Model Predictive Control for Blending Processes in Cement Plants

Model predictive control of large scale processes

Model predictive control is a receding horizon control policy in which a linear or quadratic program with linear constraints is solved on-line at each sampling instance. An algorithm is developed that allows quick computation of suboptimal control moves. The linear constraint set is approximated by an ellipsoid and a change of variables is performed so that a solution may be computed eciently ...

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...

Nonlinear Model Predictive Control for Combined Cycle Power Plants

This master thesis project serves to investigate the possibilities of Nonlinear Model Predictive Control (NMPC) using the example of enthalpy control of the BENSON HRSG (heat recovery steam generator) of a combined cycle power plant (CCPP). The general idea of NMPC is to solve an optimization problem, to find the next control action, and this optimization problem is based on a model of the syst...

Controlling Nonlinear Processes, using Laguerre Functions Based Adaptive Model Predictive Control (AMPC) Algorithm

Laguerre function has many advantages such as good approximation capability for different systems, low computational complexity and the facility of on-line parameter identification. Therefore, it is widely adopted for complex industrial process control. In this work, Laguerre function based adaptive model predictive control algorithm (AMPC) was implemented to control continuous stirred tank rea...

Fuzzy Controller for Cement Raw Material Blending