Potential and Limitations of Multi-stage Nonlinear Model Predictive Control

Robust Multi-stage Nonlinear Model Predictive Control

Model Predictive Control (MPC) has become one of the most popular control techniques in the process industry mainly because of its ability to deal with multiple-input-multipleoutput plants and with constraints. However, its performance can deteriorate in the presence of model uncertainties and disturbances. In the last years, the development of robust MPC techniques has been widely discussed, b...

Advanced-multi-step Nonlinear Model Predictive Control

Nonlinear Model Predictive Control (NMPC) has gained wide attention through the application of dynamic optimization. However, this approach is susceptible to computational delay, especially if the optimization problem cannot be solved within one sampling time. In this paper we propose an advanced-multi-step NMPC (amsNMPC) method based on nonlinear programming (NLP) and NLP sensitivity. This met...

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...

Advanced-Multi-Step and Economically Oriented Nonlinear Model Predictive Control

This dissertation addresses two issues that arise in the field of Nonlinear Model Predictive Control (NMPC): computational delay and stability of economically oriented NMPC. NMPC has gained wide attention through the application of dynamic optimization. It has the ability to handle variable bounds and multi-input-multi-output systems. However, computational delay caused by large size of nonline...

Nonlinear Model Predictive Control