Teaching Predictive Control Using Specification-based Summative Assessments

TL;DR

This paper discusses the evolution of an MPC course at Imperial College London, introducing a novel MATLAB-based assessment framework that evaluates student understanding through real-world controller design and robustness testing.

Contribution

The paper presents a comprehensive MPC course evolution and introduces a novel specification-based summative assessment framework in MATLAB for evaluating student controller designs.

Findings

Effective assessment of student understanding through real-world problem variations

Enhanced curriculum coverage from linear to nonlinear MPC and extensions

Robustness evaluation of student-designed controllers

Abstract

Including Model Predictive Control (MPC) in the undergraduate/graduate control curriculum is becoming vitally important due to the growing adoption of MPC in many industrial areas. In this paper, we present an overview of the predictive control course taught by the authors at Imperial College London between 2018 and 2021. We discuss how the course evolved from focusing solely on the linear MPC formulation to covering nonlinear MPC and some of its extensions. We also present a novel specification-based summative assessment framework, written in MATLAB, that was developed to assess the knowledge and understanding of the students in the course by tasking them with designing a controller for a real-world problem. The MATLAB assessment framework was designed to provide the students with the freedom to design and implement any MPC controller they wanted. The submitted controllers were then assessed against over 30 variations of the real-world problem to gauge student understanding of design robustness and the MPC topics from the course.