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A new servo controller for a Materials Testing Machine - MTM Final Presentation a

A new servo controller for a Materials Testing Machine - MTM Final Presentation a. David Schwartz & Uri goldfeld Supervisor : Daniel Alkalay. General System Description.

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A new servo controller for a Materials Testing Machine - MTM Final Presentation a

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  1. A new servo controllerfor a Materials Testing Machine - MTMFinal Presentation a David Schwartz & Uri goldfeld Supervisor :Daniel Alkalay

  2. General System Description The MTM system we work on is a mechanical system that allows us to test the physical properties of materials and structures. Testing is done by applying static or dynamic loads, using an hydraulic actuator in closed-loop servo control. Feedback for closed loop control uses displacement OR Strain sensors. The MTM system enables us to determine tensile/compressive strength, fatigue resistance, crack growth resistance ect.

  3. Strength: The machine applies force on a specimen in order to find out stress Vs. strain characteristics

  4. Fatigue: This test is vital for materials who are under a cyclic force for example: plane wings,bridge… Here the machine applies a periodic (Sin, Square,saw tooth) waveform and checks the behavior under different frequencies and amplitudes. Our machine:

  5. The Original System

  6. General abstract

  7. Main Project Goals The global purpose is to develop a modern computer based mechanical testing system, using current hardware and software tools. Part A: Part B: Servo + hydraulics Old control system Software controller FPGA LabView

  8. Project Goals of Part A: • Implementing a new control system for the MTM machine Located at the Material Mechanics Laboratory. • Learning LabVIEW • Learning the required control tools • Performing system identification • Implement a simulation environment • Simulate the whole system using our controller • Keeping the environment General so it’ll fit other possible systems

  9. The control loop + Command PID controller MTM - Force = 1 Displacement=2 1 Load cell 2 LVDT The machine is controlled in a closed loop. The control loop is modeled as an SISO LTI system.

  10. What is PID Set point = Command Process variable = The Sensors output

  11. What characteristics should we check

  12. Stage 1: System identification • Learning LabView deeply • Learning MatLab System identification tool • Finding the 3dB point of the hole system • Finding Overshoot% and Tsettling and Trise_time • Finding dominant poles (W n and z) • Working with function generator while controlling it using LabView • Finding Bode plot Gain + Phase of the system

  13. The measurement system Agilent Waveform generator MTM I/O card 6036 RS232 pc MatLab: LabView:

  14. The Measuring Environment We want to measure the TF of the whole system meaning, we give the command to the -> controller who calculates the control signal ->To servo ->Back throw sensors to the controller. We sniff the sensors output and calculate the TF by it • The original FULL system was running while our LabVIEW software provided the input and sampled the sensors. • The sampled sensor was the LVDT sensor • measurement influence: we used E-6036 card to sample the LVDT. If there was any influence on the results they are probably insignificant and we can handle them by autotuning.

  15. TF measurements Using LABVIEW Read generator’s output signal and MTM’s output signal via sampling card Send waveform properties To signal generator via RS232 Write to file for further Post processing with matlab Do preliminary calculations (such as gain and phase)

  16. Step response measurements Step response to 1Hz rectangular wave, amplitude 1Volt Sampling rate is 1KHz: Trise_time=106ms O.S%= 0.5% Tsettling= 140ms

  17. Finding Bode plot Gain + Phase of the system This was done using a VI that generated Freq. steps between 0.01Hz to 60Hz,sampeling the Gain and Phase for each Freq. Gain Phase

  18. MTM System’s TF • The approximated Transfer function: 8097 -------------------- s^2 + 303.5 s + 7518 The poles are real -276.2874 -27.2106 The system is over damped: Xi=1.75>1 3db point : 4.344 Hz

  19. Stage 2: Isolating the controller • Learning current system properties from available engineering documentations. • Abstracting the control system • Replace the servo valve with equivalent resistance , repeating the system identification process on the controller without the actuator. • Calculating the transfer function of the controller • The measuring system: • We use a new M-series hardware for measuring and activating the controller • Since the valve is disconnected the sensors will give the controller same value all the time thus what we will measure on the resistors will be the response of the controller as pure as possible while the whole system works

  20. The measurement system (with internal signal generator) I/O M series card MTM controller pc LabView: • Gain and phase calculations • Signal generation The Measuring Environment Generate signal in software ->input to the controller ->controller gives command to servo -> We sample the given command on the resistors • The controller is not connected to the valve but to the resistors • We do not sample any sensor but the DC_ERROR signal coming out of the controller • We expect no meaningful influence on the results caused by our measurement

  21. Bode Plot of the MTM Controller Phase(deg) Magnitude(dB)

  22. MTM controller TF Transfer function : Xi=0.96 MTM_Controller_3dB point = 921Hz The Controller’s 3dB point is 212 times then entire system 3db (4.344 Hz) Conclusion: Mainly a P controller with Kc~=2.72 2.052e008 ----------------------------- s^2 + 1.666e004 s + 7.529e007

  23. The simulation program Labview VIs: NI DAQmx read Limit +interlock check Stop program fail o.k Control signal NI DAQmx write PI H(s) Signal generator

  24. System Overview

  25. Stage 3: Building a simulation • A simulation in LabVIEW for the system was built using the acquired transfer function to simulate The servo valve and acquired PID parameters to simulate the controller • Labview auto tuning vi’s were used to find better PID parameters.Manual tuning is also possible.

  26. Simulation results Simulating Square Input to the machine

  27. Simulating Sin input to the machine

  28. Simulating Sin input to the machine

  29. Simulating Square Input to the machine

  30. Difficulties • Main difficulty is that in order to check our simulation system we need a special Amplifier which we currently don’t have. Attempts were made to use the old controller’s Amplifier but it can’t be done. • To continue we must build or buy the amplifier that will enable us to check our software directly on the servo valve

  31. Summary These are the results we got for optimal gains: • No overshot (0%) • Rise time of 15 msec • Settling time of 30 msec (to a sleeve of +- 5%) If we look at the full system identification we see that the original system had a WORSE response : • No overshot (0%) • Rise time of 100 msec • Settling time of 140 msec (to a sleeve of +- 5%) Meaning that in simulation the controller we have now is the much better than the original controller, Thus we should expect the hardware implementation to bring better results then the old controller

  32. Future Work

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