A Flight Control System for Autonomous Helicopter

1. A Flight Control System for Autonomous Helicopter

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

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

4. Introduction What is an Autonomous Helicopter??

5. 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

6. 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.

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

8. 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

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

10. 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 • Θ, pitch, Φ, yaw

11. Controller Result • Successfully achieved a hover flight • Flied forward, backward and sideward • Flied on a prefixed path

12. Flight Controller Demonstration A small demonstration of autonomous helicopter controller

13. Attitude Estimation

14. 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

15. The complementary property of attitude estimation by gyro and gravity vector

16. The complementary property of body velocity estimation by GPS and body acceleration integration

17. 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.

18. The Kalman filter x: actual state vector z: measurement vector w: process variance v: measurement variance u: control input

19. The Kalman filter

20. Gyro noise variance Can be calculated from gyro reading orientation state: Pitch, roll, yaw

21. 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.

22. The helicopter has several strong vibration sources Main rotor at 29 Hz Structural vibration at 10 Hz

23. 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

24. Filter result The result of our filter is satisfying For example, The remaining noise is +-0.1 m/s^2 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.

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

26. IMU Offset Compensation The IMU offset vector is The accelerometer reading follows: The largest error is introduced by the term

27. IMU Offset Compensation The IMU offset compensation is

28. GPS antenna offset compensation The GPS offset vector is The GPS offset compensation equation is

29. Sensor offset compensation effects

30. Kalman filter result (attitude and velocity estimation)

31. 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:

33. 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.

34. Introduction of the Hardware System

35. Hardware System • Electrical System - GPS - IMU - Compass • Mechanical Damper

36. Overall Electrical System

37. GPS

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

39. IMU

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

41. Compass

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

43. Mechanical Dampers

44. Main Feature of Dampers • 7-9 Hz Cutoff Frequency • 11 Hz in horizontal plane • 13 Hz in the vertical direction

45. Communication Between Devices

46. Overall Block Diagram

47. SPI CommunicationARM & Microprocessors

48. SPI Communication SD Card & Microprocessor

49. UART

50. Microprocessor & Servo Motor