Multi-rate data-driven model predictive control for ball mill grinding process

Industrial Internet of Things (IIoT) is widely used in ball mill grinding process to meet the intelligent requirements of production process. Through massive data collection and analysis, the Industrial Internet of Things has significantly improved production quality and enhanced economic efficiency. However, the introduction of more and more sensors with different sampling periods results in the multi-rate control problem. Meanwhile, the complex working conditions of the grinding process cause persistent changes in model parameters. To solve these problems, a multi-rate data-driven model predictive control algorithm is proposed in this paper. A multi-rate Hankel matrix is first designed according to the characteristics of sampling periods. Then the future data are predicted by using Willems fundamental lemma. A correction term is added to the data-driven model predictive controller to obtain offset-free tracking. Finally, experiments have been carried out to verify the effectiveness of the proposed method. This method offers a new approach for solving the multi-rate and time-varying problems in grinding process, and is expected to be implemented in practical mining scenarios.