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Two integrated circuits, BCD-to-decimal decoder and four-input NAND gate, form basic four, input XOR circuit. Multiple-input exclusive-OR logic is implemented by combining several basic elements. 16-input XOR gate is assembled from five NAND gates and five decoders. Same principle extended to handle more inputs.
During the final free flight (FF-5) of the shuttle's approach and landing tests, the vehicle experienced pilot-induced oscillations near touchdown. The light test data showed that pilot inputs to the hand controller reached peak-to-peak amplitudes of 20 deg at a frequency between 3 and 3.5 radians per second. The controller inputs were sufficient to exceed the priority rate limit set in the pitch axis. A nonlinear analytical study was conducted to investigate the combined effects of pilot input, rate limiting, and time delays. The frequency response of the total system is presented parametrically as a function of the three variables. In general, with no dead time, for controller inputs of 5 deg or less, the total system behaves in a linear fashion. For 10 deg of controller input, independent of the delay time, the elevon loop will be rate saturated above a frequency of 4 radians per second.
A reliability model is presented for planetary gear trains in which the ring gear is fixed, the Sun gear is the input, and the planet arm is the output. The input and output shafts are coaxial and the input and output torques are assumed to be coaxial with these shafts. Thrust and side loading are neglected. This type of gear train is commonly used in main rotor transmissions for helicopters and in other applications which require high reductions in speed. The reliability model is based on the Weibull distribution of the individual reliabilities of the transmission components. The transmission's basic dynamic capacity is defined as the input torque which may be applied for one million input rotations of the Sun gear. Load and life are related by a power law. The load life exponent and basic dynamic capacity are developed as functions of the component capacities.
A fluidic sensor providing a differential pressure signal proportional to the angular velocity of a rotary input is described. In one embodiment the sensor includes a fluid pump having an impeller coupled to a rotary input. A housing forming a constricting fluid flow chamber is connected to the fluid input of the pump. The housing is provided with a fluid flow restrictive input to the flow chamber and a port communicating with the interior of the flow chamber. The differential pressure signal measured across the flow restrictive input is relatively noise free and proportional to the square of the angular velocity of the impeller. In an alternative embodiment, the flow chamber has a generally cylindrical configuration and plates having flow restrictive apertures are disposed within the chamber downstream from the housing port. In this embodiment, the differential pressure signal is found to be approximately linear with the angular velocity of the impeller.
A technique for identifying nonlinear systems was introduced, beginning with a single input-single output system. Assuming the system is initially at rest, the first kernel (first convolution integral in the continuous case or first convolution sum in the discrete case) was calculated. A controllable and observable linear realization was then obtained in a particular canonical form. The actual nonlinear system was probed with an appropriate input (or inputs) and the output (or outputs) determined. For the linear system, the input was computed that produces the same output. In the difference between the inputs to the nonlinear and linear systems, basic information was found about the nonlinear system. There is an interesting class of nonlinear systems for which this type of identification scheme should prove to be accurate.
A digitized synchronous demodulator is constructed entirely of digital components including timing logic, an accumulator, and means to digitally filter the digital output signal. Indirectly, it accepts, at its input, periodic analog signals which are converted to digital signals by traditional analog-to-digital conversion techniques. Broadly, the input digital signals are summed to one of two registers within an accumulator, based on the phase of the input signal and medicated by timing logic. At the end of a predetermined number of cycles of the inputted periodic signals, the contents of the register that accumulated samples from the negative half cycle is subtracted from the accumulated samples from the positive half cycle. The resulting difference is an accurate measurement of the narrow band amplitude of the periodic input signal during the measurement period. This measurement will not include error sources encountered in prior art synchronous demodulators using analog techniques such as offsets, charge injection errors, temperature drift, switching transients, settling time, analog to digital converter missing code, and linearity errors.
The present parametric study of interplanetary shock propagation to 1 AU uses a two- and-one-half-dimensional MHD time-dependent model whose input conditions encompass initial shock velocity, driving-pulse duration, and pulse width at the near-sun position of 18 solar radii. It is found that, for input pulses with modest angular width and temporal duration, the propagation of the resulting interplanetary fast-forward shock waves primarily depends on the net input energy. While the properties of the reverse shocks are also a function of input energy, they depend on the specific values of the input pulse shock velocity, width, and duration.
MacPASCO, an interactive, graphic preprocessor for panel design is described. MacPASCO creates input for PASCO, an existing computer code for structural analysis and optimization of longitudinal stiffened composite panels. By using a graphical user interface, MacPASCO simplifies the specification of panel geometry and reduces user input errors, thus making the modeling and analysis of panel designs more efficient. The user draws the initial structural geometry on the computer screen, then uses a combination of graphic and text inputs to: refine the structural geometry, specify information required for analysis such as panel load conditions, and define design variables and constraints for minimum-mass optimization. Composite panel design is an ideal application because the graphical user interface can: serve as a visual aid, eliminate the tedious aspects of text-based input, and eliminate many sources of input errors.
The NASA Lewis heat pipe code was developed to predict the performance of heat pipes in the steady state. The code can be used as a design tool on a personal computer or with a suitable calling routine, as a subroutine for a mainframe radiator code. A variety of wick structures, including a user input option, can be used. Heat pipes with multiple evaporators, condensers, and adiabatic sections in series and with wick structures that differ among sections can be modeled. Several working fluids can be chosen, including potassium, sodium, and lithium, for which monomer-dimer equilibrium is considered. The code incorporates a vapor flow algorithm that treats compressibility and axially varying heat input. This code facilitates the determination of heat pipe operating temperatures and heat pipe limits that may be encountered at the specified heat input and environment temperature. Data are input to the computer through a user-interactive input subroutine. Output, such as liquid and vapor pressures and temperatures, is printed at equally spaced axial positions along the pipe as determined by the user.
A theory has been developed at the University of Virginia which explains the effects of including an ideal predictor in the forward loop of a linear error-sampled system. It has been shown that the presence of this ideal predictor tends to stabilize the class of systems considered. A prediction controller is merely a system which anticipates a signal or part of a signal before it actually occurs. It is understood that an exact prediction controller is physically unrealizable. However, in systems where the input tends to be repetitive or limited, (i.e., not random) near ideal prediction is possible. In order for the controller to act as a stability compensator, the predictor must be designed in a way that allows it to learn the expected error response of the system. In this way, an unstable system will become stable by including the predicted error in the system transfer function. Previous and current prediction controller include pattern recognition developments and fast-time simulation which are applicable to the analysis of linear sampled data type systems. The use of pattern recognition techniques, along with a template matching scheme, has been proposed as one realizable type of near-ideal prediction. Since many, if not most, systems are repeatedly subjected to similar inputs, it was proposed that an adaptive mechanism be used to 'learn' the correct predicted error response. Once the system has learned the response of all the expected inputs, it is necessary only to recognize the type of input with a template matching mechanism and then to use the correct predicted error to drive the system. Suggested here is an alternate approach to the realization of a near-ideal error prediction controller, one designed using Neural Networks. Neural Networks are good at recognizing patterns such as system responses, and the back-propagation architecture makes use of a template matching scheme. In using this type of error prediction, it is assumed that the system error responses be known for a particular input and modeled plant. These responses are used in the error prediction controller. An analysis was done on the general dynamic behavior that results from including a digital error predictor in a control loop and these were compared to those including the near-ideal Neural Network error predictor. This analysis was done for a second and third order system.
Passive technologies for the isolation of structures from vibrating sources are often inadequate. Using active control inputs applied directly to the source or designing a structure integrating the transducers required for the control inputs and the response measurements are ways of dealing with the problem. Results are given which were obtained on an experimental set up simulating this kind of problem where the form and the position of the transducers could be varied. By measuring the response of the structure integrated over a particular area the effects of particular types of modes could be taken into account to deal with specific types of input or limit particular modes of response more efficiently. Results of using different modes of vibration excitation of the receiving structure with and without control are presented for particular input frequencies. The problems of optimizing the control system to deal with multiple frequency inputs are discussed.
The principal objective is to provide a training procedure for a feed forward, back propagation neural network which greatly accelerates the training process. A set of orthogonal singular vectors are determined from the input matrix such that the standard deviations of the projections of the input vectors along these singular vectors, as a set, are substantially maximized, thus providing an optimal means of presenting the input data. Novelty exists in the method of extracting from the set of input data, a set of features which can serve to represent the input data in a simplified manner, thus greatly reducing the time/expense to training the system.
A higher-order neural network (HONN) can be designed to be invariant to changes in scale, translation, and inplane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Consequently, fewer training passes and a smaller training set are required to learn to distinguish between objects. The size of the input field is limited, however, because of the memory required for the large number of interconnections in a fully connected HONN. By coarse coding the input image, the input field size can be increased to allow the larger input scenes required for practical object recognition problems. We describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Our simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096 x 4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten passes through the training set. Furthermore, we empirically determine the limits of the coarse coding technique in the object recognition domain.
Self-tuning control algorithms are potential successors to manually tuned PID controllers traditionally used in process control applications. A very attractive design method for self-tuning controllers, which has been developed over recent years, is the long-range predictive control (LRPC). The success of LRPC is due to its effectiveness with plants of unknown order and dead-time which may be simultaneously nonminimum phase and unstable or have multiple lightly damped poles (as in the case of flexible structures or flexible robot arms). LRPC is a receding horizon strategy and can be, in general terms, summarized as follows. Using assumed long-range (or multi-step) cost function the optimal control law is found in terms of unknown parameters of the predictor model of the process, current input-output sequence, and future reference signal sequence. The common approach is to assume that the input-output process model is known or separately identified and then to find the parameters of the predictor model. Once these are known, the optimal control law determines control signal at the current time t which is applied at the process input and the whole procedure is repeated at the next time instant. Most of the recent research in this field is apparently centered around the LRPC formulation developed by Clarke et al., known as generalized predictive control (GPC). GPC uses ARIMAX/CARIMA model of the process in its input-output formulation. In this paper, the GPC formulation is used but the process predictor model is derived from the state space formulation of the ARIMAX model and is directly identified over the receding horizon, i.e., using current input-output sequence. The underlying technique in the design of identified predictive control (IPC) algorithm is the identification algorithm of observer/Kalman filter Markov parameters developed by Juang et al. at NASA Langley Research Center and successfully applied to identification of flexible structures.
A transient signal suppressor for use in a controls system which is adapted to respond to a change in a physical parameter whenever it crosses a predetermined threshold value in a selected direction of increasing or decreasing values with respect to the threshold value and is sustained for a selected discrete time interval is presented. The suppressor includes a sensor transducer for sensing the physical parameter and generating an electrical input signal whenever the sensed physical parameter crosses the threshold level in the selected direction. A manually operated switch is provided for adapting the suppressor to produce an output drive signal whenever the physical parameter crosses the threshold value in the selected direction of increasing or decreasing values. A time delay circuit is selectively adjustable for suppressing the transducer input signal for a preselected one of a plurality of available discrete suppression time and producing an output signal only if the input signal is sustained for a time greater than the selected suppression time. An electronic gate is coupled to receive the transducer input signal and the timer output signal and produce an output drive signal for energizing a control relay whenever the transducer input is a non-transient signal which is sustained beyond the selected time interval.
An antenna, which may be a search coil, is connected to an operational amplifier circuit which provides negative impedances, each of which is in the order of magnitude of the positive impedances which characterize the antenna. The antenna is connected to the inverting input of the operational amplifier; a resistor is connected between the inverting input and the output of the operational amplifier; a capacitor-resistor network, in parallel, is connected between the output and the noninverting input of the operational amplifier; and a resistor is connected from the noninverting input and the circuit common. While this circuit provides a negative resistance and a negative inductance, in series, which appear, looking into the noninverting input of the operational amplifier, in parallel with the antenna, these negative impedances appear in a series loop with the antenna positive impedances, so as to algebraically add. This circuit is tuned by varying the various circuit components so that the negative impedances are very close, but somewhat less, in magnitude, to the antenna impedances. The result is to increase the sensitivity of the antenna by lowering its effective impedance. This, in turn, increases the effective area of the antenna, which may be broadband.
NLEdit is a generic graphical user interface for the preprocessing of Fortran namelist input files. The interface consists of a menu system, a message window, a help system, and data entry forms. A form is generated for each namelist. The form has an input field for each namelist variable along with a one-line description of that variable. Detailed help information, default values, and minimum and maximum allowable values can all be displayed via menu picks. Inputs are processed through a scientific calculator program that allows complex equations to be used instead of simple numeric inputs. A custom user interface is generated simply by entering information about the namelist input variables into an ASCII file. There is no need to learn a new graphics system or programming language. NLEdit can be used as a stand-alone program or as part of a larger graphical user interface. Although NLEdit is intended for files using namelist format, it can be easily modified to handle other file formats.
In a joint research program of NASA Ames Research Center, ZF Luftfahrttechnik, the German Aerospace Research Establishment (DLR), and EUROCOPTER Deutschland, a wind tunnel test was performed to evaluate the effects of Individual Blade Control (IBC) on rotor noise. This test was conducted in the 40x80 ft wind tunnel at NASA Ames Research Center, utilizing a full scale MBB-BO 105 four-bladed rotor system. Three microphones were installed for determination of the radiated noise, two of them on a moveable traverse below the advancing blade side and one in a fixed location below the retreating side. Acoustic results are presented for flight conditions with Blade-Vortex-Interaction (BVI) noise radiation. High noise level reductions were measured for single harmonic control inputs. In addition to the single harmonic inputs, multi-harmonic inputs were evaluated by superimposing 2/rev to 6/rev harmonics. For the first time the efficiency of sharp wavelets (60 deg and 90 deg width) on acoustic noise were measured. In order to achieve an adequate wavelet shape at the blade tip, corrections were made to account for the blade torsional behavior. In parallel with the acoustic measurements, vibratory loads were measured during the BVI flight condition to correlate the effects of IBC on noise and vibrations. It is shown how noise levels and vibrations are affected by specific IBC control inputs. In addition, correlations are made between noise levels and acoustic time histories with IBC phase and amplitude variations. For one IBC input mode with high noise reducing efficiency, a sweep of the moveable microphone traverse below the advancing side shows the effect on BVI noise directivity.