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 inputstate 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.
This paper explores the dynamic modeling of a novel two stage bidirectional poppet valve and proposes a control scheme that uses a Nodal Link Perceptron Network (NLPN). The dynamic nonlinear mathematical model of this Electro-Hydraulic Control Valve (EHCV) is based on the analysis of the interactions among its mechanical, hydraulic, and electromagnetic subsystems. A discussion on experimental approaches to determine the model parameters is included along with model validation results. Finally, the control scheme is developed by proposing that the states of the EHCV follow a set of desired states, which are calculated based upon the desired valve flow conductance coefficient KV. A simulation is presented at the end to verify the proposed control scheme.
This paper describes a novel auto-calibration state-trajectory-based control method and its application to electronic flow control for independent metering systems. In this paper, the independent metering architecture that is considered uses five Electro-Hydraulic Poppet Valves (EHPV's). The proposed control method is applied to four of these valves, arranged in a Wheatstone bridge configuration, to regulate the flow of hydraulic oil coming into and out of an actuator. For simplicity, the fifth valve is operated via open-loop to control the supply pressure. Experimental data presented herein demonstrate that the control method learns the valve's conductance characteristics (i.e. the inverse input-state dynamic map of the valve) while simultaneously controlling the motion of the hydraulic actuator.
This paper describes a novel learning/adaptive state trajectory control method and its application to electronic hydraulic pressure control. The control algorithm presented herein learns the inverse input-state mapping of the plant at the same time this map is employed in the feedforward loop to force the state of the plant to asymptotically converge to a prescribed state trajectory. The algorithm accomplishes this task without requiring prior exact information about the state transition map of the plant. The novel controller is applied to an electrohydraulic poppet valve with the objective of tracking a desired supply pressure signal. In this application, the controller learns the inverse conductance characteristics of the valve. The supply pressure tracking performance subject to the proposed controller is validated through experimental data.
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