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Electronics 2017, 6(4), 88;

A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications

School of Science, Engineering and Design, Teesside University, Middlesbrough, TS1 3BA, UK
School of Health and Social Care, Teesside University, Middlesbrough, TS1 3BA, UK
Author to whom correspondence should be addressed.
Received: 15 September 2017 / Revised: 10 October 2017 / Accepted: 15 October 2017 / Published: 21 October 2017
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Model predictive control (MPC) schemes employ dynamic models of a process within a receding horizon framework to optimize the behavior of a process. Although MPC has many benefits, a significant drawback is the large computational burden, especially in adaptive and constrained situations. In this paper, a computationally efficient self-tuning/adaptive MPC scheme for a simple industrial process plant with rate and amplitude constraints on the plant input is developed. The scheme has been optimized for real-time implementation on small, low-cost embedded processors. It employs a short (2-step) control horizon with an adjustable prediction horizon, automatically tunes the move suppression (regularization) parameter to achieve well-conditioned control, and presents a new technique for generating the reference trajectory that is robust to changes in the process time delay and in the presence of any inverse response. In addition, the need for a full quadratic programming procedure to handle input constraints is avoided by employing a quasi-analytical solution that optimally fathoms the constraints. Preliminary hardware-in-the-loop (HIL) test results indicate that the resulting scheme performs well and has low implementation overhead. View Full-Text
Keywords: real-time control; industrial process control; input constraints real-time control; industrial process control; input constraints

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Short, M.; Abugchem, F. A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications. Electronics 2017, 6, 88.

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