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At least 19 recordsLinked to original sources
PubMed1994

Nonlinear adaptive control of adrenal-postpituitary imbalances and identifiability analysis.

Adrenal-postpituitary imbalances express pathological evolutions of the nonlinear biological oscillator due to hormonal coupling between adrenocortical hormones and vasopressin. This system, based on agonistic-antagonistic equilibration, can be represented by a nonlinear model to be controlled in the pathological case, in order to reach a physiological state. The modeling introduced by E. Bernard-Weil has already led to efficient therapeutics and can thus be considered realistic. We can therefore use the simulated data given by Bernard-Weil, and although our results on control are obtained by simulation, they are meaningful. The therapy is based on the idea of moving the pathological controlled system from the pathological state to the physiological one. However, it is proved that with a periodic control one is not able to achieve the precise objective. This leads us to introduce the locking concept, which allows system parameters to change and provides the basis for an adaptive and iterative control, here given by a sequence of polynomial correctors. In a few iterations we are now able to find the physiological behavior again. Moreover, as we have to identify the parameters of the considered models and control laws, we have to study their structural identifiability. We can prove, thanks to the work of Vajda and his colleagues, the global identifiability of the uncontrolled 12-parameter model. We also prove the local identifiability of the eight-parameter controllers.

Animals↗
PubMed1994

Model reference direct adaptive control of nonlinear plants using neural networks.

A learning scheme for multilayer feedforward neural networks used as direct adaptive controllers of nonlinear plants is suggested. This scheme is a supervised steepest descent one that does not require backpropagation of the error. Using a neural network controller trained with this method does not require the identification stage and this makes it superior to the other methodologies. Methods for using neural networks in plant control suggested in the literature are discussed and compared with the proposed system. The structure of the network and the training method used are explained. Simulations based on model reference control of some nonlinear plants show satisfactory performance.

Computer Simulation↗
PubMed2002

Neural adaptive control of nonlinear multivariable systems with application to a class of inverted pendulums.

In this paper multilayer neural networks (MNNs) are used to control the balancing of a class of inverted pendulums. Unlike normal inverted pendulums, the pendulum discussed here has two degrees of rotational freedom and the base-point moves randomly in three-dimensional space. The goal is to apply control torques to keep the pendulum in a prescribed position in spite of the random movement at the base-point. Since the inclusion of the base-point motion leads to a non-autonomous dynamic system with time-varying parametric excitation, the design of the control system is a challenging task. A feedback control algorithm is proposed that utilizes a set of neural networks to compensate for the effect of the system's nonlinearities. The weight parameters of neural networks updated on-line, according to a learning algorithm that guarantees the Lyapunov stability of the control system. Furthermore, since the base-point movement is considered unmeasurable, a neural inverse model is employed to estimate it from only measured state variables. The estimate is then utilized within the main control algorithm to produce compensating control signals. The examination of the proposed control system, through simulations, demonstrates the promise of the methodology and exhibits positive aspects, which cannot be achieved by the previously developed techniques on the same problem. These aspects include fast, yet well-maintained damped responses with reasonable control torques and no requirement for knowledge of the model or the model parameters. The work presented here can benefit practical problems such as the study of stable locomotion of human upper body and bipedal robots.

Algorithms↗
PubMedDate not supplied

Adaptive control at low glucose concentration of HEK-293 cell serum-free cultures.

Fed-batch cultures were implemented to study the metabolism of HEK-293 cells. Glucose, measured every 30 min by a FIA biosensor system, was maintained at 1 mM throughout the culture using an adaptive nonlinear controller based on minimal process modeling. The controller performed satisfactorily at both low and high cell concentrations without the need for retuning between different culture phases. Overall, lactate production was significantly reduced by maintaining a low glucose concentration, thus decreasing the rate of glycolysis. The rates of glucose and glutamine uptake as well as the lactate and ammonia production were compared to those obtained in batch mode with an initial glucose concentration of 21 mM. Basically, three phases were observed in both culture modes. The metabolic shift from the first to the second phase was characterized by a significant reduction in glucose consumption and lactate production while maximum growth rate was maintained. The specific respiration rate appeared unchanged during the first two phases, suggesting that no change occurred in the oxidative pathway capacity. In the third phase, cell growth became slower very likely due to glutamine limitation.

Ammonia↗
PubMed1992

Finite element methods for the biomechanics of soft hydrated tissues: nonlinear analysis and adaptive control of meshes.

This chapter addresses computationally demanding numerical formulations in the biomechanics of soft tissues. The theory of mixtures can be used to represent soft hydrated tissues in the human musculoskeletal system as a two-phase continuum consisting of an incompressible solid phase (collagen and proteoglycan) and an incompressible fluid phase (interstitial water). We first consider the finite deformation of soft hydrated tissues in which the solid phase is represented as hyperelastic. A finite element formulation of the governing nonlinear biphasic equations is presented based on a mixed-penalty approach and derived using the weighted residual method. Fluid and solid phase deformation, velocity, and pressure are interpolated within each element, and the pressure variables within each element are eliminated at the element level. A system of nonlinear, first-order differential equations in the fluid and solid phase deformation and velocity is obtained. In order to solve these equations, the contributions of the hyperelastic solid phase are incrementally linearized, a finite difference rule is introduced for temporal discretization, and an iterative scheme is adopted to achieve equilibrium at the end of each time increment. We demonstrate the accuracy and adequacy of the procedure using a six-node, isoparametric axisymmetric element, and we present an example problem for which independent numerical solution is available. Next, we present an automated, adaptive environment for the simulation of soft tissue continua in which the finite element analysis is coupled with automatic mesh generation, error indicators, and projection methods. Mesh generation and updating, including both refinement and coarsening, for the two-dimensional examples examined in this study are performed using the finite quadtree approach. The adaptive analysis is based on an error indicator which is the L2 norm of the difference between the finite element solution and a projected finite element solution. Total stress, calculated as the sum of the solid and fluid phase stresses, is used in the error indicator. To allow the finite difference algorithm to proceed in time using an updated mesh, solution values must be transferred to the new nodal locations. This rezoning is accomplished using a projected field for the primary variables. The accuracy and effectiveness of this adaptive finite element analysis is demonstrated using a linear, two-dimensional, axisymmetric problem corresponding to the indentation of a thin sheet of soft tissue. The method is shown to effectively capture the steep gradients and to produce solutions in good agreement with independent, converged, numerical solutions.

Adaptation, Physiological↗
PubMedDate not supplied

Adaptive terminal sliding mode control for high-order nonlinear dynamic systems.

An adaptive terminal sliding mode control (SMC) technique is proposed to deal with the tracking problem for a class of high-order nonlinear dynamic systems. It is shown that a function augmented sliding hyperplane can be used to develop a new terminal sliding mode for high-order nonlinear systems. A terminal SMC controller based on Lyapunov theory is designed to force the state variables of the closed-loop system to reach and remain on the terminal sliding mode, so that the output tracking error then converges to zero in finite time which can be set arbitrarily. An adaptive mechanism is introduced to estimate the unknown parameters of the upper bounds of system uncertainties. The estimates are then used as controller parameters so that the effects of uncertain dynamics can be eliminated. It is also shown that the stability of the closed-loop system can be guaranteed with the proposed control strategy. The simulation of a numerical example is provided to show the effectiveness of the new method.

Journal Article↗
PubMed1995

Adaptive fuzzy control of unstable nonlinear systems.

This paper addresses the structure and an associated on-line learning algorithm of a feedforward multilayer connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed Fuzzy Adaptive Learning Control Network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. An on-line structure/parameter learning algorithm, called FALCON-ART, is proposed for constructing the FALCON dynamically. The FALCON-ART can partition the input/output space in a flexible way based on the distribution of the training data. Hence it can avoid the problem of combinatorial growing of partitioned grids in some complex systems. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. More notably, the FALCON-ART can on-line partition the input/output spaces, tune membership functions, and find proper fuzzy logic rules dynamically without any a priori knowledge or even any initial information on these. The proposed learning scheme has been successfully used to control two unstable nonlinear systems. They are the seesaw system and the inverted wedge system.

Algorithms↗
PubMed1994

An adaptive plasma glucose controller based on a nonlinear insulin/glucose model.

The design of plasma glucose controllers traditionally relies on linear approaches. The implementation of an appropriate nonlinear model of the insulin/glucose regulatory system into an adaptive controller should predict the insulin-dependent glucose removal more reliably and hence provide better control over a wide spectrum of insulin signals. A discretized form of the model leads to a two-step procedure. First, the measured plasma glucose levels associated with the exogenous glucose infusion rates are used in the estimation of the past removal rates which, in turn, can be expressed as a weighted sum of past insulin inputs and previous values of the removal rate. Parameters of the sum are adjusted on-line by a recursive method of estimation which features a prefiltering of data to account for a corrupting coloured process noise. The same equation is in turn used to predict the time course of the insulin-dependent fractional rate of glucose removal. The performance of the controller, tested in vivo in three pigs, is presented for various intravenous or subcutaneous rapid injections and staircase infusions of insulin. Plasma glucose is maintained at an average level of 99.9 +/- 8.7% of the target value (% set point +/- coefficient of variation). The controller reacts promptly to large and rapid variations in insulin action. Although control improves with the number of glucose measurements, the prediction of glucose removal allows for some flexibility in the monitoring of the plasma glucose. Sampling frequency varied from a 2 min interval during transient periods to 7 min as steady states were reached.

Algorithms↗
PubMed1998

Adaptive Process Control in Rubber Industry.

This paper describes the problems and an adaptive solution for process control in rubber industry. We show that the human and economical benefits of an adaptive solution for the approximation of process parameters are very attractive. The modeling of the industrial problem is done by the means of artificial neural networks. For the example of the extrusion of a rubber profile in tire production our method shows good resuits even using only a few training samples.

extruder mask estimation↗
PubMed1995

Adaptive control of therapeutic drug regimens relations between clinical situations: outcomes and simulations using nonlinear dynamic models.

With Bayesian modeling and adaptive control of drug dosage regimens, serum and peripheral drug concentrations can be predicted in clinical situations using linear pharmacokinetic compartmental models (PK). Recently, several pathophysiologic and pharmacodynamic nonlinear models (PD) have been developed. The present report illustrates both their utility and limits for the computation of effects in clinical situations in the setting of actual routine and acute patient care. Patients who received therapy with aminoglycosides or/and vancomycin were selected. For each patient, after estimation of individual pharmacokinetic parameters, the computed outputs of the linear compartmental pharmacokinetic model were used as inputs for 2 different a priori nonlinear dynamic models: 1) the EFFECT modeling program, using a Hill model, and 2) the BACTCIDE program, which is a combination of a simple growth model for the organism and a Hill effect model considering both the microorganism, the antibiotic, and the patient's minimal inhibitory concentration (MIC). The programs (1) and (2) can use as inputs the computed concentrations from any of three compartments: central, peripheral, or a spherical diffusion compartment to compute drug diffusion into endocardial vegetations or abscesses. The EFFECT program can be used alone for the evaluation of drug effects. The BACTCIDE program illustrates differences in activity between concentration-dependent and time-dependent antibiotics. Such nonlinear programs are very sensitive to the MIC values.

Aged↗
PubMed1991

A control method for a nonlinear multivariable system: application to interstitial laser hyperthermia.

An original adaptive control method is presented for controlling a nonlinear multivariable system. The method, which could be described as a modified quasi-linear approach, involves dividing the source excitation into a series of pulsing rounds and is implemented as a control algorithm on a computer. The theory underlying the method is developed with reference to an application involving temperature control in interstitial laser hyperthermia. In this application, the method is both successful and necessary to achieve optimally uniform elevated temperatures in a ground beef phantom. Apart from variable and parameter definitions, the method is otherwise general and might be useful for controlling a nonlinear system in which no prior exact characterization of the system is possible. Simulations were conducted to assess the effectiveness of the method in systems for which the unit excitation response changes by factors ranging from zero to three over the total period of excitation. In each case the method has proven stable.

Algorithms↗
PubMed2002

Adaptation and gain pool summation: alternative models and masking data.

Foley [J. Opt. Soc. Am. A 11 (1994) 1710] has proposed an influential psychophysical model of masking in which mask components in a contrast gain pool are raised to an exponent before summation and divisive inhibition. We tested this summation rule in experiments in which contrast detection thresholds were measured for a vertical 1 c/deg (or 2 c/deg) sine-wave component in the presence of a 3 c/deg (or 6 c/deg) mask that had either a single component oriented at -45 degrees or a pair of components oriented at +/-45 degrees. Contrary to the predictions of Foley's model 3, we found that for masks of moderate contrast and above, threshold elevation was predicted by linear summation of the mask components in the inhibitory stage of the contrast gain pool. We built this feature into two new models, referred to as the early adaptation model and the hybrid model. In the early adaptation model, contrast adaptation controls a threshold-like nonlinearity on the output of otherwise linear pathways that provide the excitatory and inhibitory inputs to a gain control stage. The hybrid model involves nonlinear and nonadaptable routes to excitatory and inhibitory stages as well as an adaptable linear route. With only six free parameters, both models provide excellent fits to the masking and adaptation data of Foley and Chen [Vision Res. 37 (1997) 2779] but unlike Foley and Chen's model, are able to do so with only one adaptation parameter. However, only the hybrid model is able to capture the features of Foley's (1994) pedestal plus orthogonal fixed mask data. We conclude that (1) linear summation of inhibitory components is a feature of contrast masking, and (2) that the main aftereffect of spatial adaptation on contrast increment thresholds can be assigned to a single site.

Adaptation, Physiological↗
PubMed2000

Neural net based MRAC for a class of nonlinear plants.

A neural net based state feedback model reference adaptive control scheme is presented for a class of nonlinear plants. The proposed scheme employs a time varying pseudo-linear state feedback control, where the state feedback gain being generated from the output of artificial neural networks. The plant need not be in a feedback linearizable form. Both regulation and tracking control have been considered. Global stability of the scheme has been proved through Lyapunov theory. The extension of the scheme to MIMO plants is also included. Simulation studies have been conducted on an industrial robot to validate and illustrate the proposed method.

Neural Networks, Computer↗
PubMed1994

The Vienna implantable centrifugal blood pump.

Because of the inherent disadvantages of membrane pumps, rotary pumps have been increasingly investigated in recent years. As a result of improving biocompatibility, extended assistance with implantable devices is of special interest. Questions arise concerning shear stress, blood traumatization, design of seals, and specific control conditions. In their development of an implantable impeller pump, the Vienna group studied the minimization of hemolysis and thrombus formation by means of numerical simulation, visualization, and in vitro blood evaluation. The latter was revealed to be the most powerful tool for pump evaluation. With optimization of geometry, a hemolysis of in vitro: IH = 0.008; MIH = 0.58; and in vivo: 2.1 to 3 mg% plasma-free hemoglobin could be obtained. For proper control and physiological adaptation, a controller based on a nonlinear and a fuzzy strategy was developed. Furthermore, a method for evaluation of the contractility of the assisted heart during nonpulsatile support was tested by computer simulation. This paper summarizes the evaluation methods used and provide an overview of the results of pump and controller design.

Animals↗
PubMed1992

Development of a digital adaptive control system for PO2 regulation in a membrane oxygenator.

Regulation of gas exchange in artificial lungs (oxygenators) during cardiopulmonary bypass is normally achieved by manual control of the gas composition and flow in response to intermittent sampling of the arterial partial pressures of oxygen (PaO2) and carbon dioxide (PaCO2). Manual control often results in abnormal blood gases which have been implicated in patient morbidity as well as influencing perfusion safety. Fine control of PaO2 and PaCO2 may be achieved by a combination of an in-line blood gas monitoring system and a membrane type oxygenator which is automatically regulated. The overall dynamics of the oxygenation process and control system components are complex and have nonlinear, multivariable and time-varying characteristics. Consequently, an adaptive control system approach is necessary. The implementation of a digital self-tuning control regime for PaO2 is described here. The controller is based on an explicit Linear Quadratic Gaussian (LQG) self-tuning control design which is presented using a polynomial equation approach. The controller performance was investigated in in vitro experiments. The self-tuner performed satisfactorily with various sensor/oxygenator combinations for blood flow and temperature load disturbances. In contrast, a nonadaptive (proportional-integral, PI) type of control system was found to be unsuitable.

Carbon Dioxide↗
PubMed1991

Feedback control methods for task regulation by electrical stimulation of muscles.

Three feedback control algorithms of varying complexity were compared for controlling three different tasks during electrical stimulation of muscles. Two controllers use stimulus pulse width (or recruitment) modulation to grade muscle force (the fixed parameter, first-order PW controller and the adaptive controller). The third controller varies both stimulus pulse width and period simultaneously for muscle force modulation (the PW/SP controller described in the comparison paper). The three tasks tested were isometric torque control, unloaded position tracking, and control of transitions between isometric and unloaded conditions. The first task involved the muscle recruitment nonlinearity. The second task added the effects of muscle length-tension and force-velocity nonlinearities. The third task included a sudden changes in external loading conditions. The comparative evaluation was carried out in an intact cat ankle joint with stimulation of tibialis anterior and medial gastrocnemius muscles. The simplest PW controller demonstrated robust control for all tasks. The PW/SP controller improved the performance of the PW controller significantly for control of isometric torque and load transition, but only slightly for control of unloaded joint position. However, the adaptive controller did not consistently achieve a significant improvement in performance compared with the PW controller for any task. Results suggest that muscle length-tension and force-velocity nonlinearities affect the performance of these controllers similarly within the tested ranges of movement amplitudes and speeds. Abrupt changes in the system, such as those due to recruitment nonlinearity and external loading transitions, tend to limit the performance of the adaptive controller. The study provides guidelines for choosing control algorithms for neural prostheses.

Algorithms↗