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Learning control applied to Electro-Hydraulic Poppet Valves

This paper describes a novel state trajectory control method and its application to electro-hydraulic poppet valves (EHPV). The control objective is to find a control sequence that forces the state of the plant to asymptotically converge to the desired state trajectory. This is to be accomplished without requiring exact information about the state transition map of the plant. In fact, it is desired to learn the inverse input-state map of the plant at the same time state tracking control is enforced. As an application of this novel controller, the tracking of a desired supply pressure trajectory is considered. This is achieved by learning the flow conductance coefficient Kv of the EHPV. The novel state trajectory control method achieves this objective by learning the inverse input- state mapping of the valve at the same time that this mapping is used in the feedforward loop. The mapping learning is accomplished with the aid of a simple neural network structure called the nodal link perceptron network (NLPN). The NLPN is trained online via a gradient descent method to minimize the errors in the inverse input-state mapping approximation. The supply pressure tracking performance subject to the proposed controller is validated through experimental data.