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At least 181 records · Page 10

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NASA NTRS1993

Neural self-tuning adaptive control of non-minimum phase system

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity, if not unstable, closed-loop behavior. Therefore, a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response.

Ho, Long T.↗

NASA NTRS1992

A composite self tuning strategy for fuzzy control of dynamic systems

The feature of self learning makes fuzzy logic controllers attractive in control applications. This paper proposes a strategy to tune the fuzzy logic controller on-line by tuning the data base as well as the rule base. The structure of the controller is outlined and preliminary results are presented using simulation studies.

Shieh, C.-Y.↗

NASA NTRS1992

Self-tuning multivariable pole placement control of a multizone crystal growth furnace

This paper presents the design and implementation of a multivariable self-tuning temperature controller for the control of lead bromide crystal growth. The crystal grows inside a multizone transparent furnace. There are eight interacting heating zones shaping the axial temperature distribution inside the furnace. A multi-input, multi-output furnace model is identified on-line by a recursive least squares estimation algorithm. A multivariable pole placement controller based on this model is derived and implemented. Comparison between single-input, single-output and multi-input, multi-output self-tuning controllers demonstrates that the zone-to-zone interactions can be minimized better by a multi-input, multi-output controller design. This directly affects the quality of crystal grown.

Batur, C.↗

NASA NTRS1992

Learning and tuning fuzzy logic controllers through reinforcements

This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Berenji, Hamid R.↗

NASA NTRS1993

Optimal placement of tuning masses on truss structures by genetic algorithms

Optimal placement of tuning masses, actuators and other peripherals on large space structures is a combinatorial optimization problem. This paper surveys several techniques for solving this problem. The genetic algorithm approach to the solution of the placement problem is described in detail. An example of minimizing the difference between the two lowest frequencies of a laboratory truss by adding tuning masses is used for demonstrating some of the advantages of genetic algorithms. The relative efficiencies of different codings are compared using the results of a large number of optimization runs.

Ponslet, Eric↗

NASA NTRS1993

Actuator tuning for nonlinear truss structures - A neural network approach

The optimal actuator placement problem encompasses finding their best locations and best gains. The latter is referred to as tuning the actuators. This paper deals with tuning a given set of actuators in presence of known structural nonlinearities arising from gaps at some joints. For this purpose, an efficient neural network-based algorithm is proposed and illustrated using numerical simulations.

Toomarian, N.↗

NASA NTRS1993

Computational issues in optimal tuning and placement of passive dampers

The effectiveness of viscous elements in introducing damping in a structure is a function of several variables including their number, their location in the structure, and their physical properties. In this paper, the optimal damper placement and tuning problem is posed to optimize these variables. Both discrete and continuous optimization problems are formulated and solved corresponding, respectively, to the problems of placement of passive elements and to the tuning of their parameters. The paper particularly emphasizes the critical computational issues resulting from the optimization formulations. Numerical results involving a lightly damped testbed structure are presented.

Chu, C. C.↗

NASA NTRS1998

The Design of a Transparent Vertical Multizone Furnace: Application to Thermal Field Tuning and Crystal Growth

We present an innovative design of a vertical transparent multizone furnace which can operate in the temperature range of 25 C to 750 C and deliver thermal gradients of 2 C/cm to 45 C/cm for the commercial applications to crystal growth. The operation of the eight zone furnace is based on a self-tuning temperature control system with a DC power supply for optimal thermal stability. We show that the desired thermal profile over the entire length of the furnace consists of a functional combination of the fundamental thermal profiles for each individual zone obtained by setting the set-point temperature for that zone. The self-tuning system accounts for the zone to zone thermal interactions. The control system operates such that the thermal profile is maintained under thermal load, thus boundary conditions on crystal growth ampoules can be predetermined prior to crystal growth. Temperature profiles for the growth of crystals via directional solidification, vapor transport techniques, and multiple gradient applications are shown to be easily implemented. The unique feature of its transparency and ease of programming thermal profiles make the furnace useful in scientific and commercial applications for determining the optimized process parameters for crystal growth.

Duvual, Walter M. B.↗

NASA NTRS1999

A Self-Tuning Kalman Filter for Autonomous Navigation Using the Global Positioning System (GPS)

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS (Global Positioning Systems) data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.

Truong, Son H.↗

NASA NTRS1999

A Self-Tuning Kalman Filter for Autonomous Navigation using the Global Positioning System (GPS)

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.

Truong, S. H.↗

NASA NTRS1998

A Self-Tuning Kalman Filter for Autonomous Spacecraft Navigation

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman Filter and Global Positioning System (GPS) data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. Current techniques of Kalman filtering, however, still rely on manual tuning from analysts, and cannot help in optimizing autonomy without compromising accuracy and performance. This paper presents an approach to produce a high accuracy autonomous navigation system fully integrated with the flight system. The resulting system performs real-time state estimation by using an Extended Kalman Filter (EKF) implemented with high-fidelity state dynamics model, as does the GPS Enhanced Orbit Determination Experiment (GEODE) system developed by the NASA Goddard Space Flight Center. Augmented to the EKF is a sophisticated neural-fuzzy system, which combines the explicit knowledge representation of fuzzy logic with the learning power of neural networks. The fuzzy-neural system performs most of the self-tuning capability and helps the navigation system recover from estimation errors. The core requirement is a method of state estimation that handles uncertainties robustly, capable of identifying estimation problems, flexible enough to make decisions and adjustments to recover from these problems, and compact enough to run on flight hardware. The resulting system can be extended to support geosynchronous spacecraft and high-eccentricity orbits. Mathematical methodology, systems and operations concepts, and implementation of a system prototype are presented in this paper. Results from the use of the prototype to evaluate optimal control algorithms implemented are discussed. Test data and major control issues (e.g., how to define specific roles for fuzzy logic to support the self-learning capability) are also discussed. In addition, architecture of a complete end-to-end candidate flight system that provides navigation with highly autonomous control using data from GPS is presented.

Truong, Son H.↗

NASA NTRS1998

A Measurement and Simulation Based Methodology for Cache Performance Modeling and Tuning

We present a cache performance modeling methodology that facilitates the tuning of uniprocessor cache performance for applications executing on shared memory multiprocessors by accurately predicting the effects of source code level modifications. Measurements on a single processor are initially used for identifying parts of code where cache utilization improvements may significantly impact the overall performance. Cache simulation based on trace-driven techniques can be carried out without gathering detailed address traces. Minimal runtime information for modeling cache performance of a selected code block includes: base virtual addresses of arrays, virtual addresses of variables, and loop bounds for that code block. Rest of the information is obtained from the source code. We show that the cache performance predictions are as reliable as those obtained through trace-driven simulations. This technique is particularly helpful to the exploration of various "what-if' scenarios regarding the cache performance impact for alternative code structures. We explain and validate this methodology using a simple matrix-matrix multiplication program. We then apply this methodology to predict and tune the cache performance of two realistic scientific applications taken from the Computational Fluid Dynamics (CFD) domain.

Waheed, Abdul↗

NASA NTRS2003

Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories

The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.

Burchett, Bradley T.↗

NASA NTRS2005

Resonant Wingbeat Tuning Circuit using Strain-Rate Feedback for Ornithoptic Micro Aerial Vehicles

A resonant wingbeat tuning circuit automatically tunes the frequency of an actuating input to the resonant frequency of a flexible wing structure. Through the use of feedback control, the circuit produces the maximum flapping amplitude of a mechanical ornithoptic system, tracking the resonant frequency of the vibratory flapping apparatus as it vanes in response to change in flight condition, ambient pressure, or incurred wing damage.

David L Raney↗

NASA NTRS1997

Zeeman Tuning Rates in the v3 Band of NO2

Zeeman spectra of the v3 fundamental (at approximately 1620 c/m) of nitrogen dioxide (14N02) have been recorded with magnetic fields of approximately 500 Gauss using a FTS (at the Kitt Peak National Observatory), as well as a tunable diode laser. At low magnetic fields the Zeeman effect is small compared to the spin-rotation interaction, and the Zeeman tuning rates are expected to be linear with magnetic field. Measured tuning rates of the Q-branch (KA = 2 - 9, N = 2 - 6) sigma transitions are compared to those expected for 2Sigma transitions in low magnetic fields, 2mu ogB/(2N+l). These measurements are required in the data analysis of an ultra-sensitive (pptv) in situ detector based on magnetic-rotation spectroscopy.

Mahon, C. H.↗

NASA NTRS2005

A Hardware Platform for Tuning of MEMS Devices Using Closed-Loop Frequency Response

We report on the development of a hardware platform for integrated tuning and closed-loop operation of MEMS gyroscopes. The platform was developed and tested for the second generation JPL/Boeing Post-Resonator MEMS gyroscope. The control of this device is implemented through a digital design on a Field Programmable Gate Array (FPGA). A software interface allows the user to configure, calibrate, and tune the bias voltages on the micro-gyro. The interface easily transitions to an embedded solution that allows for the miniaturization of the system to a single chip.

gyroscope controls↗

NASA NTRS1995

Active noise control using noise source having adaptive resonant frequency tuning through variable ring loading

A noise source for an aircraft engine active noise cancellation system in which the resonant frequency of noise radiating structure is tuned to permit noise cancellation over a wide range of frequencies. The resonant frequency of the noise radiating structure is tuned by a plurality of drivers arranged to contact the noise radiating structure. Excitation of the drivers causes expansion or contraction of the drivers, thereby varying the edge loading applied to the noise radiating structure. The drivers are actuated by a controller which receives input of a feedback signal proportional to displacement of the noise radiating element and a signal corresponding to the blade passage frequency of the engine's fan. In response, the controller determines a control signal which is sent to the drivers, causing them to expand or contract. The noise radiating structure may be either the outer shroud of the engine or a ring mounted flush with an inner wall of the shroud or disposed in the interior of the shroud.

Pla, Frederic G.↗

NASA NTRS1995

Active noise control using noise source having adaptive resonant frequency tuning through stress variation

A noise source for an aircraft engine active noise cancellation system in which the resonant frequency of a noise radiating element is tuned to permit noise cancellation over a wide range of frequencies. The resonant frequency of the noise radiating element is tuned by an expandable ring embedded in the noise radiating element. Excitation of the ring causes expansion or contraction of the ring, thereby varying the stress in the noise radiating element. The ring is actuated by a controller which receives input of a feedback signal proportional to displacement of the noise radiating element and a signal corresponding to the blade passage frequency of the engine's fan. In response, the controller determines a control signal which is sent to the ring, causing the ring to expand or contract. Instead of a single ring embedded in the noise radiating panel, a first expandable ring can be bonded to one side of the noise radiating element, and a second expandable ring can be bonded to the other side.

Pla, Frederic G.↗