automatic control (tsrt15): lecture 1 · automatic control (tsrt15): lecture 1 ... * all lecture...
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Automatic Control (TSRT15): Lecture 1
Tianshi Chen*Division of Automatic ControlDept. of Electrical EngineeringEmail: [email protected]: 13-282226Office: B-house extrance 25-27
* All lecture notes in this course are revised on the ones used before.
I sincerely appreciate Johan Löfberg for his permission to use them.
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2Course plan
Lecture notes will (hopefully) be posted some days in advance
12 lectures
12 exercise sessions
3 mandatory laboratory sessions (all materials on homepage)Lab 1: PID-control (preparation questions in the PM)Lab 2: Control of double-tanks (preparation takes time!)Lab 3: Control of inverted pendulum (computer lab)Lablists will be sent out on email and be posted on-line
Exam: Course book, tables and formula collection allowed. Separate notes and other sheets not allowedStudy notes in book are allowed
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3Outline
Automatic control in practice
Definition of basic principlesSignal, system, Control
Fundamental Principle of Control: Feedback
Linear Dynamic systems
Design of a cruise controllerOpen vs closed-loop control, P-control
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4Automatic control
Makes ”impossible” problems solvable
Often called the ”hidden technology”
Central for Swedish technology companies
Many interesting applications!
A lot of interesting math
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5Control examples
Mobile phones
Automatic control is used to control the power in radio signals between phone and base-station.
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6Control examples
Head-phones
Active noise cancellation in head-phones use automatic control to transmit counteracting sound in anti-phase.
Similar technique for sound and vibration damping in airplanes, cars, snowboards and buildings.
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7Control examples
Hard disks
The reading arm must be positioned at they right spot as fast as possible.
Without active control, the arm oscillates after movements, and prevents reading data until it has settled.
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8Control examples
Segway
One of the most obvious consumer products,and it does not work without a control system.
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9Control examples
Modern cars
Most acronyms hides a control system!
ABS (anti-lock braking system)ESC (electronic stability control)ACE (active cornering enhancement)TCS (traction control system)ACC (adaptive cruise control)ANC (active noise control)…
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10Control examples
Heavy trucks
The aim is to utilize an on-board database with road topography information in combination with a positioning system in order to calculate fuel-optimal velocity trajectories and gear shifting schemes.
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11Control examples
Modern fighters
Designed so that they are impossible to fly manually(to obtain better performance)
Requires a control system
If the control system has a design problem, it can go very wrong. This is what happened in the Gripencrashes in 89’ and 93’
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12Control examples
Kite-Powered Cargo Ship
Has been tested in practice over the Atlantic
Reduced fuel consumption by 20%
Kite position controlled for maximalpower
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13Control examples
Extremely large telescopes
We have reached the limit on mirror size
Large telescopes are built with many small mirrors whose position is continuously controlled to focus the image(called adaptive optics)
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14Control examples
Industrial robots
Same as the hard disk
A robot arm is weak, and oscillates after movements
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15Control examples
Automatic Anaesthesia
A control system replaces the nurse (still research)
The system controls the level of consciousness
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16Control examples
Interest rates and Inflation
The Swedish bank uses state interest rate to control inflation
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17Automatic control?
The ”thing” we control can be conceptually illustrated
u(t)
w(t)
y(t)Systemr(t)
Design the control u(t) so that the system (according to the outputy(t)) behaves as wanted (reference r(t)) despite disturbances w(t).
Here, u(t), y(t), r(t) and w(t) are functions of time and called signals.
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18Control examples
System u(t) y(t) w(t) r(t)
Car Throttle,break speed Slope, air resistance
Desired speed
Anaesthesia Drugs consciousness Drug tolerance, weight
Less than dead
Economy Interest Inflation Politics Inflation goal 2%
Magnetelevation
Magnet strength
Elevation Wind Desired elevation
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19Dynamical systems
Systems memory, current output depends on past inputs
Mathematically: System described by a differential equation
A description (often approximate) of a system is called a model
Opposite: Static system
Speed and position of a car (depends on past throttle)
Room temperature (depends on past heating and outside temperature)
Economics (depends on politics, investments past years)
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20Linear systems
u(t) y(t)System
Linear system means superposition holds
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21Linear systems
Linear ordinary differential equations fulfill this
We only work with systems described by linear ordinary differential equations
More (much more) about this next lecture
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22Fundamental principle of control: Feedback
A fundamental principle in control is feedback, here illustrated on a distillation column
1. Formulate a control goal (reference signal)We want a temperature of 80º
2. Measure current temperature (measurement signal)It is now 60º
3. Apply action (control using the control signal)Increase heating!
Feedback!
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23Fundamental principle of control: Feedback
Feedback system
u(t) y(t)System
w(t)
Controllerr(t)
Feedback!
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24Control examples
Feedback system
speedthrottle
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25Control examples
Feedback system
consciousnessDrugs
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26Control examples
Feedback system
interest inflationSystem2%
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27What we will learn?
In this course we ask
How do we describe the system to be controlled
How do we analyze the system to be controlled
How do we design a controller
How do we analyze the feedback system (what can go wrong)
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28Design of cruise controller
φ
u(t): Driving/breaking force [N]y(t): Velocity of car [m/s]φ : Road slope [rad]m: Car weight [kg]α: Aerodynamic coefficient [Ns/m], αy(t): Drag force [N]
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29Open loop control
Model: m = 1000kg, α = 200Ns/m, φ = 0
Newton
Open loop: Our goal is a reference speed r(t) = 25m/s for t ` 0.Assume y(0) = 0. We test the following control law
Solution:We reach the reference speed asymptotically
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30Open loop control
u(t) y(t)
mg sin(φ)
200r(t)=25
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31Open loop control
Non-nominal model:
Wind tunnel test wrong, in reality α = 150Ns/m
Under the assumption y(0) = 0, we use the same control law and obtain
The car achieves a too high speed
Cause: we have not feeded back the true velocity!
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32Open loop control
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33Closed-loop control
Feedback the velocity!
A reasonable strategy is to throttle more when too slow
This is called proportional control, P-control, and the constant K is the only design variable in the controller
The closed-loop system
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34Closed-loop control
u(t) y(t)
mg sin(φ)
Kr(t)=25
-1
e(t)
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35Closed-loop control
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36Closed-loop control
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37But what is a controller, really?
A controller is a computer in the car, measuring speed and desired speed, and sends command signals (desired torque) to the engine
program CruiseControl
repeatr = getReferenceMeasurementy = getSpeedMeasurementu = K*(r-y);sendCommandToEngine(u)
end
y
r
u
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38Summary of this lecture
Automatic control is everywhere
We use differential equation to create models of systems
Open-loop control very sensitive to model parameters and disturbances
Feedback can reduce sensitivity significantly
Feedback u(t) = K(r(t)-y(t)) is called P-control
We still haven’t achieved perfect control, better design is needed
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39Summary of this lecture
Automatic control: “Making things behave as we want”.
Signal: Functions of time with information
System: An object driven by input signals, generating output signals
Model: A simplified description of reality. In this course, a mathematical description of the system we study
Dynamical systems: Systems where the output signal depends on past inputs
Feedback: Feed back information about the current state to the controller. Automatic control is the theory about feedback systems
Important concepts