• To develop an adaptive tracking control strategy for uncertain nonlinear systems.
• To handle nonsymmetric input saturation and immeasurable states effectively.
• To guarantee signal boundedness and provide explicit tracking error bounds.

Main Methods:

• Utilizing radial basis function neural networks (NNs) for approximating unknown system functions.
• Designing a neural network state observer to estimate immeasurable states.
• Employing an auxiliary system to analyze input saturation effects.
• Applying adaptive backstepping techniques for controller development.

Main Results:

• Achieved boundedness of all signals within the closed-loop system.
• Developed an adaptive tracking controller that accounts for input saturation.
• Established explicit bounds for the tracking error, dependent on design parameters and saturation error.
• Demonstrated the effectiveness of the proposed method through a simulation example.

Conclusions:

• The proposed adaptive tracking control approach effectively manages uncertain nonlinear systems with input saturation and unmeasurable states.
• The method provides superior performance by ensuring signal stability and offering explicit tracking error bounds.
• This work contributes a robust solution for complex control problems in engineering and robotics.