Tutorial Overview

Model Predictive Control (MPC) has a long history in the field of control engineering. It is one of the few areas that has received on-going interest from researchers in both the industrial and academic communities. Three major aspects of model predictive control make the design methodology attractive to both engineers and academics. The first aspect is the design formulation, which uses a completely multivariable system framework where the performance parameters of the multivariable control system are related to the engineering aspects of the system; hence, they can be understood and 'tuned' by engineers. The second aspect is the ability of method to handle both 'soft' constraints and hard constraints in a multivariable control framework. This is particularly attractive to industry where tight profit margins and limits on the process operation are inevitably present. The third aspect is the ability to perform process on-line optimization.

The core material of this workshop, based on the books entitled ‘Model Predictive Control System Design and Implementation using MATLAB’ (Springer, 2009) and ‘PID and Predictive Control of Electrical Drives and Power Converters using MATLAB and Simulink’ (Wiley-IEEE PRESS, 2015) by the speaker, and is suitable for engineers, students and researchers who wish to gain basic knowledge about predictive control with constraints as well as understand how to perform real time simulation and implementation using MATLAB and Simulink tools.

This two hours tutorial will cover the following topics: receding horizon control, state estimate MPC design and implementation, model predictive control with constraints.

Tutorial  participants will receive:

•      Lecture notes that clearly show how MPC can be designed and implemented

•      MATLAB programs for MPC design and implementation written by the speaker