Fast adaptive regression-based model predictive control
作者机构：Electrical and Computer EngineeringMilitary Technical CollegeCairo14627Egypt
出 版 物：《Control Theory and Technology》 (控制理论与技术（英文版）)
年 卷 期：2023年第21卷第4期
页 面：555-570页
核心收录：
学科分类：08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 080201[工学-机械制造及其自动化]
主 题：Regression analysis MPC Control Parametrization Wavelet Support vector regression(SVR) Optimization
摘 要：Model predictive control(MPC)is an optimal control method that predicts the future states of the system being controlled and estimates the optimal control inputs that drive the predicted states to the required *** computations of the MPC are performed at pre-determined sample instances over a finite time *** number of sample instances and the horizon length determine the performance of the MPC and its computational cost.A long horizon with a large sample count allows the MPC to better estimate the inputs when the states have rapid changes over time,which results in better performance but at the expense of high computational ***,this long horizon is not always necessary,especially for slowly-varying *** this case,a short horizon with less sample count is preferable as the same MPC performance can be obtained but at a fraction of the computational *** this paper,we propose an adaptive regression-based MPC that predicts the best minimum horizon length and the sample count from several features extracted from the time-varying changes of the *** proposed technique builds a synthetic dataset using the system model and utilizes the dataset to train a support vector regressor that performs the *** proposed technique is experimentally compared with several state-of-the-art techniques on both linear and non-linear *** proposed technique shows a superior reduction in computational time with a reduction of about 35–65%compared with the other techniques without introducing a noticeable loss in performance.
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