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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5

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NASA NTRS2011

Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme

A method detects and isolates in-flight sensor, actuator, and component faults for advanced propulsion systems. In sharp contrast to many conventional methods, which deal with either sensor fault or component fault, but not both, this method considers sensor fault, actuator fault, and component fault under one systemic and unified framework. The proposed solution consists of two main components: a bank of real-time, nonlinear adaptive fault diagnostic estimators for residual generation, and a residual evaluation module that includes adaptive thresholds and a Transferable Belief Model (TBM)-based residual evaluation scheme. By employing a nonlinear adaptive learning architecture, the developed approach is capable of directly dealing with nonlinear engine models and nonlinear faults without the need of linearization. Software modules have been developed and evaluated with the NASA C-MAPSS engine model. Several typical engine-fault modes, including a subset of sensor/actuator/components faults, were tested with a mild transient operation scenario. The simulation results demonstrated that the algorithm was able to successfully detect and isolate all simulated faults as long as the fault magnitudes were larger than the minimum detectable/isolable sizes, and no misdiagnosis occurred

Tang, Liang↗

NASA NTRS1992

Control of sound radiation with active/adaptive structures

Recent research is discussed in the area of active structural acoustic control with active/adaptive structures. Progress in the areas of structural acoustics, actuators, sensors, and control approaches is presented. Considerable effort has been given to the interaction of these areas with each other due to the coupled nature of the problem. A discussion is presented on actuators bonded to or embedded in the structure itself. The actuators discussed are piezoceramic actuators and shape memory alloy actuators. The sensors discussed are optical fiber sensors, Nitinol fiber sensors, piezoceramics, and polyvinylidene fluoride sensors. The active control techniques considered are state feedback control techniques and least mean square adaptive algorithms. Results presented show that significant progress has been made towards controlling structurally radiated noise by active/adaptive means applied directly to the structure.

Fuller, C. R.↗

NASA NTRS1986

Active Control Evaluation for Spacecraft (ACES)

The Air Force goal is to develop vibration control techniques for large flexible spacecraft by addressing sensor, actuator, and control hardware and dynamic testing. The Active Control Evaluation for Spacecraft (ACES) program will address the Air Force goal by looking at two leading control techniques and implementing them on a structural model of a flexible spacecraft under laboratory testing. The first phase in the ACES program is to review and to assess the High Authority Control/Low Authority Control (HAC/LAC) and Filter accomodated Model Error Sensitivity Suppression (FAMESS) control techniques for testing on the modified VCOSS structure. Appropriate sensors and actuators will be available for use with both techniques; locations will be the same for both techniques. The control actuators will be positioned at the midpoint and free end of the structure. The laser source for the optical sensor is mounted on the feed mast. The beam will be reflected from a mirror on the offset antenna onto the detectors mounted above the shaker table bay. The next phase is to develop an analysis simulation with the control algorithms implemented for dynamics verification. The third phase is to convert the control laws into high level computer language and test them in the NASA-MSFC facility. The final phase is to compile all analytical and test results for performance comparisons.

Pearson, J.↗

NASA NTRS2012

Adaptive Control in the Presence of Simultaneous Sensor Bias and Actuator Failures

The problem of simultaneously accommodating unknown sensor biases and unknown actuator failures in uncertain systems is considered in a direct model reference adaptive control (MRAC) setting for state tracking using state feedback. Sensor biases and actuator faults may be present at the outset or may occur at unknown instants of time during operation. A modified MRAC law is proposed, which combines sensor bias estimation with control gain adaptation for accommodation of sensor biases and actuator failures. This control law is shown to provide signal boundedness in the resulting system. For the case when an external asymptotically stable sensor bias estimator is available, an MRAC law is developed to accomplish asymptotic state tracking and signal boundedness. For a special case wherein biases are only present in the rate measurements and bias-free position measurements are available, an MRAC law is developed using a model-independent bias estimator, and is shown to provide asymptotic state tracking with signal boundedness.

Joshi, Suresh M.↗

NASA NTRS2014

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a Simulink(R) library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.

numerical simulation↗

NASA NTRS2015

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a SimulinkR library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.

propulsion simulation↗

NASA NTRS2014

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (40,000 pound force thrust) (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a Simulink (R) library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.

numerical simulation↗

NASA NTRS2015

Generalized Nanosatellite Avionics Testbed Lab

The Generalized Nanosatellite Avionics Testbed (G-NAT) lab at NASA Ames Research Center provides a flexible, easily accessible platform for developing hardware and software for advanced small spacecraft. A collaboration between the Mission Design Division and the Intelligent Systems Division, the objective of the lab is to provide testing data and general test protocols for advanced sensors, actuators, and processors for CubeSat-class spacecraft. By developing test schemes for advanced components outside of the standard mission lifecycle, the lab is able to help reduce the risk carried by advanced nanosatellite or CubeSat missions. Such missions are often allocated very little time for testing, and too often the test facilities must be custom-built for the needs of the mission at hand. The G-NAT lab helps to eliminate these problems by providing an existing suite of testbeds that combines easily accessible, commercial-offthe- shelf (COTS) processors with a collection of existing sensors and actuators.

Testing data and general test protocols↗

NASA NTRS1989

Enhanced modeling features within TREETOPS

The original motivation for TREETOPS was to build a generic multi-body simulation and remove the burden of writing multi-body equations from the engineers. The motivation of the enhancement was twofold: (1) to extend the menu of built-in features (sensors, actuators, constraints, etc.) that did not require user code; and (2) to extend the control system design capabilities by linking with other government funded software (NASTRAN and MATLAB). These enhancements also serve to bridge the gap between structures and control groups. It is common on large space programs for the structures groups to build hi-fidelity models of the structure using NASTRAN and for the controls group to build lower order models because they lack the tools to incorporate the former into their analysis. Now the controls engineers can accept the hi-fidelity NASTRAN models into TREETOPS, add sensors and actuators, perform model reduction and couple the result directly into MATLAB to perform their design. The controller can then be imported directly into TREETOPS for non-linear, time-history simulation.

Vandervoort, R. J.↗

NASA NTRS1996

Active Control of Noise Using Actuator/Sensor Arrays

Current research in smart structures is directed toward the integration of many actuators and sensors into a material. In this paper we investigate the possibility of using this instrumentation for active noise control from a vibrating structures. Current technology for reducing radiated sound is limited by the instrumentation for the control system. These control systems employ relatively small numbers of sensors and actuators. Hence, these control systems must rely on a model of the structure to estimate and control the global vibrations that contribute to the far field pressure. For complex, realistic structures the development of such a model is a formidable task. The model is a limiting factor in the continuing development of structural acoustics. In this paper we propose to increase the number of actuators and sensors of a smart material to offset the complexity of the model used for control design. The sensor arrays will be used to directly sense the shape of the structure rather than using a model of the structures to indirectly sense the shape of the structure. The actuator array is used to apply distributed forces to the structure, rather than using the structure itself as a load path. A control system for the active cancellation of sound is derived from standard control system methodologies.

Lindner, Douglas K.↗

NASA NTRS1991

The zeroes of controlled structures - Sensor/actuator attributes and structural modelling

The effects of sensor/actuator design and modeling decisions on the zero frequencies of single input single output structural systems are explored. A zero definition based on the high gain closed loop character of the poles of a system is used to study parametrically zeroes as a function of sensor and actuator location. An infinite order model is adopted so that the effects of finite order modeling are removed. Examples focus on point or localized actuators and sensors and simple uniform structures. The sensitivity of the zero frequencies to changes in the sensor and actuator location for both collocated and non-collocated cases are presented. Non-minimum phase behavior is visible in only certain non-collocated situations. In addition to the sensor/actuator attributes, the zeroes are shown to be a function of the truncation and discretization used to model the structural response for collocated and dual systems. Methods for improving the zero predictions in the control bandwidth are suggested.

Fleming, Farla M.↗

NASA NTRS1983

Integrated flight/propulsion control system architectures for a high speed aircraft

The present investigation is concerned with the impact of integrated flight, inlet, and engine control system architectures on system effectiveness and life cycle cost (LCC). The aircraft employed in the investigation is a modified YF-12 with a cruise Mach number of 2.5. The aircraft has two advanced technology aircraft JT-69 afterburning, low-bypass turbofan engines. Advanced technologies suitable for use in future flight/propulsion control systems are related to distributed computer networks, fault tolerant computers and software, analytical redundancy, self-checking microprocessor pairs, advanced direct drive actuators, fiber optic data buses, VLSI microcircuits, and skewed sensors. The integrated architectures were found to have fewer sensors, actuators, and power sources than the nonintegrated architectures.

Bangert, L. H.↗

NASA NTRS1996

Microelectromechanical Systems for Aerodynamics Applications

Microelectromechanical systems (MEMS) embody the integration of sensors, actuators, and electronics on a single substrate using integrated circuit fabrication techniques and compatible micromachining processes. Silicon and its derivatives form the material base for the MEMS technology. MEMS devices, including micro-sensors and micro-actuators, are attractive because they can be made small (characteristic dimension about microns), be produced in large numbers with uniform performance, include electronics for high performance and sophisticated functionality, and be inexpensive. MEMS pressure sensors, wall-shear-stress sensors, and micromachined hot-wires are nearing application in aeronautics. MEMS actuators face a tougher challenge since they have to be scaled (up) to the physical phenomena that are being controlled. MEMS actuators are proposed, for example, for controlling the small structures in a turbulent boundary layer, for aircraft control, for cooling, and for mixing enhancement. Data acquisition or control logistics require integration of electronics along with the transducer elements with appropriate consideration of analog-to-digital conversion, multiplexing, and telemetry. Altogether, MEMS technology offers exciting opportunities for aerodynamics applications both in wind tunnels and in flight

Mehregany, Mehran↗

NASA NTRS2005

Network device interface for digitally interfacing data channels to a controller via a network

The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface uses a specialized protocol for communicating across the network bus that uses a low-level instruction set and has low overhead for data communication.

Ellerbrock, Philip J.↗

NASA NTRS2004

Network device interface for digitally interfacing data channels to a controller via a network

Ellerbrock, Philip J.↗

NASA NTRS2006

Network device interface for digitally interfacing data channels to a controller via a network

The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted into digital signals and transmitted back to the controller. In one embodiment, the bus controller sends commands and data a defined bit rate, and the network device interface senses this bit rate and sends data back to the bus controller using the defined bit rate.

Ellerbrock, Philip J.↗

NASA NTRS2006

Network device interface for digitally interfacing data channels to a controller via a network

The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface is a state machine, such as an ASIC, that operates independent of a processor in communicating with the bus controller and data channels.

Ellerbrock, Philip J.↗

NASA NTRS2007

Network device interface for digitally interfacing data channels to a controller via a network

The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is converted into digital signals and transmitted to the controller. In some embodiments, network device interfaces associated with different data channels coordinate communications with the other interfaces based on either a transition in a command message sent by the bus controller or a synchronous clock signal.

Ellerbrock, Philip J.↗