The Generic Nanosatellite Bus (GNB) is a spacecraft platform designed to accommodate the integration of diverse payloads in a common housing of supporting components. The development of the GNB at the Space Flight Laboratory (SFL) under the Canadian Advanced Nanospace eXperiment (CanX) program provides accelerated access to space while reducing non-recurring engineering (NRE) costs. The work presented herein details the development of the attitude determination and control subsystem (ADCS) of the GNB. Specific work on magnetorquer coil assembly, integration, and testing (AIT) and reaction wheel testing is included. The embedded software development and unit-level testing of the GNB sun sensors are discussed. The characterization of the AeroAstro star tracker is also a major focus, with procedures and results presented here. Hardware models were developed and incorporated into SFL's in-house high-fidelity attitude dynamics and control simulation environment. This work focuses on specific contributions to the CanX-3, CanX-4&5, and AISSat-1 nanosatellite missions.

Some modern spacecraft missions require precise knowledge of the attitude, obtained from the ground processing of on-board attitude sensors. A traditional 6-state attitude determination filter, containing three attitude errors and three gyro bias errors, has been recognized for its robust performance when it is used with high quality measurement data from a star tracker for many past and present missions. However, as higher accuracies are required for attitude knowledge in the missions, systematic errors such as sensor misalignment and scale factor errors, which could often be neglected in previous missions, have become serious, and sometimes, the dominant error sources. The star tracker data have gaps and degradation caused by, for example, the Sun and Moon blocking in the filed of view and data time tag errors. Thus, attitude determination based on the gyro data without using the star tracker data is inevitably required for most missions for the period when the star tracker is unable to provide accurate data. However, any gyro-based attitude errors would eventually grow exponentially because of the uncorrected systematic errors of gyros and the uncorrected gyro random noises. An improved understanding of the gyro random noise characteristics and the estimation of the gyro scale factor errors and gyro misalignments are necessary for precise attitude determination for some present and future missions. The 6-state filters have been extended to 15-state filters to estimate the scale factor and misalignment errors of gyros especially during a high-slew maneuver and the performance of theses filters has been investigated. During a starless period, the inevitable drift of the EKF solutions, which are caused by the uncorrected gyro’s systematic errors and the gyro random noises, can be replaced with the batch solutions, which are less affected by the data gap in the star tracker. Power Spectral Density and the Allan Variance Method are used for analyzing the gyro random noises in both ICESat and simulated gyro data, which provide better information about the process noise covariance in the attitude filter. Both simulated and real data are used for analyzing and evaluating the performances of EKF and batch algorithms. / text

The attitude determination and control subsystem (ADCS) is a critical part of any satellite conducting scientific experiments that require accurate positioning (such as Earth observation and solar spectroscopy). The engineering design process of this subsystem has a long heritage; yet, it is surrounded by several limitations due to the stringent physical constraints imposed on small satellites. These limitations (e.g., limited computational capabilities, power, and volume) require an improved approach for the purpose of attitude determination (AD) and control. Previous space missions relied mostly on the extended Kalman filter (EKF) to estimate the relative orientation of the spacecraft because it yields an optimal estimator under the assumption that the measurement and process models are white Gaussian processes. However, this filter suffers from several limitations such as a high computational cost. This thesis addresses all the limitations found in small satellites by introducing a computationally efficient algorithm for AD based on a fuzzy inference system with a gradient decent optimization technique to calculate and optimize the bounds of the membership functions. Also, an optimal controller based on a fractional proportional-integral-derivative controller has been implemented to provide an energy-efficient control scheme. The AD algorithm presented in this thesis relies on the residual information of the Earth magnetic field. In contrast to current approaches, the new algorithm is immune to several limitations such as sensitivity to initial conditions and divergence problems. Additionally, its computational cost has been reduced. Simulation results illustrate a higher pointing stability, while maintaining satisfying levels of pointing accuracy and increasing reliability. Moreover, the optimal controller designed provides a shorter time delay, settling time, and steady-state error. This demonstrates that accurate attitude determination and control can be conducted in small spacecraft.

In early 2002, Queensland University of Technology (QUT) commenced to develop its own low-cost Global Positioning System (GPS) receiver with the capability for space applications such as satellites in Low Earth Orbits, and sounding rockets. This is named the SPace Applications Receiver (SPARx). This receiver development is based on the Zarlink (formerly known as Mitel) GP2000 Chip set and is a modification of the Mitel Orion 12 channel receiver design. Commercially available GPS receivers for space applications are few and expensive. The QUT SPARx based on the Mitel Orion GPS receiver design is cost effective for space applications. At QUT its use is being maximized for space applications and carrier phase processing in a cost-effective and specific way. To build upon previous SPARx software developments made from 2002 to 2003, the receiver is required to be modified to have L1 carrier phase navigation capability. Such an improvement is necessary for the receiver to be used in 3-axis attitude determination and relative navigation using carrier phase. The focus of this research is on the implementation of the L1 carrier phase measurement capability with SPARx. This is to enable the use of improved navigation algorithms. Specific emphasis is given to the areas of time synchronization, the carrier phase implementation and carrier phase differential GPS with SPARx. Test results conducted in the area of time synchronization and comparisons with other carrier phase capable GPS receivers are given, as well as an investigation of the use of SPARx in carrier phase differential GPS. Following these, conclusions and recommendations are given for further improvements to SPARx.

Experimental testing of attitude determination systems still plays an important role, despite increasing use of simulations. Testing provides a means to numerically quantify system performance, give confidence in the models and methods, and also discover and compensate for unexpected behaviours and interactions with the attitude determination system. The usefulness of the test results is dependent on an understanding of the uncertainties that contribute to the attitude error. With this understanding, the significance of the results can be assessed, and efforts to reduce attitude errors can be directed appropriately. The work of this thesis is to gain a quantitative understanding of the uncertainties that impact the attitude error of low cost spinning spacecraft using COTS camera (as Sun sensor) and MEMS magnetometer. The sensors were calibrated and the uncertainties in these calibrations were quantified, then propagated through the Triad method to uncertainties in the attitude. It was found that most systematic errors were reduced to negligible levels, except those due to timing latencies. Attitude errors achieved in the laboratory with the experimental setup were around 0.14 degrees (3σ) using either the Triad, q-method or Extended Kalman Filter with a gyro for dynamic model replacement. The errors in laboratory were dominated by magnetometer noise. Furthermore, correlated systematic errors had the effect of reducing the attitude error calculated in the laboratory. For an equivalent Sun-mag geometry in orbit, simulation showed that total attitude error would be of the order of 0.77 degrees (3σ). An uncertainty contribution analysis revealed this error was dominated by uncertainties in the inertial magnetic field model. Uncertainties in knowledge of the inertial Sun model, sensor calibration, sensor alignment and sensor noise were shown to be insignificant in comparison.

This thesis outlines an orbit determination and 3-axis attitude determination system for use on orbit as applicable to 1U CubeSats and other picosatellites. The constraints imposed by the CubeSat form factor led to the need for a simple configuration and relaxed accuracy requirements. To design a system within the tight mass, volume, and power constraints inherent to CubeSats, a balance between hardware complexity, software complexity and accuracy is sought. The proposed solution consists of a simple orbit propagator, magnetometers with a magnetic field look-up table, Sun sensors with an analytic Sun direction model, and the TRIAD method to combine vector observations into attitude information. The orbit propagator is a simple model of a circular trajectory with several frequently updated parameters and can provide orbital position data with average and maximum errors—when compared to SGP4—of less than 3.7km and 10.7km for 14 days. The magnetic field look up table provides useful information from a small memory footprint; only 480 data points provide a mean error of approximately 0.2° and a maximum error of approximately 2°—when compared to the IGRF model. The Sun’s direction is modeled, and as expected, can be modeled simply and accurately. Combining the magnetic field and Sun direction models with inaccurate sensors and the TRIAD method results in useful attitude information from a very simple system. A system with Sun sensor error standard deviation of 1° and magnetometer error standard deviation of 5° yields results with average error of only 2.74°, and 99% of the errors in this case are less than approximately 13°. The system outlined provides crude attitude determination with software and hardware requirements that are well within the capabilities of current 1U CubeSats—something that many other systems, such as Kalman filters or star trackers, cannot do. It also provides an excellent starting point for future ADCS systems, which will significantly increase the ability of CubeSats.

Current trends in spacecraft are leading to smaller, more inexpensive options whenever possible. This shift has been primarily pursued for the opportunity to open a new frontier for technologies with a small financial obligation. Limited power, processing, pointing, and communication capabilities are all common issues which must be considered when miniaturizing systems and implementing low-cost components. This thesis addresses some of these concerns by applying two methods, in attitude estimation and control. Additionally, these methods are not restricted to only small, inexpensive satellites, but offer a benefit to large-scale spacecraft as well. First, star cameras are examined for the tendency to generate streaked star images during maneuvers. This issue also comes into play when pointing capabilities and camera hardware quality are low, as is often the case in small, budget-constrained spacecraft. When pointing capabilities are low, small residual velocities can cause movement of the stars in the focal plane during an exposure, causing them to streak across the image. Additionally, if the camera quality is low, longer exposures may be required to gather sufficient light from a star, further contributing to streaking. Rather than improving the pointing or hardware directly, an algorithm is presented to retrieve and utilize the endpoints of streaked stars to provide feedback where traditional methods do not. This allows precise attitude and angular rate estimates to be derived from an image which, with traditional methods, would return large attitude and rate error. Simulation results are presented which demonstrate endpoint error of approximately half a pixel and rate estimates within 2% of the true angular velocity. Three methods are also considered to remove overlapping star streaks and resident space objects from images to improve performance of both attitude and rate estimates. Results from a large-scale Monte Carlo simulation are presented in order to characterize the performance of the method. iii Additionally, a rapid optimal attitude guidance method is experimentally validated in a groundbased, pico-scale satellite test bed. Fast slewing performance is demonstrated for an incremental step maneuver with low average power consumption. Though the focus of this thesis is primarily on increasing the capabilities of small, inexpensive spacecraft, the methods discussed have the potential to increase the capabilities of current and future large-scale missions as well.

The low size and budget of typical nanosatellite missions limit the available sensors for attitude estimation. Relatively high noise MEMS gyroscopes often must be employed when accurate knowledge of the spacecraft’s angular velocity is necessary for attitude determination and control. This thesis derived and tested in simulation the “Virtual Gyroscope” algorithm, which replaced a standard gyroscope with an array of spatially distributed accelerometers for a 1U CubeSat mission. A MEMS accelerometer model was developed and validated using Root Allan Variance, and the Virtual Gyroscope was tested both in the open loop configuration and as a replacement for a gyroscope in a Multiplicative Extended Kalman Filter. It was found that the quality of the Virtual Gyroscope’s rate measurement improved with a larger and higher quality array, but the error in the estimate was very large. The low signal-to-noise ratio and the unknown bias in the accelerometers caused the angular velocity estimate from the accelerometer array to be too poor for use in the propagation step of the Kalman filter. The Kalman filter performed better with attitude measurements alone than with the Virtual Gyroscope, even when the attitude were delivered at a low rate with added noise. Overall, the current Virtual Gyroscope algorithm that is presented in this thesis is not suitable to replace a MEMS gyroscope in a nanosatellite mission, although there is room for future improvements using bias prediction for the individual accelerometers in the array.

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