PID Control System
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The document discusses feedback control systems, specifically focusing on Proportional-Integral-Derivative (PID) control methods. It outlines the mechanics of each control type (P, PI, PD, and PID), their applications, and the significance of tuning parameters for optimal performance. Additionally, it addresses common issues and solutions related to system inertia and external forces.
PID Control System
- 1. ASHEESH K. SHAHI asheeshshahiece@gmail.com Department of Electronics and Comm., Amity School of Engineering & Technology (ASET), Amity University, Uttar Pradesh, India 1
- 2. Feedback Control Say you have a system controlled by an actuator Hook up a sensor that reads the effect of the actuator (NOT the output to the actuator) You now have a feedback loop and can use it to control your system! 2 Actuator Sensor
- 3. Introduction to PID Stands for Proportional, Integral, and Derivative control Form of feedback control 3
- 4. Simple Feedback Control (Bad) double Control (double setpoint, double current) { double output; if (current < setpoint) output = MAX_OUTPUT; else output = 0; return output; } Why won't this work in most situations? 4
- 5. Simple Feedback Control Fails Moving parts have inertia Moving parts have external forces acting upon them (gravity, friction, etc) 5
- 6. Proportional Control Get the error - the distance between the setpoint (desired value) and the actual value Multiply it by Kp, the proportional gain That's your output! double Proportional(double setpoint, double current, double Kp) { double error = setpoint - current; double P = Kp * error; return P; } 6
- 7. Proportional Tuning If Kp is too large, the sensor reading will rapidly approach the setpoint, overshoot, then oscillate around it If Kp is too small, the sensor reading will approach the setpoint slowly and never reach it 7
- 8. What can go wrong? When error nears zero, the output of a P controller also nears zero Forces such as gravity and friction can counteract a proportional controller and make it so the setpoint is never reached (steady-state error) Increased proportional gain (Kp) only causes jerky movements around the setpoint 8
- 9. Proportional-Integral Control Accumulate the error as time passes and multiply by the constant Ki. That is your I term. Output the sum of your P and I terms. double PI(double setpoint, double current, double Kp, double Ki) { double error = setpoint - current; double P = Kp * error; static double accumError = 0; accumError += error; double I = Ki * accumError; return P + I; } 9
- 10. PI controller The P term will take care of the large movements The I term will take care of any steady- state error not accounted for by the P term 10
- 11. Limits of PI control PI control is good for most embedded applications Does not take into account how fast the sensor reading is approaching the setpoint Wouldn't it be nice to take into account a prediction of future error? 11
- 12. Proportional-Derivative Control Find the difference between the current error and the error from the previous timestep and multiply by the constant Kd. That is your D term. Output the sum of your P and D terms. double PD(double setpoint, double current, double Kp, double Kd) { double error = setpoint - current; double P = Kp * error; static double lastError = 0; double errorDiff = error - lastError; lastError = error; double D = Kd * errorDiff; return P + D; } 12
- 13. PD Controller D may very well stand for "Dampening" Counteracts the P and I terms - if system is heading toward setpoint, This makes sense: The error is decreasing, so d(error)/dt is negative 13
- 14. PID Control Combine P, I and D terms! double PID(double setpoint, double current, double Kp, double Ki, double Kd) { double error = setpoint - current; double P = Kp * error; static double accumError = 0; accumError += error; double I = Ki * accumError; static double lastError = 0; double errorDiff = error - lastError; lastError = error; double D = Kd * errorDiff; return P + I + D; } 14
- 15. Effects of increasing a parameter independently PARAMETER Kp Ki Kd RISE TIME DECREASE DECREASE MINOR CHANGE OVERSHOOT INCREASE INCREASE DECREASE SETTLING TIME SMALL CHANGE INCREASE DECREASE STEADY STATE ERROR DECREASE INCREASE NO EFFECT STABILITY DEGRADE DEGRADE IMPROVE IF Kd IS SMALL 15
- 16. PID Tuning Start with Kp = 0, Ki = 0, Kd = 0 Tune P term - System should be at full power unless near the setpoint Tune Ki until steady-state error is removed Tune Kd to dampen overshoot and improve responsiveness to outside influences PI controller is good for most embedded applications, but D term adds stability 16
- 17. Effects of varying PID parameters on the step response of a system 17
- 18. PID Applications Robotic arm movement (position control) Temperature control Speed control (ENGR 151 TableSat Project) 18
- 19. Conclusion PID uses knowledge about the present, past, and future state of the system, collected by a sensor, to control In PID control, the constants Kp, Ki, and Kd must be tuned for maximum performance 19
- 20. Questions? 20