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Limited-amplitude, controlled-decay process improves the reliability of microwelding. The system consists in building a capacitor-discharge welder for control of the shape of the weld pulse. Standard welders may be modified.
This paper describes the design of a model-based autonomous planning system that will enable robots to manage a space-borne chemical laboratory. In a model-based planning system, knowledge is encapsulated in the form of models at the various layers to support the predefined system objectives. Thus the model-based approach can be considered as an extended planning paradigm which is able to base its planning, control, diagnosis, repair, and other activities on a variety of objectives-related models. A System Entity Structure/Model Base framework is employed to support autonomous system design through the ability to generate a family of planning alternatives as well as to build hierarchical event-based control structures. The model base is a multilevel, multiabstraction, and multiformalism system organized through the use of system morphisms to integrate related models.
The proportional-plus-flicker automatic pilot operates by a nonlinear principle whereby a fast-acting flicker servomotor response is combined with a low-speed proportional servomotor response for the purpose of obtaining supersonic stability and control. Essentially, the autopilot maintains a zero reference about which the output is proportional to the input. However, a flicker response overrides this proportional response at a fixed angle of gimbal displacement on either side of the zero gyroscope reference. Therefore, in contrast to other high speed control systems, the design requirements are simplified because the two components of the proportional-flicker control system are easy to build separately and they can be combined in a relatively simple manner. By application of the proportional-flicker principle, satisfactory stability can be obtained by the proper adjustment of the variable factors in the autopilot mechanism; namely, the proportional gain, the amplitude of flicker control deflection, the autopilot time-lag factor (the time-lag between flicker and proportional operation), and the point in the range that the autopilot switches from a flicker to a proportional system. There is a possibility that these factors can be adjusted so that a more rapid response time (the time to reach steady state) is obtained with the non-linear proportional-flicker autopilot than with a purely linear proportional autopilot. For the main part of this analysis, the proportional part of the system is approximated by a zero-phase-lag proportional autopilot with the assumption that the control surface moves instantaneously at the point where the system switches from flicker to proportional. Good correlation is shown between the results obtained by this method and results obtained by using a close approximation of an actual autopilot transfer function for proportional autopilot operation.
NASA’s Extensible Traffic Management (xTM) builds on the foundation and the architecture of Unmanned Aircraft Systems (UAS) Traffic Management (UTM) concept and extends it broadly to other domains, such as Advanced / Urban Air Mobility (AAM/UAM) and Upper Class E Traffic Management (ETM). These xTM concepts assume the ability to fly in airspace that is authorized to operate solely under xTM services and mostly without any air traffic control (ATC) support. However, they also assume circumstances in which the xTM vehicles would need to operate in conventional ATC-managed airspace, both during nominal and off-nominal scenarios. Due to the vast differences in the xTM vehicle performances and missions, there is a concern that ATC may have difficulty in safely managing the xTM traffic and providing appropriate services to all vehicles, unless a consistent set of roles, procedures, and data exchange requirements are defined across the diverse set of xTM vehicle operations. In this paper, we describe a set of use cases that have been identified in UTM, AAM/UAM, and ETM operations that are related to ATC interactions, and we propose to categorize these use cases across xTM domains based on common trigger events. Organizing the use cases from the perspective of ATC roles per each trigger event is expected to provide the first step in discovering common procedures and data requirements across xTM domains that could help ease the controllers’ cognitive task load and allow them to manage these interactions more safely.
The problem of control command and maneuver induced structural loads is an important aspect of any control system design. Designers must design the aircraft structure and the control architecture to achieve desired piloted control responses while limiting the imparted structural loads. The classical approach is to build the structure with high margins, restrict control surface commands to known good combinations, and train pilots to follow procedural maneuvering limitations. With recent advances in structural sensing and the continued desire to improve safety and vehicle fuel efficiency, it is both possible and desirable to develop control architectures that enable lighter vehicle weights while maintaining and improving protection against structural damage.
A .003366 scale model of the Grumman H-33 orbiter was tested in the MSFC 14 inch Trisonic Wind Tunnel. Six-component aerodynamic force and moment data was recorded over a Mach number range of 0.6 to 4.96. Both pitch runs and yaw runs at various constant angles of attack were completed. The basic model configuration was investigated. The effects of a component build-up and of various control deflections were obtained. The elevons were deflected symmetrically and asymmetrically to determine elevator and aileron effectiveness. The rudder was tested both flared and unflared and the effects of deflections were determined in the flared case. The model was tested in pitch in two intervals. The first interval was from 0 to 20 deg. Then an adaptor was set to give the sting an offset angle and 20 to 40 deg angle of attack was obtained. Characteristics in sideslip were determined by varying sideslip angle from -4 deg to 10 deg with angle of attack set at 0 deg, 10 deg, 15 deg, and 30 deg.
An apparatus is described for controlling nutation motion in a spinning body, comprised of an angular accelerometer with its input axis perpendicular to the spin axis of the body, a flywheel with an axis of rotation perpendicular to the axis of the accelerometer and to the spin axis of the body, and a motor for driving the flywheel to attenuate or build nutation. The motor is controlled by circuitry that monitors the output of the angular accelerometer and drives the motor clockwise or counterclockwise during predetermined nutation angles synchronized to the zero crossover points of the accelerometer signal centered about the nutation peaks. The motor drive is phased to damp nutation motion to zero for stabilization. To increase the noise immunity of the system, when the output of the accelerometer falls below a threshold level, the circuitry operates in an open loop, beat mode where data representing the last accelerometer signal that exceeded that threshold level is stored, and the motor drive is controlled by the stored data. In a second version, the motor is controlled to supply a predetermined amount of nutation motion to a body undergoing testing on a spin table for energy dissipation evaluation. In each version, the use of an angular accelerometer rather than a linear accelerometer or gyro to monitor nutation enables placement of the nutation control apparatus at any location relative to the spin axis of the body requiring only crude orientation and no calibration.
Sixty-four separate 50-kW peak-power transmitters are distributed throughout the 200 x 200 meter Poker Flat MST radar antenna array. The relative phase of each transmitter is automatically controlled by a 64-channel unit located in the main building at the edge of the antenna. The phase control unit is described. In operation the RF pulse from a transmitter coupler is power divided and compared with the phase reference in a mixer. The mixer output is low-pass filtered and sampled near the center of the resulting video pulse by an amplifying sample-and-hold integrated circuit. Phase control is effected by maintaining the mixer output pulse near zero volts by amplifying the sample-and-hold output which then drives the voltage-controlled phase shifter in the direction to null the mixer output. The voltage-controlled shifter achieves over 360 deg phase shift in the range from 0.7 to 24 volts. When the voltage into the shifter tracks to either voltage limit the wrap-around control resets the voltage so that the shifter is always operating within its control range.
The main objective is to introduce techniques in the areas of testing and calibration, design, and control of robotic systems. A statistical technique is described that analyzes a robot's performance and provides quantitative three-dimensional evaluation of its repeatability, accuracy, and linearity. Based on this analysis, a corrective action should be taken to compensate for any existing errors and enhance the robot's overall accuracy and performance. A comparison between robotics simulation software packages that were commercially available (SILMA, IGRIP) and that of Kennedy Space Center (ROBSIM) is also included. These computer codes simulate the kinematics and dynamics patterns of various robot arm geometries to help the design engineer in sizing and building the robot manipulator and control system. A brief discussion on an adaptive control algorithm is provided.
The Integrated Propulsion Control System (IPCS) demonstrates control of an entire supersonic propulsion module - inlet, engine afterburner, and nozzle - with an HDC 601 digital computer. The program encompasses the design, build, qualification, and flight testing of control modes, software, and hardware. The flight test vehicle is an F-111E airplane. The L.H. inlet and engine will be operated under control of a digital computer mounted in the weapons bay. A general description and the current status of the IPCS program are given.
The American Mobile Satellite Corporation (AMSC) is going to construct, launch, and operate a satellite system in order to provide mobile satellite services to the United States. AMSC is going to build, own, and operate a Network Control System (NCS) for managing the communications usage of the satellites, and to control circuit switched access between mobile earth terminals and feeder-link earth stations. An overview of the major NCS functional and performance requirements, the control system physical architecture, and the logical architecture is provided.
Results are described from a project to build a prototype expert system for automated fault isolation and correction of a regenerative CO2 removal device that is typical of functions of the air revitalization group in the Space Station environmental control and life support system (ECLSS). The software was developed using one of the powerful commercial knowledge engineering environments. The goal of the project was to evaluate the feasibility of using a software development environment to rapidly design, construct, test, and change expert system software. The use of expert systems to enhance automatic controllers and the use of information on device design and on device troubleshooting and repair procedures in developing expert systems are discussed.
This paper presents a novel chance constrained optimal control (CCOC) algorithm that chooses a control action probabilistically. A CCOC problem is to find a control input that minimizes the expected cost while guaranteeing that the probability of violating a set of constraints is below a user-specified threshold. We show that a probabilistic control approach, which we refer to as a mixed control strategy, enables us to obtain a cost that is better than what deterministic control strategies can achieve when the CCOC problem is nonconvex. The resulting mixed-strategy CCOC problem turns out to be a convexification of the original nonconvex CCOC problem. Furthermore, we also show that a mixed control strategy only needs to "mix" up to two deterministic control actions in order to achieve optimality. Building upon an iterative dual optimization, the proposed algorithm quickly converges to the optimal mixed control strategy with a user-specified tolerance.
Fifty years ago, NASA decided that the cockpit controls in spacecraft should be like the ones in airplanes. But controls based on the stick and rudder may not be best way to manually control a vehicle in space. A different method is based on submersible vehicles controlled with foot pedals. A new pilot can learn the sub's control scheme in minutes and drive it hands-free. We are building a pair of foot pedals for spacecraft control, and will test them in a spacecraft flight simulator.
The essence of a distributed control system is the modular partitioning of control function across a hardware implementation. This type of control architecture requires embedding electronics in a multitude of control element nodes for the execution of those functions, and their integration as a unified system. As the field of distributed aeropropulsion control moves toward reality, questions about building and validating these systems remain. This paper focuses on the development of hardware-in-the-loop (HIL) test techniques for distributed aero engine control, and the application of HIL testing as it pertains to potential advanced engine control applications that may now be possible due to the intelligent capability embedded in the nodes.
The essence of a distributed control system is the modular partitioning of control function across a hardware implementation. This type of control architecture requires embedding electronics in a multitude of control element nodes for the execution of those functions, and their integration as a unified system. As the field of distributed aeropropulsion control moves toward reality, questions about building and validating these systems remain. This paper focuses on the development of hardware-in-the-loop (HIL) test techniques for distributed aero engine control, and the application of HIL testing as it pertains to potential advanced engine control applications that may now be possible due to the intelligent capability embedded in the nodes.
Soft computing is a general term for algorithms that learn from human knowledge and mimic human skills. Example of such algorithms are fuzzy inference systems and neural networks. Many applications, especially in control engineering, have demonstrated their appropriateness in building intelligent systems that are flexible and robust. Although recent research have shown that certain class of neuro-fuzzy controllers can be proven bounded and stable, they are implementation dependent and difficult to apply to the design and validation process. Many practitioners adopt the trial and error approach for system validation or resort to exhaustive testing using prototypes. In this paper, we describe our on-going research towards establishing necessary theoretic foundation as well as building practical tools for the verification and validation of soft-computing systems. A unified model for general neuro-fuzzy system is adopted. Classic non-linear system control theory and recent results of its applications to neuro-fuzzy systems are incorporated and applied to the unified model. It is hoped that general tools can be developed to help the designer to visualize and manipulate the regions of stability and boundedness, much the same way Bode plots and Root locus plots have helped conventional control design and validation.
For the past few years, the Advanced Electrical Systems Branch here at NASA Glenn has been pursuing research in the area of flywheels. The purpose of these pursuits has been t o explore the potential for flywheels to replace current battery-powered systems in space. So far it has been learned that flywheels offer large momentum storage capacity, comparatively small volume, high durability, and near- complete discharge capabilities, all of which are advancements over the existing nickel hydrogen and nickel cadmium batteries. Another significant advantage of flywheels is the potential they offer for combining the function of attitude control with energy storage. During the summer of 2004, I worked with Dr. Barbara Kenny in the Advanced Electrical Systems Branch, supporting the work she is doing by analyzing and testing some new components for the new Generation-2 flywheel. To monitor the speed and angular position of the flywheel rotor, a once-around (OAR) signal along with a sensorless algorithm is used. The OAR signal is used for the magnetic bearings that keep the flywheel suspended for frictionless operation. The sensorless algorithm is used for the flywheel motor/generator control. The OAR is generated from position sensors that monitor a circular plate. The plate has a cut down the middle such that one half of the circle is on a slightly lower level than the other. Every half-turn, or 180, the sensors detect the "cut" on the plate, and trigger the OAR, telling the computer that the rotor has made half a revolution. This, however, doesn't provide needed detailed information about the angular position of the rotor, since it only provides a signal alert every half- revolution. This is enough information for the magnetic bearing control but is insufficient for the motor/generator control. A new resolver was designed such that it would give continuous angle information rather than the 180 degree information of the OAR. The new resolver has two separate observable pieces: a flat middle section to monitor vertical motion, and an angled section around the circumference, which, when observed from above, produces a sine-wave displacement through the entire 360" revolution. My first job when I arrived this summer was to calibrate the sensors that would be mounted on the inside of the flywheel casing to monitor the position (angular and vertical) of the shaft. After calibration, I used the sensors to evaluate voltage outputs created by position differences between two pairs of sensors on the angled portion of the resolver for eight different angular positions, moving the resolver vertically and laterally through its entire potential range of motion. The results of these tests will be used to determine the rotor angular (and axial) position from the sensor readings once the new flywheel unit is assembled. The sensorless algorighm mentioned above consists of two operations: the signal injection method and the back electro-motive force (EMF). The signal injection is meant to work at low speeds, while the back EMF algorithm is meant to work at higher speeds. Both work together to determine the correct estimate of rotor position and speed based on the measured motor/generator current. It was determined that we wanted to know exactly how accurate our estimation methods were, and so a resolver (a commercially available mechanical sensor mounted to the motor/generator shaft to measure rotor position and speed) and a "Resolver to Digital" (R2D) circuit board was purchased to make the comparison to the existing estimation. My work related to the R2D board has included the following: creating two connector cables (one to power the circuit and one to get readable output off the board), writing Simulink code to process the board's output, and building a dSpace panel to control and monitor the circuit. The next step in the process will be to perform tests to compare the estimated rotor position and speed from the sensorless algorithm to the actual rotor and speed from the resolver signal.