Generalized predictive control based on interval gray model with adaptive buffer operator for pattern-moving systems | JoVE Visualize

Area of Science:

Control Engineering
Complex Systems Analysis
Industrial Process Optimization

Background:

Pattern-moving systems, common in industrial processes like sintering and cement kilns, are complex nonlinear systems governed by statistical laws.
Existing control methods struggle to capture the statistical properties of these systems using deterministic variables, often overlooking their inherent nature or treating them as stochastic.
This limitation necessitates advanced control strategies capable of addressing the unique challenges posed by pattern-moving systems.

Purpose of the Study:

To develop a novel control strategy that accurately captures the statistical attributes of pattern-moving systems.
To enhance the precision of tracking dynamic pattern transitions in complex industrial processes.
To improve the disturbance rejection capabilities for complicated uncertain systems.

Main Methods:

Proposed a novel Interval Grey Adaptive Buffer Generalized Predictive Control (IGAB-GPC) utilizing the bidirectional mapping framework under Pattern Moving Theory (PMT).
Incorporated an adaptive buffer operator to mitigate oscillations in pattern class sequences based on monotonicity.
Developed an Interval Grey Model IGM(1,2)-based prediction model for uncertainty analysis and implemented a GPC control scheme with receding horizon optimization and feedback correction.

Main Results:

The IGAB-GPC demonstrated superior tracking performance compared to CARIMA-GPC and IG-GPC, reducing tracking errors by approximately two orders of magnitude.
Achieved a Mean Absolute Error (MAE) of 0.0056 and a Root Mean Squared Error (RMSE) of 0.0074, indicating high accuracy.
The integrated adaptive buffer operator, grey system modeling, and GPC effectively handled system uncertainty and improved disturbance rejection.

Conclusions:

The IGAB-GPC strategy effectively quantifies pattern category variables and precisely tracks dynamic pattern transitions in complex nonlinear systems.
The novel approach significantly enhances control accuracy and disturbance rejection for industrial processes characterized by pattern-moving dynamics.
This work provides a robust framework for controlling complicated uncertain systems from a pattern dynamic perspective.