Platform Enhancements and System Identification for ...

2006 9th Int. Conf. Control, Automation, Robotics and Vision Singapore, 5-8th December 2006

Platform Enhancements and System Identification for Control of an Unmanned Helicopter Matt Garratt, Bilal Ahmed and Hemanshu R. Pota The University of New South Wales at the Australian Defence Force Academy Canberra ACT 2600 m. garratt, b. ahmed, [email protected] Abstract— This paper discusses a common approach to systems integration and automation for two different sizes of rotary wing UAVs. A linear model has been identified using time domain methods for both platforms. The identified models will help to evaluate parameters for designing nonlinear control systems for automatic landing of UAVs on moving platforms. In this paper, the innovation is the enhancement of experimental platforms to better suit experimental research in the design of controllers.

MPC555 Autopilot

GPS Antenna

I. I NTRODUCTION There has been extensive research conducted in the area of autonomous helicopters. Several Australian institutions are involved in this research, motived by the need to exploit natural resources and secure large borders. Because of Australia’s vast coastal area, our ongoing research is focused on the launch and recovery of unmanned helicopters from small coastal patrol vessels. This research will also be helpful in decreasing risk in recovery operations for manned helicopters operating from ships. Reviewing previous work, conventional proposed control schemes for model scale helicopters include the use of H2 , H∞ based on approximate linearization [1] and subspace methods (inversion)[2]. These methods are based on linearized helicopter models about hover, uniform forward flight trim conditions or the assumption that the modes (flight) are decoupled. Previous attempts at nonlinear control of helicopters include neural network based controllers [3], hierarchal control scheme [4], differential flatness [5],[6] and back stepping designs [7]. Establishing stabilization of nonlinear control schemes proposed for under actuated systems is dealt with in [8], [9]. In this paper, we discuss the capabilities of the research platform and sensor integration, and also elaborate on our linear systems identification using time and frequency domain for the largest platform. Finally, we discuss the future direction of our research.

Battery 8000mAH

Fig. 1.

Stereo camera m

The Experimental platform - Eagle

station were provided using 19600 baud rate RF modems. The NovAtel units are configured for a 20Hz update rate. In real-time kinematic (RTK) mode the GPS units are quoted as having a circular error probable (CEP) of 2cm. We plan to perform higher risk testing with the Eagle helicopter owing to its lower replacement cost. The similarities in hardware between the two platforms make transition of controllers from one platform to the other relatively simple. A. Eagle Platform 1) MPC555 based Autopilot: The autopilot for the Eagle helicopter is based on the Motorola MPC555 micro controller. The MPC555 is a 32-bit device based on a 40Mhz Power PC core. It has a large array of peripheral equipment, which makes it suitable for embedded applications that require lots of computation, high integration, and expendability. Various single board computers based on the MPC555 are available. The autopilot hardware consists of three printed circuit boards surrounding a 40MHZ Phytec MPC555 single board computer (SBC) board. The SBC is approximately the same size as a credit card and contains 4MB of RAM and 4MB of EEPROM for program storage. The additional circuit boards are stacked in line with the SBC and provide interfacing to an 8 channel radio control receiver, servos, I2C bus, CAN-Bus, Inertial Measurement Unit (IMU) and the RF modem used for telemetry. The IMU utilizes three Analog Devices ADXL105 accelerometers, three Honeywell HMC1021S magnetometers and three Murata ENV-05D rate gyroscopes. The axes of each set of sensors are arranged in an orthogonal fashion so that the orientation and motion of the helicopter along all three

II. P LATFORMS DESCRIPTION Experiments for this research were conducted on two different helicopter platforms. The largest of these was a 70kg Yamaha R-Max helicopter, already used for autonomous helicopter research at many other institutions [10],[11]. A second platform was constructed based on a 60 size Hirobo ”Eagle” helicopter kit shown in Figure 1. The latter model was converted to electric power using a brushless DC motor to remove the complications of an internal combustion engine. Both platforms were fitted with identical NovAtel OEM4-G2LRT2 GPS units. Differential corrections from a DGPS base

c 2006 IEEE 1–4244–0342–1/06/$20.00


PC104 enclosure

1981

ICARCV 2006

axes can be determined. All inertial sensors are sampled at 1600Hz by a 16 bit Analog Devices AD977BR Analog to Digital Converter. For experimental purposes and safety, a mechanism is necessary to allow the helicopter to be switched between manual and automatic control. A Hand Over Take Over (HOTO) system has been implemented on the helicopter allowing switching between these two modes. In manual mode, a human pilot flies the helicopter with a hand held radio control transmitter. In automatic mode, the helicopter controls are set by the autopilot. The 7th receiver channel has been assigned for controlling the HOTO function and the autopilot sets automatic mode when this channel lies within a certain range of values. A switch on the pilot’s transmitter is used to set the value of the 7th channel to one of two possible values corresponding to automatic or manual. The current set of servo actuators consists of five servo channels: collective, throttle, aileron, elevator and tail rotor pitch. To simplify testing, it is desirable to be able to choose which channels the pilot controls and which channels are controlled by the autopilot at any given instance. This enables each control loop to be tuned in individually, which is much easier to cope with experimentally. To facilitate this, a ’pass-thru’ parameter can be changed from the ground, which controls the source of each channel. The pass-thru parameter only applies in automatic mode and is implemented in software. The control channels to be set by the pilot are decoded by the autopilot and passed through to the servos as a Pulse Width Modulated (PWM) signal unchanged. In manual mode, all of the servo channels are driven directly from the receiver through a hardware switch. In addition to the above HOTO system, a failsafe mechanism has been established which de-activates automatic control when a watchdog circuit is not reset periodically by the main control loop. In the event of a software failure stopping the program from running, the main control loop is unable to toggle a designated data line of the MPC555. After a period of 0.1 seconds, the failure to toggle results in control being passed back to the Radio control receiver. 2) PC104 based Flight Computer: Whilst the current MPC555 micro controller based autopilot is satisfactory for embedding a basic attitude control system and position controller, its processing power is limited. This prevents onboard high-end processing such as image processing or sophisticated state estimation algorithms. As the eventual aim is to develop advanced adaptive control algorithms a high performanceprocessing platform is desirable. With this in mind, a PC104 based flight computer was developed. A Toronto Microelectronics PC104-P3-90E 900Mhz Single board computer was selected for the PC104 flight computer. This board contains a Pentium 3 CPU, onboard video and Ethernet, 256MB RAM and an integrated compact flash card that appears to the system as an IDE hard drive. The Linux operating system was installed on a 2GB onboard compact flash (CF) card. On boot a compressed kernel and filesystem is loaded into RAM from the flash card. The entire kernel,

home directory and root filesystem is stored as a RAM disk. The largest partitions required are the /usr and /opt directories and these are mounted read-only from the flash card. Since the CF card is only mounted read-only, the filesystem is protected from damage in case of a power failure. Also, as all of the filesystem writes are conducted in RAM, the wear on the CF is very minimal. The OS includes X-windows and a full set of development tools, so that a keyboard and monitor can be connected to the PC104 and the system used as a hardware in the loop development system. The integration of the PC104 into the existing system is shown in Figure 3. An RS-232 communications link between the PC104 system and the autopilot allows data to be passed between the two systems. Attitude, rotation rate and control data will be passed from the autopilot to the PC104. High-level control information and commands are passed from the PC104 to the autopilot. The inner loop control function to control attitude is performed on the autopilot whilst the high-level navigation function are performed by the PC104 computer. A PC104 frame grabber using the BT878 chipset is present in the Eagle and is used to support image processing from a stereo camera located under the nose of the helicopter. Algorithms running on the PC104 flight computer process the image data to calculate optic flow data and helicopter height from the stereo disparity. This information has previously been used by the first author to control the helicopter during hover [12], [13]. A convenient feature of the PC104 architecture is a native USB interface. This has allowed a data logging feature using COTS memory stick devices. Thus, a high bandwidth flight test data can be recorded during flight and then transferred to a PC workstation immediately after landing for analysis. Memory sticks are now available with 4GB capacity enabling practically unlimited amounts of flight test data to be recorded at the fastest sensor sampling rates (1600Hz). B. R-Max Platform The Yamaha R-Max platform is predominantly used for crop-dusting in Japan, although a fully autonomous version has been marketed for airborne surveillance. A number of variants have been produced but the underlying systems are similar in each model. Our vehicle is an L-15 R-Max with a 30kg payload and an endurance of approximately one hour shown in Figure 2. The performance of the R-Max makes it an ideal platform for research. The R-Max comes with an attitude control inner loop. The Yamaha control system is known as the Yamaha Attitude Control System or YACS. The YACS provides inertial information to the PC104 flight computer which includes the output of 03 fibre optic rate gyroscopes and 03 accelerometers. We have added a Microstrain 3DM − GX1T M Gyro Enhanced Orientation Sensor to the RMAX to provide heading information to the flight computer. 1) PC104 based sensors integration: The same PC104 setup used on the Eagle is used on the R-Max platform and is interfaced to the YACS using the first 4 ports of an 8-port PC104 multiport serial card. Three serial lines carry 1982

information from the YACS to an external computer, and one accepts input from an external computer. The three serial lines that provide data to the external computer are termed YAS, RX and YACS. They convey information from the helicopter’s attitude sensor, radio receiver and attitude control system, respectively. The fourth serial line allows commands to be sent to either the YACS, or directly to the R-Max servo amplifier, bypassing the attitude control system. The integration of the PC104 into the overall system architecture is shown in Figure 4. The PC104 has been interfaced to a laser-rangefinder, DGPS, and the YACS. A magnetometer, designed in-house, has also been interfaced to the PC104 to provide heading data. The PC104 single board computer includes two standard serial ports. The first of these ports is currently being used to provide a serial login to the computer so that debugging and development work can occur from a laptop computer connected to the helicopter on the ground or through a bluetooth radio modem. The second port is used to communicate with the Novatel OEM2 board, allowing it to send navigation data to the PC104 and receive commands from the flight computer. A multi-port serial card is necessary to connect the PC104 to the R-Max systems and the laser-rangefinder. An RTLinux driver for this board was developed to allow deterministic communications with the laser-rangefinder, magnetometer and command and control modem.

system identification purposes. Each control axes was identified separately, so that one channel at a time can be stimulated by the control input. Both the input and output data during the manoeuvres was recorded. This data was used for system identification purposes. The data was logged into memory stick onboard platforms and transferred to PC workstation for analysis. After selecting the model structure with unknown parameters, one straight forward approach is to minimize the prediction errors [15]. Matlab function PEM from the system identification toolbox was used to estimate the unknown parameters of the system. The parameterized model of the R-Max platform that we have identified using flight test data (with out YACS) is shown in Figure 13. The identified model shown in Figure 13 should be well suited for the controller design purposes. For the controller design purposes, often we need a decoupled representation of the system. This decoupled representation of the system may be considered as a first step in the hover controller design. Next, we present the two uncoupled dynamic systems along with the identification results. A. Longitudinal-vertical dynamics Consider a longitudinal-vertical dynamics of the platform as follows: x˙ = Ax + Bu (9) where

III. S YSTEM I DENTIFICATION

   Ax =    

The UAV model can be described as a 6DOF rigid body model coupled with the main rotor dynamics. The linearized model proposed in [11] is given as: u˙ v˙ w˙ p˙

= = = =

(−w0 q + v0 r) − gθ + Xu u + Xa a (−u0 r + w0 p) − gφ +Yu v +Yb b +Yped δ ped (−v0 p + u0 q) − gφ + Zw w + Zcol δcol Lu u + Lv v + Lb b + Lw w

q˙ = Mu u + Mv v + Ma a + Mw w + Mcol δcol r˙ = Nr r + Nped δ ped

(1) (2) (3)

τ f a˙

= −b − τ f p − Ba a + Blat (δlat + Kd d) +Blon δlon = −a − τ f q − Ab b + Alat δlat +Alon (δlon + Kc c)

0 Xth Xu 0 0 Mu 0 1 0 0 −1 0 0 0 0 0 −1 0 

0  0   0  B=  Alon/t f  0 Clon/ts

(4) (5) (6)

The simplified coupling between the cyclic pitch and the flapping angles including the effect of stabilizer bar is given as: τ f b˙



Xa Ma 0 −1/t f Za 0

0 Mcol 0 0 Zcol 0

0 0 Mw 0 0 0 0 Ac/t f Zw 0 0 −1/ts

       

 u=

δlon δcol

        (10)

     and   

u q θ a w c

 (11)

B. Lateral-directional dynamics Consider a lateral-directional dynamics of the platform as follows: x˙ = Ax + Bu (12)

(7) where (8)



    Ax =     

where Kd and Kc represent the gearing of the stabiliser bar Bell-mixer. The yaw controller implemented in the YACS can be accounted for as a PI feedback system. The reader can refer [14] for the nomenclature of above equations. The flight experiments were conducted for the hover flight condition. The pilot performed different manoeuvres for the 1983

Yv 0 g g Lv 0 0 Lb 0 1 0 0 0 −1 0 −1/t f Nv N p 0 0 0 0 0 0 0 −1 0 0

0 0 0 0 Nr Kr 0

  0 0 v   0 0   p    0 0  φ     0 Bd/t f   b    Nf 0  r   Kf 0  rfb  d 0 −1/ts (13)

     B=    

0 0 0 Blat/t f 0 0 Dlta/ts

Y ped 0 0 0 N ped 0 0

      and    

 u=

δlat δ ped

[2] V. Gavrilets, I. Martinos, B. Mettler, and E. Feron, “Control logic for automated aerobatic flight of a miniature helicopter,” in AIAA Guidance, Navigation, and Control Conference and Exhibit, Monterey, California, 2002, p. 8. [3] J. V. R. Prasad, A. J. Calise, Y. Pei, and J. E. Corban, “Adaptive nonlinear controller synthesis and flight test evaluation on an unmanned helicopter,” in IEEE International Conference on Control Applications, vol. 1, 1999, p. 137. [4] C. P. Sanders, P. A. DeBitetto, E. Feron, H. F. Vuong, and N. Leveson, “Hierarchical control of small autonomous helicopters,” in 37th IEEE Conference on Decision & Control, vol. 4, Tampa, Florida USA, 1998, p. 3629. [5] T. J. Koo and S. Sastry, “Differential flatness based full authority helicopter control design,” in Decision and Control 1999. Proceedings of the 38th IEEE Conference on, vol. 2, 1999, p. 1982. [6] ——, “Output tracking control design of a helicopter model based on approximate linearization,” in 37th IEEE Conference on Decision & Control, vol. 4, Tampa, Florida USA, 1998, p. 3635. [7] E. Frazzoli, M. A. Dahleh, and E. Feron, “Trajectory tracking control design for autonomous helicopters using a backstepping algorithm,” in American Control Conference,2000. Proceedings of the 2000, vol. 6, 2000, p. 4102. [8] A. Isidori, L. Marconi, and A. Serrani, “Robust nonlinear motion control of a helicopter,” in Decision and Control, 2001. Proceedings of the 40th IEEE Conference on, vol. 5, 2001, p. 4586. [9] ——, “Robust nonlinear motion control of a helicopter,” Automatic Control, IEEE Transactions on, vol. 48, no. 3, p. 413, 2003, 0018-9286. [10] B. Mettler, M. B. Tischler, and T. Kanade, “System identification of a model-scale helicopter,” Robotics Institute, Carnegie Mellon University, Tech. Rep. CMU-RI-TR-00-03, January 2000 2000. [11] ——, “System identification modeling of a small-scale unmanned helicopter,” Journal of the American Helicopter Society, p. 14, 2001. [12] M. A. Garratt and J. S. Chahl, “Visual control of an autonomous helicopter,” in AIAA 41st Aerospace Sciences Meeting and Exhibit, January 2003. [13] M. V. Srinivasan, S. W. Zhang, J. S. Chahl, G. Stange, and M. A.Garratt, “An overview of insect inspired guidance for application in ground and airborne platforms,” in Instn Mech. Engrs, Aerospace Engineering, 2004. [14] E. Bogdanov, A. Wan and G. Harvey, “SDRE flight control for X-cell and R-Max autonomous helicopters,” in Proceedings of the 43rd IEEE Conference on Decision and Control, vol. 2, 2004, pp. 1196–1203. [15] L. Ljung, System identification : theory for the user. Englewood Cliffs, N.J : Prentice-Hall, 1987.

 (14)

C. Identification results The identified parameters using the two above representations for 15 random data sets each of 501 samples of time domain flight data around hover condition are shown in Table I. The identification and the validation results are shown in Figure (5–12). TABLE I E STIMATED PARAMETERS OF YAMAHA R-M AX AROUND HOVER FLIGHT CONDITION

Parameters Xu Mu Ma tf Za Zw ts Mw Ac Alon Zcol Mcol Clon Yv Lv Lb Bd Nv Np Nr Nf Kr Kf Yped Blat N ped Dlat

value −0.1707 −0.1649 135.6672 0.0684 −9.7492 0.4934 0.2576 0.0032 0.5541 0.0114 −45.837 0.7687 0.0588 0.2842 −0.0013 166.669 −1.7994 0.0037 −3.542 −14.4581 −46.0293 9.0974 −28.9313 10.6526 0.0667 46.031 0.1889

x) ¯2 σi = ∑(x− n−1 0.1252 0.0502 1.0989 0.0330 −− 0.0686 0.0697 0.0442 0.1210 0.0070 1.8000 0.1628 0.0096 0.5060 0.1255 −− 1.3218 1.3E − 18 1.3E − 15 0 0 3.6E − 15 7.3E − 15 −− 0.0190 0 0.3568

¯ ∑ |x−x| n

0.0880 0.0375 0.5559 0.0223 −− 0.0389 0.0428 0.0218 0.0912 0.0053 1.1444 0.0870 0.0081 0.4044 0.0953 −− 0.7605 1.3E − 18 1.3E − 15 0 0 3.5E − 15 7.1E − 15 −− 0.0150 0 0.2014

IV. C ONCLUSION AND FUTURE WORK Considerable work is often required to add processing hardware and sensors to commercial UAV platforms before they can be used for experimental research. Enhanced platforms are needed to identify system models and design controllers for the UAVS. This paper presents a linear model for the R-Max helicopter. Research work is now in progress to identify nonlinear model parameters from the linear model to design nonlinear controllers for automating the landing of both platforms. R EFERENCES [1] H. Shim, T. J. Koo, F. Hoffmann, and S. Sastry, “A comprehensive study of control design for an autonomous helicopter,” in Decision and Control 1998. Proceedings of the 37th IEEE Conference on,, vol. 4, Tampa, Florida USA, 1998, p. 3653.

Fig. 2.

1984

The Experimental platform - Yamaha R-Max

Groundstation Commands

Bluetooth Modem

Com2

900 MHz RF Modem

MPC555 Autopilot

Com1

DGPS Corrections Com2

GPS

Motion Data, Commands

Stereo Camera

Com1

Com1 Inertial Measurement Unit(IMU)

BT878 FrameGrabber

Com2

PC104 Flight log

Memory Stick

USB

Fig. 3.

Integration of PC104 and autopilot with sensors onboard Eagle platform 900 MHz RF Modem

900 MHz RF Modem

Remote Login

Com1

Com1

GPS Base Station

8 port Serial Interface

PC104 Flight Computer

Novatel OEM4 DGPS Com2 Memory stick for logging flight test data

Com1

Com2

Com2

USB

Com3 Com4

High Speed Interface Card Com5 Range Data Laserrangefinder

Magnetometer

Fig. 4.

36 MHz Radio Control Reciever

Com1

Sensor Data

Yamaha Attitude Control System (YACS)

Com2

RC Data

Com3

Control Data

Com4

Servo Commands

Com6 Com7 Com8 (Spare) 2.4 GHz RF Modem

LRF Commands

Ground Control Computer

Groundstation Commands, Telemetry

2.4 GHz RF Modem

Heading Data

Integration of PC104 with YACS and sensors onboard R-Max platform

Yamaha − R−Max identification results

4

10

Measured Output model Fit: 64.57%

Measured Output model Fit: 60.07%

5 q (deg/s)

θ (deg)

2 0

0 −5

−2 −10

−4

0

2

4

6

8

10

−15

12

0

2

4

6

time (sec)

Fig. 5.

8

10

12

time (sec)

Identification result of pitch angle

Fig. 6.

Identification result of pitch-rate

Yamaha − R−Max validation results 4 2 0 −2 −4

Measured Output model Fit: 45.2%

5 q (deg/s)

θ (deg)

10

Measured Output model Fit: 40.47%

0 −5 −10

0

2

4

6

8

10

12

0

2

4

6 time (sec)

8

10

12

time (sec)

Fig. 7.

Validation of pitch angle

Fig. 8.

1985

Validation of pitch-rate

20

5

Measured Output

Measured Output

model Fit: 54.03%

10 p (deg/s)

φ (deg)

model Fit: 84.65%

0

0 −10

−5 0

2

4

Fig. 9.

6 time(sec)

8

10

−20 0

12

2

Identification result of roll angle

4

Fig. 10.

5

6 time(sec)

8

10

12

Identification result of roll-rate

20

Measured Output

Measured Output

model Fit: 83.5%

model Fit: 47.99%

p (deg/s)

φ (deg)

10 0

0 −10

−5 0

2

4

6 time(sec)

Fig. 11.

8

10

−20 0

12

2

Validation of roll angle

           Ax =           

−0.17 0 −0.28 −0.16 0 0 0 0 0 0 0 0 0



0 0.28 −0.001 −0.16 0 0 0 0 0 0.003 0 0 0

0 0 0 0 1 0 0 −0.06 0 −3.54 0 0 −0.25

0 0 0  0 0 10.65   0 0 0   0 0 0   0 0 0   0 0 0  0.001 0.011 0 B=   0.0667 0.004 0   0 0 0   0 0 46.031   0 0 0   0 0.058 0 0.188 0 0 a, b, c, d u, v, w φ, θ, ψ p, q, r δlat ,δlon ,δped ,δcol

4

6 time(sec)

Fig. 12.

0 0 0 0 0 1 −0.06 0 0 0 0 −0.25 0 0 0 0 0.7687 0 0 0 0 −45.837 −3.29 0 0 0

0 g 0 0 0 0 0 0 0 0 0 0 0

−g 0 0 0 0 0 0 0 0 0 0 0 0

−g 0 0 135.66 0 0 −1 0.67 −9.74 0 0 0 0

0 g 166.66 0 0 0 −0.45 −1 −131.2 0 0 0 0

0 0 0 0.003 0 0 0 0 0.49 0.085 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.55 0 0 0 1.0 0 0 −14.45 −46.02 0 9.09 −28.93 0 0 0 −1 0 0 0

                     



and

 δlat  δlon   u=  δped  δcol

longitudinal and lateral rotor and stabilizer bar flapping angles longitudinal, lateral, and vertical speed roll, pitch, and yaw angles in helicopter reference frame roll, pitch, and yaw rates in helicopter reference frame aileron, elevator, pedestal and collective control inputs

Fig. 13.

Identified model using R-Max flight data

1986

8

10

12

Validation of roll-rate

0 0 0 0 0 0 0 −1.79 0 0 0 0 −1

                     

u v p q φ θ a b w r rf b c d

                     