Online learning state trajectory control applied to Electro-Hydraulic Poppet Valves (EHPV) is considered herein. The control problem is to track a desired flow conductance coefficient KV for pressure or flow control applications. In general terms, the control methodology employed herein computes the input signal sent to the valve from the addition of three components. The first component comes from an experimentally approximated inverse input-output map of the system which gives a nominal input. The second component is computed through a neural network structure called the Nodal Link Perceptron Network that learns online the adjustment of this nominal map. The third component is an adaptive proportional feedback control input. This last component uses two system parameters known as the Jacobian and the Controllability parameter, which are estimated online via a recursive least squares algorithm with forgetting factor. The proposed controller is explored through experimental data on a pressure control application and the results are discussed.
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机译：本文认为，应用于电液提升阀（EHPV）的在线学习状态轨迹控制。控制问题是跟踪期望的流量导电系数KV用于压力或流量控制应用。通常，这里采用的控制方法计算从添加三个组件的发送到阀门的输入信号。第一组件来自系统的实验近似的逆输入输出映射，其提供标称输入。通过称为Nodal Link Perceptron网络的神经网络结构来计算第二组件，该网络结构在线学习在线的调整该标称图。第三组分是自适应比例反馈控制输入。最后一个组件使用称为Jacobian和可控性参数的两个系统参数，其通过递归最小二乘算法在线估计，遗忘因子。通过关于压力控制应用的实验数据探索所提出的控制器，并讨论了结果。
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