Operation Output-Feedback Predictive Control Based on Model Order Reduction and Predictive Optimization in Industrial Processes

Modern industrial processes are essentially complex multivariable systems with high interaction among variables. Moreover, in modern process control, it is imperative not only to track a setting point for the controlled object but also the performance indicators for the entire operation process, where the optimal operational feedback control of multivariable industrial processes is derived. In this paper, we propose an operation output-feedback predictive control (OOFPC) method for multivariable industrial processes based on model order reduction and model predictive optimization. Firstly, causality networks are employed to extract potential correlations between variables to reduce the high-dimensional feature space. Subsequently, a setpoint compensator is developed within the framework of model predictive control (MPC), taking into account both performance indicators and operational constraints. Finally, closed-loop control with stable adjustment of the controlled object is achieved by utilizing proportional-integral-derivative (PID) controllers. Feedback correction and rolling optimization enable optimal setpoint tracking and stable closed-loop control. Experiments on a grinding process control case demonstrate the effectiveness of the proposed control strategy.