A non-linear model-based control architecture for a single-stage grinding mill circuit closed with a hydro cyclone is proposed. The control architecture aims to achieve independent control of circuit throughput and product quality, and consists of a non-linear model predictive controller for grinding mill circuit control, and a dynamic inversion controller to control the fast sump dynamics. A particle filter is used to estimate the mill states, and an algebraic routine is used to estimate the sump states. The observers make use of real-time continuous measurements commonly available on industrial plants. Simulation results show that control objectives can be achieved by the controller despite the presence of measurement noise and disturbanc...
In the raymond mill grinding process, precise control of operating load is vital for the high produc...
A constrained Model Predictive Static Programming (MPSP) method is implemented in simulation to a si...
The growing complexity of the control systems and the increased use of nonlinear models cause a dram...
A non-linear model-based control architecture for a single-stage grinding mill circuit closed with a...
This paper focuses on the design of a nonlinear model predictive control (NMPC) scheme for a cement ...
The recently developed reference-command tracking version of model predictive static programming (MP...
This paper presents the development of a nonlinear model predictive controller (NMPC) applied to a c...
Electromagnetic mill installation for dry grinding represents a complex dynamical system that requir...
This paper presents the development of a non-linear model predictive controller (NMPC) applied to a ...
In minerals processing control is vital to increasing performance and output of a plant. Model predi...
A step-wise algebraic routine is used to fit a dynamic non-linear model, specifically developed for ...
The states and unknown parameters of a simplified non-linear grinding mill circuit model for process...
A hybrid non-linear model predictive controller (HNMPC) is developed for a run-of-mine ore grinding ...
Abstract—This brief investigates the feasibility of applying a ro-bust nonlinear model predictive co...
Control problems of engineering interest such as industrial grinding circuit (IGC) are essential for...
In the raymond mill grinding process, precise control of operating load is vital for the high produc...
A constrained Model Predictive Static Programming (MPSP) method is implemented in simulation to a si...
The growing complexity of the control systems and the increased use of nonlinear models cause a dram...
A non-linear model-based control architecture for a single-stage grinding mill circuit closed with a...
This paper focuses on the design of a nonlinear model predictive control (NMPC) scheme for a cement ...
The recently developed reference-command tracking version of model predictive static programming (MP...
This paper presents the development of a nonlinear model predictive controller (NMPC) applied to a c...
Electromagnetic mill installation for dry grinding represents a complex dynamical system that requir...
This paper presents the development of a non-linear model predictive controller (NMPC) applied to a ...
In minerals processing control is vital to increasing performance and output of a plant. Model predi...
A step-wise algebraic routine is used to fit a dynamic non-linear model, specifically developed for ...
The states and unknown parameters of a simplified non-linear grinding mill circuit model for process...
A hybrid non-linear model predictive controller (HNMPC) is developed for a run-of-mine ore grinding ...
Abstract—This brief investigates the feasibility of applying a ro-bust nonlinear model predictive co...
Control problems of engineering interest such as industrial grinding circuit (IGC) are essential for...
In the raymond mill grinding process, precise control of operating load is vital for the high produc...
A constrained Model Predictive Static Programming (MPSP) method is implemented in simulation to a si...
The growing complexity of the control systems and the increased use of nonlinear models cause a dram...