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Formation control of multiple wheeled mobile robots based on model predictive control

Published online by Cambridge University Press:  18 February 2022

Najla Nfaileh
Affiliation:
Advanced Service Robots (ASR) Laboratory, Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Khalil Alipour*
Affiliation:
Advanced Service Robots (ASR) Laboratory, Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Bahram Tarvirdizadeh
Affiliation:
Advanced Service Robots (ASR) Laboratory, Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Alireza Hadi
Affiliation:
Advanced Service Robots (ASR) Laboratory, Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
*
*Corresponding author. E-mail: [email protected]

Abstract

This paper considers the problem of formation control for a team of nonholonomic wheeled mobile robots considering obstacle avoidance. A new control algorithm based on the model predictive control (MPC) and the nonlinear dynamics of the system is presented here. The control algorithm is applied to the nonlinear system using two different controllers including linear MPC and nonlinear MPC. The virtual structure formation approach and artificial potential field method are employed to determine the reference trajectories of the robots and to solve the problem of obstacle avoidance. A control algorithm consisting of two parts is proposed to track the trajectories and maintain the team’s formation. Two advantages of using MPC techniques are the ability to consider control and state constraints which are of high importance in practical applications. The main contribution of this paper is the design of two robust control systems to disturbance with respect to actuator saturation limits. Simulation results demonstrate the effectiveness and robustness of the proposed control algorithm in trajectory tracking and formation maintenance in the presence of disturbance and actuator limits.

Type
Research Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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