A Data-driven MPC Algorithm for Bridge Cranes

Han Qiu Bao, Jing An, Meng Chu Zhou, Qi Kang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

A crane system often works in a complex environment. Traditional system identification methods are difficult to accurately model crane system identification's real dynamic performance, whether online or offline. Therefore, a Data-driven model predictive control (D-MPC) algorithm is proposed in this paper. Based on Bayesian optimization, the crane's local linear dynamic model is learned to improve the rapidly anti-swing and precise positioning. By collecting data through closed-loop experiments and using Bayes to construct the Gaussian model for guiding learning, the controller parameters and prediction model with the best closed-loop performance can be found. Simulation results show that we explicitly find the dynamics model that produces the best control performance for the actual system, and the method can quickly suppress the swing and realize the accurate trolley positioning. The results verify the proposed control algorithm's effectiveness, feasibility and superior performance of the proposed method by comparing it with double-closed-loop proportional-integral- derivative (PID).

Original languageEnglish (US)
Title of host publication2020 International Conference on Advanced Mechatronic Systems, ICAMechS 2020
PublisherIEEE Computer Society
Pages328-332
Number of pages5
ISBN (Electronic)9781728165301
DOIs
StatePublished - Dec 10 2020
Externally publishedYes
Event2020 International Conference on Advanced Mechatronic Systems, ICAMechS 2020 - Hanoi, Viet Nam
Duration: Dec 10 2020Dec 13 2020

Publication series

NameInternational Conference on Advanced Mechatronic Systems, ICAMechS
Volume2020-December
ISSN (Print)2325-0682
ISSN (Electronic)2325-0690

Conference

Conference2020 International Conference on Advanced Mechatronic Systems, ICAMechS 2020
Country/TerritoryViet Nam
CityHanoi
Period12/10/2012/13/20

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Mechanical Engineering

Keywords

  • Bayesian optimization
  • data-driven
  • model predictive control

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