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At least 127 records · Page 7Linked to original sources
PubMed2006

Tracking control of cascade systems based on passivity: the nonadaptive and adaptive cases.

A new cascade passivity-based control scheme for tracking purposes is proposed in this paper. The proposed scheme is valid for a certain class of nonlinear systems even with unstable zero dynamic, and it is also useful for regulation and stabilization purposes. The cases where all system parameters are assumed to be known (nonadaptive case) and also the case when they are unknown (adaptive case) are considered. Some simulation examples are studied to analyze the behavior of the proposed scheme.

Algorithms↗
PubMed1989

Phase I trial using adaptive control dosing of hexamethylene bisacetamide (NSC 95580).

Hexamethylene bisacetamide (HMBA), a potent differentiating agent, was administered to patients with refractory malignant tumors. Thirteen patients received 30 evaluable courses. HMBA was given by continuous i.v. infusion for 5 days. Therapy was repeated every 28 days, if patients had recovered from toxicity. The starting dose was 24 g/m2/day. Because our previous trial had shown wide interpatient variability in HMBA pharmacokinetics and excess toxicity at HMBA plasma concentrations greater than 2 mM (HMBA doses between 24 and 33.6 g/m2/day), we attempted to individualize each patient's dose based on a dosing scheme using an adaptive (feedback) control algorithm, which assumed linear clearance for HMBA. In all courses, a plasma sample was assayed daily and infusion rates were adjusted to achieve an HMBA plasma concentration of 1.5-2.0 mM (300-400 mg/liter). The patients included 12 men and 1 woman with a median age of 56 years (range, 34-76) and median Karnofsky performance status of 90% (range, 60-100). All patients had received prior chemotherapy and 9 patients had also received radiation therapy. The linear adaptive control algorithm was reasonably precise, with a mean absolute error of 0.28 (SE 0.04) mM. However, adjustments in infusion rate systematically overshot the desired change in steady state concentration, probably due to nonlinear clearance of HMBA. For levels within 24 h of a change in infusion rate, this resulted in significant bias, with a mean error of 0.24 (SE 0.09) mM. The mean absolute error was 0.40 (SE 0.06) mM. A second adaptive control algorithm, using a pharmacokinetic model with parallel first-order (renal) clearance and Michaelis-Menten (nonrenal) clearance and using Bayesian parameter estimation with a priori estimates based on our previous phase I trial, proved to be much more precise than the linear method and was unbiased when applied retrospectively to the same observations, with a mean error (within 24 h of a change in infusion rate) of 0.02 (SE 0.06) mM and a mean absolute error of 0.22 (SE 0.03) mM. Toxicity was reversible in all cases. Neurotoxicity, consisting of hallucinations, agitation, somnolence, or confusion, was seen in 2 patients. Four patients complained of insomnia or anxiety. Mild asymptomatic acidosis was seen in 3 patients. Other toxicity included grade 1-2 nausea and vomiting (10 patients), grade 2 diarrhea (2 patients), grade 3 thrombocytopenia (3 patients), grade 1-3 leukopenia (3 patients), and oral herpes simplex infection (4 patients). Mild reversible renal insufficiency (measured by creatinine clearance) was seen in 8 patients.(ABSTRACT TRUNCATED AT 400 WORDS)

Acetamides↗
PubMed2000

Normalization: contrast-gain control in simple (Fourier) and complex (non-Fourier) pathways of pattern vision.

Results from two types of texture-segregation experiments considered jointly demonstrate that the heavily-compressive intensive nonlinearity acting in static pattern vision is not a relatively early, local gain control like light adaptation in the retina or LGN. Nor can it be a late, within-channel contrast-gain control. All the results suggest that it is inhibition among channels as in a normalization network. The normalization pool affects the complex-channel (second-order, non-Fourier) pathway in the same manner in which it affects the simple-channel (first-order, Fourier) pathway, but it is not yet known whether complex channels' outputs are part of the normalization pool.

Form Perception↗
PubMed1995

Information processing in neural networks by means of controlled dynamic regimes.

This paper is concerned with the modeling of neural systems regarded as information processing entities. I investigate the various dynamic regimes that are accessible in neural networks considered as nonlinear adaptive dynamic systems. The possibilities of obtaining steady, oscillatory or chaotic regimes are illustrated with different neural network models. Some aspects of the dependence of the dynamic regimes upon the synaptic couplings are examined. I emphasize the role that the various regimes may play to support information processing abilities. I present an example where controlled transient evolutions in a neural network, are used to model the regulation of motor activities by the cerebellar cortex.

Cerebellum↗
PubMedDate not supplied

[The adaptive regulation of the nonlinear dynamics of brain electrical activity].

The reinforcing automated stimulation of the emotional positive hypothalamic areas which was contingent upon the multiperiodical events in the EEG structure increased the number of episodes with non-linear dynamics. It resulted in an increase in the frequency of the intracranial self-stimulation. Under conditions of controlled experiment a possibility was shown of the intentional experimental formation of the EEG episodes with different types of non-linear dynamics. At the stages preceding the associative learning, the application of fractal analysis enabled revealing a complex character of non-linearity in the bands of the EEG dominant frequencies with a slight tendency to a dominant process. The associative learning produced one dominant non-linear process which determined the dynamics of the whole system. The neurophysiological characteristics of the given adaptive process were determined as well as the difference between this process and the response to control stimulation.

Adaptation, Physiological↗
PubMed1990

Modeling, adaptive control, and optimal drug therapy.

Drug therapy, its clearly development, and the advent of pharmacokinetic models are described, from the original work of Teorell, through that of Augsberger and Kruger-Thiemer, to the present. Adaptive control of such models, long known in engineering, began in therapeutics with methods for linear and nonlinear least-squares regression, and has progressed to the Maximum Aposteriori Probability (MAP) Bayesian method. Strategies for optimal monitoring of serum concentrations are described and their clinical results briefly evaluated. Lastly, the new method of Approximate Optimal Closed-Loop (AOCL) control is described, in which the therapeutic regimen is used at the same time to probe (learn about) the patient's model approximately optimally. The new method considers the expected values of planned future serum concentrations (or other responses), in addition to the traditional measurement of past serum concentrations. This should optimize the process of learning about a patient's model while treating him at the same time.

Bayes Theorem↗
PubMedDate not supplied

Proper orthogonal decomposition based optimal neurocontrol synthesis of a chemical reactor process using approximate dynamic programming.

The concept of approximate dynamic programming and adaptive critic neural network based optimal controller is extended in this study to include systems governed by partial differential equations. An optimal controller is synthesized for a dispersion type tubular chemical reactor, which is governed by two coupled nonlinear partial differential equations. It consists of three steps: First, empirical basis functions are designed using the 'Proper Orthogonal Decomposition' technique and a low-order lumped parameter system to represent the infinite-dimensional system is obtained by carrying out a Galerkin projection. Second, approximate dynamic programming technique is applied in a discrete time framework, followed by the use of a dual neural network structure called adaptive critics, to obtain optimal neurocontrollers for this system. In this structure, one set of neural networks captures the relationship between the state variables and the control, whereas the other set captures the relationship between the state and the costate variables. Third, the lumped parameter control is then mapped back to the spatial dimension using the same basis functions to result in a feedback control. Numerical results are presented that illustrate the potential of this approach. It should be noted that the procedure presented in this study can be used in synthesizing optimal controllers for a fairly general class of nonlinear distributed parameter systems.

Models, Chemical↗
PubMed1997

A model for the design and evaluation of algorithms for closed-loop cardiovascular therapy.

Developing a clinically useful closed-loop drug delivery system can be extremely time consuming and costly. One approach to reducing the time and cost associated with developing closed-loop systems is to reduce the number of animal experiments and perform an extensive set of simulation studies. Through simulations, a closed-loop controller's performance can be evaluated over a complete spectrum of the patient population, including boundary conditions. Simulation studies are repeatable, offering significant advantages in comparing modifications in control algorithms. Finally, simulation studies can be performed in a fraction of the time required for animal studies, at a fraction of the cost. We have developed a simulator, that included a nonlinear pulsatile-flow cardiovascular model, a physiological regulatory mechanism, and the pharmacology of four frequently titrated cardiovascular drugs. This simulator has already been used in the design and evaluation of two closed-loop algorithms-a self-tuning regulator (STR) and a multiple model adaptive controller (MMAC)-for blood pressure control during and after cardiac surgery.

Algorithms↗
PubMed2003

Computation of dynamic adsorption with adaptive integral, finite difference, and finite element methods.

Analysis of diffusion-controlled adsorption and surface tension in one-dimensional planar coordinates with a finite diffusion length and a nonlinear isotherm, such as the Langmuir or Frumkin isotherm, requires numerical solution of the governing equations. This paper presents three numerical methods for solving this problem. First, the often-used integral (I) method with the trapezoidal rule approximation is improved by implementing a technique for error estimation and choosing time-step sizes adaptively. Next, an improved finite difference (FD) method and a new finite element (FE) method are developed. Both methods incorporate (a). an algorithm for generating spatially stretched grids and (b). a predictor-corrector method with adaptive time integration. The analytical solution of the problem for a linear dynamic isotherm (Henry isotherm) is used to validate the numerical solutions. Solutions for the Langmuir and Frumkin isotherms obtained using the I, FD, and FE methods are compared with regard to accuracy and efficiency. The results show that to attain the same accuracy, the FE method is the most efficient of the three methods used.

Adsorption↗
PubMed1995

Multiple drug hemodynamic control by means of a supervisory-fuzzy rule-based adaptive control system: validation on a model.

A control device that uses an expert system approach for a two input-two output system has been developed and evaluated using a mathematical model of the hemodynamic response of a dog. The two inputs are the infusion rates of two drugs: sodium nitroprusside (SNP) and dopamine (DPM). The two controlled variables are the mean arterial pressure and the cardiac output. The control structure is dual mode, i.e., it has two levels: a critical conditions (coarse) control mode and a noncritical conditions (fine) control mode. The system switches from one to the other when threshold conditions are met. Different "controller parameters sets"-including the values for the threshold conditions-can be given to the system which will lead to different controller outputs. Both control modes are rule-based, and supervisory capabilities are added to ensure adequate drug delivery. The noncritical control mode is a fuzzy logic controller. The system includes heuristic features typically considered by anesthesiologists, like waiting periods and the observance of a "forbidden dosage range" for DPM infusion when used as an inotrope. An adaptation algorithm copes with the wide range of sensitivities to SNP found among different individuals, as well as the time varying sensitivity frequently observed in a single patient. The control device is eventually tested on a nonlinear model, designed to mimic the conditions of congestive heart failure in a dog. The test runs show a highest overshoot of 3 mmHg with nominal SNP sensitivity. When tested with different simulated SNP sensitivities, the controller adaptation produces a faster response to lower sensitivities, and reduced oscillations to higher sensitivities. The simulations seem to show that the system is able to drive and adequately keep the two hemodynamic variables within prescribed limits.

Adaptation, Physiological↗
PubMed2005

A multi-channel adaptive nonlinear filtering structure realizing some properties of the hearing system.

An adaptive nonlinear signal-filtering model of the cochlea is proposed based on the functional properties of the inner ear. The model consists of the cochlear filtering segments taking into account the longitudinal, transverse and radial pressure wave propagation. On the basis of an analytical description of different parts of the model and the results of computer modeling, the biological significance of the nonlinearity of signal transduction processes in the outer hair cells, their role in signal compression and adaptation, the efferent control over the characteristics of the filtering structures (frequency selectivity and sensitivity) are explained.

Cochlea↗
PubMed1995

Neural network control of functional neuromuscular stimulation systems: computer simulation studies.

A neural network control system has been designed for the control of cyclic movements in Functional Neuromuscular Stimulation (FNS) systems. The design directly addresses three major problems in FNS control systems: customization of control system parameters for a particular individual, adaptation during operation to account for changes in the musculoskeletal system, and attaining resistance to mechanical disturbances. The control system was implemented by a two-stage neural network that utilizes a combination of adaptive feedforward and feedback control techniques. A new learning algorithm was developed to provide rapid customization and adaptation. The control system was evaluated in a series of studies on a computer simulated musculoskeletal model. The model of electrically stimulated muscle used in the study included nonlinear recruitment, linear dynamics, and multiplicative nonlinear torque-angle and torque-velocity scaling factors. The skeletal model consisted of a one-segment planar system with passive constraints on joint movement. Results of the evaluation have demonstrated that the control system can provide automated customization of the feedforward controller parameters for a given musculoskeletal system. It can account for changes in the musculoskeletal system by adapting the feedforward controller parameters on-line and it can resist the effects of mechanical disturbances. These results suggest that this design may be suitable for the control of FNS systems and other dynamic systems.

Adaptation, Physiological↗
PubMed1995

Contrast gain control: a bilinear model for chromatic selectivity.

We report the results of psychophysical experiments on color contrast induction. In earlier work [Vision Res. 34, 3111 (1994)], we showed that modulating the spatial contrast of an annulus in time induces an apparent modulation of the contrast of a central disk, at isoluminance. Here we vary the chromatic properties of disk and annulus systematically in a study of the interactions among the luminance and the color-opponent channels. Results show that induced contrast depends linearly on both disk and annulus contrast, at low and moderate contrast levels. This dependence leads us to propose a bilinear model for color contrast gain control. The model predicts the magnitude and the chromatic properties of induced contrast. In agreement with experimental results, the model displays chromatic selectivity in contrast gain control and a negligible effect of contrast modulation at isoluminance on the appearance of achromatic contrast. We show that the bilinear model for chromatic selectivity may be realized as a feed-forward multiplicative gain control. Data collected at high contrast levels are fit by embellishing the model with saturating nonlinearities in the contrast gain control of each color channel.

Adaptation, Ocular↗
PubMed1990

Receptor interactions in modulating ventilatory activity.

The ventilatory control system utilizes a variety of sensory receptor groups, including chemoreceptors and mechanoreceptors, to provide feedback concerning the status of controlled variables. Most ventilatory responses to altered receptor inputs generally involve a complex interaction between several receptor groups, central integrative mechanisms, and other modulatory inputs (e.g., "state," hormonal, or neurotransmitter status). Because the control system is complex, nonlinear, and dynamic, the ultimate ventilatory response elicited by a given stimulus is not easy to predict based on the reflex effects of individual receptor groups studied in isolation. A full understanding of the role that sensory receptors play in ventilatory control requires information concerning interactions among receptor groups and with other elements of the control system. The complexity of the problem and the lack of a uniform definition of the term "interaction" has hindered research in this area. An interaction is defined as a nonadditive relationship between independent inputs to the system. Within this definition, five domains of interaction are described. 1) Algebraic interactions occur in ventilation and/or its components because of their multiplicative and nonlinear relationship. 2) Closed-loop interactions occur because of the prevalence of feedback loops within the respiratory control system. 3) Neural interactions reflect central nervous system integration of simultaneous receptor inputs and are demonstrated when feedback loops are opened. Three subdomains of neural interactions are defined: modulatory, dynamic, and range-specific neural interactions. 4) Mechanical interactions result from nonlinear transformations of motoneuron output into mechanical actions. 5) Adaptive interactions occur when paired receptor or modulatory inputs alter future responses. To understand the role of any sensory receptor group in ventilatory control, it is necessary to define its interactions with other control system elements in each of these domains. Understanding the mechanisms of these interactions requires detailed information about the physical system subserving ventilatory control (mechanics and gas exchange) and the relevant properties of the neural network coordinating their actions.

Adaptation, Physiological↗
PubMed1996

Pharmacodynamic modeling of prolonged administration of etoposide.

PURPOSE: A refined pharmacodynamic model for toxicity is necessary for successful adaptive control of the administration of an anticancer drug to avoid toxicity. We sought to establish a pharmacodynamic model of leukopenia in a 14-day administration of etoposide. METHODS: Pharmacokinetic data of 32 patients treated with etoposide infused over 14 days in a phase I study (20 patients) or in an adaptive control study (12 patients) were used to develop a model for the prediction of a leukocyte nadir count. The concentrations of both estimated unbound and total etoposide at steady state, as well as patient demographic factors, were included in linear and nonlinear models. The unbound fraction of etoposide was estimated using an equation based on serum albumin and total bilirubin. The efficacy of the models was evaluated in terms of correlation coefficient (r), mean predictive error (MPE) and root mean square error (RMSE). RESULTS: For both total and unbound drug concentration, a nonlinear model predicted leukopenia more precisely and with less bias than a linear model, and unbound drug explained more variability of leukopenia than total drug concentration in both linear and nonlinear models. The best model was a nonlinear model with three variables of unbound concentration, pretreatment leukocyte count and prior treatment (r = 0.76, MPE +/- SEM = 0.07 +/- 0.17 x 10(3)/microl, RMSE = 0.95 x 10(3) microl), which was better than the best linear model. CONCLUSIONS: The nonlinear model using unbound etoposide concentration explained the interpatient variability of leukocyte nadir count to a fairly large extent. Although the model provided useful information on the pharmacodynamics of etoposide, it was still imprecise and a more refined model is necessary for application to an adaptive control study.

Antineoplastic Agents, Phytogenic↗
PubMed1992

The effect of adaptation on the differential sensitivity of the S-cone color system.

This paper presents a psychophysical dissection of the S-cone color system. Experiments were guided by a skeletal model that assumed a first stage consisting of S-, M- and L-cones, and a second stage of the opponent combination of the S and L+M signals. The response of the S-cone system was isolated by measuring difference thresholds between lights that were equiluminant tritanopic confusion pairs and thus differed only in S-cone excitation. Two types of mechanisms that control sensitivity in the S-cone system were identified: (i) static mechanisms that have a restricted range and thus limit discrimination to a small range of inputs; and (ii) adaptive mechanisms that change the state of the system in response to changes in steady illumination, so that the system is sensitive to small changes from the adapting light. These mechanisms were localized by lights that stimulated the S-cone system while keeping the signal constant at either the S, the L+M, or the post-opponent stage. The response function of the static mechanism was estimated by measuring difference thresholds at judgment points other than the steady adapting light. This procedure was repeated at a number of adaptation lights to examine the properties of adaptive mechanisms. The data were consistent with an elaborated model that included identical multiplicative gain control mechanisms in the S and L+M pre-opponent branches, and a post-opponent static sigmoidal nonlinearity with different amounts of compression for positive and negative opponent inputs.

Adaptation, Ocular↗
PubMed2005

Exponential epsilon-regulation for multi-input nonlinear systems using neural networks.

This paper considers the problem of robust exponential epsilon-regulation for a class of multi-input nonlinear systems with uncertainties. The uncertainties appear not only in the feedback channel but also in the control channel. Under some mild assumptions, an adaptive neural network control scheme is developed such that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded and, under the control scheme with initial data starting in some compact set, the states of the closed-loop system is guaranteed to exponentially converge to an arbitrarily specified epsilon-neighborhood about the origin. The important contributions of the present work are that a new exponential uniformly ultimately bounded performance is proposed and that the design parameters and initial condition set can be determined easily. The development generalizes and improves earlier results for the single-input case.

Algorithms↗
PubMed2004

Quantification of dark adaptation dynamics in retinitis pigmentosa using non-linear regression analysis.

PURPOSE: Non-linear regression analysis was used to determine dark adaptation indices in people with retinitis pigmentosa and in control subjects. METHODS: Dark adaptation data were collected for 13 people with retinitis pigmentosa and 21 controls using the Goldmann-Weekers Dark Adaptometer. Data were analysed using an exponential non-linear regression model and dark adaptation indices derived. The results were compared to age-related values. RESULTS: The mean cone threshold of the group with RP (4.73 +/- 0.19 log units) was significantly greater than that found in the control group (3.69 +/- 0.12 log units). The rate of cone dark adaptation in the RP group was not significantly different from that of the control group. The a break in the RP group (6.46 +/- 0.70 minutes) was delayed when compared to the control group (4.29 +/- 0.21 minutes) and the rate of rod dark adaptation in the RP group was slower (10 +/- 2 per cent per minute) than that of the control group (15 +/- 1 per cent per minute). CONCLUSIONS: This study has shown that a relatively simple data analysis can provide a more quantitative and intuitive description of dark adaptation rates in people with retinal disease. This technique will enable more effective use of dark adaptometry as a supplement to objective electrophysiology, when monitoring people with retinitis pigmentosa.

Adult↗