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ECE 4951

ECE 4951. Lecture 2: Power Switches and PID Control of Motorized Processes. Power SemiConductors. High Voltage (100’s of Volts) High Current (10’s of Amps) High Power Transistors, SCR’s Power Diode Power BJT, IGBT Power MOSFET Thyristor (Power SCR), GTO. High Power DC Switch.

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ECE 4951

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  1. ECE 4951 Lecture 2: Power Switches and PID Control of Motorized Processes

  2. Power SemiConductors • High Voltage (100’s of Volts) • High Current (10’s of Amps) • High Power Transistors, SCR’s • Power Diode • Power BJT, IGBT • Power MOSFET • Thyristor (Power SCR), GTO

  3. High Power DC Switch • Use Power Transistor as a Switch (On/Off) on a Power Circuit • Small Signal (Low power) Controls Large Signal (Like a Relay) • Combine with Inductors and Capacitors for Wave-Shaping

  4. Power MOSFETs • Simple to Bias • Hundreds of Volts • Tens of Amps • Low Gate Voltages • Vgs < +/- 20 Volts (DO NOT EXCEED) • Fairly Fast Switching times (200 nS)

  5. DC-DC Chopper • Power Transistor “Chops” High Voltage DC into Low Voltage DC (DC to DC Transformation)

  6. Chopper Output Waveforms • Transistor Chops Voltage into Square Wave • Inductor Smoothes Current

  7. Biasing Circuit for P-MOSFET Switch • Design Goals: • 5V Logic to turn on/off switch • Want MOSFET lightly in saturation when on (Vgs=10-15V) [Avoid approaching Vgs=+/-20V] • Want to control a 24V circuit • Want to protect Logic Source from Transients

  8. Design of Biasing Circuit for MOSFET Switch IMPORTANT: |Vgs| < 20 Volts!

  9. Motor Types • AC, DC or Universal • DC Motors: • Wound Stator • Series • Shunt • Compound (both) • Permanent Magnet Stator • Brushless (Permanent Magnet Rotor)

  10. Speed – Torque Characteristicsof DC Motors • Shunt – Constant Speed • Series – ‘Traction Motor’ • Compound – Anywhere in between • PM Motor – Constant Speed like a Shunt Motor

  11. Gear Heads • Common to take a High-Speed, Low Torque motor (Permanent Magnet) and match it to a Gear Head. T = P/ω • Produces a Low-Speed, High Torque Motor where speed can be varied with applied voltage.

  12. ServoMotor • Designed for Classic Feedback Control • Shaft position is encoded and available to control system as an electronic signal • Shaft encoder usually matched with a gearhead PM motor (or equivalent)

  13. Feedback Control • Reference Signal Calls for Specific Shaft Position, θ • Controller responds, with actual shaft position fed back to achieve swift and accurate outcome

  14. Stepper Motor • Designed for Pulsed Input(s) • Each Pulse advances (or retards) shaft by a fixed angle (No feedback needed to know θ)

  15. PID Control • A closed loop (feedback) control system, generally with Single Input-Single Output (SISO) • A portion of the signal being fed back is: • Proportional to the signal (P) • Proportional to integral of the signal (I) • Proportional to the derivative of the signal (D)

  16. When PID Control is Used • PID control works well on SISO systems that can are or can be approximated to 2nd Order, where a desired Set Point can be supplied to the system control input • PID control handles step changes to the Set Point especially well: • Fast Rise Times • Little or No Overshoot • Fast settling Times • Zero Steady State Error • PID controllers are often fine tuned on-site, using established guidelines

  17. Control Theory • Consider a DC Motor turning a Load: • Shaft Position, Theta, is proportional to the input voltage

  18. Looking at the Motor: • Electrically (for Permanent Magnet DC):

  19. Looking at the Motor • Mechanically:

  20. Combining Elect/Mech Torque is Conserved: Tm = Te 1 and 2 above are a basis for the state-space description

  21. This Motor System is 2nd Order • So, the “plant”,G(s) = K / (s2 + 2as + b2) • Where a = damping factor, b = undamped freq. • And a Feedback Control System would look like:

  22. Physically, We Want: • A 2nd Order SISO System with Input to Control Shaft Position:

  23. Adding the PID: • Consider the block diagram shown: • C(s) could also be second order….(PID)

  24. PID Block Diagram:

  25. PID Mathematically: • Consider the input error variable, e(t): • Let p(t) = Kp*e(t) {p proportional to e (mag)} • Let i(t) = Ki*∫e(t)dt {i integral of e (area)} • Let d(t) = Kd* de(t)/dt {d derivative of e (slope)} AND let Vdc(t) = p(t) + i(t) + d(t) Then in Laplace Domain: Vdc(s) = [Kp + 1/s Ki + s Kd] E(s)

  26. PID Implemented: Let C(s) = Vdc(s) / E(s) (transfer function) C(s) = [Kp + 1/s Ki + s Kd] = [Kp s + Ki + Kd s2] / s (2nd Order) THEN C(s)G(s) = K [Kd s2 + Kp s + Ki] s(s2 + 2as + b2) AND Y/R = Kd s2 + Kp s + Ki s3 + (2a+Kd)s2 + (b2+Kp) s + Ki

  27. Implications: • Kd has direct impact on damping • Kp has direct impact on resonant frequency In General the effects of increasing parameters is: Parameter: Rise Time Overshoot Settling Time S.S.Error Kp Decrease Increase Small Change Decrease Ki Decrease Increase Increase Eliminate Kd Small Change Decrease Decrease None

  28. Tuning a PID: • There is a fairly standard procedure for tuning PID controllers • A good first stop for tuning information is Wikipedia: • http://en.wikipedia.org/wiki/PID_controller

  29. Deadband • In noisy environments or with energy intensive processes it may be desirable to make the controller unresponsive to small changes in input or feedback signals • A deadband is an area around the input signal set point, wherein no control action will occur

  30. Time Step Implementation of Control Algorithms (digital controllers) • Given a continuous, linear time domain description of a process, it is possible to approximate the process with Difference Equations and implement in software • Time Step size (and/or Sampling Rate) is/are critical to the accuracy of the approximation

  31. From Differential Equation to Difference Equation: • Definition of Derivative: dU = lim U(t + Δt) – U(t) dt Δt0Δt • Algebraically Manipulate to Difference Eq: U(t + Δt) = U(t) + Δt*dU dt (for sufficiently small Δt) • Apply this to Iteratively Solve First Order Linear Differential Equations (hold for applause)

  32. Implementing Difference Eqs: • Consider the following RC Circuit, with 5 Volts of initial charge on the capacitor: • KVL around the loop: -Vs + Ic*R + Vc = 0, Ic = C*dVc/dt OR dVc/dt = (Vs –Vc)/RC

  33. Differential to Difference with Time-Step, T: • Differential Equation: dVc/dt = (Vs –Vc)/RC • Difference Equation by Definition: Vc(kT+T) = Vc(kT) + T*dVc/dt • Substituting: Vc(kT+T) = Vc(kT) + T*(Vs –Vc(kT))/RC

  34. Coding in SciLab: R=1000 C=1e-4 Vs=10 Vo=5 //Initial Value of Difference Equation (same as Vo) Vx(1)=5 //Time Step dt=.01 //Initialize counter and time variable i=1 t=0 //While loop to calculate exact solution and difference equation while i<101, Vc(i)=Vs+(Vo-Vs)*exp(-t/(R*C)), Vx(i+1)=Vx(i)+dt*(Vs-Vx(i))/(R*C), t=t+dt, i=i+1, end

  35. Results:

  36. Integration by Trapezoidal Approximation: • Definition of Integration (area under curve): • Approximation by Trapezoidal Areas

  37. Trapezoidal Approximate Integration in SciLab: //Calculate and plot X=5t and integrate it with a Trapezoidal approx. //Time Step dt=.01 //Initialize time and counter t=0 i=2 //Initialize function and its trapezoidal integration function X(1)=0 Y(1)=0 //Perform time step calculation of function and trapezoidal integral while i<101,X(i)=5*t,Y(i)=Y(i-1)+dt*X(i-1)+0.5*dt*(X(i)-X(i-1)), t=t+dt, i=i+1, end //Plot the results plot(X) plot(Y)

  38. Results:

  39. Coding the PID • Using Difference Equations, it is possible now to code the PID algorithm in a high level language p(t) = Kp*e(t)  P(kT) = Kp*E(kT) i(t) = Ki*∫e(t)dt  I(kT+T) = Ki*[I(kT)+T*E(kT+T)+.5(E(kT+T)-E(kT))] d(t) = Kd* de(t)/dt  D(kT+T) = Kd*[E(kT+T)-E(kT)]/T

  40. Example: Permanent Magnet DC Motor State-Space Description of the DC Motor: 0. θ’ = ω (angular frequency) 1. Jθ’’ + Bθ’ = KtIa  ω’ = -Bω/J + KtIa/J • LaIa’ + RaIa = Vdc - Kaθ’  Ia’ = -Kaω/La –RaIa/La +Vdc/La In Matrix Form:

  41. Scilab Emulation of PM DC Motor using State Space Equations

  42. DC Motor with PID control //PID position control of permanent magnet DC motor //Constants Ra=1.2;La=1.4e-3;Ka=.055;Kt=Ka;J=.0005;B=.01*J;Ref=0;Kp=5;Ki=1;Kd=1 //Initial Conditions Vdc(1)=0;Theta(1)=0;Omega(1)=0;Ia(1)=0;P(1)=0;I(1)=0;D(1)=0;E(1)=0 //Time Step (Seconds) dt=.001 //Initialize Counter and time i=1;t(1)=0 //While loop to simulate motor and PID difference equation approximation while i<1500, Theta(i+1)=Theta(i)+dt*Omega(i), Omega(i+1)=Omega(i)+dt*(-B*Omega(i)+Kt*Ia(i))/J, Ia(i+1)=Ia(i)+dt*(-Ka*Omega(i)-Ra*Ia(i)+Vdc(i))/La, E(i+1)=Ref-Theta(i+1), P(i+1)=Kp*E(i+1), I(i+1)=Ki*(I(i)+dt*E(i)+0.5*dt*(E(i+1)-E(i))), D(i+1)=Kd*(E(i+1)-E(i))/dt, Vdc(i+1)=P(i+1)+I(i+1)+D(i+1), //Check to see if Vdc has hit power supply limit if Vdc(i+1)>12 then Vdc(i+1)=12 end t(i+1)=t(i)+dt, i=i+1, //Call for a new shaft position if i>5 then Ref=10 end end

  43. Results:

  44. References: • Phillips and Nagle, Digital Control System Analysis and Design, Prentice Hall, 1995, ISBN-10: 013309832X WAKE UP!!

  45. Team Assignment: • Build a Power Switch using an N-MOS transistor (IRF 830) and an opto-isolator (PS-2501). Use your programmable device to turn a 12 V circuit on and off.

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