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

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.

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

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:

The Original System

General abstract

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 FPGALabView Softwarecontroller

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

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

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

What characteristics should we check

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

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

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.

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

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

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. GainPhase

MTM System’s TF The approximated Transfer function: The approximated Transfer function: s^ s s^ s The poles are real The system is over damped: Xi=1.75>1 3db point : Hz

Stage 2: Isolating the controller Learning current system properties from available engineering documentations. Learning current system properties from available engineering documentations. Abstracting the control system Abstracting the control system Replace the servo valve with equivalent resistance, repeating the system identification process on the controller without the actuator. 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 Calculating the transfer function of the controller The measuring system: 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

I/O M series card The measurement system (with internal signal generator) MTM controller LabView: Gain and phase Gain and phase calculations calculations Signal generation Signal generation pc 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

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

MTM controller TF 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 ( Hz) Conclusion: Mainly a P controller with Kc~= e e s^ e004 s e007

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

System Overview

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 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. Labview auto tuning vi’s were used to find better PID parameters.Manual tuning is also possible.

Simulation results Simulating Square Input to the machine

Simulating Sin input to the machine

Simulating Square Input to the machine

Difficulties Main difficulty is that in order to check our simulation system we need a special Amplifier which we currently don’t have. 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. 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 To continue we must build or buy the amplifier that will enable us to check our software directly that will enable us to check our software directly on the servo valve on the servo valve

Summary These are the results we got for optimal gains: No overshot (0%) No overshot (0%) Rise time of 15 msec Rise time of 15 msec Settling time of 30 msec (to a sleeve of +- 5%) 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%) No overshot (0%) Rise time of 100 msec Rise time of 100 msec Settling time of 140 msec (to a sleeve of +- 5%) 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

Future Work assignmentsDates Learning the LabVIEW FPGA module On going (Long) Defining the architecture of the combined system I.E what will be on FPGA and what on the LabVIEW_RT Two-Three weeks Emulate our system before using the FPGA One-Two weeks Use the whole system as one synchronized unit and checking it as the new full controller Two-Three weeks Project book and Final presentation One-Two week