A Flight Control System for Autonomous Helicopter

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A Flight Control Framework for Self-sufficient Helicopter. Individuals. Bunch Individuals: Jacky, SHEN Jie Frank, WANG Tao Marl, Mama Mo Administrators: Prof. QIU Li Prof. LI Zexiang. Presentation Stream. Presentation Controlling a Model Helicopter Disposition Estimation Equipment and Programming

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Slide 1

A Flight Control System for Autonomous Helicopter

Slide 2

People Group Members: Jacky, SHEN Jie Frank, WANG Tao Marl, MA Mo Supervisors: Prof. QIU Li Prof. LI Zexiang

Slide 3

Presentation Flow Introduction Controlling a Model Helicopter Attitude Estimation Hardware and Software Results and Further Work

Slide 4

Introduction What is an Autonomous Helicopter??

Slide 5

Introduction What is Autonomous Helicopter? An Autonomous Helicopter is a helicopter who is completely or semi-controlled by on-board knowledge and processing power. Pictures are from CMU: http://www-2.cs.cmu.edu/afs/cs/extend/chopper

Slide 6

What could an Autonomous Helicopter do? Travel to an assigned territory on an endorsed way while maintaining a strategic distance from impediments. Look and find protest of enthusiasm for the assigned zone. Outwardly bolt on to and track or, if vital, seek after the items. Send back pictures to a ground station while following the items.

Slide 7

What could an Autonomous Helicopter do? Pictures are from CMU: http://www-2.cs.cmu.edu/afs/cs/extend/chopper/www/goals.html

Slide 8

Objectives and Goal Step 1: On-load up electronic framework improvement Step 2: Data accumulation from human controlled flights Step 3: Algorithm reenactments in PC Step 4: On-load up constant calculation usage and testing Goal: Achieving a drift flight

Slide 9

Controlling a Helicopter - Dynamic Model From MIT: http://gewurtz.lids.mit.edu/index.htm

Slide 10

Controlling a Helicopter - Dynamic Model From MIT: http://gewurtz.lids.mit.edu/index.htm u, v, w, the speed in x, y, z pivot p, q, r, the edge speed in 3 hub Θ , pitch, Φ , yaw

Slide 11

Controller Result Successfully accomplished a float flight Flied forward, in reverse and sideward Flied on a prefixed way

Slide 12

Flight Controller Demonstration A little exhibition of self-sufficient helicopter controller

Slide 13

Attitude Estimation

Slide 14

Information required by flight controller The parameter we have to gauge: Body introduction: pitch, move, yaw Body straight speed vector: Vb (u, v, w) Body precise speed vector: Wb (p, q, r) NED position : x, y, z

Slide 15

The corresponding property of state of mind estimation by gyro and gravity vector

Slide 16

The correlative property of body speed estimation by GPS and body quickening mix

Slide 17

What is Kalman fitler? The Kalman channel is a various information, numerous yield computerized channel that can ideally appraise, continuously, the conditions of a framework in light of its boisterous yields. The Kalman channel evaluates a procedure by utilizing a type of criticism control: the channel appraises the procedure state sooner or later and after that acquires input as (boisterous) estimations.

Slide 18

The Kalman channel x: genuine state vector z: estimation vector w: handle fluctuation v: estimation difference u: control input

Slide 19

The Kalman channel

Slide 20

Gyro clamor change Can be figured from gyro perusing introduction state: Pitch, move, yaw

Slide 21

Variance presented by the subsequent blunder of the "increasing speed free" suspicion Pitch, Roll, Yaw Calculated from Accelerometer ASSUMMING the helicopter don't has anyone straight quickening.

Slide 22

The helicopter has a few in number vibration sources Main rotor at 29 Hz Structural vibration at 10 Hz

Slide 23

Low-pass sifting Fortunately vibration clamors and helicopter flow are not in a similar recurrence, we can low-pass the information to kill the commotion. Equipment dumper : cutoff recurrence 7-9Hz FIR channel: cutoff recurrence 5Hz

Slide 24

Filter result The aftereffect of our channel is fulfilling For instance, The rest of the commotion is +-0.1 m/s^2 in x hub acc sensor and around +-1 degree/s in y hub gyro perusing. The commotion will be further wiped out in the Kalman channel and mix operation.

Slide 25

Sensor Offset Effect GPS Antenna C.G. IMU Sensor

Slide 26

IMU Offset Compensation The IMU counterbalance vector is The accelerometer perusing takes after: The biggest mistake is presented by the term

Slide 27

IMU Offset Compensation The IMU balance pay is

Slide 28

GPS recieving wire balance remuneration The GPS balance vector is The GPS balance pay condition is

Slide 29

Sensor balance pay impacts

Slide 30

Kalman channel result (mentality and speed estimation)

Slide 31

Measure demeanor from acc gps and compass Background: In strap down latency route channel, the disposition data ought to be consistently measured from the accelerometer, GPS, and attractive sensor. In static circumstance the main increasing speed accelerometer detected is the gravitational constrain, the contribute and roll the Euler points can be measured by the accompanying strategy:

Slide 33

However, when in element condition the accelerometer detected the static gravitational drive as well as direct quickening which can be gotten from subsidiary of GPS ground speed perusing. Since the first and the second some portion of are no longer zero so the initial two segment of will make the and no longer simple to comprehend, in this way a decent technique ought to be produced to tackle this issue.

Slide 34

Introduction of the Hardware System

Slide 35

Hardware System Electrical System -GPS -IMU -Compass Mechanical Damper

Slide 36

Overall Electrical System

Slide 37

GPS

Slide 38

Main Feature of GPS 5 Hz Position Velocity and Time (PVT) yield Robust Signal Tracking Satellite Based Augmentation System

Slide 39

IMU

Slide 40

Main Feature of IMU 96 Hz Sampling Rate MEMS Technology Digital Outputs +/ - 2g Acceleration Measurement Range User-configurable FIR Filters

Slide 41

Compass

Slide 42

Main Feature of Compass 1° Heading Accuracy, 0.1° Resolution 15Hz Response Time UART/SPI Interface

Slide 43

Mechanical Dampers

Slide 44

Main Feature of Dampers 7-9 Hz Cutoff Frequency 11 Hz in even plane 13 Hz in the vertical bearing

Slide 45

Communication Between Devices

Slide 46

Overall Block Diagram

Slide 47

SPI Communication ARM & Microprocessors

Slide 48

SPI Communication SD Card & Microprocessor

Slide 49

UART

Slide 50

Microprocessor & Servo Motor

Slide 51

Result Achievements & Further Development

Slide 52

Achievements PD Controller effectively executed Attitude estimation Hover Flight

Slide 53

Estimation Result

Slide 54

Further Development Maneuver Flight Possibility Vision Tracking

Slide 55

Q & A

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