scholarly journals Neural Network Observer-Based Finite-Time Formation Control of Mobile Robots

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Caihong Zhang ◽  
Tairen Sun ◽  
Yongping Pan
Keyword(s):  
Neural Network ◽  
Mobile Robots ◽  
Trajectory Tracking ◽  
Finite Time ◽  
Formation Control ◽  
Nonholonomic Mobile Robots ◽  
Control Laws ◽  
Leader Following ◽  
Simulation Results ◽  
Neural Network Observer

This paper addresses the leader-following formation problem of nonholonomic mobile robots. In the formation, only the pose (i.e., the position and direction angle) of the leader robot can be obtained by the follower. First, the leader-following formation is transformed into special trajectory tracking. And then, a neural network (NN) finite-time observer of the follower robot is designed to estimate the dynamics of the leader robot. Finally, finite-time formation control laws are developed for the follower robot to track the leader robot in the desired separation and bearing in finite time. The effectiveness of the proposed NN finite-time observer and the formation control laws are illustrated by both qualitative analysis and simulation results.

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

Leader-Following Formation Control of Nonholonomic Mobile Robots With Velocity Observers

2020 ◽  
Vol 25 (4) ◽  
pp. 1747-1755
Author(s):  
Xinwu Liang ◽  
Hesheng Wang ◽  
Yun-Hui Liu ◽  
Zhe Liu ◽  
Weidong Chen
Keyword(s):  
Mobile Robots ◽  
Formation Control ◽  
Nonholonomic Mobile Robots ◽  
Leader Following

scholarly journals An algorithm for formation control of mobile robots

2013 ◽  
Vol 10 (1) ◽  
pp. 59-72 ◽  
Author(s):  
Aleksandar Cosic ◽  
Marko Susic ◽  
Stevica Graovac ◽  
Dusko Katic
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Trajectory Tracking ◽  
Formation Control ◽  
Control Structure ◽  
Kinematic Model ◽  
The Real ◽  
Avoidance Control ◽  
Simulation Results ◽  
High Level

Solution of the formation guidance in structured static environments is presented in this paper. It is assumed that high level planner is available, which generates collision free trajectory for the leader robot. Leader robot is forced to track generated trajectory, while followers? trajectories are generated based on the trajectory realized by the real leader. Real environments contain large number of static obstacles, which can be arbitrarily positioned. Hence, formation switching becomes necessary in cases when followers can collide with obstacles. In order to ensure trajectory tracking, as well as object avoidance, control structure with several controllers of different roles (trajectory tracking, obstacle avoiding, vehicle avoiding and combined controller) has been adopted. Kinematic model of differentially driven two-wheeled mobile robot is assumed. Simulation results show the efficiency of the proposed approach.

Adaptive Neural-Network Control of Mobile Robot Formations Including Actuator Dynamics

2013 ◽  
Vol 303-306 ◽  
pp. 1768-1773 ◽  
Author(s):  
Yan Dong Li ◽  
Ling Zhu ◽  
Ming Sun
Keyword(s):  
Neural Network ◽  
Mobile Robots ◽  
Formation Control ◽  
Network Control ◽  
Lyapunov Theory ◽  
Stability And Convergence ◽  
Parameter Uncertainties ◽  
Nonholonomic Mobile Robots ◽  
Actuator Dynamics ◽  
Adaptive Neural Network Control

For the formation control problem of multiple nonholonomic mobile robots with actuator and formation dynamics, this paper propsed a new control strategy that integrated kinematic controller with input voltages controller of actuator. This control law was designed by backstepping technique based on formation control structure of leader-follower. The RBFNN was adopted to achieve on-line estimation for the dynamics nonlinear uncertain part for follower and leader robots. The adaptive robust controller was adopted to compensate modeling errors of neural network. This strategy not only solved the problem of parameters and non-parameter uncertainties of mobile robots, but also ensured the desired trajectory tracking of robot formation in the case of maintaining formation. The stability and convergence of the control system were proved by using the Lyapunov theory. The simulation results showed the effectiveness of this proposed method.

scholarly journals Nonlinear model predictive control for trajectory tracking of nonholonomic mobile robots

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141876046 ◽  
Author(s):  
Tiago P Nascimento ◽  
Carlos Eduardo Trabuco Dórea ◽  
Luiz Marcos G Gonçalves
Keyword(s):  
Mobile Robots ◽  
Nonlinear Model ◽  
Trajectory Tracking ◽  
Autonomous Vehicles ◽  
Tracking Control ◽  
Nonholonomic Mobile Robots ◽  
Trajectory Tracking Control ◽  
Control Laws ◽  
Novel Approach ◽  
Predictive Controller

Trajectory tracking for autonomous vehicles is usually solved by designing control laws that make the vehicles track predetermined feasible trajectories based on the trajectory error. This type of approach suffers from the drawback that usually the vehicle dynamics exhibits complex nonlinear terms and significant uncertainties. Toward solving this problem, this work proposes a novel approach in trajectory tracking control for nonholonomic mobile robots. We use a nonlinear model predictive controller to track a given trajectory. The novelty is introduced by using a set of modifications in the robot model, cost function, and optimizer aiming to minimize the steady-state error rapidly. Results of simulations and experiments with real robots are presented and discussed verifying and validating the applicability of the proposed approach in nonholonomic mobile robots.

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