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Results for “PROPORTIONAL CONTROL”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Apparatus for Measuring Total Emissivity of Small, Low-Emissivity Samples

An apparatus was developed for measuring total emissivity of small, lightweight, low-emissivity samples at low temperatures. The entire apparatus fits inside a small laboratory cryostat. Sample installation and removal are relatively quick, allowing for faster testing. The small chamber surrounding the sample is lined with black-painted aluminum honeycomb, which simplifies data analysis. This results in the sample viewing a very high-emissivity surface on all sides, an effect which would normally require a much larger chamber volume. The sample and chamber temperatures are individually controlled using off-the-shelf PID (proportional integral derivative) controllers, allowing flexibility in the test conditions. The chamber can be controlled at a higher temperature than the sample, allowing a direct absorptivity measurement. The lightweight sample is suspended by its heater and thermometer leads from an isothermal bar external to the chamber. The wires run out of the chamber through small holes in its corners, and the wires do not contact the chamber itself. During a steady-state measurement, the thermometer and bar are individually controlled at the same temperature, so there is zero heat flow through the wires. Thus, all of sample-temperature-control heater power is radiated to the chamber. Double-aluminized Kapton (DAK) emissivity was studied down to 10 K, which was about 25 K colder than any previously reported measurements. This verified a minimum in the emissivity at about 35 K and a rise as the temperature dropped to lower values.

Tuttle, James↗
NASA NTRS2002

A Nonlinear Digital Control Solution for a DC/DC Power Converter

A digital Nonlinear Proportional-Integral-Derivative (NPID) control algorithm was proposed to control a 1-kW, PWM, DC/DC, switching power converter. The NPID methodology is introduced and a practical hardware control solution is obtained. The design of the controller was completed using Matlab (trademark) Simulink, while the hardware-in-the-loop testing was performed using both the dSPACE (trademark) rapid prototyping system, and a stand-alone Texas Instruments (trademark) Digital Signal Processor (DSP)-based system. The final Nonlinear digital control algorithm was implemented and tested using the ED408043-1 Westinghouse DC-DC switching power converter. The NPID test results are discussed and compared to the results of a standard Proportional-Integral (PI) controller.

Zhu, Minshao↗
NASA NTRS1957

Experimental Comparison of Speed : Fuel-flow and Speed-area Controls on a Turbojet Engine for Small Step Disturbances

Optimum proportional-plus-integral control settings for speed - fuel-flow control, determined by minimization of integral criteria, correlated well with analytically predicted optimum settings. Engine response data are given for a range of control settings around the optimum. An inherent nonlinearity in the speed-area loop necessitated the use of nonlinear controls. Response data for two such nonlinear control schemes are presented.

ENGINES, CONTROL - TURBOJET↗
NASA NTRS1955

Analog Study of Interacting and Noninteracting Multiple-loop Control Systems for Turbojet Engines

The results of an analog investigation of several turbojet-engine control configurations is presented in this report. Both proportional and proportional-plus-integral controllers were studied, and compensating terms for engine interaction were added to the control system. Data were obtained on the stability limits and the transient responses of these various configurations. Analytical expressions in terms of the component transfer functions were developed for the configurations studied, and the optimum form for the compensation terms was determined. It was found that the addition of the integral term, while making the system slower and more oscillatory, was desirable in that it made the final values of the system parameters independent of source of disturbance and also eliminated droop in these parameters. Definite improvement in system characteristics resulted from the use of proper compensation terms. At comparable gain points the compensated system was faster and more stable. Complete compensation eliminated engine interaction, permitting each loop to be developed to an optimum point independently.

Pack, George J↗
NASA NTRS2018

Dynamics and Control of a Tethered Enhanced Gravity Tractor Performing Asteroid Deflection

The dynamics and control of an Enhanced Gravity Tractor (EGT) augmented with a tether for deflecting an asteroid are studied. A conventional EGT consists of collected asteroidal mass collocated with the spacecraft. Because of the presence of a tether, the collected mass is placed where the EGT would have been without a tether, and the spacecraft is placed farther away from the asteroid. Doing so improves the fuel efficiency and safety margin of the EGT operation without significantly sacrificing the gravitational attraction between the asteroid and the EGT. The tether is modeled as a series of particles connected by spring-dashpot systems. Physical properties of the tether are selected to be similar to those of the SPECTRA-1000, Kevlar-29, and Kevlar-49 fibers. It is assumed that control is applied only to the spacecraft, and there is no active control associated with the collected mass. A Proportional-Derivative (PD) controller is employed to maintain the spacecraft and the collected mass at desired positions relative to the asteroid. Numerical simulations of tethered EGT operations at 2008 EV5, Itokawa, Apophis, and a fictitious ellipsoidal asteroid are performed. It is demonstrated that a PD controller is capable of accomplishing the control objectives. The gravity gradient and the control force keep the tether stretched throughout a normal tethered EGT operation, and the load on the tether is well within the design limit of the tether material. While including multiple particles in the tether model is essential in capturing details of tether vibration, the number of particles does not significantly affect the motions of the collected mass and the spacecraft. In addition, the distance from the asteroid mass center to the collected mass should be chosen judiciously in the case of a rotating slender asteroid; some distance ranges should be avoided as excessive lateral oscillations can be excited by resonance between the asteroid rotation and tether pendular motion.

Shen, Haijun↗
DOE OSTI2025

Uncertainty quantification and sensitivity analysis of a nuclear thermal propulsion reactor startup sequence

The research presented in this article describes progress in applying stochastic methods, uncertainty quantification, parametric studies, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated via the radiation transport code Griffin to simulate neutronics. Our goal is to develop a reduced-order (surrogate) model that can be rapidly sampled with perturbations to multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums (CDs), which is itself controlled by a hybrid proportional-integral-derivative (PID) controller actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time [Λ] and effective delayed neutron fraction [ β eff ] from a transient Griffin simulation executed via Griffin’s improved quasi-static solver to provide the kinetic parameters) as inputs to functions that control the CD rotation angle. By investigating numerous stochastic approaches, we developed a dual-purpose surrogate model of the NTP system, using polynomial regression in the Multiphysics Object-Oriented Simulation Environment (MOOSE) Stochastic Tools Module (STM). The trained model can be rapidly sampled while simultaneously perturbing various input parameters, such as coefficients on the PID control or temperature (directly affecting the neutron cross section). The surrogate model delivers accurate (within 5%) results at speeds orders of magnitude faster (minutes, not days of computational time) than the base model. Once the surrogate model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs as well as the effects of these inputs on the model output. For example, coefficients used in the PID control system may vary due to some type of physical interference, or uncertainty may exist in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters, and operational boundaries can be determined. The goal of this work is to support development of an advanced control system for operating CDs in a functioning NTP system. This work is a scoping study of the MOOSE STM.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
NASA NTRS1975

On the control, stability, and waiting time in a slotted ALOHA random-access system

This paper explores some of the boundaries in performance of slotted ALOHA systems by analyzing a simple and almost optimal centrally supervised control. The control results in a very simple Markov chain model and allows an examination of stability, conditional waiting time distribution of transmitting terminals, and many other system measures. The key to the simplicity is to have a probability of successful packet transmission that is independent of the number of transmitting terminals. In considering waiting time, we calculate the mean and other moments of the waiting time of a terminal when it enters the system to find (n - 1) other terminals already there competing for the channel. Under this control, the average time is proportional to n. The control requires exact knowledge of the number of terminals contending for the channel, and hence is not implementable, except as an approximation.

Ferguson, M. J.↗
NASA NTRS1990

Terminology and concepts of control and Fuzzy Logic

Viewgraphs on terminology and concepts of control and fuzzy logic are presented. Topics covered include: control systems; issues in the design of a control system; state space control for inverted pendulum; proportional-integral-derivative (PID) controller; fuzzy controller; and fuzzy rule processing.

Aldridge, Jack↗
NASA NTRS1994

A neural network controller for automated composite manufacturing

At McDonnell Douglas Aerospace (MDA), an artificial neural network based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns an approximate inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional plus integral (PI) controller. However after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A Cerebellar Model Articulation Controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM compatible 386 PC with an A/D board interface to the machine.

Lichtenwalner, Peter F.↗
NASA NTRS2005

Integration of an Autopilot for a Micro Air Vehicle

Two autopilots providing autonomous flight capabilities are presented herein. The first is the Pico-Pilot, demonstrated for the 12-inch size class of micro air vehicles. The second is the MicroPilot MP2028(sup g), where its integration into a 36-inch Zagi airframe (tailless, elevons only configuration) is investigated and is the main focus of the report. Analytical methods, which include the use of the Advanced Aircraft Analysis software from DARCorp, were used to determine the stability and control derivatives, which were then validated through wind tunnel experiments. From the aerodynamic data, the linear, perturbed equations of motion from steady-state flight conditions may be cast in terms of these derivatives. Using these linear equations, transfer functions for the control and navigation systems were developed and feedback control laws based on Proportional, Integral, and Derivative (PID) control design were developed to control the aircraft. The PID gains may then be programmed into the autopilot software and uploaded to the microprocessor of the autopilot. The Pico-Pilot system was flight tested and shown to be successful in navigating a 12-inch MAV through a course defined by a number of waypoints with a high degree of accuracy, and in 20 mph winds. The system, though, showed problems with control authority in the roll and pitch motion of the aircraft: causing oscillations in these directions, but the aircraft maintained its heading while following the prescribed course. Flight tests were performed in remote control mode to evaluate handling, adjust trim, and test data logging for the Zagi with integrated MP2028(sup g). Ground testing was performed to test GPS acquisition, data logging, and control response in autonomous mode. Technical difficulties and integration limitations with the autopilot prevented fully autonomous flight from taking place, but the integration methodologies developed for this autopilot are, in general, applicable for unmanned air vehicles within the 36-inch size class or larger that use a PID control based autopilot.

Platanitis, George↗
NASA NTRS2005

Simulation Analysis of Computer-Controlled pressurization for Mixture Ratio Control

A procedural code (C++) simulation was developed to investigate potentials for mixture ratio control of pressure-fed spacecraft rocket propulsion systems by measuring propellant flows, tank liquid quantities, or both, and using feedback from these measurements to adjust propellant tank pressures to set the correct operating mixture ratio for minimum propellant residuals. The pressurization system eliminated mechanical regulators in favor of a computer-controlled, servo- driven throttling valve. We found that a quasi-steady state simulation (pressure and flow transients in the pressurization systems resulting from changes in flow control valve position are ignored) is adequate for this purpose. Monte-Carlo methods are used to obtain simulated statistics on propellant depletion. Mixture ratio control algorithms based on proportional-integral-differential (PID) controller methods were developed. These algorithms actually set target tank pressures; the tank pressures are controlled by another PID controller. Simulation indicates this approach can provide reductions in residual propellants.

Alexander, Leslie A.↗
DOE OSTI2024

Uncertainty Quantification and Sensitivity Analysis of Non-Nuclear Advanced Controls Testbed Reactor Mockup

The research presented in this report describes our progress in applying stochastic methods and uncertainty quantification, parametric study, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated with Griffin, with the goal of developing a reduced order (surrogate) model which can be rapidly sampled while perturbing multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums, which are controlled by a hybrid proportional, integral and derivative (PID) controller, actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time and $\beta$ from a transient Griffin simulation executed with the improved quasi-static method to provide the kinetic parameters) as inputs to functions which control the CD rotation angle. Using a number of stochastic method approaches, we developed a dual purpose training-surrogate model of the NTP system using polynomial regression. The trained model can be rapidly sampled while simultaneously perturbing various input parameters of the model, such as coefficients on the PID control, or temperature (directly affect the neutron cross section). The surrogate model delivers accurate results orders-of-magnitude faster (minutes, not days) than the base model. Once the base model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs and their effect on the output. For example, coefficients used in the PID control system may vary due to some physical interference, or there may be uncertainty in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters and operational boundaries can be determined. The goal of this work is to support development of an advanced control system to operate CDs in a functioning NTP system.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1993

Dynamic response and control of a jet-transport aircraft encountering a single-axis vortex

The dynamic responses of a jet-transport aircraft to two types of single-axis wind vortex encounters are studied. Aircraft attitude, flight path angle, and aerodynamic angle excursions are analyzed and dominating dynamic forcing effects are identified for each encounter. A simple departure-preventing LQR controller is designed to demonstrate the benefits of using automatic control to reduce the wind vortex hazard. A Proportional-Integral-Filter controller structure successfully regulates the critical parameters, roll angle, phi, and sideslip angle, beta, for the two different vortex encounters considered in this study.

Spilman, Darin R.↗
NASA NTRS2003

Intrinsic Hardware Evolution for the Design and Reconfiguration of Analog Speed Controllers for a DC Motor

Evolvable hardware provides the capability to evolve analog circuits to produce amplifier and filter functions. Conventional analog controller designs employ these same functions. Analog controllers for the control of the shaft speed of a DC motor are evolved on an evolvable hardware platform utilizing a second generation Field Programmable Transistor Array (FPTA2). The performance of an evolved controller is compared to that of a conventional proportional-integral (PI) controller. It is shown that hardware evolution is able to create a compact design that provides good performance, while using considerably less functional electronic components than the conventional design. Additionally, the use of hardware evolution to provide fault tolerance by reconfiguring the design is explored. Experimental results are presented showing that significant recovery of capability can be made in the face of damaging induced faults.

Gwaltney, David A.↗
NASA NTRS2003

Hardware Evolution of Analog Speed Controllers for a DC Motor

Evolvable hardware provides the capability to evolve analog circuits to produce amplifier and filter functions. Conventional analog controller designs employ these same functions. Analog controllers for the control of the shaft speed of a DC motor are evolved on an evolvable hardware platform utilizing a Field Programmable Transistor Array (FPTA). The performance of these evolved controllers is compared to that of a conventional proportional-integral (PI) controller.

Gwaltney, David A.↗
NASA NTRSDate not supplied

A Comparison of Model Predictive Control Architectures for Application to Electrified Aircraft Propulsion Systems

As electrified aircraft propulsion (EAP) systems continue to mature, more sophisticated hardware and software are being developed to balance operations among electric machines and gas-turbine engines. In hybrid-electric propulsion systems, the increased complexity resulting from integrating turbine-engine shafts with electric machines necessitates control methodologies to account for various physical domains. Ideal controllers for hybrid-electric engines manage systems, subsystems, and their interactions in a coordinated fashion, able to account for safety and performance goals while being computationally efficient. In a previous work, linear model predictive control (MPC) schemes were implemented in centralized and distributed frameworks on a nonlinear turbofan engine model as a proof of concept. However, these schemes were not evaluated for computational complexity, prompting further study. The research presented here develops hierarchical MPC schemes to reduce the computational burden of the previous MPC schemes. A two-tier framework is implemented, where a slower sampling MPC controls electric machines and determines fan-speed tracking goals for a faster sampling controller, which is either a MPC or a proportional-integral (PI) controller. The proposed designs are compared to the centralized MPC investigated previously, and performance is measured via fan speed tracking error, energy storage state-of-charge, and computation time. Results reveal that the hierarchical MPC scheme employing a lower-level PI controller improves computation time while maintaining comparable tracking and state-of-charge regulation to the centralized scheme.

model predictive control↗
NASA NTRSDate not supplied

A Comparison of Model Predictive Control Architectures for Application to Electrified Aircraft Propulsion Systems

As electrified aircraft propulsion (EAP) systems continue to mature, more sophisticated hardware and software are being developed to balance operations among electric machines and gas-turbine engines. In hybrid-electric propulsion systems, the increased complexity resulting from integrating turbine-engine shafts with electric machines necessitates control methodologies to account for various physical domains. Ideal controllers for hybrid-electric engines manage systems, subsystems, and their interactions in a coordinated fashion, able to account for safety and performance goals while being computationally efficient. In a previous work, linear model predictive control (MPC) schemes were implemented in centralized and distributed frameworks on a nonlinear turbofan engine model as a proof of concept. However, these schemes were not evaluated for computational complexity, prompting further study. The research presented here develops hierarchical MPC schemes to reduce the computational burden of the previous MPC schemes. A two-tier framework is implemented, where a slower sampling MPC controls electric machines and determines fan-speed tracking goals for a faster sampling controller, which is either a MPC or a proportional-integral (PI) controller. The proposed designs are compared to the centralized MPC investigated previously, and performance is measured via fan speed tracking error, energy storage state-of-charge, and computation time. Results reveal that the hierarchical MPC scheme employing a lower-level PI controller improves computation time while maintaining comparable tracking and state-of-charge regulation to the centralized scheme.

model predictive control↗