The resistance displacement feedback sensor KTC-500 (The maximum distance is 500 mm, the output voltage is 0−5 V), and computer control system with 12 bit Yanhua high speed A/D D/A card PCI-1712 is added. The simulation and experiment studies have been carried out to investigate the performance of speed control and position servo of the direct-drive volume control system. The simulation parameters of the above system are the SRM speed constant  Kv=5 r/(s•V); the SRM time constant  TD=0.078 s; the hydraulic oil volume elastic modulus  βe=700 MPa; the leaking coefficient of piston cylinder  λ=7×10−11 m³/(Pa•s); the piston-side section area of piston cylinder A1=3.14×10−2 m2; the mass of the moving parts m=120 kg; the piston-side volume of piston cylinder  V1=1.57×10−2 m³; the pump delivery  Dp=     6×10−5 m3/r; the natural frequency of hydraulic system ωh1=605.408 rad/s; and the damping ratio of hydraulic system ξh1=2.577×10−3. The initial control and objective curve are planned manually in order to ensure that the expected objective of ILC can be achieved. Firstly, a fixed SRM rotation rate curve is selected as the input of initial control. Then, the rotation rate curve is transformed into the velocity curve of piston movement according to linear proportion association. Finally, the piston displacement curve (shown in Fig.6) that possesses the characteristics of accelerating- constant-decelerating and positioning operations can be obtained through integral operation on the velocity curve. The simulation experiments are carried out to investigate the convergence speed and precision of ILC with the conventional PD and fuzzy PD learning arithmetic. The results are shown in Fig.7. The curves describe the variation of maximum absolute error in iterative learning process. It can be seen that the convergence speed of the conventional ILC is slow, and the PD iterative learning parameters play a significant role in convergence speed and precision. As shown in Fig.7(a), when the learning gain is small, the convergence speed at earlier stage is quickly while it is slow at later stage, and the convergence precision is low, the maximum error is 0.016 V after 100 times of iteration. With the increase of the learning gain, as shown in Figs.7(b) and (c), the convergence speed at earlier stage becomes slow, and the overshot vibration becomes violently, while at later stage, the convergence speed is quicker and precision is higher. The maximum error is 0.008 V after 76 times of iteration (shown in Fig.7(c)). For fuzzy PD ILC, the fuzzy control strategy is used to adaptively regulate learning gains based on error and change in error. For example, when the error and change in error are bigger, lower learning gain is adopted to avoid violent vibration, and when the error and change in error are smaller, higher learning gain is used to accelerate the convergence speed. Therefore, the convergence speed can be accelerated. The simulation results shown in Fig.7(d) demonstrate that the maximum error decreases to 0.008 V after 46 times of iteration with fuzzy PD ILC. It is obvious that the fuzzy PD ILC arithmetic can significantly improve the convergence speed and precision of iterative learning for hydraulic press position servo control. Figs.8(a) and (b) describe the process of approaching objective curve and the maximum absolute error change curve obtained by fuzzy PD ILC in experiments, whose iterative objective and initial control are the same as those of stimulation. The maximum tracking error 0.02 V is acquired after 12 times of iteration when the fuzzy PD ILC is applied to carrying out displacement curve tracking. And due to the precision of sensor and performance of system, the maximum error maintains at 0.02 V when the iterative experiment continues. This is in accordance with the precision obtained in positioning control with closed-loop PID. Thus, the arithmetic proposed in this work can obtain the expected control signal with quicker speed and overcome the problems existing in conventional open-loop ILC. A novel control strategy is   Fig.8 Experimental results of open-loop fuzzy PD ILC:      (a) Iterative and expected output; (b) Fuzzy PD ILC error curve  presented to fulfill the servo control of pressing process for direct-drive volume control hydraulic press.  6 Conclusions  (1) The SRM driven technique is introduced into the fields of hydraulic press. The traditional valve-controlled hydraulic servo system is replaced by SRM direct-drive volume control system. The electro-hydraulic position servo control of hydraulic press slider is realized. (2) A kind of fuzzy ILC algorithm that can adaptively adjust the ILC gains using fuzzy strategy is put forward. The fuzzy algorithm principle and implementation process of adaptive adjustment ILC gains based on error and change in error is presented. A new approach is provided to improve the convergence rate of ILC. (3) The simulated and experimental results show that the gain parameters of ILC have an important influence upon the convergence rate and precision, the fuzzy ILC can evidently accelerate the convergence rate in the process of ILC for electro-hydraulic servo system, and the displacement curve tracking control of hydraulic press slider is achieved with the higher precision of maximum error of 0.02 V after 12 iterations in experiments.  开关磁阻电机液压机电液伺服系统英文文献和中文翻译(4):http://www.751com.cn/fanyi/lunwen_40872.html