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Quantitative descriptions of human control properties are formulated that take into account the dynamics of muscle actuation units, neuromuscular sensor units, and sensory feedback mechanics. Detailed dynamic measurements on neurologically disordered patients are correlated with clinical manifestations of various neurological syndromes to quantify operator behavior.
The technology advancement topics described are a part of the supporting research and technology (SRT) program conducted to support the major Stirling engine development program. This support focuses on developing alternatives or backups to the engine development in critical areas. These areas are materials, seals control, combustors and system analysis. Specific objectives and planned milestone schedules for future activities as now envisioned are described. These planned SRT activities are related to the timeline of the engine development program that they must support.
The basic parameters relevant to the design of network timing systems describe the random and systematic time departures of the system elements, i.e., master (or reference) clocks, transmission links, and other clocks controlled over the links. The quantitative relations between these parameters were established and illustrated by means of numerical examples based on available measured data. The examples were limited to a simple PLL control system but the analysis can eventually be applied to more sophisticated systems at the cost of increased computational effort.
The development of Riccati iteration, a tool for the design and analysis of linear control systems is examined. First, Riccati iteration is applied to the problem of pole placement and order reduction in two-time scale control systems. Order reduction, yielding a good approximation to the original system, is demonstrated using a 16th order linear model of a turbofan engine. Next, a numerical method for solving the Riccati equation is presented and demonstrated for a set of eighth order random examples. A literature review of robust controller design methods follows which includes a number of methods for reducing the trajectory and performance index sensitivity in linear regulators. Lastly, robust controller design for large parameter variations is discussed.
Military aircraft research opportunities for the future are briefly surveyed. Aircraft control theory, design analysis, systems integration and flight characteristics are discussed.
This paper summarizes several related research projects supported by NASA which seek to apply computer models to space medicine and physiology. These efforts span a wide range of activities, including mathematical models used for computer simulations of physiological control systems; power spectral analysis of physiological signals; pattern recognition models for detection of disease processes; and computer-aided diagnosis programs.
The Naval Postgraduate School in the AE 4871 Advanced Spacecraft Design course designed a communications satellite (HILACS) that will provide a continuous UHF communications link between stations located north of the region covered by geosynchronous communications satellites. The communications payload will operate only for that portion of the orbit necessary to provide specific coverage. The satellite orbit is elliptic with perigee at 1204 km in the Southern Hemisphere and an apogee at 14,930 km with 63.4 degrees inclination. Analysis and design of each of the subsystems was done to the extent possible within the constraints of an eleven week quarter and the design and analysis tools available. Work was completed in orbital analysis, the reaction control system, attitude control subsystem, electric power subsystem, telemetry, tracking, and control, thermal control subsystem, and the structures subsystem. The design team consisted of 12 students. Additional support was provided by the Jet Propulsion Laboratory and the Naval Research Laboratory.
In the design and analysis of robust control systems for uncertain plants, representing the system transfer matrix in the form of what has come to be termed an M-delta model has become widely accepted and applied in the robust control literature. The M represents a transfer function matrix M(s) of the nominal closed loop system, and the delta represents an uncertainty matrix acting on M(s). The nominal closed loop system M(s) results from closing the feedback control system, K(s), around a nominal plant interconnection structure P(s). The uncertainty can arise from various sources, such as structured uncertainty from parameter variations or multiple unsaturated uncertainties from unmodeled dynamics and other neglected phenomena. In general, delta is a block diagonal matrix, but for real parameter variations delta is a diagonal matrix of real elements. Conceptually, the M-delta structure can always be formed for any linear interconnection of inputs, outputs, transfer functions, parameter variations, and perturbations. However, very little of the currently available literature addresses computational methods for obtaining this structure, and none of this literature addresses a general methodology for obtaining a minimal M-delta model for a wide class of uncertainty, where the term minimal refers to the dimension of the delta matrix. Since having a minimally dimensioned delta matrix would improve the efficiency of structured singular value (or multivariable stability margin) computations, a method of obtaining a minimal M-delta would be useful. Hence, a method of obtaining the interconnection system P(s) is required. A generalized procedure for obtaining a minimal P-delta structure for systems with real parameter variations is presented. Using this model, the minimal M-delta model can then be easily obtained by closing the feedback loop. The procedure involves representing the system in a cascade-form state-space realization, determining the minimal uncertainty matrix, delta, and constructing the state-space representation of P(s). Three examples are presented to illustrate the procedure.
A review is given of the conditions and preparations for studying the performance of a robotic laboratory manipulator under microgravity conditions and by means of dynamic simulations. The robotic arm is fitted with accelerometers, incorporated into a materials-transfer workcell, and flown on a microgravity simulator. A software package based on the Lagrangian form of the equations of motion is used for dynamic analysis and control-systems development of the arm.
The authors present an analysis of why control systems are ineffective in compensating for acceleration disturbances due to Coulomb friction. Linear arguments indicate that the effects of Coulomb friction on a body are most difficult to reject when the control actuator is separated from the body of compliance. The linear arguments were illustrated in a nonlinear simulation of optimal linear tracking control in the presence of nonlinear friction. The results of endpoint acceleration measurements for four robot designs are presented and are compared with simulation and to equivalent measurements on a human. It is concluded that Coulomb friction in common bearings and transmission induces unacceptable levels of endpoint acceleration, that these accelerations cannot be adequately attenuated by control, and that robots for microgravity work will require special design considerations for inherently low friction.
This paper describes the initial configuration of the NASA Marshall Space Flight Center (MSFC) Controls/Structures Interaction Advanced Development Facility (CSI ADF), which is a ground test facility (GTF) for the proposed Controls, Astrophysics, and Structures Experiment in Space (CASES). The laboratory has been developed for the purpose of implementing, testing, and evaluating CSI modeling, control system design, failure analysis, and system identification techniques on a representative large space structure. The facility has been configured to represent as closely as possible an actual flight article.
The quantification of model uncertainty is becoming increasingly important as robust control is an important tool for control system design and analysis. This paper presents an algorithm that effectively characterizes the model uncertainty in terms of parametric and nonparametric uncertainties. The algorithm utilizes the frequency domain model error which is estimated from the spectra of output error and input data. The parametric uncertainty is represented as an interval transfer function while the nonparametric uncertainty is bounded by a designed error bound transfer function. Both discrete and continuous systems are discussed in this paper. The algorithm is applied to the Mini-Mast example, and the detail analysis is given.
R and D projects on electricity generating wind turbines were conducted at the NASA Lewis Research Center from 1973 to 1988. Most projects were sponsored by the U.S. Department of Energy (DOE), a major element of its Federal Wind Energy Program. Another large wind turbine project was by the Bureau of Reclamation of the U.S. Department of Interior (DOI). From 1988 to 1995, NASA wind energy activities have been directed toward the transfer of technology to commercial and academic organizations. As part of these technology transfer activities, previously unpublished manuscripts have been assembled and presented here to share the wind turbine research results with the wind energy community. A variety of wind turbine technology topics are discussed: Wind and wake models; Airfoil properties; Structural analysis and testing; Control systems; Variable speed generators; and acoustic noise. Experimental and theoretical results are discussed.
Simulation of high speed propulsion systems may be divided into two categories, nonlinear and linear. The nonlinear simulations are usually based on multidimensional computational fluid dynamics (CFD) methodologies and tend to provide high resolution results that show the fine detail of the flow. Consequently, these simulations are large, numerically intensive, and run much slower than real-time. ne linear simulations are usually based on large lumping techniques that are linearized about a steady-state operating condition. These simplistic models often run at or near real-time but do not always capture the detailed dynamics of the plant. Under a grant sponsored by the NASA Lewis Research Center, Cleveland, Ohio, a new method has been developed that can be used to generate improved linear models for control design from multidimensional steady-state CFD results. This CFD-based linear modeling technique provides a small perturbation model that can be used for control applications and real-time simulations. It is important to note the utility of the modeling procedure; all that is needed to obtain a linear model of the propulsion system is the geometry and steady-state operating conditions from a multidimensional CFD simulation or experiment. This research represents a beginning step in establishing a bridge between the controls discipline and the CFD discipline so that the control engineer is able to effectively use multidimensional CFD results in control system design and analysis.
Nexus was a technology demonstrator project that was designed to bridge from the Hubble Space Telescope (HST) to its successor, the Next Generation Space Telescope (NGST). This paper focuses on the process used in designing the ground segment for Nexus and the lessons learned in its development. Ground station cost drivers were: (1) Contact time, (2) Cost of transporting data between the ground stations and control center, and (3) Cost savings via ground automation. We found that reducing the communication requirement in just the first 100 days could have reduced the total ground station cost by 40%. Contact time cost dwarfed the cost trade between automation development and off-shift operations personnel. Real-time Telemetry and Control (T&C) system analysis was divided into: (1) Potential reuse of the Nexus real-time (T&C) system for NGST, (2) Feasibility of using a 'Finite State-Based Modeling' product, and (3) Selecting a Commercial Off the Shelf (COTS) versus Government Off The Shelf (GOTS) products. We found that each of the products evaluated in detail (ASIST, EPOCH 2000, and ITOS) could adequately support basic mission requirements. Lessons learned were: (1) Include operations at the beginning of the mission, and (2) Develop an operations concept as soon as possible.
Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.
The "NanoSail-D" mission is currently scheduled for launch onboard a Falcon-1 Launch Vehicle in the early June 2008 timeframe. The NanoSail-D spacecraft will consist of a solar sail subsystem stowed in a 2U volume and a 1U spacecraft bus, provided by Ames Research Center. The primary objectives of the NanoSail-D technology demonstration mission are to fabricate, stow and deploy on-orbit a solar sail and perform a de-orbit maneuver to demonstrate a potential orbital debris mitigation technology. The NanoSail-D mission is being developed through a collaborative effort between the NASA Marshall Space Flight Center and the NASA Ames Research Center Small Spacecraft Office. Details of the NanoSail-D system will be presented, including: 1) design details of the solar sail reflective membrane quadrants, gossamer booms, deployment system and passive attitude control system, 2) design analysis results including structural, thermal, environmental, orbital debris and safety, and 3) test results including deployment, ascent venting, launch vibration and PPOD integration verification.
Version 3.31 of Modeling and Analysis for Controlled Optical Systems (MACOS) has been released. MACOS is an easy-to-use computer program for modeling and analyzing the behaviors of a variety of optical systems, including systems that have large, segmented apertures and are aligned with the technology of wavefront sensing and control. Two previous versions were described in "Improved Software for Modeling Controlled Optical Systems" (NPO-19841) NASA Tech Briefs, Vol. 21, No. 12 (December 1997), page 42 and "Optics Program Modified for Multithreaded Parallel Computing" (NPO-40572) NASA Tech Briefs, Vol. 30, No. 1 (January 2006) page 13a. The present version incorporates the following enhancements over prior versions: a) A powerful system-optimization facility includes algorithms for linear, nonlinear, unconstrained, and constrained optimization of optical systems under a variety of settings. b) There is now enhanced capability to perturb optical components individually and on subsystem levels, and to optimize system performance by adjusting selected individual components as well as subsystems. c) Capabilities for modeling a variety of new optical aperture types have been added. d) Effects of multilayer thin-film coats on optical surfaces can now be taken into account when tracing polarized rays. e) Major software-engineering work was performed to make MACOS more reliable, flexible, and manageable for purposes of maintenance and further development.