Introduction to Control Theory

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# Introduction to Control Theory - PowerPoint PPT Presentation

Introduction to Control Theory. COMP417 Instructor: Philippe Giguère. Actuators. Earlier lecture presented actuators Electrical motor Now how do we use them in an intelligent manner?. Open-loop?.

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## Introduction to Control Theory

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1. Introduction to Control Theory COMP417 Instructor: Philippe Giguère

2. Actuators • Earlier lecture presented actuators • Electrical motor • Now how do we use them in an intelligent manner?

3. Open-loop? • We want to spin a motor at a given angular velocity. We can apply a fixed voltage to it, and never check to see if it is rotating properly. • Called open loop.

4. Open-loop? • What if there is a changing load on the motor? • Our output velocity will change! speed w no torque at max speed stall torque torque t

5. Closing the loop • Let’s measure the actual angular velocities. • Now we can compensate for changes in load by feeding back some information.

6. Control Theory • Roots in another science: Cybernetics Cybernetics is the study of feedback and derived concepts such as communication and control in living organisms, machines and organisations. • Expression was coined by Norbert Weiner in 1948.

7. Early Example of Feedback System • James Watt’s “Centrifugal Governor” in 1788. • Regulates the steam engine speed.

8. Other Simple Examples • Body temperature regulation • If cold, shiver (muscles produce heat) • If hot, sweat (evaporation takes away heat) • Maintaining social peace • If a crime is found (sensor), the guilty party is punished (actuator). • Cruise control in cars • You set a speed, CC will increase fuel intake uphill, and decrease it downhill. • Etc…

9. Why Control Theory • Systematic approach to analysis and design • Transient response • Consider sampling times, control frequency • Taxonomy of basic controllers (PID, open-loop, Model-based, Feedforward…) • Select controller based on desired characteristics • Predict system response to some input • Speed of response (e.g., adjust to workload changes) • Oscillations (variability) • Assessing stability of system

10. Characteristics of Feedback System • Power amplification • Compare neural signal power (mW) vs muscle power output (tens of W) • Means it is an active system, as opposed to passive. • Actuator • Feedback • measurement (sensor) • Error signal • Controller

11. Classic Feedback Diagram

12. Controller Design Methodology Start System Modeling Controller Design Block diagram construction Controller Evaluation Transfer function formulation and validation Objective achieved? Stop Y Model Ok? N Y N

13. Control System Goals • Regulation • thermostat • Tracking • robot movement, adjust TCP window to network bandwidth • Optimization • best mix of chemicals, minimize response times

14. Approaches to System Modeling • First Principles • Based on known laws. • Physics, Queueing theory. • Difficult to do for complex systems. • Experimental (System Identification) • Requires collecting data • Is there a good “training set”?

15. Common Laplace Transfom Name f(t) F(s) Impulse d 1 Step Ramp Exponential Sine Damped Sine

16. Transfer Function • Definition: H(s) = Y(s) / X(s) • Relates the output of a linear system to its input. • Describes how a linear system responds to an impulse… called impulse response • (remember! Convolving with impulse yields the same thing. Hence probing a system with and impulse is finding its transfer function…) • All linear operations allowed • Scaling, addition, multiplication

17. Block Diagram • Expresses flows and relationships between elements in system. • Blocks may recursively be systems. • Rules • Cascaded elements: convolution. • Summation and difference elements

18. Laplace Transform of Classic Feedback Sysem

19. Key Transfer Functions

20. First Order System

21. Response of System • Impulse • Step Exponential

22. Steady-State vs. Transient • Step Response illustrates how a system response can be decomposed into two components: • Steady-state part: • Transient

23. Second Order System

24. Second Order Response • Typical response to step input is: overshoot -- % of final value exceeded at first oscillation rise time -- time to span from 10% to 90% of the final value settling time -- time to reach within 2% or 5% of the final value

25. Basic Controller Function

26. Effect of Controller Functions • Proportional Action • Simplest Controller Function • Integral Action • Eliminates steady-state error • Can cause oscillations • Derivative Action (“rate control”) • Effective in transient periods • Provides faster response (higher sensitivity) • Never used alone

27. Control Performance (2nd O), P-type Kp = 20 Kp = 50 Kp = 500 Kp = 200

28. Steady-state Errors, P-type Kp = 50 Kp = 200

29. Control Performance, PI - type Kp = 100 Ki = 50 Ki = 200

30. You’ve been integrated... Kp = 100 instability & oscillation

31. Control Performance, PID-type Kp = 100 Kd = 5 Kd = 2 Ki = 200 Kd = 10 Kd = 20

32. PID final control

33. PID Tuning How to get the PID parameter values ? (1) If we know the transfer function, analytical methods can be used (e.g., root-locus method) to meet the transient and steady-state specs. (2) When the system dynamics are not precisely known, we must resort to experimental approaches. Ziegler-Nichols Rules for Tuning PID Controller: Using only Proportional control, turn up the gain until the system oscillates w/o dying down, i.e., is marginally stable. Assume that K and P are the resulting gain and oscillation period, respectively. Then, use for P control for PI control for PID control Kp = 0.5 K Kp = 0.45 K Kp = 0.6 K Ziegler-Nichols Tuning for second or higher order systems Ki = 1.2 / P Ki = 2.0 / P Kd = P / 8.0

34. Layered Approach to Control • Robotic systems often have a layered approach to control: movie

35. Multiple Loops Inner loops are generally “faster” that outer loop.

36. Advanced Control Topics • Adaptive Control • Controller changes over time (adapts). • MIMO Control • Multiple inputs and/or outputs. • Predictive Control • You measure disturbance and react before measuring change in system output. • Optimal Control • Controller minimizes a cost function of error and control energy. • Nonlinear systems • Neuro-fuzzy control. • Challenging to derive analytic results.

37. RC Servo • Is a controller + actuator in a small package. • Not expensive 10\$-100\$. • Those in 417 will use some of these.

38. PWM • PWM -- “pulse width modulation • Duty cycle: • The ratio of the “On time” and the “Off time” in one cycle. • Determines the fractional amount of full power delivered to the motor. • Why: • Transistor acts as a resistor when partially on. • Leads to energy being wasted  heat.