scholarly journals Distributed Consensus-Based Robust Adaptive Formation Control for Nonholonomic Mobile Robots with Partial Known Dynamics

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Zhaoxia Peng ◽  
Shichun Yang ◽  
Guoguang Wen ◽  
Ahmed Rahmani
Keyword(s):  
Control Problem ◽  
Mobile Robots ◽  
Formation Control ◽  
Control Strategies ◽  
Reference Trajectory ◽  
Consensus Problem ◽  
Distributed Consensus ◽  
Nonholonomic Mobile Robots ◽  
Robust Adaptive ◽  
Virtual Leader

This paper investigates the distributed consensus-based robust adaptive formation control for nonholonomic mobile robots with partially known dynamics. Firstly, multirobot formation control problem has been converted into a state consensus problem. Secondly, the practical control strategies, which incorporate the distributed kinematic controllers and the robust adaptive torque controllers, are designed for solving the formation control problem. Thirdly, the specified reference trajectory for the geometric centroid of the formation is assumed as the trajectory of a virtual leader, whose information is available to only a subset of the followers. Finally, numerical results are provided to illustrate the effectiveness of the proposed control approaches.

scholarly journals Implementation of Leader-Follower Formation Control of a Team of Nonholonomic Mobile Robots

2017 ◽  
Vol 12 (6) ◽  
pp. 871 ◽  
Author(s):  
Augie Widyotriatmo ◽  
Endra Joelianto ◽  
Agung Prasdianto ◽  
Hafidz Bahtiar ◽  
Yul Yunazwin Nazaruddin
Keyword(s):  
Mobile Robots ◽  
Formation Control ◽  
Control Method ◽  
Artificial Potential Field ◽  
Multiple Robots ◽  
Nonholonomic Mobile Robots ◽  
Role Assignment ◽  
Virtual Leader ◽  
And Control

A control method for a team of multiple mobile robots performing leader-follower formation by implementing computing, communication, and control technol-ogy is considered. The strategy expands the role of global coordinator system andcontrollers of multiple robots system. The global coordinator system creates no-collision trajectories of the virtual leader which is the virtual leader for all vehicles,sub-virtual leaders which are the virtual leader for pertinent followers, and virtualfollowers. The global coordinator system also implements role assignment algorithmto allocate the role of mobile robots in the formation. The controllers of the individualmobile robots have a task to track the assigned trajectories and also to avoid collisionamong the mobile robots using the artificial potential field algorithm. The proposedmethod is tested by experiments of three mobile robots performing leader-followerformation with the shape of a triangle. The experimental results show the robustnessof formation of mobile robots even if the leader is manually moved to the arbitrarylocation, and so that the role of a leader is taken by the nearest mobile robot to thevirtual leader.

Formation Control and Trajectory Tracking of Nonholonomic Mobile Robots

2018 ◽  
Vol 26 (6) ◽  
pp. 2250-2258 ◽  
Author(s):  
Akshit Saradagi ◽  
Vijay Muralidharan ◽  
Vishaal Krishnan ◽  
Sandeep Menta ◽  
Arun D. Mahindrakar
Keyword(s):  
Mobile Robots ◽  
Trajectory Tracking ◽  
Formation Control ◽  
Nonholonomic Mobile Robots

scholarly journals Robust Tracking Control of Mobile Robot Formation with Obstacle Avoidance

2007 ◽  
Vol 2007 ◽  
pp. 1-10 ◽  
Author(s):  
Tiantian Yang ◽  
Zhiyuan Liu ◽  
Hong Chen ◽  
Run Pei
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Formation Control ◽  
Optimization Problem ◽  
Reference Trajectory ◽  
Robust Tracking Control ◽  
Wheeled Mobile Robots ◽  
Lmi Optimization ◽  
Control Scheme

We consider the formation control problem of multiple wheeled mobile robots with parametric uncertainties and actuator saturations in the environment with obstacles. First, a nonconvex optimization problem is introduced to generate the collision-free trajectory. If the robots tracking along the reference trajectory find themselves moving close to the obstacles, a new collision-free trajectory is generated automatically by solving the optimization problem. Then, a distributed control scheme is proposed to keep the robots tracking the reference trajectory. For each interacting robot, optimal control problem is generated. And in the framework of LMI optimization, a distributed moving horizon control scheme is formulated as online solving each optimal control problem at each sampling time. Moreover, closed-loop properties inclusive of stability andH∞performance are discussed. Finally, simulation is performed to highlight the effectiveness of the proposed control law.

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