Pattern-based learning and control for a class of sampled-data nonlinear systems.

Area of Science:

• * Industrial Control Systems Engineering
• * Adaptive Control Theory
• * Machine Learning in Control

Background:

• * Traditional control schemes fail in dynamic industrial environments with changing system parameters.
• * Sampled-data systems require advanced control strategies to manage multiple operating scenarios.
• * Complexity arises from time-varying dynamics and the need for real-time adaptation.

Purpose of the Study:

• * To develop a robust pattern-based learning and control strategy for sampled-data systems.
• * To address challenges posed by multiple, dynamic operating scenarios in industrial processes.
• * To enhance system stability and control performance under varying conditions.

Main Methods:

• * Two-phase identification: designing sampled-data neural network (NN) controllers and constructing a candidate controller library using deterministic learning (DL) theory.
• * Second identification phase: precise identification of closed-loop system dynamics using estimators.
• * Recognition and control phase: rapid detection of scenario changes via minimum residual principle and selection of appropriate learning controllers.

Main Results:

• * Accurate approximation of unknown system dynamics using NNs based on DL theory.
• * Effective identification of system dynamics under normal controller operation.
• * Rapid and accurate detection of control scenario changes.
• * Successful selection of suitable learning controllers ensuring stability and performance.

Conclusions:

• * The proposed pattern-based learning control strategy effectively handles dynamic industrial processes.
• * The approach ensures system stability and high-performance control across multiple operating scenarios.
• * Simulation results validate the efficacy of the adaptive control method.