Fault Detection and Isolation in Attitude Control Subsystem of Spacecraft Formation Flying using Extended Kalman Filters

In this thesis, the problem of fault detection and isolation in the attitude control subsystem of spacecraft formation flying is considered. For this purpose, first the attitude dynamics of a single spacecraft is analyzed and a nonlinear model is defined for our problem. This is followed up by generating the model of the spacecraft formation flight using the attitude model and controlling the formation based on virtual structure control scheme. In order to design the fault detection method, an extended Kalman filter is utilized which is a nonlinear stochastic state estimation method. Three fault detection architectures, namely, centralized, decentralized, and semi-decentralized are designed based on extended Kalman filters. Moreover, the `residual generation and threshold selection techniques are proposed for these architectures. The capabilities of the architectures for fault detection are studied through extensive numerical simulations. Using a confusion matrix evaluation system, it is shown that the centralized architecture can achieve the most reliable results relative to the semi-decentralized and decentralized architectures. Furthermore, the results confirm that the fault detection in formations with angular velocity measurements achieve higher level of accuracy, true faulty, and precision, along with lower level of false healthy misclassification as compared to the formations with only attitude measurements. In order to isolate the faults, structured residuals are designed for the decentralized, semi-decentralized, and centralized architectures. By using the confusion matrix tables, the results from each isolation technique are presented for different fault scenarios. Finally, based on the comparisons made among the architectures, it is shown that the centralized architecture has the highest accuracy in isolating the faults in the formations. Furthermore, the results confirm that fault isolation in formations with angular velocity measurements achieve higher level of accuracy when compared to formations with only attitude measurements

Design and implementation of resilient attitude estimation algorithms for aerospace applications

Satellite attitude estimation is a critical component of satellite attitude determination and control systems, relying on highly accurate sensors such as IMUs, star trackers, and sun sensors. However, the complex space environment can cause sensor performance degradation or even failure. To address this issue, FDIR systems are necessary. This thesis presents a novel approach to satellite attitude estimation that utilizes an InertialNavigation System (INS) to achieve high accuracy with the low computational load. The algorithm is based on a two-layer Kalman filter, which incorporates the quaternion estimator(QUEST) algorithm, FQA, Linear interpolation (LERP)algorithms, and KF. Moreover, the thesis proposes an FDIR system for the INS that can detect and isolate faults and recover the system safely. This system includes two-layer fault detection with isolation and two-layered recovery, which utilizes an Adaptive Unscented Kalman Filter (AUKF), QUEST algorithm, residual generators, Radial Basis Function (RBF) neural networks, and an adaptive complementary filter (ACF). These two fault detection layers aim to isolate and identify faults while decreasing the rate of false alarms. An FPGA-based FDIR system is also designed and implemented to reduce latency while maintaining normal resource consumption in this thesis. Finally, a Fault Tolerance Federated Kalman Filter (FTFKF) is proposed to fuse the output from INS and the CNS to achieve high precision and robust attitude estimation.The findings of this study provide a solid foundation for the development of FDIR systems for various applications such as robotics, autonomous vehicles, and unmanned aerial vehicles, particularly for satellite attitude estimation. The proposed INS-based approach with the FDIR system has demonstrated high accuracy, fault tolerance, and low computational load, making it a promising solution for satellite attitude estimation in harsh space environment

TechSat 21 and Revolutionizing Space Missions using Microsatellites

The Air Force Research Laboratory (AFRL) TechSat 21 flight experiment demonstrates a formation of three microsatellites flying in formation to operate as a “virtual satellite.” X-band transmit and receive payloads on each of the satellites form a large sparse aperture system. The satellite formation can be configured to optimize such varied missions as radio frequency (RF) sparse aperture imaging, precision geolocation, ground moving target indication (GMTI), single-pass digital terrain elevation data (DTED), electronic protection, single-pass interferometric synthetic aperture radar (IF-SAR), and high data-rate, secure communications. Benefits of such a microsatellite formation over single large satellites include unlimited aperture size and geometry, greater launch flexibility, higher system reliability, easier system upgrade, and low cost mass production. Key research has focused on the areas of formation flying and sparse aperture signal processing and been sponsored and guided by the Air Force Office of Scientific Research (AFOSR). The TechSat 21 Program Preliminary Design Review (PDR) was held in April 2001 and incorporated the results of extensive system trades to achieve a light-weight, high performance satellite design. An overview of experiment objectives, research advances, and satellite design is presented

Distributed estimation and control technologies for formation flying spacecraft by Philip Andrew Ferguson.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2003.Includes bibliographical references (p. 115-120).S.M

A Hierarchical Architecture for Cooperative Actuator Fault Estimation and Accommodation of Formation Flying Satellites in Deep Space

A new cooperative fault accommodation algorithm based on a multi-level hierarchical architecture is proposed for satellite formation flying missions. This framework introduces a high-level (HL) supervisor and two recovery modules, namely a low-level fault recovery (LLFR) module and a formation-level fault recovery (FLFR) module. At the LLFR module, a new hybrid and switching framework is proposed for cooperative actuator fault estimation of formation flying satellites in deep space. The formation states are distributed among local detection and estimation filters. Each system mode represents a certain cooperative estimation scheme and communication topology among local estimation filters. The mode transitions represent the reconfiguration of the estimation schemes, where the transitions are governed by information that is provided by the detection filters. It is shown that our proposed hybrid and switching framework confines the effects of unmodeled dynamics, disturbances, and uncertainties to local parameter estimators, thereby preventing the propagation of inaccurate information to other estimation filters. Moreover, at the LLFR module a conventional recovery controller is implemented by using estimates of the fault severities. Due to an imprecise fault estimate and an ineffective recovery controller, the HL supervisor detects violation of the mission error specifications. The FLFR module is then activated to compensate for the performance degradations of the faulty satellite by requiring that the healthy satellites allocate additional resources to remedy the problem. Consequently, fault is cooperatively recovered by our proposed architecture, and the formation flying mission specifications are satisfied. Simulation results confirm the validity and effectiveness of our developed and proposed analytical work

Development of a guidance, navigation and control architecture and validation process enabling autonomous docking to a tumbling satellite

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.Includes bibliographical references (p. 307-324).The capability to routinely perform autonomous docking is a key enabling technology for future space exploration, as well as assembly and servicing missions for spacecraft and commercial satellites. Particularly, in more challenging situations where the target spacecraft or satellite is tumbling, algorithms and strategies must be implemented to ensure the safety of both docking entities in the event of anomalies. However, difficulties encountered in past docking missions conducted with expensive satellites on orbit have indicated a lack of maturity in the technologies required for such operations. Therefore, more experimentation must be performed to improve the current autonomous docking capabilities. The main objectives of the research presented in this thesis are to develop a guidance, navigation and control (GN&C) architecture that enables the safe and fuel-efficient docking with a free tumbling target in the presence of obstacles and anomalies, and to develop the software tools and verification processes necessary in order to successfully demonstrate the GN&C architecture in a relevant environment. The GN&C architecture was developed by integrating a spectrum of GN&C algorithms including estimation, control, path planning, and failure detection, isolation and recovery algorithms.(cont.) The algorithms were implemented in GN&C software modules for real-time experimentation using the Synchronized Position Hold Engage and Reorient Experimental Satellite (SPHERES) facility that was created by the MIT Space Systems Laboratory. Operated inside the International Space Station (ISS), SPHERES allow the incremental maturation of formation flight and autonomous docking algorithms in a risk-tolerant, microgravity environment. Multiple autonomous docking operations have been performed in the ISS to validate the GN&C architecture. These experiments led to the first autonomous docking with a tumbling target ever achieved in microgravity. Furthermore, the author also demonstrated successful docking in spite of the presence of measurement errors that were detected and rejected by an online fault detection algorithm. The results of these experiments will be discussed in this thesis. Finally, based on experiments in a laboratory environment, the author establishes two processes for the verification of GN&C software prior to on-orbit testing on the SPHERES testbed.by Simon Nolet.Sc.D

Fault Detection, Isolation and Identification of Formation Flying Satellites using Wavelet-Entropy and Neural Networks

The main objective of this thesis is to develop a fault detection, isolation and identification (FDII) scheme based on Wavelet Entropy (WE) and Artificial Neural Network (ANN) for reaction wheels (RW) that are employed as actuators in the attitude control subsystem (ACS) of a satellites to perform the formation flying (FF) missions. In this thesis two FDII approaches are proposed, i) Spacecraft-level fault diagnosis and ii) Formation-level fault diagnosis. In the "spacecraft-level" fault diagnosis scheme in order to analysis faults, absolute attitude and angular measurements from a satellite are considered as diagnostic signals. In order to detect the fault, the wavelet-entropy technique is employed on diagnostic signals and the sum of the absolute wavelet entropies (SAWE) of the diagnostic signals are obtained and compared with an appropriately selected threshold. If the SAWE passes the threshold the faulty condition is established. In order to isolate the fault in a satellite the angular velocity measurements in a satellite are considered as diagnostic signals and the relative wavelet energy (RWE) of these signals are obtained and compared to a threshold. In our proposed fault identification scheme, the attitude measurements in a satellite are considered and the detail and approximation coefficients of the wavelet signals are obtained and these coefficients are used as inputs to an artificial neural network to identify the type of the fault in a satellite. Using a confusion matrix evaluation system we demonstrate that our spacecraft-level FDII can detect, isolate and identify the high severity faults in a satellite however this scheme cannot detect low severity faults in a satellite. Our proposed "formation-level" FDII scheme utilizes data collected from the relative attitudes and relative angular velocity measurements of the formation flying satellites. In this fault diagnosis scheme, the relative attitude and relative angular velocity measurements in a satellite with respect to each its neighbor's in a formation are considered as diagnostic signals. In order to detect the fault, the relative attitude measurements in a satellite are considered as diagnostic signals. The wavelet-entropy technique is utilized on diagnostic signals and the SAWEs with respect to each satellite's neighbor are obtained. These SAWEs are then compared with an appropriately selected threshold. The faulty satellite is determined if these SAWEs pass the thresholds. In order to isolate the fault in a faulty satellite, the relative angular velocity measurements are considered as diagnostic signals. The RWE of these signals are obtained and compared to a threshold. In our proposed fault identification scheme, the relative attitude measurements in a satellite are considered as diagnostic signals. In this scheme, the RWEs of the diagnostic signals are obtained and used as inputs to an artificial neural network to identify the type of the fault in a satellite. According to the simulation results, our proposed FDII scheme can detect, isolate and identified both low severity and high severity faults in the reaction wheels of satellite

Fault detection, isolation, and identification for nonlinear systems using a hybrid approach

This thesis presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems; taking advantage of both system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution are a bank of adaptive neural parameter estimators (NPE) and a set of single-parameterized fault models. The NPEs continuously estimate unknown fault parameters (FP) that are indicators of faults in the system. In view of the availability of full-state measurements, two NPE structures, namely series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. On the contrary, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Simple neural network architecture and update laws make both schemes suitable for real-time implementations. A fault tolerant observer (FTO) is then designed to extend the FDII schemes to systems with partial-state measurement. The proposed FTO is a neural state estimator that can estimate unmeasured states even in presence of faults. The estimated and the measured states then comprise the inputs to the FDII schemes. Simulation results for FDII of reaction wheels of a 3-axis stabilized satellite in presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solution under both full and partial-state measurements