A Flight Control System for Autonomous Helicopter

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A Flight Control System for Autonomous Helicopter
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People

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

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Presentation Flow

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

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Introduction

• What is an
• Autonomous
• Helicopter??

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IntroductionWhat is Autonomous Helicopter?

• An Autonomous Helicopter is a helicopter who
  is fully or semi- controlled by on-board
  intelligence and computing power.

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

• Fly to a designated area on a prescribed path
  while avoiding obstacles.
• Search and locate object of interest in the
  designated area.
• Visually lock on to and track or, if necessary,
  pursue the objects.
• Send back images to a ground station while
  tracking the objects.

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

• Step 1 On-board electronic system
  development
• Step 2 Data collection from
• human controlled flights
• Step 3 Algorithm simulations in PC
• Step 4 On-board real-time algorithm
  implementation and testing
• Goal Achieving a hover 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 velocity in x, y, z axis
• p, q, r, the angle velocity in 3 axis
• T, pitch, F, yaw

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Controller Result

• Successfully achieved a hover flight
• Flied forward, backward and sideward
• Flied on a prefixed path

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Flight Controller Demonstration

• A small demonstration
• of autonomous
• helicopter controller

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Attitude Estimation
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Information needed by flight controller

• The parameter we need to estimate
• Body orientation pitch, roll, yaw
• Body linear velocity vector Vb (u, v, w)
• Body angular velocity vector Wb (p, q, r)
• NED position x, y, z

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The complementary property of attitude estimation
by gyro and gravity vector
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The complementary property of body velocity
estimation by GPS and body acceleration
integration
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What is Kalman fitler?

• The Kalman filter is a multiple-input,
  multiple-output digital filter that can optimally
  estimate, in real time, the states of a system
  based on its noisy outputs.
• The Kalman filter estimates a process by using
  a form of feedback control the filter estimates
  the process state at some time and then obtains
  feedback in the form of (noisy) measurements.

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The Kalman filter
x actual state vector z measurement vector w
process variance v measurement variance u
control input
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The Kalman filter
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Gyro noise variance
Can be calculated from gyro reading
orientation state Pitch, roll, yaw
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Variance introduced by the resulting error of
the acceleration free assumption
Pitch, Roll, Yaw Calculated from Accelerometer
ASSUMMING the helicopter do NOT has any body
linear acceleration.
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The helicopter has several strong vibration
sources
Main rotor at 29 Hz
Structural vibration at 10 Hz
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Low-pass filtering

• Fortunately vibration noises and helicopter
  dynamics are not in the same frequency, we can
  low-pass the data to eliminate the noise.
• Hardware dumper
• cutoff frequency 7-9Hz
• FIR filter
• cutoff frequency 5Hz

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Filter result

• The result of our filter is satisfying
• For example, The remaining noise is
• -0.1 m/s2 in x axis acc sensor and around -1
  degree/s in y axis gyro reading. The noise will
  be further eliminated in the Kalman filter and
  integration operation.

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Sensor Offset Effect
GPS Antenna
C.G.
IMU Sensor
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IMU Offset Compensation

• The IMU offset vector is
• The accelerometer reading follows
• The largest error is introduced by the term

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IMU Offset Compensation

• The IMU offset compensation is

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GPS antenna offset compensation

• The GPS offset vector is
• The GPS offset compensation equation is

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Sensor offset compensation effects
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Kalman filter result (attitude and velocity
estimation)
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Measure attitude from acc gps and compass

• Background
• In strap down inertia navigation filter, the
  attitude information should be continuously
  measured from the accelerometer, GPS, and
  magnetic sensor.
• In static situation the only acceleration
  accelerometer sensed is the gravitational force,
  the pitch and roll in the Euler angles can be
  measured by the following method

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• However, when in dynamic environment the
  accelerometer sensed not only the static
  gravitational force but also linear acceleration
  which can be obtained from derivative of GPS
  ground velocity reading.
• Because the first and the second part of are no
  longer zero so the first two column of will make
  the and no longer easy to solve, thus a good
  method should be developed to solve this problem.

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• 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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GPS
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Main Feature of GPS

• 5 Hz Position Velocity and Time
• (PVT) output
• Robust Signal Tracking
• Satellite Based Augmentation System

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IMU
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Main Feature of IMU

• 96 Hz Sampling Rate
• MEMS Technology
• Digital Outputs
• /- 2g Acceleration Measurement Range
• User-configurable FIR Filters

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Compass
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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 horizontal plane
• 13 Hz in the vertical direction

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• Communication
• Between Devices

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Overall Block Diagram
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SPI CommunicationARM Microprocessors
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SPI Communication SD Card Microprocessor
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UART
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Microprocessor Servo Motor
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• Result
• Achievements
• Further
• Development

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Achievements

• PD Controller successfully implemented
• Attitude estimation
• Hover Flight

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Estimation Result
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Further Development

• Maneuver Flight Possibility
• Vision Tracking

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• Q A