Superconvergence of Online Optimization for Model Predictive Control

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...

Model Predictive Control of Distributed Energy Resources with Predictive Set-Points for Grid-Connected Operation

This paper proposes an MPC - based (model predictive control) scheme to control active and reactive powers of DERs (distributed energy resources) in a grid - connected mode (either through a bus with its associated loads as a PCC (point of common coupling) or an MG (micro - grid)). DER may be a DG (distributed generation) or an ESS (energy storage system). In the proposed scheme, the set - poin...

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...