Pattern-Based NN Control of a Class of Uncertain Nonlinear Systems

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Uncertain nonlinear systems pose significant challenges for traditional control methods.
• Adaptive neural network (NN) controllers offer potential for improved performance but require efficient identification and adaptation strategies.

Purpose of the Study:

• To develop a pattern-based neural network (NN) control approach for uncertain nonlinear systems.
• To enhance closed-loop stability and tracking performance through situation recognition and controller selection.

Main Methods:

• Phase (i) identification: Adaptive NN controllers designed for stability and tracking, with dynamics identified via deterministic learning and stored in radial basis function (RBF) NNs.
• Phase (ii) identification: System dynamics under normal control identified via deterministic learning for abnormal conditions, with dynamical estimators built.
• Recognition and control phases: Rapid recognition of recurring situations using estimators, followed by selection of a pre-trained NN controller.

Main Results:

• The pattern-based control approach achieves closed-loop stability and improved tracking performance.
• The method demonstrates a humanlike control process, enabling fast decision-making in dynamic environments.
• Simulation results validate the effectiveness of the proposed control strategy.

Conclusions:

• The pattern-based NN control offers a novel framework for controlling uncertain nonlinear systems.
• This approach facilitates rapid situation recognition and adaptive controller selection.
• The method shows promise for applications requiring fast decision and control in dynamic and uncertain conditions.