Disturbance rejection of ball mill grinding circuits using DOB and MPC

Hardware-in-the-loop simulation platform for supervisory control of mineral grinding process

“…Until now, there have been some attempts on solving this supervisory control problem. These begin with some model-based control and optimization methods, such as real-time optimization (RTO) [25], model predictive control (MPC) [2,[26][27][28] and adaptive decoupled control [29,30]. But, these methods are hard to be applied in practical MGPs, as accurate modeling is difficult to achieve or the established models do not accurately describe the actual dynamic processes.…”

“…As a crucial component of the beneficiation process, mineral grinding process (MGP) is used to grind the run-of-mine ore into suitable particle size such that the valuable mineral constituent can be exposed to be recovered in the subsequent classification process [1,2]. In the past, the process control research of the MGP was focused on the basic loop control methods to ensure the process variables follow their setpoints in a stable operation.…”

Grinding in Ball Mills: Modeling and Process Control

“…An approach involving simultaneous decoupling of interactions and closed loop pole assignment is proposed in [24]. A control system incorporating disturbance observer and MPC is designed in [50]. Innovative control platforms are described in [43] and a comparison of multivariable PI, fuzzy and model predictive control is presented in [40].…”

Robust control of nonlinear MAGLEV suspension system with mismatched uncertainties via DOBC approach

“…The disturbance observer technique was first presented by Ohishi et al [2] for a position servo system in the late 1980s. During the last three decades, DOBC schemes for linear and nonlinear systems have been proposed and applied successfully in various practical areas, e.g., servo control systems [3,4], robotic systems [5,6,7], hard disk drive systems [8,9], flight control systems [10,11], and process control systems [12,13,14].…”