Adaptive neural network tracking control-based reinforcement learning for wheeled mobile robots with skidding and slipping

To track the desired trajectories of the wheeled mobile robot (WMR) with time-varying forward direction, a reinforcement learning-based adaptive neural tracking algorithm is proposed for the nonlinear discrete-time (DT) dynamic system of the WMR with skidding and slipping. And, the typical model is...

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Published inNeurocomputing (Amsterdam) Vol. 283; pp. 20 - 30
Main Authors Li, Shu, Ding, Liang, Gao, Haibo, Chen, Chao, Liu, Zhen, Deng, Zongquan
Format Journal Article
LanguageEnglish
Published Elsevier B.V 29.03.2018
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Online AccessGet full text
ISSN0925-2312
1872-8286
DOI10.1016/j.neucom.2017.12.051

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Abstract To track the desired trajectories of the wheeled mobile robot (WMR) with time-varying forward direction, a reinforcement learning-based adaptive neural tracking algorithm is proposed for the nonlinear discrete-time (DT) dynamic system of the WMR with skidding and slipping. And, the typical model is transformed into an affine nonlinear DT system, the constraint of the coupling robot input torque is extended to pseudo dead zone (PDZ) control input. Three neural networks (NNs) are introduced as action NNs to approximate the unknown modeling item, the skidding and the slipping item and the PDZ item, whereas another NN is employed as critic NN to approximate the strategy utility function. Then, the critic and action NN adaptive laws are designed through the standard gradient-based adaptation method. The uniform ultimate boundedness (UUB) of all signals in the affine nonlinear DT WMR system can be ensured, while the tracking error converging to a small compact set by zero. Numerical simulations are conduced to validate the proposed method.
AbstractList To track the desired trajectories of the wheeled mobile robot (WMR) with time-varying forward direction, a reinforcement learning-based adaptive neural tracking algorithm is proposed for the nonlinear discrete-time (DT) dynamic system of the WMR with skidding and slipping. And, the typical model is transformed into an affine nonlinear DT system, the constraint of the coupling robot input torque is extended to pseudo dead zone (PDZ) control input. Three neural networks (NNs) are introduced as action NNs to approximate the unknown modeling item, the skidding and the slipping item and the PDZ item, whereas another NN is employed as critic NN to approximate the strategy utility function. Then, the critic and action NN adaptive laws are designed through the standard gradient-based adaptation method. The uniform ultimate boundedness (UUB) of all signals in the affine nonlinear DT WMR system can be ensured, while the tracking error converging to a small compact set by zero. Numerical simulations are conduced to validate the proposed method.
Author Deng, Zongquan
Liu, Zhen
Gao, Haibo
Li, Shu
Ding, Liang
Chen, Chao
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Keywords Neural network
Wheeled mobile robot
Adaptive tracking control
Reinforcement learning
Language English
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SSID ssj0017129
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Snippet To track the desired trajectories of the wheeled mobile robot (WMR) with time-varying forward direction, a reinforcement learning-based adaptive neural...
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elsevier
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StartPage 20
SubjectTerms Adaptive tracking control
Neural network
Reinforcement learning
Wheeled mobile robot
Title Adaptive neural network tracking control-based reinforcement learning for wheeled mobile robots with skidding and slipping
URI https://dx.doi.org/10.1016/j.neucom.2017.12.051
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