Model-based controllers have positioned themselves in industrial applications, working mainly on top of a layer of PID controllers. Their implementation takes an important amount of time because of the required PID tuning and the model characterization/identification. This paper presents a strategy to perform on-line adaptation for the dynamic matrix coefficients in a DMC controller. Based on the observed PH (Prediction Horizon) elements of the response and controller signal vectors, and based on a non-residing control horizon controller design, the direct control problem is reformulated using the full-effect dynamic matrix (PHxPH) as an unknown. Data is collected and used in two directions: training a RAWN Network (Random Allocation Weight Neural Network), to describe recently observed process behavior, and to solve a least-squares problem for a set of linear equations where the unknowns are the characteristic response coefficients. The paper presents the effect of both approaches, illustrating the adaptation algorithms operation in a highly nonlinear process where the controller is designed in a low-gain region. Then the process operating condition is shifted so that it moves to a high-gain region to observe controller response.
Neural-Network Based On-Line Adaptation of Model Predictive Controller for Dynamic Systems With Uncertain Behavior
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Sanjuan, ME. "Neural-Network Based On-Line Adaptation of Model Predictive Controller for Dynamic Systems With Uncertain Behavior." Proceedings of the ASME 2005 International Mechanical Engineering Congress and Exposition. Dynamic Systems and Control, Parts A and B. Orlando, Florida, USA. November 5–11, 2005. pp. 1033-1040. ASME. https://doi.org/10.1115/IMECE2005-82609
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