Taylor Series-Based Fuzzy Model Predictive Control for Wheeled Robots

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Libo Yang - , Guangdong University of Science and Technology (Author)
  • Mei Guo - , Xiangnan University (Author)
  • Ardashir Mohammadzadeh - , Shenyang University of Technology (Author)
  • Amir Mosavi - , TUD Dresden University of Technology, Óbuda University, Slovak University of Technology (Author)

Abstract

In this paper, a new hybrid method for controlling a wheeled robot is introduced. Model predictive control (MPC) is the main controller and a fuzzy controller is used as a compensator. The wheeled robot is a nonlinear, multi-input–multi-output system that requires new and combined methods for precise control. In order to stabilize the system the appropriate control input is set, and at the same time, attention is paid to the reference signal tracking. In the simulation section, several different scenarios are applied and parameter uncertainties and their effects on the controller’s performance are evaluated. The simulation results show the success and efficiency of the proposed method.

Details

Original languageEnglish
Article number2498
JournalMathematics
Volume10
Issue number14
Publication statusPublished - Jul 2022
Peer-reviewedYes

Keywords

Keywords

  • artificial intelligence, computational intelligence, fuzzy control, mobile robots, model predictive control, robotics, soft computing, Taylor series, wheeled robots