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Results for “nonlinear adaptive control”

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PubMed2003

Generation of entanglement between frequency bands by nonlinear fiber propagation and spectral pulse shaping.

A novel fiber-based scheme for generating quadrature entanglement between a specific pair of frequency bands is proposed. The scheme is based on use of a nonlinear fiber, a spectral pulse shaper, and an adaptive feedback loop. It is numerically predicted that at least -5 dB of the quadrature entanglement will be created by preparation of an appropriate initial phase spectrum. Optimal control of the four-wave mixing terms of the Kerr Hamiltonian is crucial for improvement of the entanglement.

Journal Article↗

PubMed2004

Adaptation through minimization of the phase lag in coupled nonidentical systems.

We show that the internal control of adaptation can be obtained from the properties of the phase lag that results from phase synchronization of two nonidentical chaotic oscillators. The direction and magnitude of the phase lag depend upon the relative internal properties of the coupled units, and they can be used as indicators during the adjustment of dynamics, i.e., adaptation of the target unit to match that of the control. The properties of the phase lag are obtained using a method based on the estimation of properties of the distributions of relative event times of both (target and control) units. The phase lag dependent mechanism to control the adaptation process was applied to a system of nonidentical Rössler oscillators and a system of nonidentical Lorenz oscillators. We also elucidate its importance as a control mechanism of the changes of neuronal activity showing its application to neural adaptation.

Animals↗

PubMedDate not supplied

Cellular signal adaptation with damage control at low doses versus the predominance of DNA damage at high doses.

Ionizing radiation is known to potentially interfere with cellular functions at all levels of cell organization and induces DNA lesions apparently with an incidence linearly related to D, also at low doses. On the other hand, low doses have also been observed to initiate a slowly appearing temporary protection against causation and accumulation of DNA lesions, involving the radical detoxification system, DNA repair and removal of DNA damage. This protection apparently does not operate at high doses; it has been described to be nonlinear, increasing initially with D, beginning to decrease when D exceeds approximately 0.1-0.2 Gy, and eventually disappearing at higher D. The various adaptive responses have been shown to last individually from hours to weeks in different cell types and resemble responses to oxidative stress. Damage to DNA is continuously and endogenously produced mainly by reactive oxygen species (ROS) generated in a normal oxidative metabolism. This endogenous DNA damage quantitatively exceeds DNA damage from low-dose irradiation, by several orders of magnitude. Thus, the protective responses following acute low-dose irradiation may be presumed to mainly counteract the endogenous DNA damage. Accordingly, the model described here uses two dose-effect functions, a linear one for causing and a nonlinear one for protecting against DNA damage from whatever cause in the irradiated cells and tissues. The resulting net dose-risk function strongly suggests that the incidence of cancer versus dose in the irradiated tissues is much less likely to be linear than to exhibit a threshold. The observed cancer incidence may even fall below the spontaneous incidence, when D to cells is below approximately 0.2 Gy. However incomplete, these data support a reexamination of the LNT hypothesis.

Adaptation, Physiological↗

PubMed2000

The Ornstein-Uhlenbeck model for decision time in cognitive tasks: an example of control of nonlinear network dynamics.

The Ornstein-Uhlenbeck (OU) model for human decision-making has been successfully applied to account for response accuracy and response time (RT) data in recent two-choice decision models. A variant of the OU model is shown to arise from the response dynamics of a nonlinear network consisting of randomly connected neural processing units. When feedback control of the network is effected by the stimulus onset, the average network response is an autocorrelated random signal satisfying the stochastic differential equation for the OU process. An alternative, more general, stimulus detection procedure is proposed which involves the use of an adaptive Kalman filter process to track any temporal change in autoregressive parameters. The predicted decision time distributions suggest that both the OU and the Kalman filter processes can serve as alternative models for RT data in experimental tasks.

Cognition↗

PubMed2004

Strategies for brain shift evaluation.

For the analysis of the brain shift phenomenon different strategies were applied. In 32 glioma cases pre- and intraoperative MR datasets were acquired in order to evaluate the maximum displacement of the brain surface and the deep tumor margin. After rigid registration using the software of the neuronavigation system, a direct comparison was made with 2D- and 3D visualizations. As a result, a great variability of the brain shift was observed ranging up to 24 mm for cortical displacement and exceeding 3 mm for the deep tumor margin in 66% of all cases. Following intraoperative imaging the neuronavigation system was updated in eight cases providing reliable guidance. For a more comprehensive analysis a voxel-based nonlinear registration was applied. Aiming at improved speed of alignment we performed all interpolation operations with 3D texture mapping based on OpenGL functions supported in graphics hardware. Further acceleration was achieved with an adaptive refinement of the underlying control point grid focusing on the main deformation areas. For a quick overview the registered datasets were evaluated with different 3D visualization approaches. Finally, the results were compared to the initial measurements contributing to a better understanding of the brain shift phenomenon. Overall, the experiments clearly demonstrate that deformations of the brain surface and deeper brain structures are uncorrelated.

Brain Neoplasms↗

PubMed1994

Bifurcation, chaos and suppression of chaos in FitzHugh-Nagumo nerve conduction model equation.

We study the effect of constant and periodic membrane currents in neuronal axons described by the FitzHugh-Nagumo equation in its wave form. Linear stability analysis is carried out in the absence of periodic membrane current. Occurrence of chaotic motion, (i) in the absence of both constant and periodic membrane currents, (ii) with constant current only, (iii) with periodic membrane current only, and (iv) with both constant and periodic currents is investigated for specific parametric choices. We show how chaos sets in through a cascade of period doubling bifurcations. We then demonstrate the possibility of control of chaos using various control mechanisms. Specifically, we show the control of chaos by (i) adaptive control mechanism, (ii) periodic parametric perturbation and (iii) stabilization of unstable periodic orbits.

Animals↗

PubMed1998

Contrast-modulation flicker: dynamics and spatial resolution of the light adaptation process.

We report a perceptual phenomenon that originates from a nonlinear operation during the visual process, and we use these observations to study the functional organization of the responsible nonlinearity; the regulation of visual sensitivity to light. When the contrast of a high frequency grating was modulated while its spatial and temporal average luminance was kept constant, observers saw brightness changes or desaturation in the field. If the contrast was modulated periodically between zero and a peak value, observers saw vivid flicker (contrast-modulation flicker), and this flicker could be seen even when the grating was too fine to be visually resolved as a pattern. This uniform-field flicker can be nulled by a modulation of space-average luminance at the contrast-modulation frequency, with appropriate phase and modulation depth. Contrast-modulation flicker is still measurable with gratings at 100 cycles/deg. The dynamics of contrast-modulation flicker suggest that it results from an early sensitivity-controlling mechanism, acting very rapidly (within about 20 msec). Its dependence on stimulus spatial frequency implies a strictly local luminance nonlinearity, one that either resides within individual photoreceptors or operates on signals from individual receptors.

Adaptation, Ocular↗

PubMed1990

Microgenetic patterns of adaptation on the Stroop task by patients with bronchial asthma and duodenal peptic ulcer.

Microgenetic patterns of adaptation on the Stroop task were assessed by means of the Serial Color-Word Test given 21 patients with bronchial asthma and 20 with duodenal peptic ulcer, who were compared with 41 normal controls matched for sex, age, and education. Two measures were calculated on each of the five trials of the test, one of linear change and one of nonlinear change in reading times. As predicted, patients presented more frequently patterns characterized by high nonlinear change and less frequently stabilized patterns (low linear and nonlinear change of reading speed). Linear and nonlinear change were then calculated on the five linear change scores and the five nonlinear change scores; again patients presented more frequently patterns characterized by high nonlinear change on both the linear change scores and nonlinear change scores and less frequently stabilized patterns. These findings indicate strong similarities between the adaptation patterns of patients with ulcer and asthma and the microgenetic patterns previously known to characterize neurotic and psychotic patients. Furthermore, scores on the Serial Color-Word Test also differentiated between ulcer and asthma groups.

Adult↗

PubMed2004

An adaptive Hinfinity controller design for bank-to-turn missiles using ridge Gaussian neural networks.

A new autopilot design for bank-to-turn (BTT) missiles is presented. In the design of autopilot, a ridge Gaussian neural network with local learning capability and fewer tuning parameters than Gaussian neural networks is proposed to model the controlled nonlinear systems. We prove that the proposed ridge Gaussian neural network, which can be a universal approximator, equals the expansions of rotated and scaled Gaussian functions. Although ridge Gaussian neural networks can approximate the nonlinear and complex systems accurately, the small approximation errors may affect the tracking performance significantly. Therefore, by employing the Hinfinity control theory, it is easy to attenuate the effects of the approximation errors of the ridge Gaussian neural networks to a prescribed level. Computer simulation results confirm the effectiveness of the proposed ridge Gaussian neural networks-based autopilot with Hinfinity stabilization.

Algorithms↗

PubMed2001

Stable operation of an elastic three-segment leg.

Quasi-elastic operation of joints in multi-segmented systems as they occur in the legs of humans, animals, and robots requires a careful tuning of leg properties and geometry if catastrophic counteracting operation of the joints is to be avoided. A simple three-segment model has been used to investigate the segmental organization of the leg during repulsive tasks like human running and jumping. The effective operation of the muscles crossing the knee and ankle joints is described in terms of rotational springs. The following issues were addressed in this study: (1) how can the joint torques be controlled to result in a spring-like leg operation? (2) how can rotational stiffnesses be adjusted to leg-segment geometry? and (3) to what extend can unequal segment lengths and orientations be advantageous? It was found that: (1) the three-segment leg tends to become unstable at a certain amount of bending expressed by a counterrotation of the joints; (2) homogeneous bending requires adaptation of the rotational stiffnesses to the outer segment lengths; (3) nonlinear joint torque-displacement behaviour extends the range of stable leg bending and may result in an almost constant leg stiffness; (4) biarticular structures (like human gastrocnemius muscle) and geometrical constraints (like heel strike) support homogeneous bending in both joints; (5) unequal segment lengths enable homogeneous bending if asymmetric nominal angles meet the asymmetry in leg geometry; and (6) a short foot supports the elastic control of almost stretched knee positions. Furthermore, general leg design strategies for animals and robots are discussed with respect to the range of safe leg operation.

Animals↗

PubMed1991

Modeling interpatient pharmacodynamic variability of etoposide.

This report describes approaches to modeling interpatient pharmacodynamic variability of etoposide effect as measured by white blood cell count nadir. Such models may be utilized in adaptive control dosing to individualize the dose administered to a patient with the aim of lessening the risk of severe myelosuppression. We have successfully employed adaptive control dosing of etoposide administered by 72-hour continuous infusion based on our prior pharmacodynamic model for white blood cell count nadir. Data from our most recent series of 41 patients were used to investigate new linear and nonlinear pharmacodynamic models. We then cross-validated our best models on data from an independent series of 27 similarly treated patients. We identified an unbiased model that exhibited high precision in both data sets (root mean square errors of 1.11 x 10(3) and 1.20 x 10(3) cells/microL, respectively). The optimal model was a nonlinear Hill model defined by 24-hour etoposide concentration, pretreatment white blood cell count, and pretreatment serum albumin level. The level of albumin was found to be both a component of kinetic (protein binding) and dynamic (patient health) variability. Patients with lower pretreatment albumin levels are at higher risk of severe myelosuppression during etoposide therapy.

Bias↗

PubMed2005

Adaptation and generalization in acceleration-dependent force fields.

Any passive rigid inertial object that we hold in our hand, e.g., a tennis racquet, imposes a field of forces on the arm that depends on limb position, velocity, and acceleration. A fundamental characteristic of this field is that the forces due to acceleration and velocity are linearly separable in the intrinsic coordinates of the limb. In order to learn such dynamics with a collection of basis elements, a control system would generalize correctly and therefore perform optimally if the basis elements that were sensitive to limb velocity were not sensitive to acceleration, and vice versa. However, in the mammalian nervous system proprioceptive sensors like muscle spindles encode a nonlinear combination of all components of limb state, with sensitivity to velocity dominating sensitivity to acceleration. Therefore, limb state in the space of proprioception is not linearly separable despite the fact that this separation is a desirable property of control systems that form models of inertial objects. In building internal models of limb dynamics, does the brain use a representation that is optimal for control of inertial objects, or a representation that is closely tied to how peripheral sensors measure limb state? Here we show that in humans, patterns of generalization of reaching movements in acceleration-dependent fields are strongly inconsistent with basis elements that are optimized for control of inertial objects. Unlike a robot controller that models the dynamics of the natural world and represents velocity and acceleration independently, internal models of dynamics that people learn appear to be rooted in the properties of proprioception, nonlinearly responding to the pattern of muscle activation and representing velocity more strongly than acceleration.

Acceleration↗

PubMed1995

Muscle-joint models incorporating activation dynamics, moment-angle, and moment-velocity properties.

Muscle input/output models incorporating activation dynamics, moment-angle, and moment-velocity factors are commonly used to predict the moment produced by muscle during nonisometric contractions; the three factors are generally assumed to be independent. We examined the ability of models with independent factors, as well as models with coupled factors, to fit input/output data measured during simultaneous modulation of the fraction of muscle stimulated (recruitment) and joint angle inputs. The models were evaluated in stimulated cat soleus muscles producing ankle extension moment, with regard to their potential applications in neuroprostheses with either fixed parameters or parameter adaptation. Both uncoupled and coupled models predicted the output moment well for random angle perturbation sizes ranging from 10 degrees to 30 degrees. For the uncoupled model, the best parameter values depended on the range of perturbations and the mean angle. Introducing coupling between activation and velocity in the model reduced this parameter sensitivity; one set of model parameter values fit the data for all perturbation sizes and also fit the data under isometric or constant stimulation conditions. Thus, the coupled model would be the most appropriate for applications requiring fixed parameter values. In contrast, with continuous parameter adaptation, errors due to changing test conditions decreased more quickly for the uncoupled model, suggesting that it would perform well in adaptive control of neuroprostheses.

Animals↗

PubMed2006

Combining genetic algorithms and Lyapunov-based adaptation for online design of fuzzy controllers.

This paper proposes a hybrid approach for the design of adaptive fuzzy controllers (FCs) in which two learning algorithms with different characteristics are merged together to obtain an improved method. The approach combines a genetic algorithm (GA), devised to optimize all the configuration parameters of the FC, including the number of membership functions and rules, and a Lyapunov-based adaptation law performing a local tuning of the output singletons of the controller, and guaranteeing the stability of each new controller investigated by the GA. The effectiveness of the proposed method is confirmed using both numerical simulations on a known case study and experiments on a nonlinear hardware benchmark.

Algorithms↗

PubMed2002

Head and eye movements in visual search using night vision goggles.

BACKGROUND: Night-vision goggles (NVGs) provide only a restricted field of view and have other characteristics that may affect the head and eye movements used in visual search. METHODS: We measured head scan patterns, the magnitude and duration of gaze saccades, and fixation duration as subjects searched computer generated imagery either with or without NVGs. Subjects searched for either a large (6 degrees) target on a low-detail background (high conspicuity condition) or a small (2 degrees) target on a high-detail background (low conspicuity condition). RESULTS: All subjects displayed head-scan patterns that were qualitatively similar to those reported in the literature. Although both head-scan speed and amplitude were higher for the NVG condition as compared with the no-NVG condition, the difference was not statistically significant. Head-scan speed did vary significantly with target conspicuity, however. Gaze saccade amplitude varied with target conspicuity but only when NVGs were used. Fixation duration did not vary with either NVG use or target conspicuity. A two-parameter (exponent and scaling parameter) power function was fitted to the amplitude-duration data. The power-function exponents varied from about 0.30 to 0.44, but there was a concommitant variation in the scaling parameter, and the result was no significant difference in the form of the power functions fitted to the data. CONCLUSIONS: NVG use did not significantly affect any of the individual head or eye movement variables involved in searching the computer-generated imagery studied here. However, the decrease in gaze-saccade amplitude with increased target conspicuity when NVGs were used is evidence of the sensitivity of the head and eye movement measurement techniques used here, and suggests that all available measures of response efficiency be considered when evaluating NVGs using complex stimuli.

Adult↗

PubMed1999

Impact of nonlinear heat transfer on temperature control in regional hyperthermia.

We describe an optimization process specially designed for regional hyperthermia of deep-seated tumors in order to achieve desired steady-state temperature distributions. A nonlinear three-dimensional heat transfer model based on temperature-dependent blood perfusion is applied to predict the temperature. Using linearly implicit methods in time and adaptive multilevel finite elements in space, we are able to integrate efficiently the instationary nonlinear heat equation with high accuracy. Optimal heating is obtained by minimizing an integral objective function which measures the distance between desired and model predicted temperatures. A sequence of minima is calculated from successively improved constant-rate perfusion models employing a damped Newton method in an inner iteration. We compare temperature distributions for two individual patients calculated on coarse and fine spatial grids and present numerical results of optimizations for a Sigma 60 Applicator of the BSD 2000 Hyperthermia System.

Algorithms↗

PubMed1988

Size adaptation of Turing prepatterns.

Spontaneous pattern formation may arise in biological systems as primary and secondary bifurcations to nonlinear parabolic partial differential equations describing chemical reaction-diffusion systems. Such Turing prepatterns have a specified geometry as long as D/R2 (the diffusion coefficient of the morphogen D divided by the square of a characteristic length) is confined to a (usually) limited interval. As real biochemical systems like cleaving eggs or early embryos vary considerably in size, Turing prepatterns are unable to maintain a specified prepattern-geometry, unless D/R2 is varied as well. We show, that actual biochemical control systems may vary Dapp/R2, where Dapp (kappa) is an apparent diffusion constant, dependent on enzyme regulated rate constants, and that such simple control systems allow Turing structures to adapt to size variations of at least a factor 10(3) (linearly), not only in large connected cell systems, but in single cells as well.

Diffusion↗

PubMed2003

Robust control of linear ceramic motor drive with LLCC resonant technique.

This study presents a robust control system for a linear ceramic motor (LCM) that is driven by a high-frequency voltage source inverter using two-inductance two-capacitance (LLCC) resonant technique. The structure and driving principle of the LCM are introduced. Because the dynamic characteristics and motor parameters of the LCM are nonlinear and time varying, a robust control system is designed based on the hypothetical dynamic model to achieve high-precision position control. The presentation of robust control for the LCM drive system is divided into three parts, which comprise state feedback controller, feed-forward controller, and uncertainty controller. The adaptation laws of control gains in the robust control system are derived in the sense of Lyapunov stability theorem such that the stability of the control system can be guaranteed. It not only has the learning ability similar to intelligent control, but also its control framework is more simple than intelligent control. With the proposed robust control system, the controlled LCM drive possesses the advantages of good tracking control performance and robustness to uncertainties. The effectiveness of the proposed robust control system is verified by experimental results in the presence of uncertainties. In addition, the advantages of the proposed control system are indicated in comparison with the traditional integral-proportional (IP) position control system.

Journal Article↗