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Results for “nonlinear adaptive control”

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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↗

PubMed1999

HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.

This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.

Journal Article↗

PubMed1990

Sites of sensitivity control within a long-wavelength cone pathway.

A flashed-field increment threshold paradigm was used to examine sites of sensitivity control within a long-wavelength cone pathway. The data were fit with a model containing two static nonlinearities, one at the receptors and the second at a red/green opponent stage. The nonlinearities are modified by multiplicative and subtractive processes of adaptation. Comparisons of the model's parameters with physiological measures of the long-wavelength cones suggest that, in the dark, sensitivity is controlled by the opponent site. At high adapting intensities, receptor nonlinearities may limit sensitivity under some conditions. The data also suggest that the spectral tuning of the opponent site varies with adapting intensity.

Color Perception↗

PubMed1991

Model for color vision and light adaptation.

A multizone color model is described. It has nonlinear receptor gain control, two postreceptor opponent-colors processing stages, and neural compression late in the visual pathway. It is assumed that gain control can be activated by receptor responses from a test light itself (self-adaptation) and (or) by receptor responses from other adapting fields. Apparent brightnesses and visual discriminations are mediated by the first processing stage, and apparent hues and saturations are mediated by the second stage. The model accounts for a wide range of data, including nonlinear hue shifts in the color solid, various apparent brightness effects, visual discriminations for achromatic and chromatic lights under various adaptation conditions, and effects of chromatic adaptation on color appearances.

Adaptation, Ocular↗

PubMed1992

Control strategies for nonlinear dynamics of muscle relaxant anaesthesia.

Using modern nonlinear identification techniques, dose-response dynamics for two muscle relaxant trials have been obtained. A so-called NARMAX model has been found for Vecuronium applied to a dog, and Atracurium given to a human. With the relaxant dynamics structure thus obtained, the work proceeded to the control phase. Simple three-term PID controllers were first designed with their parameters being optimised, off-line, using the Simplex method. The non-adaptive nature of this class of controllers makes their robustness open to question when the system parameters for which they have been optimised change. Hence, adaptive controllers in the form of linear and nonlinear generalised minimum variance self-tuners, generalised predictive control and nonlinear k-step ahead predictive controllers are also considered. All of these latter control approaches are shown to be satisfactory, in terms of transient and steady-state performance.

Algorithms↗

PubMed2006

Comparative performance of linear and nonlinear neural networks to predict irregular breathing.

Breathing adaptation during external-beam radiotherapy is a matter of great concern because uncompensated tumour motion requires extended treatment margins that endanger sensitive tissue. Compensation strategies include beam gating, collimator tracking and robotic beam re-alignment. All of these schemes have a system latency of up to several hundred milliseconds, which calls in turn for predictive control loops. Irregularities in breathing make prediction difficult. We have evaluated the performance of two classes of control loop algorithms-the linear adaptive filter and the adaptive nonlinear neural network-for highly irregular patient breathing behaviours. The neural network demonstrated robust adaptability to all of the observed breathing patterns while the linear filter failed in a significant percentage of cases. For those cases where the linear filter could function, it made less accurate predictions than the neural network. Because the neural network presents no additional computational burden in the control loop we conclude that it is the preferred choice among heuristic predictive algorithms.

Algorithms↗

PubMed2004

Fuzzy logic detection of medically serious suicide attempt records in major psychiatric disorders.

Clinical prediction of suicide is a complicated task. The focus for improved suicide risk detection is on the subgroup of individuals whose high suicide risk remains unrecognized by clinicians. We sought to evaluate the accuracy of Fuzzy Adaptive Learning Control Network (FALCON) neural networks, a nonlinear algorithm, in identification of this subgroup. The study sample included the Computerized Scale for risk of Suicide, including 21 suicide risk factors (including the target variable) drawn from 987 patient records, completed by staff clinicians during face-to-face interviews of hospitalized patients. FALCON evaluated all records in two steps: a) 612 for training and 375 for validation, and b) 887 for training and 100 for validation. The existence of previous medically serious suicide attempts (MSSAs) was chosen as the target variable because it is generally recognized as the strongest suicide risk factor. Sensitivity, specificity, and unknown answers among MSSA and non-MSSA were as follows: 612/375 FALCON, 91%, 85%, 11%, 15%; 887/100 FALCON, 94%, 82%, 20%, 14.5%, respectively. Trained FALCON, a nonlinear neural network, achieves respectable accuracy in detecting MSSA patients based on 20 suicide risk factors. Trained FALCON may therefore assist in identification of subgroup of individuals who remain unrecognized by clinicians and contribute to prevention of suicide.

Adolescent↗

PubMed2000

Dynamic algorithm for parameter estimation and its applications

We consider a dynamic method, based on synchronization and adaptive control, to estimate unknown parameters of a nonlinear dynamical system from a given scalar chaotic time series. We present an important extension of the method when the time series of a scalar function of the variables of the underlying dynamical system is given. We find that it is possible to obtain synchronization as well as parameter estimation using such a time series. We then consider a general quadratic flow in three dimensions and discuss the applicability of our method of parameter estimation in this case. In practical situations one expects only a finite time series of a system variable to be known. We show that the finite time series can be repeatedly used to estimate unknown parameters with an accuracy that improves and then saturates to a constant value with repeated use of the time series. Finally, we suggest an important application of the parameter estimation method. We propose that the method can be used to confirm the correctness of a trial function modeling an external unknown perturbation to a known system. We show that our method produces exact synchronization with the given time series only when the trial function has a form identical to that of the perturbation.

Journal Article↗

PubMed2000

Controller designs for constant cutting force turning machine control

A simulation study of a constant cutting force metal turning process is investigated. The process is a challenging control problem due to its nonlinear and time varying dynamics. Simulated implementations of PID, adaptive and non-adaptive sliding mode and model reference adaptive controllers were developed. Tests of the closed loop systems were performed for a range of cutting conditions including specific machined part contours that are presented here to verify the force tracking capability and flexibility of each control scheme. Results indicate that careful design and use of a fixed-gain sliding mode controller with output feedback can give roughly equivalent performance to that of more complex adaptive controllers.

Journal Article↗

PubMed2002

Simulating closed- and open-loop voluntary movement: a nonlinear control-systems approach.

In many recent human motor control models, including feedback-error learning and adaptive model theory (AMT), feedback control is used to correct errors while an inverse model is simultaneously tuned to provide accurate feedforward control. This popular and appealing hypothesis, based on a combination of psychophysical observations and engineering considerations, predicts that once the tuning of the inverse model is complete the role of feedback control is limited to the correction of disturbances. This hypothesis was tested by looking at the open-loop behavior of the human motor system during adaptation. An experiment was carried out involving 20 normal adult subjects who learned a novel visuomotor relationship on a pursuit tracking task with a steering wheel for input. During learning, the response cursor was periodically blanked, removing all feedback about the external system (i.e., about the relationship between hand motion and response cursor motion). Open-loop behavior was not consistent with a progressive transfer from closed- to open-loop control. Our recently developed computational model of the brain--a novel nonlinear implementation of AMT--was able to reproduce the observed closed- and open-loop results. In contrast, other control-systems models exhibited only minimal feedback control following adaptation, leading to incorrect open-loop behavior. This is because our model continues to use feedback to control slow movements after adaptation is complete. This behavior enhances the internal stability of the inverse model. In summary, our computational model is currently the only motor control model able to accurately simulate the closed- and open-loop characteristics of the experimental response trajectories.

Adaptation, Biological↗

PubMed1992

Adaptative control of above knee electro-hydraulic prosthesis.

Conventional designs of an above-knee prosthesis are based on mechanisms with mechanical properties (such as friction, spring and damping coefficients) that remain constant during changing cadence. These designs are unable to replace natural legs due to the lack of active knee joint control. Since the nonlinear and time-varying dynamic coupling between the thigh and the prosthetic limb is high during swing phase, an adaptive control is employed to control the knee joint motion. Two dimensional simulation indicates that the adaptive controller can improve the appearance of gait pattern. It is adaptable to walking speed and can compensate for the variations of hip moment, hip trajectory and toe-off conditions.

Adaptation, Physiological↗

PubMed2003

Simulation results for on-line optimization of a batch bioreactor using nonlinear filtering and optimal control.

The computation of optimal control profiles for batch bioreactors is based on the use of simple and empirical dynamic models. Since these models present some level of uncertainty, the difference between the model dynamics and the reactor dynamics can have significant effects in the reliability of the calculated profile. To develop near optimal control trajectories considering this drawback, we propose to calculate successive control profiles on a moving time horizon using a mathematical model in which the kinetic parameters are estimated by an observer. The desired objective is to generate a near optimal control trajectory adapted to the "running" fermentation. This idea results in a nonlinear estimator plus an optimizer arrangement that so far has not been applied to batch fermentors. Numerical simulations are performed on xanthan-gum batch fermentations and reasonably good results are obtained.

Algorithms↗

PubMed1982

Synthesis of nonlinear control surfaces by a layered associative search network.

An approach to solving nonlinear control problems is illustrated by means of a layered associative network composed of adaptive elements capable of reinforcement learning. The first layer adaptively develops a representation in terms of which the second layer can solve the problem linearly. The adaptive elements comprising the network employ a novel type of learning rule whose properties, we argue, are essential to the adaptive behavior of the layered network. The behavior of the network is illustrated by means of a spatial learning problem that requires the formation of nonlinear associations. We argue that this approach to nonlinearity can be extended to a large class of nonlinear control problems.

Animals↗

PubMed1999

A mathematical model of the adaptive control of human arm motions.

This paper discusses similarities between models of adaptive motor control suggested by recent experiments with human and animal subjects, and the structure of a new control law derived mathematically from nonlinear stability theory. In both models, the control actions required to track a specified trajectory are adaptively assembled from a large collection of simple computational elements. By adaptively recombining these elements, the controllers develop complex internal models which are used to compensate for the effects of externally imposed forces or changes in the physical properties of the system. On a motor learning task involving planar, multi-joint arm motions, the simulated performance of the mathematical model is shown to be qualitatively similar to observed human performance, suggesting that the model captures some of the interesting features of the dynamics of low-level motor adaptation.

Algorithms↗

PubMed2006

Generalized projective synchronization of chaotic systems with unknown dead-zone input: observer-based approach.

In this paper we investigate the synchronization problem of drive-response chaotic systems with a scalar coupling signal. By using the scalar transmitted signal from the drive chaotic system, an observer-based response chaotic system with dead-zone nonlinear input is designed. An output feedback control technique is derived to achieve generalized projective synchronization between the drive system and the response system. Furthermore, an adaptive control law is established that guarantees generalized projective synchronization without the knowledge of system nonlinearity, and/or system parameters as well as that of parameters in dead-zone input nonlinearity. Two illustrative examples are given to demonstrate the effectiveness of the proposed synchronization scheme.

Algorithms↗

PubMed2003

Wavelet neural network control for linear ultrasonic motor drive via adaptive sliding-mode technique.

A wavelet neural network (WNN) control system is proposed to control the moving table of a linear ultrasonic motor (LUSM) drive system to track periodic reference trajectories in this study. The design of the WNN control system is based on an adaptive sliding-mode control technique. The structure and operating principle of the LUSM are introduced, and the driving circuit of the LUSM, which is a voltage source inverter using two-inductance two capacitance (LLCC) resonant technique, is introduced. Because the dynamic characteristics and motor parameters of the LUSM are nonlinear and time varying, a WNN control system is designed based on adaptive sliding-mode control technique to achieve precision position control. In the WNN control system, a WNN is used to learn the ideal equivalent control law, and a robust controller is designed to meet the sliding condition. Moreover, the adaptive learning algorithms of the WNN and the bound estimation algorithm of the robust controller are derived from the sense of Lyapunov stability analysis. The effectiveness of the proposed WNN control system is verified by some experimental results in the presence of uncertainties.

Journal Article↗

PubMed2003

Dynamical mechanisms underlying contrast gain control in single neurons.

Recent neurophysiological experiments have revealed that the linear and nonlinear kernels of the transfer function in sensory neurons are not static. Rather, they are adaptive to the contrast or the variance of time-varying input stimuli, exhibiting a contrast gain control phenomenon. We investigated the underlying biophysical causes of this phenomenon by simulating and analyzing the leaky integrate-and-fire and the Hodgkin-Huxley neuronal models. Our findings indicate that contrast gain control may result from the synergistic cooperation of the nonlinear dynamics of spike generation and the statistical properties of the stimuli. The resulting statistics-dependent stimulus threshold is shown to be a key factor underlying the adaptation of frequency tuning and amplitude gain of a neuron's transfer function in different stimulus environments.

Animals↗

PubMed1984

Adaptation of receptive field spatial organization via multiplicative lateral inhibition.

The interactions among electrically independent neurons via synapses mediating voltage controlled conductance become primarily multiplicative lateral inhibition. This nonlinear lateral inhibition among members of an array of neurons causes adaptation of the organization of the spatial receptive field. A proof of the adaptation is given and applications of the results to studies on insect visual interneurons are discussed. Given a simple hypothesis of spatial gradient of order of conductance dependence on neighboring cell voltage, a sharpening of spatial tuning of the receptive field is predicted with increased background level along with an increased linearization of the neuronal response function.

Adaptation, Physiological↗