Velocity Tracking Control Based on Reinforcement Learning Neural Network
A new approach for tracking vehicle speeds by robotic driver during emission testing is presented.Based on neural network and combined with adaptive capability of reinforcement learning,it can execute velocity tracking control through on-line learning of neural network.Using the data obtained from a test vehicle,a neural network model of automotive for velocity tracking is developed at first.A neural network controller is designed based on reinforcement learning neural network framework to achieve adaptive control of velocity tracking.During simulation study,the velocity control neural network model is used to train primary controller rather than the actual test vehicle,and the developed and well-trained self-learning neural network controller is applied to velocity tracking control.Results show that the developed neural network controller has good performance of velocity tracking,and control efficacy is obvious.
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