Zahra Yaghoubi and Hassan Zarabadipour
Neural-network controller, anti-control of chaos, mobile robot system, feedback-linearization controller
In this paper, a new method for controlling a mobile robot system is proposed. Utilizing chaotic features, this paper presents a new method to synchronize a mechanical system with a chaotic system. To optimize the energy consumption and reduce the costs in this method, a hybrid neural-network (NN) controller is designed using anti-control of chaos. The objective of this paper is to introduce a method in which a mobile robot system is being controlled through chaotification via NNs as its identifier and controller. First, the feedback-linearization controller is used to trace the desired path, and then in parallel with this classic learning controller, the NN is used. Finally, this NN is replaced and trained on-line using the back propagation algorithm with sample data collected from feedback-linearization controller. In this study, anti-control of chaos means that the tracking error, instead of assuming to approach zero, is synchronized with small ratio of chaotic gyroscope system amplitude. To show how the feedback linearization and NN are applied, the trajectory tracking in the presence of noise is examined and the performance of the proposed method is estimated through simulation and cost function calculation.
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