Low cost validation test-bed for small 's attitude determination and control

Renato Miyagusuku, Miguel A Chicchon, John K Rojas, Klebes R Arias and Elizabeth R Villota Centro de Tecnologias de Informacion y Comunicaciones, Universidad Nacional de Ingenieria, Lima, Peru [email protected]

Abstract The final stage of a satellite project, and notably the most important one, is the satellite’s testing. While some of the required tests are relatively easy and inexpensive to perform; others involve sophisticated equipments that are rarely available, or affordable, when dealing with a university project. Tests that validate the attitude determination and control system (ADCS) fit into the latter category; for, the recreation of satellite’s environment at open space in a controlled environment is not a trivial matter. Several equipments are mandatory, all of them with prices ranging in the tens of thousands of dollars. The cost and difficulty of owning this type of facility may forbid low funded projects from getting appropriate testing. Nevertheless, at Universidad Nacional de Ingenieria, where the CubeSat project Chasqui I is currently under final testing, a real time hardware in the loop (RT HIL) simulator for ADCS validation has been developed at low cost and ease of implementation. It is expected that this RT HIL simulator will provide a tool for standardized testing of the ADCS embedded system; thus, improving the whole satellite’s reliability without incurring into major budget expenses and enticing low funding projects to start small satellite development.

Key Words: Hardware In The Loop, Attitude Determination and Control Systems, Small .

I. INTRODUCTION computer) and ADCS embedded system (target board). Section Real Time Hardware In the Loop (RT HIL) is a simulation 5 shows simulated results obtained by the Chasqui I’s ADCS technique used to test prototype or final hardware for simple when tested in accordance to the proposed RT HIL simulation. or complex systems with real time constraints. It works by Finally, Section 6 addresses conclusions and future work. creating a plant’s computer simulation and emulating sensing II. CHASQUI I’S ADCS EMBEDDED SYSTEM and actuation for interaction between the computer simulation Any ADCS design for small satellites present inherent and the tested hardware. Sensors’ emulator sends responses restrictions on mass and power. ADCS’s allocated mass for from the computer simulator to the tested hardware (target a 10 cm3 cubic satellite is in the range of 150-200 g, while board) and embedded control algorithms in target board output power consumption cannot exceed 1 W. Under such tight actuator control signals to the actuators’ emulator; actuation constraints, at Universidad Nacional de Ingenieria, within the information is fed back to the simulated plant successfully development of its first one-unit CubeSat satellite project adding hardware to the control loop. RT HIL simulation Chasqui I, a hybrid magnetic system has been presented herein offers, based on this idea, an effective way of devised so that active control can be tested and limited attitude testing the attitude determination and control system (ADCS) control guaranteed by passive control [7]. Permanent embedded system by directly adding it to a feedback loop with and hysteresis foils were selected for passive actuation whereas a complex real-time simulated small satellite. magnetorquers were selected for active actuation. There exist several satellite simulators and HIL interfaces Passive control main goals are rotational energy dissipa- in the market; however, they are costly and not always easy to tion and partial orientation for communications. Rotational modify, as many of them are not open source. The alternative energy dissipation is achieved by using hysteresis foils as provided in this paper uses software with a graphic user their magnetic properties enable them to damp rotational interface (GUI) developed under a MATLAB license, for plant energy as the satellite rotates [4]. One-axis attitude control to simulation (modeling of satellite’s dynamics and its environ- guarantee communications is achieved by using two permanent ment) -with available codes at "www.chasqui.uni.edu.pe", and magnets, which generate a constant magnetic moment that not expensive hardware (less than $150), for emulation of aligns the satellite to the geomagnetic field tangent; this partial sensors and actuators’ responses with real time requirements orientation is enough for communication requirements. and communication protocols necessary for interfacing. The Unlike passive control, active control requires a MCU RT HIL simulator here described has successfully been tested not only to compute control algorithms but also to process with Chasqui I’s ADCS, enabling embedded system testing information about the satellite’s states, thus it also requires and validation. the use of sensors and determination algorithms. However, it Section 2 presents ADCS’s embedded system, designed is the active actuation the one responsible for the most energy according to Chasqui I’s objectives and constraints. An ADCS consumption. simulator is detailed in Section 3, where mathematical models have been used to simulate the satellite’s behavior in a MAT- A. ADCS embedded hardware LAB environment. Section 4 describes the RT HIL hardware Embedded hardware for Chasqui I’s ADCS includes a low necessary for interaction between ADCS simulator (personal power high performance 32-bit MCU and several sensors for

1 DAQ PROCESS

INIT

DAQ RUN DAQ DIAGNOSIS

Fig. 1. Chasqui I’s ADCS operation modes

attitude determination. The selected MCU was a Freescale III. ADCS SIMULATOR MCF51QE128, with UART, SPI and I2C peripherals, PWM ADCS simulation software comprises the 6-DoF motion of generators and A/D modules. Sensors include a one 3-axis a rigid body at open space conditions of any low Earth orbit, magnetometer (MicroMag3), three 1-axis gyros (ADIS16265) Chasqui I’s particular study case is 600 km. While angular and six analog sun sensors (Super Solar). Main sensors’ displacements are not constrained, linear displacements are characteristics are shown at Table I. constrained to be in the satellite’s orbit. Quaternions are em- ployed to represent angular displacements due to its non sin- MicroMag3 ADIS16265 SunSensor gular representation, as opposed to Euler angles, and its faster Scale factor 31.24 µT 0.06336 o/s – o computation, when compared to rotation matrices. A solely Bias 0.003137 µT 0.025 /s – Bandwidth 175 kHz 330 Hz – modification of the inertia tensor parameter permits to simulate Variance 0.0063 µT 0.0056 o/s 0.0043 mA different pico-, nano- or micro-satellite dynamics. The effect o o Saturation ± 1100 µT ±160 /s ± 85 of aerodynamic drag, solar pressure and gravity gradients on Sampling 0.125 ms 0.125 ms 0.125 ms Quantization 0.015 µT 0.07812 o/s 0.0878 o the satellite rotational dynamics is modeled as forces that generate perturbation torques. For orbit propagation, an SGP4 TABLE I SENSORS’ CHARACTERISTICS model is implemented while a IGRF model is used to obtain Earth’s magnetic field. Magnetic passive actuation elements that modify the satellite’s dynamics have been added to the B. ADCS embedded software simulator, these include permanent magnets and hysteresis foils. Modeled sensors are gyroscopes, magnetometers and sun ADCS embedded software for attitude stabilization and sensors, all with modifiable-by-user white noise and precision; orientation is programmed based on the state machine shown and modeled actuators are 3-axis magnetorquers, also with the at Fig. 1. The state machine presents 4 states, called process: option to modify their state function, range and precision. All DAQ, DCA, ACTUATION and CCMI. the models mentioned above are implemented using standard MATLAB-SIMULINK blocks. It is expected that these models • DAQ: acquires data from sensors and carries out diagno- sis to check its validity. will be improved or new ones added by the small satellites’ community so that the ADCS simulator can grow richer and • DCA: performs determination and control algorithms when required. A B-dot for attitude stabilization; a be validated by more projects. TRIAD for attitude determination together with a LQR A. Reference System or continuous sliding mode controller for attitude control. For accurate modeling of the satellite’s attitude, three well • ACTUATION: sends required PWM signals to magnetor- known reference frames are used: i) the inertial frame centered quers, performs rotational energy evaluation for verifica- on Earth, called Earth Center Inertial frame (ECI); having its tion of system’s appropriate behavior, and stores data. x-axis pointing to the Vernal Equinox, the x-, and y-axis are in • CCM: idle mode where ADCS waits for commands. a plane parallel to the Equatorial plane, and the z-axis points

2 D. Geomagnetic field zI IGRF model has been selected to simulate the geomagnetic field. This model is widely employed by the academic commu- xI yI nity and represents the joint work of several IAGA teams. The model is based on spherical harmonic expansion. Every 5 years the IGRF model is reviewed; last version is the IGRF-11 which Fig. 2. Reference frames presents a 13th degree expansion. Detailed model description to the North Pole, ii) the frame centered on the satellite and can be found in literature [8] and the series coefficients are its orbit, called Orbital frame (O), its y-axis is opposite to the available at NASA’s web page [5]. satellite’s angular velocity vector, the z-axis is in the orbital E. Sun vector plane pointing towards the Earth’s center and the x-axis is Sun vector is the representation of Sun’s position with in the satellite’s linear velocity direction, and iii) the frame respect to the satellite. However, as the distance between the centered on the satellite’s body, called Body frame (B), with Sun and the Earth (1.427 108 km) is much bigger than the axes being the satellite’s main inertial axes. Fig. 2 shows these distance between the Earth and the satellite (6.978 103 km), main reference frames. it can be assumed that the Sun’s position with respect to the B. Rigid body equations satellite is the same as with respect to the Earth. In order to Angular dynamics can be expressed as dh/dt = τ in an obtain the Sun vector, a standard technique is used [10]. inertial frame, where h is the overall angular momentum and τ F. Perturbation torques is the sum of magnetic control (τ = m×B ) and perturbation c The following perturbation torque models have been im- torques (τ ). Expressing this equation in the satellite’s body p plemented in the attitude simulator: gravity gradients, aerody- frame as a state function of the angular velocity: namic drags, and sun pressure, as they can be considered as the ˙ b −1 b b b Ωib =I (τ − Ωib × IΩib), (1) most influential ones [3], [1]. Equations for these perturbations are presented at (4), (5) and (6). b where Ωib is the angular velocity vector from the inertial frame to the body frame, expressed in the body frame; and I is the −3GMEarth o o τgg = o z × Iz , (4) satellite’s inertia tensor. |z |5 Angular rates from inertial frame to body frame (Ωb ) relate ib 1 2 o b τa = r × ρV SCDx , (5) to the angular rates from orbital frame to body frame (Ωob = 2 [ωx ωy ωz]) by: EEarth b b o b τsp = r × ACpus. (6) Ωib = Ro Ωio + Ωob, (2) c o where Ωio is the angular rate associated with the satellite’s G. Sensors angular velocity caused by rotation around the Earth, consid- b Sensors have been modeled taking into account several ered constant for most cases; and Ro the rotation matrix to factors, such as: sensors’ scale and bias factors (obtained go from orbital to body frame. by calibration procedures), their dynamical response (con- Angular kinematics employs quaternion representation sidering sensors’ bandwidth and modeling the response as a T where q0 is the scalar part and q = [q1 q2 q3] is the vector second order system), measurement uncertainty (characterized part; then differential quaternions can be expressed as: by sensors’ data standard deviation), and sensors’ span and precision. Table I shows selected Chasqui I ADCS sensors’ 0 −ω −ω −ω x y z characteristics for modeling. dq  ω 0 ω −ω  = x z y . (3) dt  ωy −ωz 0 ωx  H. Actuators    ωz ωy −ωx 0  1) Magnetorquer: Magnetorquers are electromagnetic coils that transforms PWM signals into magnetic moments with C. Orbit the aid of an electronic interface. Chasqui I magnetorquers’ Translational dynamics are calculated using a SGP4 model, model were obtained by applying PWM signals to fabricated convenient for low Earth orbit cases. Even though no control magnetorquers and measuring their current outputs. For the is performed regarding linear positions or velocities, still x-axis magnetorquer the following system was identified: accurate satellite position is needed as it serves as input to the Sun and geomagnetic field models, which generate the Sun 0.006z + 0.01362 Icoil(z)= . (7) and magnetic field vectors in the inertial frame. SGP4 is the z2 − 0.4975z − 0.4688 simplification of a model that considers gravitational effects Relationship between current and produced magnetic moments and aerodynamic drag. It is initialized by a series of parameters are obtained from: wrapped together in a two line element (TLE) input, generated by NORAD. τcoil = NIcoilA. (8)

3 where N is the number of turns for the coil, and A is the coil transversal area. Several additional tests were performed to this magnetorquer to validate the model’s behavior [2]. 2) Permanent magnets: A permanent is made from a hard magnetic material, one that keeps magnetized for a long time after a strong enough magnetizing field has been applied to it. Permanent magnet’s interaction with the geomagnetic field causes a torque described by:

τmag = mmag × BEarth, (9) where mmag is the magnet’s magnetic moment:

Vmag mmag = Bmag, (10) Fig. 3. ADCS simulator µ0 between the ADCS simulator and designed ADCS target board with V being the magnet’s volume and B being the mag mag and also provides real time requirements and emulation of magnet’s intrinsic induction. sensors and actuators. Real time requirements are achieved by 3) Hysteresis foils: An hysteresis material is made from precise timing using the interface hardware interrupt system a soft magnetic material, one that easily saturates when ex- and short computer calculation times. RS-232 communication posed to external magnetizing fields, and easily demagnetizes enables interaction with a personal computer; while interface after removing such fields. Many analytical models exist for board communication protocols emulate sensors and actuators’ hysteresis material’s analysis, going from simple ones like responses and enable interaction with the ADCS embedded the “switch” or a “switch” with a slope model, to more system. For Chasqui I, a Freescale DemoQE128 board with a complex ones like the Preisach or the Jiles-Atherton models MC9S08QE128 MCU and an 8 MHz clock and additional 12 [4], [6], [9]. The simple “switch” model was chosen for being low-pass filters, designed to satisfy emulation requirements. computationally faster and almost as precise. Importantly, no extra code is added to the ADCS target board I. Communications for RT HIL testing, thus obtained results show exactly how RS-232 communication protocol was selected because of final embedded system will respond. its ease of use and implementation. A 128 kbps baud rate Total hardware cost does not add up to more than $150 as was selected for Chasqui I RT HIL simulator as it was the shown at Table IV (prices online to November 2011). fastest baud rate that provided 100% reliability of data transfer Item Qty Unit cost [$] Total cost [$] between the computer and the RT HIL interface. MATLAB’s DemoQE128 board 1 99.00 99.00 feature designed to block the simulation in progress until TU-S9 USB-serial 1 17.99 17.99 expected data is received through the serial port was employed RC filter 12 0.50 6.00 to sample the simulation with the RT HIL interface. Total 122.99 J. ADCS simulator implementation TABLE II RT HIL INTERFACE BOARD COST The ADCS simulator was implemented in a MATLAB- A. Operation SIMULINK environment. This simulator comprises rigid body ADCS simulator computes the satellite’s kinematics and kinematics and dynamics, environment perturbations, and sen- dynamics. Based on this information and several environment sors and actuators models; remaining models, Sun, SGP4 and models (described at Section III), the simulator determines IGRF models, are taken as lists. Sun, SGP4 and IGRF models sensors’ measurements at any given time. Sensed information are run in advanced in order to accelerate computational times; is sent through the RS-232 port to the RT HIL interface at the resulting data is stored in lists. This can be done without 128 kbps. RT HIL interface emulates sensors’ responses and diminishing performance as the SGP4 algorithm does not acquires target board’s outputs. Actuators’ outputs are stored need actualizations in less than 3 days period of simulation and sent to the simulator through the RS-232 port at each (making linear positions and velocities only dependent on the computation cycle, thus successfully closing the simulation TLE initial input); IGRF results are only dependent on linear loop. positions, which are constrained to the satellite’s orbit, and A state machine describing before mentioned actions to- Sun model is only dependent on time. Figure 3 shows the gether with several hardware interruptions has been pro- Chasqui I’s ADCS simulator. grammed in the MCU. Importantly, real time considerations IV. RT HIL INTERFACE are taken into account for the HIL simulation design. Main hardware needed for the RT HIL interface are a USB- B. State machine serial adapter and any kind of MCU development board; also The RT HIL interface’s state machine comprises 4 states: i) additional hardware may be required for sensors and actuators’ actuators’ emulation, ii) RS-232 send, iii) sensors’ emulation responses emulation. This hardware permits the interaction and iv) idle. These states are further discussed below.

4 10 kΩ 10 kΩ State or Function Calls per Total time Vin Vout Interruption name cycle [µs] 0.2 μF 0.2 μF Actuators’ emulation adc_read() 3 574.566 RS232 send send_short() 3 522.732 Sensor’s emulation set_pwm() 6 4.296 Fig. 4. Two stage RC filter schematic RTC RTC_ISR 1 2.147 SCI SCI_RX_ISR 18 83.160

100 reception_data() 1 199.814 SPI decode_command 6 159.12 80 send_first_byte 6 16.824 60 send_second_byte 6 15.066 40 TABLE III 20 Max value MEASURED TIMES FROM THE RT HIL INTERFACE

Vin [PWM Duty] Min value 0 0 10 20 30 40 50 60 70 80 90 100 Vout [ADC read] protocols, such as RS-232. Functions have been developed so Fig. 5. Two stage RC filter input-output response SCI enables not only int8 data type transmitions but also shorts and doubles. SCI receives a total of 18 bytes, being 6 bytes for 1) Actuators’ emulation: As explained at Section III-H, magnetometer’s data (three int16 elements), other 6 bytes for ADCS actuators are 3 magnetorquers driven by PWM signals. gyro’s data (three int16 elements) and 6 bytes for sun sensors’ The RT HIL interface filters PWM signals so that steady data (six int8 elements). analog voltages can be read by the interface MCU’s analog- 3) Serial peripheral interface: A serial peripheral interface to-digital (A/D) converter. The interface filter is a two stage (SPI) enables serial communication between a master MCU RC passive filter, disposed for each PWM channel. RC passive and a slave one, or peripherals circuits, i.e. sensors. For filter’s schematic is shown at Fig. 4. The filters provided 8 bit Chasqui I ADCS case, four sensors are emulated using SPI reliable measurements. Figure 5 shows the filter’s input-output communications, one 3-axis magnetometer and three 1-axis response for 1 kHz PWM signals. The values obtained from gyros. As sensors are emulated, the SPI is configured as a the A/D channels relate to the magnetic moments generated slave, receiving commands from the target board and sending by the magnetorquers. data accordingly. Furthermore, a 23 ms timer interrupt is used 2) RS232 send: For communications with the the ADCS to emulate MicroMag3’s time required to obtain data from simulator, periodical communications through the computer’s each axis. serial port are performed. Incoming data with sensors’ infor- D. Real time analysis mation can take place at any given moment, while outgoing In order to successfully meet real time constraints, all RT data with actuators’ information takes place one per computing HIL interface functions process times are measured. Table III cycle. This state sends a total of 6 bytes (actuators’ information shows the measured times obtained from the RT HIL interface format is int16, and is stored in a 3 element array). function1. Considering those times, magnetometer’s 23.3 ms 3) Sensors’ emulation: Three different kinds of sensors wait response and satellite’s time response (around 1 orbit for must be emulated for the ADCS target board. Two of them stabilization and 4 for orientation); sampling time was chosen (magnetometer and gyros) involve SPI protocols, and are to be 100 ms. implemented with hardware interrupts (detailed at Section IV-C); the third one (sun sensors) generate analog voltages. E. Hardware connections If 8 bit digital to analog converters were to be used, and Figure 6 shows hardware connections between RT HIL considering 6 sun sensors, 48 digital outputs would be required interface and the target’s board (6 PWM channels, 6 A/D for sun sensors’ emulation. However, cheap digital-to-analog channels, SPI module, 7 GPIO pins), and the RT HIL interface converters (D/C) can be implemented using PWM signals and SCI connection with the computer. filtering. Herein, the second approach was selected. F. RT HIL interface implementation 4) Idle: Represents an idle state, where the MCU waits for According to descriptions given in this section an RT HIL the next computing cycle to start. interface board was implemented in order to test Chasqui I’s C. Hardware interrupts ADCS. Board implementation is shown at Fig. 7.

Hardware interrupts have the highest task priority in the V. CHASQUI I’S ADCS RT HIL RESULTS MCU. For the RT HIL interface, the hardware interrupts used are described below. A test of Chasqui I stabilization with RT HIL simulation 1) Real time counter: For Chasqui I ADCS MCU’s real is performed using a DemoQE128 as target board, not on time counter (RTC) uses a 1 kHz internal low power oscillator the real ADCS board as we are only interested in testing configured so that every 0.1 s, the RTC interrupt would restart embedded software at this point. In order to fully test the computation cycle making the transition from idle state to ADCS embedded system, an RT HIL port must be added to actuators’ emulation state. Chasqui I ADCS board design, this port should enable access 2) Serial communication interface: A serial communica- 1Detailed information of each function and the whole MCU program can tion interface (SCI) enables communications through serial be found at "www.chasqui.uni.edu.pe"

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