Teaching Industrial Internet-of-Things-Based Model-Predictive Controller

Abstract

Contribution: This article explores how the Industrial Internet of Things (IIoT) could be leveraged to enhance the teaching/learning experience of advanced control techniques [e.g., model-predictive control (MPC)] for complex systems (nonlinear and multivariable) for undergraduate students. Background: The IIoTs' features, such as ubiquitous sensing, open connectivity, and distributed control, are expected to transform the way control is implemented in the industries. The students need to be prepared for this development. Courses on advanced control techniques should be revamped considering this change. In particular, deploying advanced controllers in IIoT scenarios could make the students ready for Industrie 4.0 and enhance the teaching-learning experience. Intended Outcomes: To reduce the cost for setting up laboratories; to make students appreciate IIoT benefits in industries and study the enhancements in teaching/learning advanced control techniques, such as MPC. The focus is on implementing MPC for a complex process, i.e., nonlinear, multivariable, and having interactions and their deployment on IIoT hardware. Method: A ten-day course on IIoTs' benefits and implementing advanced control techniques for a complex process with lecturing and hands-on sessions for undergraduate students is used. The course focuses on understanding basic concepts to deploy advanced control techniques on IIoT hardware in industries. Findings: The learning experience is enthralling, and the students are appreciative of the IIoT benefits to the industries and in their learning experience, which is demonstrated by their in-depth understanding of concepts on system complexity and implementing MPC.