Thompson tchobanian ni_li)

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Thompson tchobanian ni_li)

  1. 1. Improving A PID Controller Using Fuzzy Logic Andrew Thompson Ni Li Ara Tchobanian Professor: Riadh Habash TA: Hanliu Chen
  2. 2. Problem <ul><li>Although PID controllers are able to provide adequate control for simple systems, they are unable to compensate for disturbances. </li></ul><ul><li>We will use Fuzzy Logic controllers to improve the PID controllers ability to handle disturbances. </li></ul>
  3. 3. Hypothesis <ul><li>We feel like all the designs for the fuzzy compensator will be an improvement upon the PID controller and will have greater ability to deal with disturbances. </li></ul>
  4. 4. IEEE Papers
  5. 5. Group Contribution <ul><li>Andrew Thompson: </li></ul><ul><ul><li>Research and development of Fuzzy precompensator design and rules </li></ul></ul><ul><ul><li>Research and development of PID Controller </li></ul></ul><ul><li>Ni Li: </li></ul><ul><ul><li>Research and development of various Fuzzy logic compensator (PD, PI) designs and rules </li></ul></ul><ul><li>Ara Tchobanian: </li></ul><ul><ul><li>Research and modeling of DC motor </li></ul></ul><ul><ul><li>Research and development of PID Controller </li></ul></ul>
  6. 6. Procedure <ul><li>We first needed to decide upon a system which we could control using a PID controller as well as be able to introduce a disturbance. </li></ul><ul><li>We chose to model a basic DC motor. </li></ul>
  7. 7. DC Motor <ul><li>We used the following values for the model of the DC Motor </li></ul><ul><li>moment of inertia of the rotor </li></ul><ul><li>J = 0.01 kg.m 2 /s 2 </li></ul><ul><li>damping ratio of the mechanical system b = 0.1 Nms </li></ul><ul><li>electromotive force constant K = 0.01 Nm/Amp </li></ul><ul><li>electric resistance R = 1 ohm </li></ul><ul><li>electric inductance L = 0.5 H </li></ul><ul><li>input V: Source Voltage </li></ul><ul><li>output Θ’: Speed of motor </li></ul>
  8. 8. DC Motor Model
  9. 9. Step 2 <ul><li>We next had to design a PID controller to control the speed of the motor. </li></ul>
  10. 10. PID Controller <ul><li>We wanted the PID controller to satisfy the following criteria: </li></ul><ul><ul><li>Settling time less than 2 seconds </li></ul></ul><ul><ul><li>Overshoot less than 5% </li></ul></ul><ul><ul><li>Steady-state error less than 1% </li></ul></ul><ul><li>By using trial and error, and examining the step response we obtained the following gains: </li></ul><ul><li>Kp = 100, Ki = 200, Kd = 10 </li></ul>
  11. 11. PID Model
  12. 12. Step 3 <ul><li>The final step in the development of our controllers was to design various forms Fuzzy logic compensators in order to improve the performance of the PID controller and to allow it to account for the disturbance. </li></ul><ul><li>We designed three types of Fuzzy logic Compensators </li></ul><ul><ul><li>Fuzzy PI </li></ul></ul><ul><ul><li>Fuzzy PD </li></ul></ul><ul><ul><li>Fuzzy Precompensated </li></ul></ul>
  13. 13. Fuzzy logic Introduction Fuzzy logic is a problem-solving control system methodology that lends itself to implementation in systems ranging from simple, small, embedded micro-controllers to large, networked, multi-channel PC or workstation-based data acquisition and control systems. It can be implemented in hardware, software, or a combination of both. Inputs Rules Output
  14. 14. Fuzzy Precompensated PID Membership Functions, and Fuzzy Rule Sets
  15. 15. Surface and Rule Sets
  16. 16. Fuzzy Precompensated PID Model
  17. 17. Fuzzy logic Equation for the fuzzy PI <ul><li>Kp*X + Ki*Y = Z </li></ul>The output for the fuzzy Y example input for Ki The gain for Ki X example input for Kp The gain for Kp
  18. 18. Membership functions for the PI component. <ul><li>(a) Input membership functions. </li></ul><ul><li>(b) Output membership functions. </li></ul>L A) B) Optical High Low
  19. 19. Fuzzy Logic Rules for the PI <ul><li>The P is Low and I is Low then output is –R </li></ul><ul><li>The P is Low and I is Optimal then output is –(R+S)/2 </li></ul><ul><li>The P is Low and I is High then output is -S </li></ul><ul><li>The P is Optimal and I is Low then output is (–R+S)/2 </li></ul><ul><li>The P is Optimal and I is Optimal then output is 0 </li></ul><ul><li>The P is Optimal and I is High then output is (R-S)/2 </li></ul><ul><li>The P is High and I is Low then output is S </li></ul><ul><li>The P is High and I is optimal then output is (R+S)/2 </li></ul><ul><li>The P is High and I is High then output is R </li></ul>Where R=L 1 *K i +L 2 *K p S=L 2 *K p +L 1 *K i
  20. 20. Linear Fuzzy PI Control Table Surface Viewer R (R+S)/2 S High ( P ) (R-S)/2 0 -(R-S)/2 Optimal ( P ) -S -(R+S)/2 -R Low ( P ) High ( D ) Optimal ( D ) Low ( D) Output
  21. 21. Fuzzy PI Model
  22. 22. Fuzzy PD Membership Functions
  23. 23. Fuzzy PD Model
  24. 24. Simulation Results Step Response PID Fuzzy Precompensated FuzzyPI Fuzzy PD
  25. 25. Simulation Results Step Response with sine disturbance PID Fuzzy Precompensated FuzzyPI Fuzzy PD
  26. 26. Simulation Results Step Response with Gaussian Noise disturbance PID Fuzzy Precompensated FuzzyPI Fuzzy PD
  27. 27. Simulation Results Sine Input PID Fuzzy Precompensated FuzzyPI Fuzzy PD
  28. 28. Simulation Results Sine Input with sine disturbance PID Fuzzy Precompensated FuzzyPI Fuzzy PD
  29. 29. Simulation Results Sine Response with Gaussian Noise disturbance PID Fuzzy Precompensated FuzzyPI Fuzzy PD

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