北京交大机电专硕专业外语文献电液提升阀的智能控制

1、Intelligent controls for electro-hydraulic poppet valves XX XXXX1512XXXX（对电液提升阀的智能控制研究）Patrick Opdenbosch, Nader Sadegh, Wayne BookThe Georgia Institute of Technology, Atlanta, USAcontents4.CONTROL LAW AND STABILITY1.ABSTRACT5.ELECTRONIC CONTROL OF HYDRAULIC PRESSURE2.INTRODUCTION6.CONCLUSIONS3.CONT

2、ROL OBJECTIVE AND INPUT-STATE APPROXIMATORTo DO:To DO: This paper describes an intelligent controller that combines an auto-calibration state-trajectory-based control method with a simple algorithm that enables fault detection. More specifically, the proposed control algorithm is designed to learn t

3、he inverse input-state map of an Electro-Hydraulic Poppet Valve (EHPV) with the aid of a Nodal Link Perceptron Network (NLPN). While in operation, the controller enforces the tracking of a desired hydraulic supply pressure profile at the same time that it learns the inverse input-state calibration m

4、ap and monitors the latter for deviations from prescribed bounds. 本文描述了一个整合了以自校正状态轨迹为基础的控制方法的智能控制器，并且这种智能控制方法可以进行误差检测。更具体的说，期望得到的控制算法是为了研究带有节点感知器网络(NLPN)的电液提升阀（EHPV）的反向输入状态映射。在试验中，控制器可以追踪所追踪所期望的供液压力期望的供液压力数据的同时，得到反向输入状态校正映射并且控制其偏差在偏差在规定的范围内规定的范围内。ABSTRACTINTRODUCTIONThe EHPV considered herein is sho

5、wn in Fig. 1.Fig. 1. The Electro-Hydraulic Poppet Valve (EHPV) PQK/vThe EHPVs performance is characterized by its flow coefficient or conductance.The control algorithm presented herein simultaneously learns the valves conductance and uses this knowledge to control the valve while achieving the track

6、ing of a desired supply pressure profile. INTRODUCTION First, there would be no need to obtain extensive offline Kv calibrations for valves of the same capacity/size. Second, it enables the inclusion of degradation monitoring and fault detection schemes by observing the valves deviation from expecte

7、d performance.Fig. 1. The Electro-Hydraulic Poppet Valve (EHPV) The contribution of this paper is the presentation of an essen-tially model-free control law for the EHPV, that tunes a simple adaptive look-up table with the inverse input-state calibration map of the EHPV, and the inclusion of fault d

8、etection while the EHPV is used in a pressure control application. CONTROL OBJECTIVE AND INPUT-STATE APPROXIMATOR The objective is to find a control input sequence uk kd to drive the state of the plant to converge asymptotically to xkd, a prescribed state trajectory, while learning the inverse input

9、-state map of the plant and monitoring performance deviations from expected behavior. 010),(xxuxFxkkkk（1）The plant (in this case the EHPV)is assumed to have a nonlinear discrete-times tate space representation with a fixed sampling time Ts of the form:CONTROL OBJECTIVE AND INPUT-STATE APPROXIMATOR E

10、ach entry of the input space of the NLPN in the basis function vector is computed from)()(,1jjinjIixxj else 0, if,)()(), if,)()()(1, 1, 11, 1, 1mj,jmm,jjjmjmjmjm,j,jm-jjmjmjmjjxxxxxFig. 2. Sample NLPN 2D input space partition showing piece-wise linear basis functions of unitary magnitude.(2)CONTROL

11、LAW AND STABILITY The proposed control law consists of two parts, one used to learn the inverse input-state calibration map and the other to use this learned map for feedforward control.)(lim)(suplimooeAkkkkFig. 3. Complete architecture for the NBIM control law.(3)ELECTRONIC CONTROL OF HYDRAULIC PRE

12、SSURE The actual flow coefficient of the valve Kv, used for feedback, is computed using Eq. (6) by substituting the measured pressure P s instead of Psd. RdsdspdvPPyAPQK)(6) Tracking of the desired flow conductance is illustrated in Fig. 6 where it can be seen that good performance can be achieved w

13、ith active learning/adaptation. Fig.6Fig.7 The tracking performance of the resulting supply pressure is shown in Fig.7. Likewise, improved tracking of the desired pressure profile was achieved with learning/adaptation.ELECTRONIC CONTROL OF HYDRAULIC PRESSURECONCLUSION (1)This paper presented the app

14、lication of an online auto-calibration based control method for the EHPV. (2)The control law presented herein simultaneously learned the inverse input-state calibration map of the EHPV while forcing its conductance to follow a prescribed desired trajectory. This was accomplished by treating the EHPV

15、 as single-state variable nonlinear plant and using a generic input-state map obtained from steady state data to initialized the NLPN. (3)Experimental results showed the performance of the valve under this control action was superior than the performance resulting from simply using an open-loop static look-up table based controller. (4)A