Introduction to Control Theory

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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? 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.

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

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

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.

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

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…

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

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

Classic Feedback Diagram

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

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

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”?

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

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

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

Laplace Transform of Classic Feedback Sysem

Key Transfer Functions

First Order System

Response of System Impulse Step Exponential

Step Response illustrates how a system response can be decomposed into two components: Steady-state part: Transient

Second Order System

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

Basic Controller Function

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

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

Kp = 50 Kp = 200

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

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

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

PID final control

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 = 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

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

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

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.

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

PWM PWM -- “pulse width modulation Duty cycle: Why:
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.