Taylor Series-Based Fuzzy Model Predictive Control for Wheeled Robots

Libo Yang, Mei Guo, Ardashir Mohammadzadeh and Amir Mosavi
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Libo Yang: School of Mechanical and Electrical Engineering, Guangdong University of Science and Technology, Dongguan 523083, Guangdong, China
Mei Guo: School of Computer and Artificial Intelligence, Xiangnan University, Chenzhou 423000, Hunan, China
Ardashir Mohammadzadeh: Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang 110870, China
Amir Mosavi: Faculty of Civil Engineering, Technische Universität Dresden, 01067 Dresden, Germany

Mathematics, 2022, vol. 10, issue 14, 1-13

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.

Keywords: soft computing; fuzzy control; model predictive control; artificial intelligence; wheeled robots; Taylor series; robotics; mobile robots; computational intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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