Nonlinear model predictive control with logic constraints for COVID-19 management

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 based/model predictive control with constraints and with/without nonlinear observer

A literature survey from the area of nonlinear model predictive control (MPC) is presented. After a brief review of the main characteristics of linear MPC algorithms various classes of nonlinear MPC algorithms based on state-space models, such as nonlinear extensions of dynamic matrix control (DMC), Newton-type algorithms and nonlinear programming (NLP) based algorithms are discussed. These alg...

Distributed Nonlinear Model Predictive Control for Dynamic Supply Chain Management

The purpose of this paper is to demonstrate the application of a new theory developed for distributed nonlinear model predictive control (NMPC) to a promising and exciting future domain for NMPC – that of dynamic management of supply chain networks. Recent work by the first author provides a distributed implementation of NMPC for application in large scale systems comprised of cooperative dynam...

Nonlinear Model Predictive Control

Fault-Tolerant Control of a Nonlinear Process with Input Constraints

A Fault-Tolerant Control (FTC) methodology has been presented for nonlinear processes being imposed by control input constraints. The proposed methodology uses a combination of Feedback Linearization and Model Predictive Control (FLMPC) schemes. The resulting constraints in the transformed process will be dependent on the actual evolving states, making their incorporation in the de...