Movement control based on model predictive control with disturbance suppression using kalman filter including disturbance estimation

Takashi Ohhira, Akira Shimada

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

This study proposes a movement control system based on model predictive control (MPC), and a Kalman filter (KF) that can consider the influences of noise and disturbance. The KF estimates not only the motion state but also the disturbance of the controlled objects affected by noise. Disturbance is introduced by the stationary disturbance, by the system noise, and by observation noise. An MPC system filtered by the KF is robust and suppresses disturbances using the special design method proposed in this study. The feasibility of the MPC-based control system is confirmed under conditions of strong intermittent disturbances, such as road surface and sensor noises, and a friction force with less time variation. Finally, the proposed method is tested in simulations of a cart traveling in a straight line. The superior simulation results over the existing MPC system validate the proposed control system.

Original languageEnglish
Pages (from-to)387-395
Number of pages9
JournalIEEJ Journal of Industry Applications
Volume7
Issue number5
DOIs
Publication statusPublished - 2018

Keywords

  • Disturbance estimation
  • Disturbance suppression
  • Kalman filter (KF)
  • Model predictive control (MPC)
  • Motion control
  • Multivariable control

ASJC Scopus subject areas

  • Automotive Engineering
  • Energy Engineering and Power Technology
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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