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A digital logic controller provides instruction execution times on the order of 50 nanoseconds and employing read-only memory outputting instructions to a pipeline register, a portion of each instruction providing a status-select control signal and address signals for controlling selection of the next instruction from the read-only memory.
The United States space transportation plans are discussed with emphasis on returning the Titan and the Shuttle to operational status. In particular, Shuttle enhancements via advanced solid rocket boosters and replacement of solid rocket boosters by liquid rocket boosters are examined. The Shuttle-C vehicle is then discussed as the next logical step that could provide a heavy launch capability in the early 1990s. The Shuttle-C will provide assured and flexible access to space for large Centaur-class payloads, for Space Station assembly, and for planetary missions.
Adaptive, self-organizing concurrent systems (ASOCS) that combine self-organization with massive parallelism for such applications as adaptive logic devices, robotics, process control, and system malfunction management, are presently discussed. In ASOCS, an adaptive network composed of many simple computing elements operating in combinational and asynchronous fashion is used and problems are specified by presenting if-then rules to the system in the form of Boolean conjunctions. During data processing, which is a different operational phase from adaptation, the network acts as a parallel hardware circuit.
The author presents a condensed exposition of some basic ideas underlying fuzzy logic and describes some representative applications. The discussion covers basic principles; meaning representation and inference; basic rules of inference; and the linguistic variable and its application to fuzzy control.
This paper describes an abstract reference model of cooperating rule-based systems (CRBSs). The model is intended to assist in the planning, specification, development, and verification of space information systems involving multiple, distributed, communicating rule-based systems. The functions performed in ground control systems are described, and five types of rule-based systems that could be used in such centers are identified. The interactions between rule-based systems are examined, and the unique requirements of such systems are addressed. A four-layer logical model is discussed which provides a framework for discussing solutions to unique CRBS requirements. The elements and operations at each layer are described.
Voltage regulator bypassed to supply starting current. Starting or "pullup" circuit supplies large inrush of current required by erasable programmable logic device (EPLD) while being turned on. Operates only during such intervals of high demand for current and has little effect any other time. Performs needed bypass, acting as current-dependent shunt connecting battery or other source of power more nearly directly to EPLD. Input capacitor of regulator removed when starting circuit installed, reducing probability of damage to transistor in event of short circuit in or across load.
A primary flight display format was integrated with the flight guidance and control system logic in support of various flight tests conducted with the NASA Transport Systems Research Vehicle B-737-100 airplane. The functional operation of the flight guidance mode control panel and the corresponding primary flight display formats are presented.
Computations required for temporal reasoning can be prohibitively expensive if fully general representations are used. Overly simple representations, such as totally ordered sequence of time points, are inadequate for use in a nonlinear task planning system. A middle ground is identified which is general enough to support a capable nonlinear task planner, but specialized enough that the system can support online task planning in real time. A Temporal Logic System (TLS) was developed during the Intelligent Task Automation (ITA) project to support robotic task planning. TLS is also used within the ITA system to support plan execution, monitoring, and exception handling.
A model is developed to assist in the planning, specification, development, and verification of space information systems involving distributed rule-based systems. The model is based on an analysis of possible uses of rule-based systems in control centers. This analysis is summarized as a data-flow model for a hypothetical intelligent control center. From this data-flow model, the logical model of cooperating rule-based systems is extracted. This model consists of four layers of increasing capability: (1) communicating agents, (2) belief-sharing knowledge sources, (3) goal-sharing interest areas, and (4) task-sharing job roles.
A simulation study is described which predicts the susceptibility of an advanced control system to electrical transients resulting in logic errors, latched errors, error propagation, and digital upset. The system is based on a custom-designed microprocessor and it incorporates fault-tolerant techniques. The system under test and the method to perform the transient injection experiment are described. Results for 2100 transient injections are analyzed and classified according to charge level, type of error, and location of injection.
The following topics are covered in viewgraph form: (1) the Penelope Project; (2) the logic of an experimental automatic guidance control system for a 737; (3) Larch/Ada specification; (4) some failures of informal description; (5) description of mode changes caused by switches; (6) intuitive description of window status (chosen vs. current); (7) design of the code; (8) and specifying the code.
Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.
The results of the initial testing of the Generalized Gate Level Logic Simulator (GGLOSS) are discussed. The simulator is a special purpose fault simulator designed to assist in the analysis of the effects of random hardware failures on fault tolerant digital computer systems. The testing of the simulator covers two main areas. First, the simulation results are compared with data obtained by monitoring the behavior of hardware. The circuit used for these comparisons is an incomplete microprocessor design based upon the MIL-STD-1750A Instruction Set Architecture. In the second area of testing, current simulation results are compared with experimental data obtained using precursors of the current tool. In each case, a portion of the earlier experiment is confirmed. The new results are then viewed from a different perspective in order to evaluate the usefulness of this simulation strategy.
The type of plant considered is one that can be modeled by a nondeterministic finite-state machine P. The regulator is a deterministic finite state machine R. The closed-loop system is formed by connecting P and R in a regulator configuration. Formulas in a propositional temporal language are used to describe the behavior of the closed-loop system. It is shown that there is a mechanical procedure which, for a given P and R, and a temporal formula Psi, will determine in a finite number of steps whether or not Psi must be true. This 'decidability' result could be proven using other known results on temporal logic. The proof given here shows that the behavior of the closed-loop system may safely be assumed to be ultimately periodic. The results are illustrated on two discrete-event system examples.
The development philosophy of the fuzzy logic controller is explained, details of the rules and membership functions used are given, and the early results of testing of the control system for a representative range of scenarios are reported. The fuzzy attitude controller was found capable of performing all rotational maneuvers, including rate hold and rate maneuvers. It handles all orbital perturbations very efficiently and is very responsive in correcting errors.
This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.
The logic flowgraph methodology (LFM), a method for modeling hardware in terms of its process parameters, has been extended to form an analytical tool for the analysis of integrated (hardware/software) embedded systems. In the software part of a given embedded system model, timing and the control flow among different software components are modeled by augmenting LFM with modified Petrinet structures. The objective of the use of such an augmented LFM model is to uncover possible errors and the potential for unanticipated software/hardware interactions. This is done by backtracking through the augmented LFM mode according to established procedures which allow the semiautomated construction of fault trees for any chosen state of the embedded system (top event). These fault trees, in turn, produce the possible combinations of lower-level states (events) that may lead to the top event.
A concept utilizing fuzzy theory has been developed for a camera tracking system to provide support for proximity operations and traffic management around the Space Station Freedom. Fuzzy sets and fuzzy logic based reasoning are used in a control system which utilizes images from a camera and generates required pan and tilt commands to track and maintain a moving target in the camera's field of view. This control system can be implemented on a fuzzy chip to provide an intelligent sensor for autonomous operations. Capabilities of the control system can be expanded to include approach, handover to other sensors, caution and warning messages.