Establishment of model predictive control theory with real-time modelling capabilities and its application to predictive fault-tolerant control

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K04548
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 21040:Control and system engineering-related
• Research Institution: Hiroshima University
• Principal Investigator: Wada Nobutaka 広島大学, 先進理工系科学研究科(工), 教授 (50335709)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: モデル予測制御 / 耐故障制御 / 状態推定 / パラメータ推定

Outline of Final Research Achievements

The objective of this project is to construct a model predictive control algorithm that can accurately estimate and predict the time-variation of a controlled object based on the measurement in real time, and maintain stability and optimality of the control system. First, for a system described as a linear parameter variable (LPV) system, a model predictive control algorithm has been constructed that can guarantee stability of the closed-loop system under parameter variation and is relatively computationally inexpensive. Next, for the system described as an LPV system, we have developed a method for estimating the state and time-varying parameters from the input-output data. The constructed estimation algorithm is reduced to the iterative computation of convex quadratic program.

Free Research Field

制御工学

Academic Significance and Societal Importance of the Research Achievements

本研究の成果を活用することにより，事後データを活用することで，モデル変動が生じた際にも，制御系の安定性を保持しつつ高い制御性能を維持することを可能とするシステム制御論を構築することが可能となる．この方法はモデル予測制御を基礎としていることから，入力や状態の制約を考慮した制御性能の最適化を図ることが可能である．また，提案する推定手法単体でも，制御対象の入出力データから状態や物理パラメータを高精度に推定することが可能であり，実用上有用である．