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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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Report describes development of next-generation control system for variable-polarity plasma arc (VPPA) welding. When fully developed, system expected to incorporate advanced sensors and adaptive control of position of and current in welding torch.
The transformation of engine control systems from centralized to distributed architecture is both necessary and enabling for future aeropropulsion applications. The continued growth of adaptive control applications and the trend to smaller, light weight cores is a counter influence on the weight and volume of control system hardware. A distributed engine control system using high temperature electronics and open systems communications will reverse the growing trend of control system weight ratio to total engine weight and also be a major factor in decreasing overall cost of ownership for aeropropulsion systems. The implementation of distributed engine control is not without significant challenges. There are the needs for high temperature electronics, development of simple, robust communications, and power supply for the on-board electronics.
Analytical studies on the theoretical aspects of thrust vector control of large space vehicles were conducted. A system for attitude control of the space shuttle vehicle was developed. Major accomplishments of the project are: (1) investigation of a model-reference adaptive control scheme for controlling the space shuttle attitude and (2) determination of optimum placement of reaction control jet engines on space shuttles.
The focus of the work is to develop and perform a set of research projects using laboratory models of satellite robots. These devices use air cushion technology to simulate in two dimensions the drag-free, zero-g conditions of space. Five research areas are examined: cooperative manipulation on a fixed base; cooperative manipulation on a free-floating base; global navigation and control of a free-floating robot; an alternative transport mode call Locomotion Enhancement via Arm Push-Off (LEAP), and adaptive control of LEAP.
Over the past decade, the goal of reducing the fatal accident rate of large transport aircraft has resulted in research aimed at the problem of aircraft loss-of-control. Starting in 1999, the NASA Aviation Safety Program initiated research that included vehicle dynamics modeling, system health monitoring, and reconfigurable control systems focused on flight regimes beyond the normal flight envelope. In recent years, there has been an increased emphasis on adaptive control technologies for recovery from control upsets or failures including damage scenarios. As part of these efforts, NASA has developed the Airborne Subscale Transport Aircraft Research (AirSTAR) flight facility to allow flight research and validation, and system testing for flight regimes that are considered too risky for full-scale manned transport airplane testing. The AirSTAR facility utilizes dynamically-scaled vehicles that enable the application of subscale flight test results to full scale vehicles. This paper describes the modeling and simulation approach used for AirSTAR vehicles that supports the goals of efficient, low-cost and safe flight research in abnormal flight conditions. Modeling of aerodynamics, controls, and propulsion will be discussed as well as the application of simulation to flight control system development, test planning, risk mitigation, and flight research.
The NASA F/A-18 tail number (TN) 853 full-scale Integrated Resilient Aircraft Control (IRAC) testbed has been designed with a full array of capabilities in support of the Aviation Safety Program. Highlights of the system's capabilities include: 1) a quad-redundant research flight control system for safely interfacing controls experiments to the aircraft's control surfaces; 2) a dual-redundant airborne research test system for hosting multi-disciplinary state-of-the-art adaptive control experiments; 3) a robust reversionary configuration for recovery from unusual attitudes and configurations; 4) significant research instrumentation, particularly in the area of static loads; 5) extensive facilities for experiment simulation, data logging, real-time monitoring and post-flight analysis capabilities; and 6) significant growth capability in terms of interfaces and processing power.
This paper focuses on the application of expert systems technology to the automatic detection, verification and correction of anomalous rocket engine operations through interfacing with an intelligent adaptive control system. The design of a reliable and intelligent propulsion control and monitoring system is outlined which includes the architecture of an Integrated Expert System (IES) serving as the core component. The IES functions include automatic knowledge acquisition, integrated knowledge base, and fault diagnosis and prediction methodology. The results of fault analysis and diagnostic techniques are presented for an example fault in the SSME main combustion chamber injectors.
A decentralized suboptimal linear quadratic control design procedure which combines substructural synthesis, model reduction, decentralized control design, subcontroller synthesis, and controller reduction is proposed for the design of reduced-order controllers for flexible structures. The procedure starts with a definition of the continuum structure to be controlled. An evaluation model of finite dimension is obtained by the finite element method. Then, the finite element model is decomposed into several substructures by using a natural decomposition called substructuring decomposition. Each substructure, at this point, still has too large a dimension and must be reduced to a size that is Riccati-solvable. Model reduction of each substructure can be performed by using any existing model reduction method, e.g., modal truncation, balanced reduction, Krylov model reduction, or mixed-mode method. Then, based on the reduced substructure model, a subcontroller is designed by an LQ optimal control method for each substructure independently. After all subcontrollers are designed, a controller synthesis method called substructural controller synthesis is employed to synthesize all subcontrollers into a global controller. The assembling scheme used is the same as that employed for the structure matrices. Finally, a controller reduction scheme, called the equivalent impulse response energy controller (EIREC) reduction algorithm, is used to reduce the global controller to a reasonable size for implementation. The EIREC reduced controller preserves the impulse response energy of the full-order controller and has the property of matching low-frequency moments and low-frequency power moments. An advantage of the substructural controller synthesis method is that it relieves the computational burden associated with dimensionality. Besides that, the SCS design scheme is also a highly adaptable controller synthesis method for structures with varying configuration, or varying mass and stiffness properties.
This paper considers the use of neural networks (NN's) in controlling a nonlinear, stochastic system with unknown process equations. The NN is used to model the resulting unknown control law. The approach here is based on using the output error of the system to train the NN controller without the need to construct a separate model (NN or other type) for the unknown process dynamics. To implement such a direct adaptive control approach, it is required that connection weights in the NN be estimated while the system is being controlled. As a result of the feedback of the unknown process dynamics, however, it is not possible to determine the gradient of the loss function for use in standard (back-propagation-type) weight estimation algorithms. Therefore, this paper considers the use of a new stochastic approximation algorithm for this weight estimation, which is based on a 'simultaneous perturbation' gradient approximation that only requires the system output error. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations.
A computer system is reported that uses adaptive voting to tolerate failures and operates in a fail-operational, fail-safe manner. Each of four computers is individually connected to one of four external input/output (I/O) busses which interface with external subsystems. Each computer is connected to receive input data and commands from the other three computers and to furnish output data commands to the other three computers. An adaptive control apparatus including a voter-comparator-switch (VCS) is provided for each computer to receive signals from each of the computers and permits adaptive voting among the computers to permit the fail-operational, fail-safe operation.
In this paper an adaptive vibration reduction control (AVRC) design is described for a Stirling cryocooler combined with a passive balancer. The AVRC design was based on a mass-spring model of the cooler and balancer, and the AVRC algorithm described in this paper was based on an adaptive binary search. Results are shown comparing the baseline uncontrolled cooler with no balancer, the cooler with the balancer, and, finally, the cooler with the balancer and the AVRC. The comparison shows that it may be possible to meet stringent vibration reduction requirements without an active balancer.
Supervisory decision making and control behavior within a C(3) oriented, ground based weapon system is being studied. The program involves empirical investigation of the sequence of control strategies used during engagement of aircraft targets. An engagement is conceptually divided into several stages which include initial information processing activity, tracking, and ongoing adaptive control decisions. Following a brief description of model parameters, two experiments which served as initial investigation into the accuracy of assumptions regarding the importance of situation assessment in procedure selection are outlined. Preliminary analysis of the results upheld the validity of the assumptions regarding strategic information processing and cue-criterion relationship learning. These results indicate that this model structure should be useful in studies of supervisory decision behavior.
Modeling and control design for flexible manipulators, both from an experimental and analytical viewpoint, are described. From the application perspective, an ongoing effort within the laboratory environment at the Ohio State University, where experimentation on a single link flexible arm is underway is described. Several unique features of this study are described here. First, the manipulator arm is slewed by a direct drive dc motor and has a rigid counterbalance appendage. Current experimentation is from two viewpoints: (1) rigid body slewing and vibration control via actuation with the hub motor, and (2) vibration suppression through the use of structure-mounted proof-mass actuation at the tip. Such an application to manipulator control is of interest particularly in design of space-based telerobotic control systems, but has received little attention to date. From an analytical viewpoint, parameter estimation techniques within the closed-loop for self-tuning adaptive control approaches are discussed. Also introduced is a control approach based on output feedback and frequency weighting to counteract effects of spillover in reduced-order model design. A model of the flexible manipulator based on experimental measurements is evaluated for such estimation and control approaches.
The effect was investigated of the synthesis approach (single or multirate) on the machine requirements for a digital control system for the space shuttle boost vehicle. The study encompassed four major work areas: synthesis approach trades, machine requirements trades, design analysis requirements and multirate adaptive control techniques. The primary results are two multirate autopilot designs for the low Q and maximum Q flight conditions that exhibits equal or better performance than the analog and single rate system designs. Also, a preferred technique for analyzing and synthesizing multirate digital control systems is included.
This paper briefly describes the NASA F-8 Digital Fly-By-Wire (DFBW) program and Langley Research Center's role in investigating and promoting advanced control laws for possible flight experimentation. The primary purpose of the paper is to provide background material for papers describing some of the F-8 DFBW adaptive control law studies. The paper provides a brief description of the Phase II DFBW F-8 aircraft and its control system, discusses some of the advanced control law study objectives and guidelines, and provides some mathematical models which are useful in the control analysis problem.
A multilayered approach to the symbolic control of complex electromechanical assemblies, such as spaceborne thermal, power, and life subsystems, is discussed. An example of this approach is given in some recent tests of the Thermal Expert System (TEXSYS) in control of the Boeing Aerospace Thermal Bus System (BATBS), a prototype two-phase Space Station Freedom thermal bus. The BATBS hardware requires read-update-act cycles of under a minute, and it is subject to dynamic reconfiguration while operating. These performance requirements are addressed by layering model-based expert system software on a conventional numerical data acquisition and control system. TEXSYS test results demonstrate both nominal control and fault recovery actions with the BATBS. Dynamic modification of the symbolic model used in this approach is compared to that of a classical numerical adaptive controller.
To help develop technologies for growing edible biomass (food crops) in space, Kennedy Space Center partnered with Orbital Technologies Corporation (ORBITEC), of Madison, Wisconsin, through the Small Business Innovation Research (SBIR) program. One result of this research was the High Efficiency Lighting with Integrated Adaptive Control (HELIAC) system, components of which have been incorporated into a variety of agricultural greenhouse and consumer aquarium lighting features. The new lighting systems can be adapted to a specific plant species during a specific growth stage, allowing maximum efficiency in light absorption by all available photosynthetic tissues.
The paper presents a modified model reference adaptive control (M-MRAC) for multi-input multi-output nonlinear dynamical systems with time varying parametric uncertainties and bounded external disturbances. It uses a prediction model to rapidly generate adaptive estimates of the system's uncertainties with adjustable errors that converge to a small neighborhood of the origin. A sufficient condition is derived to specify the region of attraction in the space of initialization errors, design parameters and external commends. The approach is applied to thrust controlled multi-rotor air vehicles operating in an urban environment. It is shown that the designed controller can provide a good tracking of a given trajectory in the unknown urban wind field, assuming that the maximum thrust generated by the rotors is known. The performance of the algorithms are demonstrated in simulations.