2017
DOI: 10.3390/electronics6040088
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A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications

Abstract: 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… Show more

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Cited by 14 publications
(9 citation statements)
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“…To reduce this, the simplified form presented by (28) can be expressed considering a Constraint Horizon [37] for input constraints ( ≤ ) and another for output constraints ( ≤ ).…”
Section: Constrained Gpc Formulationmentioning
confidence: 99%
“…To reduce this, the simplified form presented by (28) can be expressed considering a Constraint Horizon [37] for input constraints ( ≤ ) and another for output constraints ( ≤ ).…”
Section: Constrained Gpc Formulationmentioning
confidence: 99%
“…1: The proposed architecture thereof). In [27], a lightweight MPC scheme able to adjust its prediction horizon has been presented and evaluated on a simple industrial process plant. However, in [27] horizon changes are driven by the need of tuning the reference trajectory of the model.…”
Section: Introductionmentioning
confidence: 99%
“…This difficulty can be circumvented by finding explicit control laws using controlled invariant contractive sets as the solution of a multiparametric programming problem which results in a piecewise affine (PWA) law over a polyhedral set satisfying the constraints. Additionally, new controller techniques such as predictive (Short and Abugchem 2017), fuzzy (Khan et al 2015) and neural (Zelentsov and Denysiuk 2019) are not as frequently used in the industry as PID.…”
Section: Introductionmentioning
confidence: 99%