Onboard electronics of UAVs


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1 AARMS Vol. 5, No. 2 (2006) TECHNOLOGY Onboard electronics of UAVs ANTAL TURÓCZI, IMRE MAKKAY Department of Electronic Warfare, Miklós Zrínyi National Defence University, Budapest, Hungary Recent advances in computer and sensing technology have made it possible to develop autonomous aerial robotic systems. Fixedwing unmanned aerial vehicles (UAVs) have been in service for some years and are being used routinely for military and meteorological purposes. Autonomous aircraft capable of vertical takeoff and landing (VTOL) have also been considered in recent years because they are suitable for intelligence, surveillance and inspection applications, due to their hovering capability. The growing demand for reliable civil UAVs speeded up research work in this field. In this paper, a control system approach is stated for mini and micro UAVs. The theory of physical behaviour of rigid bodies, such as UAVs, is also described besides the characteristics of fourrotor VTOL type UAVs. Introduction The onboard electronics in the mini and micro UAVs has to provide almost the same performance as they do in the larger ones. That means a lot of different sensors and units have to be implemented in the smallest size and the lightest weight possible. Since the smaller size, the smaller inertia and controllability, the flight control system of small UAVs has to be more sophisticated. Our aim is to develop and build a control system that is suitable and flexible enough for most of our intended applications regardless of the airframe. The only difference between the individual control systems would be the airframedependent software. Figure 2 outlines the main blocks and their communication in our system approach. This article focuses on the inertial part of the system. Received: March 6, 2006 Address for correspondence: ANTAL TURÓCZI Department of Electronic Warfare Miklós Zrínyi National Defence University H 1581 Budapest, P.O. Box 15, Hungary Inertial navigation, stabilization Inertial navigation systems are used in areas such as military and civil aviation or space technology where precision and reliability are the most important elements. In the beginning the size and the weight of these systems were not suitable for mobile applications like UAVs. But recent progress in Micro ElectroMechanical System
2 (MEMS) led to sophisticated lowcost sensor products. With these single chip accelerometer and gyro sensors small, light and cheap inertial navigation systems can be made. 1 In order to describe spatial movement of a rigid body we need six parameters: three translation and three rotation parameters. Three acceleration sensors and three gyros can form an orthogonal system that can provide x, y, z acceleration and angular velocity. Position and orientation information can be obtained by integration of the individual translation and rotation components. Figure 1 depicts the theory of this method. 2 x( = a(dt 2 ϕ( = ω(dt Figure 1: Six parameters of freedom The finite precision of the sensors is the main limitation of the system performance. Integrating a small continuous error in the measured acceleration results in a big error in speed, integrated a second time in a huge error in distance. The same applies to angular rate errors. Therefore the sensors and the data processing, including the error correction must be very precise to get an accurate inertial navigation platform. With the introduction of GPS, or electronic compass or the gvector as a reference or all together the entire accuracy can be improved. But these concepts require careful considerations about the calibration method for not disturbing the whole control system. 1,3 If the adequate acceleration and angular rate values are available we can describe the change of state of the airframe over time. This involves a short briefing of rigid body physics. The following differential equation describes the motion of a rigid body: d dt S( = d dt x( v( R( ω ( R( =. P( F( ( ) ( ) L t τ t 238 AARMS 5(2) (2006)
3 AARMS 5(2) (2006) 239
4 The linear velocity v( represents the rate of change of the position x( over time. Point x is at the centre of mass. The body may also be spinning. The angular velocity ω( encodes both the axis and speed of rotation. The columns of the 3 3 rotation matrix R( represent the transformed axes of the socalled body space, which is a coordinate system fixed to the airframe with origin in the centre of a mass. The rate of change of the linear momentum P( and the angular momentum L( over time equal to the total force F( and torque τ( applied to the airframe. In order to set up the momentum equations we need two parameters describing the airframe itself. These are mass M and the inertia tensor I: P( = Mv( L( = I( ω( The 3 3 inertia tensor is a matrix which describes how the shape and mass distribution of the airframe is affected by the angular velocity. This matrix is computed in body space and transformed as needed to world space. The calculation and the transformation methods are: 2 I xx I body = I yx I zx I xy I yy I zx I xz I yz I zz where I xx = I yy = I zz = 2 2 ( y + z ) dv 2 2 ( x + z ) dv 2 2 ( x + y ) dv and I xy = I yx = I yz = I zy = I xz = I zx = xy yx xz dv dv dv I( = R( I ( tr ) body T Now, we got over the physics part of the problem but here comes the control problem of it. Since we do not know the exact solution of our differential equation we cannot use classical linear control methods on the highly nonlinear system model. In addition to the nonlinear differential equations the individual control signals are not independent of each other in most of the cases. That is the case in our ongoing research work, which is a quadrotor helicopter design. Fourrotor design The fourrotor platform is not a new idea. The first attempts at implementing such designs were unsuccessful because it is almost impossible to manually control the four rotors. The task is more difficult for modelsized helicopters because of their small 240 AARMS 5(2) (2006)
5 inertia. The solution of this problem is designing an onboard controller system that is capable of autonomous hovering and can stabilize the fourrotor aircraft. Technologically, the smaller time constant require accurate sensors and fast response time from the computational unit and also from the propulsion system. 4,5 Figure 3: Fourrotor arrangement All helicopters, mostly the smaller ones, are dynamically instable because of the lack of natural damping. The rotors have to be constantly controlled to achieve appropriate thrust. In conventional helicopters thrust is controlled by adjusting the motor power and by adjusting the angle of attack of the rotor blades. Adjusting the motor power is not an efficient way of control because of the large inertia of the engine and the rotor. But adjusting the rotor pitch causes an immediate change on the thrust. To move the helicopter in horizontal directions, however, the rotor blade pitch has to be adjusted during one turn, which means a complex motion of the rotor blades. 6 In the fourrotor platform the tail rotor and control of the rotor blade pitch can be abandoned. The tail rotor is not needed since the counterrotating rotors can balance the craft. The adjustment of the blade pitch is not needed either using electric motors because they respond quickly enough to control thrust only by adjusting motor power. Increasing or decreasing the angular velocity of the motors controls the motion of the aircraft, up, down pitch, yaw and roll (Figure 4). But the individual control signals are not independent of each other. They must be changed simultaneously in order to maintain stable flight. This interaction is derived from the highly nonlinear physical nature of the construction. 4,7,8 That means multiple input multiple output robust controller is needed. AARMS 5(2) (2006) 241
6 Figure 4: Controlling the fourrotor platform: a. More power to the left rotors produces a leftthrust. b. More power to the diagonally arranged rotors produces horizontal rotation 4 Electric Propulsion The electric motor is a convenient propulsion system for the fourrotor platform. Unlike combustion engines electric motors have much smaller inertia and far better efficiency and can easily be controlled. Besides, their construction is simple consequently more reliable. The disadvantage of electric power compared to gasoline power is the significantly smaller stored energy to weight ratio and shorter operation time. But new battery technologies and recent advances in fuelcell technology, driven by the rapidly expanding market of mobile applications, promise fast development in energy density of batteries. Alternatively longer operation time can be achieved by using hybrid power system similarly to hybrid cars. That means a combustion engine driving a generator that charges a battery power supply. Conclusion In this paper, the physical basement of controlling UAVs has been presented. Being nonlinear mechanical system the stabilization of naturally instable UAVs requires non classical, robust controller. Unlike conventional methods of controlling linear systems the implementation of robust controllers involves different way of thinking and requires more sophisticated mathematical background from the engineer. A special arrangement, a fourrotor platform eliminates the mechanical complexity of traditional tail rotor helicopters. The main advantage of this layout is that the helicopter is controlled only by the adjustment of the power of the four electric motors. The implementation of an experimental autopilot is currently on the way. 242 AARMS 5(2) (2006)
7 References 1. INTERSENSE: Inertial Cube2 Manual A. WITKIN, D. BARAFF: Physically Based Modeling: Principles and Practice. Online Siggraph 97 Course notes ( 3. J. BORENSTEIN, H. R. EVERETT, L. FENG, D. WEHE: Mobile robot positioning Sensors and techniques. Journal of Robotic Systems, Special Issue on Mobile Robots. 14 (4) J. BORENSTEIN: The HoverBot An electrically powered flying robot. Unpublished White Paper 5. T. HAMEL, R. MAHONY, R. LOZANO, J. OSTROWSKI: Dynamic Modelling and Configuration Stabilization for an X4Flyer. 15th Triennial World Congress, Barcelona, Spain M. CHEN, M. HUZMEZAN: A Combined MBPC/ 2 DOF H Controller for a Quad Rotor UAV. Unpublished White Paper 8. L. A. YOUNG, E. W. AIKEN, J. L. JOHNSON, R. DEMBLEWSKI, J. ANDREWS, J. KLEM: New Concepts and Perspectives on MicroRotorcraft and Small Autonomous RotaryWing Vehicles. Unpublished White Paper AARMS 5(2) (2006) 243
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