This work presents an alternative to the least squares optimization used in Dynamic Matrix Control (DMC). Instead of calculating future moves by minimizing the sum of the squares of the future errors (least squares), each future error is individually minimized. Each minimization results in an individual recommendation for the lone future move and the actuated move would be an average of all the individual recommendations. The work presents an analytical study of the closed-loop dynamics of the method and it is used here mainly to prove the ability of the method to perform control as well as estimate the closed-loop time constants. The performance of the method is illustrated and compared to a benchmark DMC via simulation.
Model Predictive Control; Dynamic Matrix Control; Least Squares Alternative
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Cite this paper
Samer Mansour, Jalal Karam. (2017) Simulations of Average Simplified Predictive Control. International Journal of Control Systems and Robotics, 2, 128-131
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