Wavelet Fuzzy Brain Emotional Learning Control System Design for MIMO Uncertain Nonlinear Systems.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Controlling multiple-input multiple-output (MIMO) uncertain nonlinear systems presents significant challenges.
• Existing control strategies often struggle with system uncertainties and dynamic complexities.

Purpose of the Study:

• To introduce an efficient and novel control scheme for MIMO uncertain nonlinear systems.
• To enhance system performance metrics including approximation ability, learning, and convergence rate.

Main Methods:

• A wavelet fuzzy brain emotional learning controller (WFBELC) model integrating wavelet functions, fuzzy theory, and brain emotional neural networks.
• Online parameter adjustment using the gradient descent method.
• Lyapunov function utilization to ensure rapid convergence.

Main Results:

• The proposed WFBELC model demonstrated superior performance in controlling uncertain nonlinear systems.
• The controller effectively improved approximation ability, learning performance, and convergence rates.
• Validation through case studies on Duffing-Holmes chaotic system and Chua's chaotic circuit confirmed model superiority.

Conclusions:

• The WFBELC model offers a more satisfactory control performance for MIMO uncertain nonlinear systems.
• The proposed controller is highly suitable for mitigating the impact of uncertainties in complex nonlinear systems.