Formation Control of Non-Holonomic Mobile Robots: Predictive Data-Driven Fuzzy Compensator

Jinfeng Wang, Hui Dong (), Fenghua Chen, Mai The Vu (), Ali Dokht Shakibjoo and Ardashir Mohammadzadeh ()
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Jinfeng Wang: School of Construction Equipment Engineering and Technology, Zhejiang College of Construction, Hangzhou 311231, China
Hui Dong: School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Fenghua Chen: School of Intelligent Manufacturing, Zhejiang Guangsha Vocational and Technical University of Construction, Dongyang 322100, China
Mai The Vu: School of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
Ali Dokht Shakibjoo: Department of Electrical Engineering, Ahrar Institute of Technology and Higher Education, Rasht 63591-41931, Iran
Ardashir Mohammadzadeh: Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang 110870, China

Mathematics, 2023, vol. 11, issue 8, 1-21

Abstract: A key research topic in the field of robotics is the formation control of a group of robots in trajectory tracking problems. Using organized robots has many advantages over using them individually, such as efficient use of resources, increased reliability due to cooperation, and better resistance against defects. To achieve this, a controller is proposed that steers the leader robot and subsequent follower robots asymptotically to a reference trajectory. The basic controller is feedback linearization. To ensure stability against perturbations, a compensator based on type-3 fuzzy logic systems (T3-FLSs) and a data-driven control strategy is designed. The approach involves employing a finite number of open-loop data and using the model-based predictive controller (MPC) approach to acquire sufficient criteria for stability. An infinite-horizon function is minimized online, which allows the data-based control policy to be considered the optimal control method. The gains of the constrained data-based control signal are computed at each time step to enhance accuracy. Applying the data-based state feedback controller to the system yields positive and stable state trajectories with appropriate transient responses. The suggested data-driven compensator is guaranteed to handle constraints. A practical example is simulated to evaluate the proposed strategy.

Keywords: mobile robots; type-3 fuzzy logic; direct data-driven control; MPC; positive systems; constrained input/state (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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