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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

On controller tuning for a flexible-link manipulator with varying payload

In this article experimental results are presented for system identification and control of a single-link flexible manipulator carrying an unknown, varying payload. The control objective is to maintain endpoint position accuracy in the presence of flexure effects after rapid movement due to a rigid body slew-angle commanded position. Various time-domain parameter estimation techniques are used to identify ARMA model representations to be employed in controller tuning schemes for vibration compensation. Only endpoint acceleration measurements and motor shaft angle measurements are utilized in relatively simple PID control schemes, which are tuned as dictated by a varying, unknown payload.

Yurkovich, Stephen↗
NASA NTRS1990

Aircraft interior noise reduction by alternate resonance tuning

Existing interior noise reduction techniques for aircraft fuselages perform reasonably well at higher frequencies, but are inadequate at lower frequencies, particularly with respect to the low blade passage harmonics with high forcing levels found in propeller aircraft. A method is being studied which considers aircraft fuselage lined with panels alternately tuned to frequencies above and below the frequency that must be attenuated. Adjacent panels would oscillate at equal amplitude, to give equal source strength, but with opposite phase. Provided these adjacent panels are acoustically compact, the resulting cancellation causes the interior acoustic modes to become cutoff, and therefore be non-propagating and evanescent. This interior noise reduction method, called Alternate Resonance Tuning (ART), is currently being investigated both theoretically and experimentally. This new concept has potential application to reducing interior noise due to the propellers in advanced turboprop aircraft as well as for existing aircraft configurations.

Bliss, Donald B.↗
NASA NTRS1991

Tuning maps for setpoint changes and load disturbance upsets in a three capacity process under multivariable control

Tuning maps are an aid in the controller tuning process because they provide a convenient way for the plant operator to determine the consequences of adjusting different controller parameters. In this application the maps provide a graphical representation of the effect of varying the gains in the state feedback matrix on startup and load disturbance transients for a three capacity process. Nominally, the three tank system, represented in diagonal form, has a Proportional-Integral control on each loop. Cross coupling is then introduced between the loops by using non-zero off-diagonal proportional parameters. Changes in transient behavior due to setpoint and load changes are examined by varying the gains of the cross coupling terms.

Litt, Jonathan S.↗
NASA NTRS1990

Climatologically tuned reflectivity-rain rate relations and links to area-time integrals

An effort is made to determine relationships between reflectivity (Z) and rain rate (R) which are tuned to the local climatology. The development of such relations was motivated by the need to understand the role of precipitation in controlling general circulation and in affecting such phenomena as ENSO. Attention is given to methods of deriving such relations and how they are linked to area integral rainfall measurements. In essence, the relation is tuned so that the probability distribution of reflectivity, P(Z), replicates that of R over some predetermined space-time climatic domain. Thus, the accurate measurement of the average R over any smaller domain depends on how closely the sampled P(Z) approximates the climatic P(Z). The probability matching method used is a modification of the approach of Calheiros and Zawadzki (1987) and Rosenfeld (1980). The technique is applied to data from Germany and the eastern tropical Atlantic (GATE).

Atlas, David↗
NASA NTRS1992

Learning and tuning fuzzy logic controllers through reinforcements

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, 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 network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated 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 NTRS1992

Optimizing tuning masses for helicopter rotor blade vibration reduction including computed airloads and comparison with test data

An optimization procedure is developed for locating tuning masses on a rotor blade so that vibratory loads are minimized and hub-shear harmonics are reduced without adding a large mass penalty. The airloads are computed by means of a helicopter analysis for the cases of three vs six tuning masses, with attention given to the prediction of changes in airloads. Frequencies, airloads, and hub loads are computed with the CAMRAD/JA helicopter analysis code and the Conmin general-purpose optimization program. The hub shear is found to be significantly reduced in both cases with the added mass, and the reduction of hub shear is demonstrated under three flight conditions. Comparisons with wind-tunnel data demonstrate that the correlation of mass location is good and the relationship between mass location and flight speed is predicted well by the model.

Pritchard, Jocelyn I.↗
NASA NTRS1992

Nb/Al-Al2O3/Nb junctions with inductive tuning elements for a very low noise 205-250 GHz heterodyne receiver

The superconductor-insulator-superconductor (SIS) junction is the most sensitive nonlinear element for millimeter-wave heterodyne detection. An Nb/Al-Al203/Nb junction fabrication process has been developed which allows the use of planar tuning circuits integrated with the junctions. These tuning elements permit the use of junctions with relatively large areas and small current densities with excellent results. Recent measurements have yielded a double sideband receiver noise temperature less than 50 K from 205 to 240 GHz and 44 K at 230 GHz. This Nb/Al-Al203/Nb trilayer technology is also being extended to the fabrication of sub-square-micron area planar junctions for submillimeter-wavelengths.

Lichtenberger, Arthur W.↗
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.↗