Fast Model Predictive Control Using Online Optimization

Model Predictive Control Using Polynomial Optimization

Sag Mill Optimization Using Model Predictive Control

Semi-Autogenous Grinding mills can be optimized for maximum ore throughput or maximum grinding energy efficiency. In both cases, precise control of the mill weight is critical. Model predictive control provides an additional tool to improve the control of Semi-Autogenous Grinding mills and is often able to reduce process variability beyond the best performance that can be obtained with proporti...

Improved Optimization Process for Nonlinear Model Predictive Control of PMSM

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

MODEL PREDICTIVE CONTROL and optimization

An extended state space (ESS) model, familiar in subspace identification theory, is used for the development of a model based predictive control algorithm for linear model structures. In the ESS model, the state vector consists of system outputs, which eliminates the need for a state estimator. A framework for model based predictive control is presented. Both general linear state space model st...

Semi-Active Suspension Control Using "Fast" Model-Predictive Techniques

The problem considered in this paper is the design and analysis of a control strategy, for semi-active suspensions in road vehicles, based on Model Predictive Control (MPC) techniques. The optimal control law we compute using predictive techniques aims to optimize, the suspension performance, by minimizing a quadratic cost function while ensuring that the magnitude of the forces generated by th...