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

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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

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

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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

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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

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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

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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.

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

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The Kalman channel

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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

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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

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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.

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Sensor Offset Effect GPS Antenna C.G. IMU Sensor

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

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IMU Offset Compensation The IMU balance pay is

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

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Sensor balance pay impacts

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Kalman channel result (mentality and speed estimation)

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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:

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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

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

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Overall Electrical System

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

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Slide 40

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

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

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Mechanical Dampers

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

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Communication Between Devices

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Overall Block Diagram

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SPI Communication ARM & Microprocessors

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SPI Communication SD Card & Microprocessor

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Microprocessor & Servo Motor

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Result Achievements & Further Development

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Achievements PD Controller effectively executed Attitude estimation Hover Flight

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Estimation Result

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Further Development Maneuver Flight Possibility Vision Tracking

Slide 55

Q & A