autonomous navigation for flying robots lecture 4.2

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Computer Vision Group Prof. Daniel Cremers Autonomous Navigation for Flying Robots Lecture 4.2 : Feedback Control Jürgen Sturm Technische Universität München

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Page 1: Autonomous Navigation for Flying Robots Lecture 4.2

Computer Vision Group Prof. Daniel Cremers

Autonomous Navigation for Flying Robots Lecture 4.2 : Feedback Control

Jürgen Sturm

Technische Universität München

Page 2: Autonomous Navigation for Flying Robots Lecture 4.2

Motivation: Position Control

Move the quadrotor to a desired location

How can we generate a suitable control signal ?

Current location (observed through sensors)

Jürgen Sturm Autonomous Navigation for Flying Robots 2

Page 3: Autonomous Navigation for Flying Robots Lecture 4.2

Controller (you!)

Sensor (you!)

Feedback Control – Generic Idea

Jürgen Sturm Autonomous Navigation for Flying Robots 3

Desired value

35°

Turn hotter (not colder)

System

25°

35°

45°

25°

35°

45°

Measured temperature

error

Page 4: Autonomous Navigation for Flying Robots Lecture 4.2

Feedback Control – Block Diagram

Jürgen Sturm Autonomous Navigation for Flying Robots 4

Controller

System

Sensor

Reference Measured error + -

Control State

Measured state

Page 5: Autonomous Navigation for Flying Robots Lecture 4.2

Proportional Control

P-Control:

Jürgen Sturm Autonomous Navigation for Flying Robots 5

Page 6: Autonomous Navigation for Flying Robots Lecture 4.2

Effect of Noise

What effect has noise in the process/measurements?

Poor performance for K=1

How can we fix this?

Jürgen Sturm Autonomous Navigation for Flying Robots 6

Page 7: Autonomous Navigation for Flying Robots Lecture 4.2

Proper Control with Noise

Lower the gain… (K=0.15)

Jürgen Sturm Autonomous Navigation for Flying Robots 7

Page 8: Autonomous Navigation for Flying Robots Lecture 4.2

What do High Gains do?

High gains are always problematic (K=2.15)

Jürgen Sturm Autonomous Navigation for Flying Robots 8

Page 9: Autonomous Navigation for Flying Robots Lecture 4.2

What happens if sign is messed up?

Check K=-0.5

Jürgen Sturm Autonomous Navigation for Flying Robots 9

Page 10: Autonomous Navigation for Flying Robots Lecture 4.2

Saturation

In practice, often the set of admissible controls u is bounded

This is called (control) saturation

Jürgen Sturm Autonomous Navigation for Flying Robots 10

Page 11: Autonomous Navigation for Flying Robots Lecture 4.2

Delays

In practice most systems have delays

Can lead to overshoots/oscillations/de-stabilization

One solution: lower gains (why is this bad?)

Jürgen Sturm Autonomous Navigation for Flying Robots 11

Page 12: Autonomous Navigation for Flying Robots Lecture 4.2

Delays

What is the total dead time of this system?

Can we distinguish delays in the measurement from delays in actuation?

Jürgen Sturm Autonomous Navigation for Flying Robots 12

Measurement

Controller –

System

1000ms delay in water pipe

200ms delay in sensing

Page 13: Autonomous Navigation for Flying Robots Lecture 4.2

Smith Predictor

Allows for higher gains

Requires (accurate) system model

Jürgen Sturm Autonomous Navigation for Flying Robots 13

Controller –

System with delay

Delay-free system model

Delay model –

Sensor

Page 14: Autonomous Navigation for Flying Robots Lecture 4.2

Smith Predictor

Assumption: System model is available, 5 seconds delay

Smith predictor results in perfect compensation

Why is this unrealistic in practice?

Jürgen Sturm Autonomous Navigation for Flying Robots 14

Page 15: Autonomous Navigation for Flying Robots Lecture 4.2

Smith Predictor

Time delay (and system model) is often not known accurately (or changes over time)

What happens if time delay is overestimated?

Jürgen Sturm Autonomous Navigation for Flying Robots 15

Page 16: Autonomous Navigation for Flying Robots Lecture 4.2

Smith Predictor

Time delay (and plant model) is often not known accurately (or changes over time)

What happens if time delay is underestimated?

Jürgen Sturm Autonomous Navigation for Flying Robots 16

Page 17: Autonomous Navigation for Flying Robots Lecture 4.2

Lessons Learned

Control problem

Feedback control

Proportional control

Delay compensation

Next video:

PID control

Position control for quadrotors

Jürgen Sturm Autonomous Navigation for Flying Robots 17