最適探索空間サイズの学習による非線形モデル予測制御の遺伝的最適化の高速化
Accelerating genetic optimization of nonlinear model predictive control by learning optimal search space size
非線形モデル予測制御の遺伝的アルゴリズム最適化において、制御周期ごとに最適な探索空間サイズを回帰モデルで予測し、計算時間を17〜45%短縮し収束率を35〜47%向上させる手法を提案した。
著者: Eslam Mostafa, Hussein A. Aly, Ahmed Elliethy
分類: math.OC, cs.CC, cs.NE, cs.RO
原文アブストラクト
Genetic algorithm (GA) is typically used to solve nonlinear model predictive control's optimization problem. However, the size of the search space in which the GA searches for the optimal control inputs is crucial for its applicability to fast-response systems. This paper proposes accelerating the genetic optimization of NMPC by learning optimal search space size. The approach trains a multivariate regression model to adaptively predict the best smallest size of the search space in every control cycle. The proposed approach reduces the GA's computational time, improves the chance of convergence to better control inputs, and provides a stable and feasible solution. The proposed approach was evaluated on three nonlinear systems and compared to four other evolutionary algorithms implemented in a processor-in-the-loop fashion. The results show that the proposed approach provides a 17-45\% reduction in computational time and increases the convergence rate by 35-47\%. The source code is available on GitHub.