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

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

A self-learning rule base for command following in dynamical systems

In this paper, a self-learning Rule Base for command following in dynamical systems is presented. The learning is accomplished though reinforcement learning using an associative memory called SAM. The main advantage of SAM is that it is a function approximator with explicit storage of training samples. A learning algorithm patterned after the dynamic programming is proposed. Two artificially created, unstable dynamical systems are used for testing, and the Rule Base was used to generate a feedback control to improve the command following ability of the otherwise uncontrolled systems. The numerical results are very encouraging. The controlled systems exhibit a more stable behavior and a better capability to follow reference commands. The rules resulting from the reinforcement learning are explicitly stored and they can be modified or augmented by human experts. Due to overlapping storage scheme of SAM, the stored rules are similar to fuzzy rules

Investigation of Natural and Man-Made Radiation Effects on Crews on Long Duration Space Missions

Over the past several years, NASA has studied a variety of mission scenarios designed to establish a permanent human presence on the surface of Mars. Nuclear electric propulsion (NEP) is one of the possible elements in this program. During the initial stages of vehicle design work, careful consideration must be given to not only the shielding requirements of natural space radiation, but to the shielding and configuration requirements of the on-board reactors. In this work, the radiation transport code MCNP has been used to make initial estimates of crew exposures to reactor radiation fields for a specific manned NEP vehicle design. In this design, three 25 MW(sub th), scaled SP-100-class reactors are shielded by three identical shields. Each shield has layers of beryllium, tungsten, and lithium hydride between the reactor and the crew compartment. Separate calculations are made of both the exiting neutron and gamma fluxes from the reactors during beginning-of-life, full-power operation. This data is then used as the source terms for particle transport in MCNP. The total gamma and neutron fluxes exiting the reactor shields are recorded and separate transport calculations are then performed for a 10 g/sq cm crew compartment aluminum thickness. Estimates of crew exposures have been assessed for various thicknesses of the shield tungsten and lithium hydride layers. A minimal tungsten thickness of 20 cm is required to shield the reactor photons below the 0.05 Sv/y man-made radiation limit. In addition to a 20-cm thick tungsten layer, a 40-cm thick lithium hydride layer is required to shield the reactor neutrons below the annual limit. If the tungsten layer is 30-cm thick, the lithium hydride layer should be at least 30-cm thick. These estimates do not take into account the photons generated by neutron interactions inside the shield because the MCNP neutron cross sections did not allow reliable estimates of photon production in these materials. These results, along with natural space radiation shielding estimates calculated by NASA Langley Research Center, have been used to provide preliminary input data into a new Macintosh-based software tool. A skeletal version of this tool being developed will allow rapid radiation exposure and risk analyses to be performed on a variety of Lunar and Mars missions utilizing nuclear-powered vehicles

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

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

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

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