Adjustable-Error-Based Adaptive Neural Network Tracking Control for Uncertain Nonlinear Systems

Area of Science:

• Control Engineering
• Artificial Intelligence
• Nonlinear System Analysis

Background:

• Traditional neural network (NN) control struggles with adjustable approximation errors.
• This limitation impacts precision and tracking accuracy in uncertain nonlinear systems.
• Existing methods lack flexibility in managing the error between NN approximators and unknown functions.

Purpose of the Study:

• To develop an adjustable-error neural network (NN) approximator.
• To integrate this approximator into an adaptive neural tracking controller for uncertain nonlinear systems.
• To enhance approximation accuracy and tracking error convergence.

Main Methods:

• Designed an adjustable-error NN approximator with adjustable parameters.
• Incorporated the approximator into an adaptive neural tracking controller.
• Utilized Lyapunov stability theory for system analysis and tracking error convergence.

Main Results:

• Achieved higher tracking error accuracy compared to traditional methods.
• Demonstrated improved approximation accuracy for unknown nonlinear functions.
• Verified the closed-loop system stability and tracking error convergence.

Conclusions:

• The proposed adjustable-error NN approximator enhances adaptive neural control.
• The novel controller design offers superior performance in uncertain nonlinear systems.
• Simulation and experimental results validate the effectiveness of the proposed scheme.