DESIGNING A ROBUST ADAPTIVE TRACKING BACKTEPPING CONTROLLER CONSIDERING ACTUATOR SATURATION FOR A WHEELED MOBILE ROBOT TO COMPENSATE UNKNOWN SLIPPAGE

This article highlights a robust adaptive tracking backstepping control approach for a nonholonomic wheeled mobile robot (WMR) by which the bad problems of both unknown slippage and uncertainties are dealt with. The radial basis function neural network (RBFNN) in this proposed controller assists unknown smooth nonlinear dynamic functions to be approximated. Furthermore, a technical solution is also carried out to avoid actuator saturation. The validity and efficiency of this novel controller, finally, are illustrated via comparative simulation results.

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Globally stable adaptive tracking control of a wheeled mobile robot using RBF neural network as feedforward compensator

2013 25th Chinese Control and Decision Conference (CCDC) ◽

10.1109/ccdc.2013.6561241 ◽

2013 ◽

Author(s):

Jian Wu ◽

Dong Zhao ◽

Weisheng Chen

Keyword(s):

Neural Network ◽

Mobile Robot ◽

Tracking Control ◽

Rbf Neural Network ◽

Wheeled Mobile Robot ◽

Adaptive Tracking Control ◽

Adaptive Tracking ◽

Globally Stable

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Robust adaptive dynamic programming based online tracking control algorithm for real wheeled mobile robot with omni-directional vision system

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331215620267 ◽

2016 ◽

Vol 39 (6) ◽

pp. 832-847 ◽

Cited By ~ 10

Author(s):

Nguyen Tan Luy

Keyword(s):

Neural Network ◽

Dynamic Programming ◽

Mobile Robot ◽

System Dynamics ◽

Vision System ◽

Dynamic Programming Algorithm ◽

Wheeled Mobile Robot ◽

Adaptive Dynamic Programming ◽

Adaptive Dynamic ◽

Robust Adaptive

This paper proposes a new method to design an online robust adaptive dynamic programming algorithm (RADPA) for a wheeled mobile robot which is equipped with an omni-directional vision system. To integrate kinematic and dynamic controllers into the unique controller, we transform the strict feedback system dynamics into tracking error dynamics. Then, we propose a control scheme which uses only one neural network rather than three proposed in the actor-critic-based control schemes for the two-player zero-sum game problem. A neural network weight update law is designed for approximating the solution of the Hamilton–Jacobi–Isaacs equation without knowing knowledge of internal system dynamics. To implement the scheme, we propose the online RADPA, in which control and disturbance laws are updated simultaneously in an iterative loop. The convergence and stability of the online RADPA are proven by Lyapunov techniques. Simulations and experiments on a wheeled mobile robot testbed are carried out to verify the effectiveness of the proposed algorithm.

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Robust adaptive tracking control of wheeled mobile robot

Robotics and Autonomous Systems ◽

10.1016/j.robot.2016.01.002 ◽

2016 ◽

Vol 78 ◽

pp. 36-48 ◽

Cited By ~ 45

Author(s):

Linjie Xin ◽

Qinglin Wang ◽

Jinhua She ◽

Yuan Li

Keyword(s):

Mobile Robot ◽

Tracking Control ◽

Wheeled Mobile Robot ◽

Adaptive Tracking Control ◽

Adaptive Tracking ◽

Robust Adaptive

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ADAPTIVE CONTROL OF MOBILE ROBOTS USING A NEURAL NETWORK

International Journal of Neural Systems ◽

10.1142/s0129065701000643 ◽

2001 ◽

Vol 11 (03) ◽

pp. 211-218 ◽

Cited By ~ 2

Author(s):

Celso de Sousa ◽

Elder Moreira Hermerly

Keyword(s):

Neural Network ◽

Adaptive Control ◽

Mobile Robot ◽

Mobile Robots ◽

Robot Navigation ◽

Control Approach ◽

On Line ◽

Simulation Results ◽

Previous Learning

A Neural Network - based control approach for mobile robot is proposed. The weight adaptation is made on-line, without previous learning. Several possible situations in robot navigation are considered, including uncertainties in the model and presence of disturbance. Weight adaptation laws are presented as well as simulation results.

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