Engineering topics

Parlos, Alexander G.

Publications and source records attributed to Parlos, Alexander G..

17 records

NASA NTRS2005

Condition Assessment and End-of-Life Prediction System for Electric Machines and Their Loads

An end-of-life prediction system developed for electric machines and their loads could be used in integrated vehicle health monitoring at NASA and in other government agencies. This system will provide on-line, real-time condition assessment and end-of-life prediction of electric machines (e.g., motors, generators) and/or their loads of mechanically coupled machinery (e.g., pumps, fans, compressors, turbines, conveyor belts, magnetic levitation trains, and others). In long-duration space flight, the ability to predict the lifetime of machinery could spell the difference between mission success or failure. Therefore, the system described here may be of inestimable value to the U.S. space program. The system will provide continuous monitoring for on-line condition assessment and end-of-life prediction as opposed to the current off-line diagnoses.

Parlos, Alexander G.↗

NASA NTRS2005

Adaptive Filtering Using Recurrent Neural Networks

A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

Parlos, Alexander G.↗

NASA NTRS2004

Algorithm for Training a Recurrent Multilayer Perceptron

An improved algorithm has been devised for training a recurrent multilayer perceptron (RMLP) for optimal performance in predicting the behavior of a complex, dynamic, and noisy system multiple time steps into the future. [An RMLP is a computational neural network with self-feedback and cross-talk (both delayed by one time step) among neurons in hidden layers]. Like other neural-network-training algorithms, this algorithm adjusts network biases and synaptic-connection weights according to a gradient-descent rule. The distinguishing feature of this algorithm is a combination of global feedback (the use of predictions as well as the current output value in computing the gradient at each time step) and recursiveness. The recursive aspect of the algorithm lies in the inclusion of the gradient of predictions at each time step with respect to the predictions at the preceding time step; this recursion enables the RMLP to learn the dynamics. It has been conjectured that carrying the recursion to even earlier time steps would enable the RMLP to represent a noisier, more complex system.

Parlos, Alexander G.↗

NASA NTRS1998

Investigation and Feasibility Assessment of TOPAZ-2 Derivations for Space Power Applications

The ability to provide continuous power at significant levels is of utmost importance for many space missions, from simple satellite operations to manned Mars missions. One of the main problems faced in delivering solar or chemical space power in the tens of kW range, is the increasingly massive nature of the power source and the costs associated with its launch, operation and maintenance. A national program had been initiated to study the feasibility of using certain advanced technologies in developing an efficient lightweight space power source. The starting point for these studies has been the Russian TOPAZ-2 space reactor system, with the ultimate goal to aid in the development of a TOPAZ-2 derivative which will be ready for flight by the year 2000. The main objective of this project has been to perform feasibility assessment and trade studies which would allow the development of an advanced space nuclear power system based on the in-core thermionic fuel element technology currently used in the Russian TOPAZ-2 reactor. Two of the important considerations in developing the concept are: (1) compliance of the current TOPAZ-2 and of any advanced designs with U.S. nuclear safety expectations, and (2) compliance of the design with the seven years lifetime requirement. The project was composed of two major phases. The initial phase of the project has concentrated on understanding the TOPAZ-2 thermionic reactor in sufficient detail to allow several follow-on tasks. The primary interest during this first phase has been given on identifying the potential of the TOPAZ-2 design for further improvements. The second phase of the project has focused on the feasibility of a TOPAZ-2 system capable of delivering 30-50 kWe. Towards the elimination of single-point failures in the load voltage regulation system an active voltage regulator has been designed to be used in conjunction with the available shunt load voltage regulator. The possible use of a dual-loop, model-based adaptive control system for load-following in the TOPAZ-2 has also been investigated. The objective of this fault-tolerant, autonomous control system is to deliver the demanded electric power at the desired voltage level, by appropriately manipulating the neutron power through the control drums. As a result, sufficient thermal power is produced to meet the required demand in the presence of dynamically changing system operating conditions and potential sensor failures. The designed controller is proposed for use in combination with the currently available shunt regulators, or as a back-up controller when other means of power system control, including some of the sensors, fail.

Parlos, Alexander G.↗

NASA NTRS1995

Neural node network and model, and method of teaching same

The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing.

Parlos, Alexander G.↗

NASA NTRS1992

Adaptive attitude control and momentum management for large-angle spacecraft maneuvers

The fully coupled equations of motion are systematically linearized around an equilibrium point of a gravity gradient stabilized spacecraft, controlled by momentum exchange devices. These equations are then used for attitude control system design of an early Space Station Freedom flight configuration, demonstrating the errors caused by the improper approximation of the spacecraft dynamics. A full state feedback controller, incorporating gain-scheduled adaptation of the attitude gains, is developed for use during spacecraft on-orbit assembly or operations characterized by significant mass properties variations. The feasibility of the gain adaptation is demonstrated via a Space Station Freedom assembly sequence case study. The attitude controller stability robustness and transient performance during gain adaptation appear satisfactory.

Parlos, Alexander G.↗

NASA NTRS1992

Adaptive attitude stability and control for Space Station/Orbiter berthing operations

The fully coupled, systematically linearized equations of motion are used for attitude control system design of an early Space Station Freedom flight configuration. These equations are valid around any estimated spacecraft equilibrium point. A full state feedback controller, incorporating gain scheduled adaptation of the attitude gains, is developed for use during spacecraft on-orbit operations characterized by significant mass properties variations. Feasibility of the gain adaptation is demonstrated via a Space Station Freedom/Orbiter berthing operation scenario. The attitude controller transient performance during gain adaptation appear satisfactory.

Parlos, Alexander G.↗

NASA NTRS1992

Fault-tolerant adaptive control for load-following in static space nuclear power systems

The possible use of a dual-loop model-based adaptive control system for load following in static space nuclear power systems is investigated. The proposed approach has thus far been applied only to a thermoelectric space nuclear power system but is equally applicable to other static space nuclear power systems such as thermionic systems.

Parlos, Alexander G.↗

NASA NTRS1991

Parameter estimation in space systems using recurrent neural networks

The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

Parlos, Alexander G.↗

NASA NTRS1991

Core reactivity estimation in space reactors using recurrent dynamic networks

A recurrent multilayer perceptron network topology is used in the identification of nonlinear dynamic systems from only the input/output measurements. The identification is performed in the discrete time domain, with the learning algorithm being a modified form of the back propagation (BP) rule. The recurrent dynamic network (RDN) developed is applied for the total core reactivity prediction of a spacecraft reactor from only neutronic power level measurements. Results indicate that the RDN can reproduce the nonlinear response of the reactor while keeping the number of nodes roughly equal to the relative order of the system. As accuracy requirements are increased, the number of required nodes also increases, however, the order of the RDN necessary to obtain such results is still in the same order of magnitude as the order of the mathematical model of the system. It is believed that use of the recurrent MLP structure with a variety of different learning algorithms may prove useful in utilizing artificial neural networks for recognition, classification, and prediction of dynamic systems.

Parlos, Alexander G.↗

NASA NTRS1990

Active vibration control techniques for flexible space structures

Two proposed control system design techniques for active vibration control in flexible space structures are detailed. Control issues relevant only to flexible-body dynamics are addressed, whereas no attempt was made to integrate the flexible and rigid-body spacecraft dynamics. Both of the proposed approaches revealed encouraging results; however, further investigation of the interaction of the flexible and rigid-body dynamics is warranted.

Parlos, Alexander G.↗

NASA NTRS1990

Bounding the states of systems with unknown-but-bounded disturbances

Control systems with hard constraints on certain variables are characterized, in an analytical review of recent investigations based on an unknown-but-bounded description of magnitude uncertainties. Hard-constraint problems typically arise in the design of controllers for potentially hazardous systems such as nuclear power plants. Consideration is given to norm bounds based on matrix measures, extensions of Schweppe's (1968) ellipsoid bounds, and set-theoretic regulator design. The performance of these approaches is evaluated by means of numerical simulations involving a third-order nonlinear steam-boiler model; the results are presented in graphs, and it is found that ellipsoid bounds are tightest in the general case, but that box bounds are even tighter for linear systems with Metzler system matrices.

Tsai, Wei K.↗

NASA NTRS1990

Attitude control/momentum management and payload pointing in advanced space vehicles

The design and evaluation of an attitude control/momentum management system for highly asymmetric spacecraft configurations are presented. The preliminary development and application of a nonlinear control system design methodology for tracking control of uncertain systems, such as spacecraft payload pointing systems are also presented. Control issues relevant to both linear and nonlinear rigid-body spacecraft dynamics are addressed, whereas any structural flexibilities are not taken into consideration. Results from the first task indicate that certain commonly used simplifications in the equations of motions result in unstable attitude control systems, when used for highly asymmetric spacecraft configurations. An approach is suggested circumventing this problem. Additionally, even though preliminary results from the second task are encouraging, the proposed nonlinear control system design method requires further investigation prior to its application and use as an effective payload pointing system design technique.

Parlos, Alexander G.↗

NASA NTRS1990

Attitude control/momentum management of the Space Station Freedom for large angle torque-equilibrium-attitude configurations

An attitude-control and momentum-management (ACMM) system for the Space Station in a large-angle torque-equilibrium-attitude (TEA) configuration is developed analytically and demonstrated by means of numerical simulations. The equations of motion for a rigid-body Space Station model are outlined; linearized equations for an arbitrary TEA (resulting from misalignment of control and body axes) are derived; the general requirements for an ACMM are summarized; and a pole-placement linear-quadratic regulator solution based on scheduled gains is proposed. Results are presented in graphs for (1) simulations based on configuration MB3 (showing the importance of accounting for the cross-inertia terms in the TEA estimate) and (2) simulations of a stepwise change from configuration MB3 to the 'assembly complete' stage over 130 orbits (indicating that the present ACCM scheme maintains sufficient control over slowly varying Space Station dynamics).

Parlos, Alexander G.↗

NASA NTRS1990

A nonlinear optimization approach for disturbance rejection in flexible space structures

In this paper the design of an active control law for the rejection of persistent disturbances in large space structures is presented. The control system design approach is based on a deterministic model of the disturbances, with a model-based-compensator (MBC) structure, optimizing the magnitude of the disturbance that the structure can tolerate without violating certain predetermined constraints. In addition to closed-loop stability, the explicit treatment of state, control and control rate constraints, such as structural displacement, control actuator effort, and compensator time guarantees that the final design will exhibit desired performance characteristics. The technique is applied for the vibration damping of a simple two bay truss structure which is subjected to persistent disturbances, such as shuttle docking. Preliminary results indicate that the proposed control system can reject considerable persistent disturbances by utilizing most of the available control, while limiting the structural displacements to within desired tolerances. Further work, however, for incorporating additional design criteria, such as compensator robustness to be traded-off against performance specifications, is warranted.

Parlos, Alexander G.↗

NASA NTRS1990

Active rejection of persistent disturbances in flexible space structures

A dynamic compensator for active rejection of persistent disturbances in flexible space structures is designed on the principle of the H(infinity)-optimization of the sensitivity transfer function matrix. A general state space solution is formulated to the multiinput multioutput H(infinity)-optimal control problem, allowing the use of the H(infinity)-optimal synthesis algorithm for the state-space models of space structures that result from model order reduction. Disturbances encountered in flexible space structures, such as shuttle docking, are investigated using the high-mode and the reduced-order models of a cantilevered two-bay truss, demonstrating the applicability of the H(infinity)-optimal approach.

Hwang, Cheng-Neng↗

NASA NTRS1990

Active disturbance rejection in large flexible space structures

The design of an active control law for the rejection of persistent disturbances, in large space structures is presented. The control system design approach is based on a deterministic model of the disturbances and it optimizes the magnitude of the disturbance that the structure can tolerate without violating certain predetermined constraints. In addition to closed-loop stability, the explicit treatment of state, control, and control rate constraints, such as structural displacement and control actuator effort, guarantees that the final design will exhibit desired performance characteristics. The technique is applied to a simple two-bay truss structure, and its response is compared with that obtained using a linear-quadratic-Gaussian/loop-transfer-recovery (LQG/LTR) compensator. Preliminary results indicate that the proposed control system can reject persistent disturbances of greater magnitude by utilizing most of the available control, while limiting the structural displacements to within desired tolerances.

Parlos, Alexander G.↗