Robert W. Mah

On-line gyro-based, mass-property identification for thruster-controlled spacecraft using recursive least squares

Spacecraft control, state estimation, and fault-detection-and-isolation systems are affected by unknown variations in the vehicle mass properties. It is often difficult to accurately measure inertia terms on the ground, and mass properties can change on-orbit as fuel is expended, the configuration changes, or payloads are added or removed. Recursive least squares-based algorithms that use gyro signals to identify the center of mass and inverse inertia matrix are presented. They are applied in simulation to 3 thruster-controlled vehicles: the X-38 and Mini-AERCam under development at NASA-JSC, and the S4, an air-bearing spacecraft simulator at the NASA-Ames Smart Systems Research Lab (SSRL).

Gyro-based maximum-likelihood thruster fault detection and identification

When building smaller, less expensive spacecraft, there is a need for intelligent fault tolerance vs. increased hardware redundancy. If fault tolerance can be achieved using existing navigation sensors, cost and vehicle complexity can be reduced. A maximum-likelihood-based approach to thruster fault detection and identification (FDI) for spacecraft is developed here and applied in simulation to the X-38 space vehicle. The system uses only gyro signals to detect and identify hard, abrupt, single- and multiple-jet on- and off-failures. Faults are detected within one second and identified within one to five seconds.

Motion-Based Mass- and Thruster-Property Identification for Thruster-Controlled Spacecraft

*† ‡ Spacecraft control, state estimation, and fault-detection-and-isolation (FDI) systems are all affected by unknown variations in the vehicle mass and thruster properties. It is often difficult to accurately measure inertia terms on the ground, and mass properties can change on-orbit as fuel is expended, the configuration changes, or payloads are added or removed. Multiple concurrent recursive least squares identification (MCRLS ID) algorithms using gyros and accelerometers to monitor vehicle motions are derived and used here to identify on-line the vehicle inertia tensor, inverse inertia tensor, center of mass, thruster force magnitude, and total mass. Originally developed for application to the X-38 v.201 spacecraft thruster FDI system, the algorithms have been extended, refined, and implemented on the MIT SPHERES experimental spacecraft, which are now awaiting launch to the ISS for space-based testing. The MCRLS ID architecture makes approximations that enable application of linear parameter estimation methods to applications where the unknown parameters do not appear linearly in the regression equation. As compared to alternative methods, this approach favors efficiency of algorithm development, re-configurability, and on-line implementation over absolute algorithmic accuracy, which is often limited by other modeling deficiencies or system uncertainties. An accurate and computationally efficient filtering method for estimating angular acceleration from raw gyro signals is presented. The algorithms have been tested extensively in 2-D air-bearing testing on the flight hardware and during brief 0-g testing on the NASA 0-g KC-135. The SPHERES will launch to the International Space Station on STS-121, with a series of experiments following soon after. The experimental plan is presented.

Motion-Based Thruster Fault Detection and Isolation

An automatic thruster fault detection and isolation (FDI) system is a key autonomy component for thruster-controlled spacecraft. Sensor-rich FDI systems often use additional sensors such as pressure and temperature sensors in the thruster nozzles; however when building and operating smaller, less expensive spacecraft, there is a call for intelligent fault tolerance that does not increase hardware complexity, cost, and mass. A maximumlikelihood-based approach to thruster FDI is presented that can detect and isolate hard, abrupt, singleand multiple-jet onand off-faults using only existing navigation sensors such as gyros or accelerometers. Therefore, this fault-tolerant capability could be provided as a software-only addition to a new spacecraft or modification to an existing one. These algorithms can be implemented on-board using the spacecraft’s existing processor(s), onboard using a stand-alone processor, or on the ground, processing sensor information communicated to the ground stations from the spacecraft. This FDI system was originally developed through application to two specific thruster-controlled spacecraft then under development at NASA Johnson Space Center: the X-38 v.201 experimental spacecraft and the Mini AERCam. Faults are detected within one second and identified within one to five seconds in most cases for the X-38. In extended testing for the X-38, faults were correctly identified in 99.9994% of the test cases. The algorithm has since been streamlined, optimized for implementation, and extensively tested in air-bearing experiments using the MIT SPHERES experimental spacecraft. The SPHERES are scheduled for launch to the International Space Station on STS-121, with a series of experiments to provide space-flight validation following soon after.

The NASA Smart Probe Project for Real-Time Multiple Microsensor Tissue Recognition

Background: Remote surgery requires automated sensors, effectors and sensor-effector communication. The NASA Smart Probe Project has focused on the sensor aspect. Methods: The NASA Smart Probe uses neural networks and data from multiple microsensors for a unique tissue signature in real time. Animal and human trials use several probe configurations: (1) 8-microsensor probe (2.5 mm in diameter) for rodent studies (normal and subcutaneous mammary tumor tissues), and (2) 21-gauge needle probe with 3 spectroscopic fibers and an impedance microelectrode for breast cancer diagnosis in humans. Multisensor data are collected in real time (update 100 times/s) using PCs. Results: Human data (collected by NASA licensee BioLuminate) from 15 women undergoing breast biopsy distinguished normal tissue from both benign tumors and breast carcinoma. Tumor margins and necrosis are rapidly detected. Conclusion: Real-time tissue identification is achievable. Potential applications, including probes incorporating nanoelectrode arrays, are presented.

Multimodality Stereotactic Brain Tissue Identification: The NASA Smart Probe Project

Real-time tissue identification can benefit procedures such as stereotactic brain biopsy, functional neurosurgery and brain tumor excision. Optical scattering spectroscopy has been shown to be effective at discriminating cancer from noncancerous conditions in the colon, bladder and breast. The NASA Smart Probe extends the concept of ‘optical biopsy’ by using neural network techniques to combine the output from 3 microsensors contained within a cannula 2.7 mm in diameter (i.e. the diameter of a stereotactic brain biopsy needle). Experimental data from 5 rats show the clear differentiation between tissues such as brain, nerve, fat, artery and muscle that can be achieved with optical scattering spectroscopy alone. These data and previous findings with other modalities such as (1) analysis of the image from a fiberoptic neuroendoscope and (2) the output from a microstrain gauge suggest the Smart Probe multiple microsensor technique shows promise for real-time tissue identification in neurosurgical procedures.

Fault Tolerant Relative Navigation using Inertial and Relative Sensors

Many emerging applications of space, ground, marine, and air vehicles require relative automated navigation with respect to other vehicles and objects. Disturbances in the environment may cause faults in relative navigation sensors. For sensors based on cameras or laser range finders, events as common as lighting changes, glint, or obstruction by debris could potentially cause spurious responses. Relative navigation is safety critical‐fault tolerance must be addressed. We propose a fault detection, identification, and recovery architecture using multiple moving horizon estimators, each for a separate hypothesis of the fault state of the system. The hypothesis with maximum empirical likelihood is selected. Detected and identified faults are reported to the main navigation filter, which may then discard the relative navigation sensor data, and instead temporarily rely on the inertial navigation system. The guidance system may also act on the identified fault state, taking actions to recover the system to a safe state. This logic is demonstrated in simulation for the automated rendezvous and docking (AR&D) of spacecraft‐a key technology for the near future demands of the space program. The simulation results demonstrate that faulty relative sensors may seriously aect the navigation solution. The proposed fault detection scheme has demonstrated an ability to identify faults in these sensors and take them oine before they disrupt navigation and lead to mission failure.

Real‐time multiple microsensor tissue recognition and its potential application in the management of prostate cancer

The present geno-proteomic and cytomic era and the even more exciting developments in bio-informatics have ushered with it an exponential increase in our understanding of disease processes. This is being increased further by the application of novel technologies for the diagnosis and management of solid malignancies. One example is the role of multiple microsensor tissue recognition (MMTR) in real-time. The use of neural networks and data from multiple microsensors to identify a unique tissue signature in real-time is at the forefront of tissue recognition. In preliminary studies, this technology has been used to aid in the diagnosis and resection of breast cancer. The extension of this to other solid malignancies, especially prostate cancer, is critical and holds great promise.

Open Architecture for Integrated Vehicle Health Management

A consensus in aerospace industry is that for IVHM technology to fully achieve its promise there is a need in establishing open architectures and standards. An open architecture would facilitate interaction of aircraft primes, subsystem OEMs, aircraft operators, government regulators, and research community in development and integration of IVHM systems from best available components. To answer this need, the IVHM Project in the NASA Aviation Safety Program is working on establishing an architectural framework for IVHM interoperability. This paper describes a NASA-led effort in IVHM architecture. The interoperability framework includes standardization of operations, functions, protocols, and information management models. NASA IVHM Project sponsors many new technology developments and expects that establishing the architectural framework would facilitate the technology transition to industry.

Adaptive DFT-based fringe tracking and prediction at IOTA

An automatic fringe tracking system has been developed and implemented at the Infrared Optical Telescope Array (IOTA). In testing during May 2002, the system successfully minimized the optical path differences (OPDs) for all three baselines at IOTA. Based on sliding window discrete Fourier transform (DFT) calculations that were optimized for computational efficiency and robustness to atmospheric disturbances, the algorithm has also been tested extensively on off-line data. Implemented in ANSI C on the 266 MHz PowerPC processor running the VxWorks real-time operating system, the algorithm runs in approximately 2.0 milliseconds per scan (including all three interferograms), using the science camera and piezo scanners to measure and correct the OPDs. Preliminary analysis on an extension of this algorithm indicates a potential for predictive tracking, although at present, real-time implementation of this extension would require significantly more computational capacity.