Reinforcement Learning for Engine Idle Speed Control

ICMTMA), 2010 International Conference(2010)

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Abstract
A control method of neural network controller with reinforcement learning is proposed to implement idle speed control of an automobile engine to reduce fluctuation of the idle speed. Firstly, the reinforcement-learning neural network is demonstrated in detail. Then, the control scheme of the reinforcement-learning controller is designed to experiment. Q learning algorithm, as one of methods of reinforcement learning, is used for learning of the neural network, which is based on evaluating the system performance and giving credit for successful actions. After the proposed controller is trained fully, the contrast experiments are implemented on an engine test bench between the proposed controller and the original controller. Experimental results show that the reinforcement learning controller has better performance in speed fluctuation and its frequency and fuel economy than that of the original controller. And, the transition of the transient idle speed controlled by the proposed controller is more smooth and stable. Meanwhile, exhaust emissions are tested during the conditions controlled by the two types of controllers respectively. And results demonstrate that the proposed controller has better fuel economy because of its lower exhaust emissions.
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Key words
automobiles,internal combustion engines,learning (artificial intelligence),neurocontrollers,velocity control,q learning algorithm,automobile engine,engine idle speed control,fuel economy,neural network controller,reinforcement-learning neural network,control,engine,idle speed,neural network,reinforcement learning,engines,automotive engineering,control engineering,fluctuations,testing,speed control,learning artificial intelligence,valves,frequency,system performance,neural networks,artificial neural networks
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