Adaptive Model Predictive Control for Mobile Robots with Localization Fluctuation Estimation

Author:

Meng Jie12ORCID,Xiao Hanbiao12,Jiang Liyu3,Hu Zhaozheng12,Jiang Liquan4,Jiang Ning5ORCID

Affiliation:

1. Intelligent Transportation Systems Research Center, Wuhan University of Technology, 1178 Heping Avenue, Wuhan 430000, China

2. Chongqing Research Institute, Wuhan University of Technology, 598 Liangjiang Avenue, Chongqing 400000, China

3. Hubei Institute of Measurement and Testing Technology, 2 Maodianshan Middle Road, Wuhan 430000, China

4. The State Key Laboratory of New Textile Materials and Advanced Processing Technologies, Wuhan Textile University, 1 Yangguang Avenue, Wuhan 430000, China

5. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430000, China

Abstract

Mobile robots are widely employed in various fields to perform autonomous tasks. In dynamic scenarios, localization fluctuations are unavoidable and obvious. However, common controllers do not consider the impact of localization fluctuations, resulting in violent jittering or poor trajectory tracking of the mobile robot. For this reason, this paper proposes an adaptive model predictive control (MPC) with an accurate localization fluctuation assessment for mobile robots, which balances the contradiction between precision and calculation efficiency of mobile robot control. The distinctive features of the proposed MPC are three-fold: (1) Integrating variance and entropy—a localization fluctuation estimation relying on fuzzy logic rules is proposed to enhance the accuracy of the fluctuation assessment. (2) By using the Taylor expansion-based linearization method—a modified kinematics model that considers that the external disturbance of localization fluctuation is established to satisfy the iterative solution of the MPC method and reduce the computational burden. (3) An improved MPC with an adaptive adjustment of predictive step size according to localization fluctuation is proposed, which alleviates the disadvantage of a large amount of the MPC calculation and improves the stability of the control system in dynamic scenes. Finally, verification experiments of the real-life mobile robot are offered to verify the effectiveness of the presented MPC method. Additionally, compared with PID, the tracking distance and angle error of the proposed method decrease by 74.3% and 95.3%, respectively.

Funder

National Engineering Research Center for Water Transport Safety

Natural Science Foundation of Hubei Province

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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