Abstract
The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model.
Highlights
In recent years, as wheeled mobile robots (WMRs) have been implemented more popularly in unstructured environments, motion control problems have received considerable attention in the automation field
The phenomenon of wheel slippage always exists in certain real-life motion tasks that have critical effects on the locomotion of mobile robots, which cannot be ignored for accurate tracking control
The weights of the neural networks (NNs) are applied in the state model of the unscented Kalman filter (UKF) and the output of the NN presents the deviation model, and the observation model of the UKF is the integrating form of the kinematics model
Summary
The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model
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