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PubMed2001

Adaptive control of cyclic movements as muscles fatigue using functional neuromuscular stimulation.

For individuals with spinal cord injuries, functional neuromuscular stimulation (FNS) systems can be used to activate paralyzed muscles in order to restore function, provide exercise, or assist in movement therapy. In previous work, the pattern generator/pattern shaper (PG/PS) adaptive controller was evaluated on subjects with spinal cord injuries and was able to automatically adjust stimulation parameters to account for individual subject differences and system response nonlinearities. In this study, the PG/PS control system was utilized in extended trials. Results indicated that the controller adapted stimulation patterns in an online manner to account for changes in system properties due to fatigue.

Adult↗
PubMed2006

Adaptation to sine-wave gratings selectively reduces the contrast gain of the adapted stimuli.

Adapting to sinusoidal gratings selectively reduces contrast sensitivity to subsequent test stimuli. To investigate the perceptual processes underlying selective adaptation, we developed an external noise plus adaptation paradigm and a theoretical framework based on a noisy observer model (the contrast-gain-control Perceptual Template Model [cgcPTM]). After adapting to a 45 deg, 2-Hz counter-flickering sine grating of 0.8 contrast, observers performed two-interval forced-choice detection of Gabors of matched spatial frequency, tilted at either 45 or 135 deg and embedded in one of six levels of white external noise (Experiment 1) or embedded in orientation band-pass-filtered external noise (Experiment 2). On the basis of the cgcPTM, we found that adaptation selectively reduced the contrast gain of the perceptual template at the adapted spatial frequency and orientation without altering either pre- or post-gain-control (additive and multiplicative) noises or changing transducer nonlinearity. Modeled as notches on the perceptual templates, the estimated full orientation bandwidth of adaptation at half height was about 8.3 deg.

Adaptation, Physiological↗
PubMed2001

Model-based control of FES-induced single joint movements.

A crucial issue of functional electrical stimulation (FES) is the control of motor function by the artificial activation of paralyzed muscles. Major problems that limit the success of current FES systems are the nonlinearity of the target system and the rapid change of muscle properties due to fatigue. In this study, four different strategies, including an adaptive algorithm, to control the movement of the freely swinging shank were developed on the basis of computer simulations and experimentally evaluated on two subjects with paraplegia due to a complete thoracic spinal cord injury. After developing a nonlinear, physiologically based model describing the dynamic behavior of the knee joint and muscles, an open-loop approach, a closed-loop approach, and a combination of both were tested. In order to automate the individual adjustments cited above, we further evaluated the performances of an adaptive feedforward controller. The two parameters chosen for the adaptation were the threshold pulse width and the scaling factor for adjusting the active moment produced by the stimulated muscle to the fitness of the muscle. These parameters have been chosen because of their significant time variability. The first three controllers with fixed parameters yielded satisfactory result. An additional improvement was achieved by applying the adaptive algorithm that could cope with problems due to muscle fatigue, thus permitting on-line identification of critical parameters of the plant. Although the present study is limited to a simplified experimental setup, its applicability to more complex and functional movements can be expected.

Biomechanical Phenomena↗
PubMedDate not supplied

From wheels to wings with evolutionary spiking circuits.

We give an overview of the EPFL indoor flying project, whose goal is to evolve neural controllers for autonomous, adaptive, indoor micro-flyers. Indoor flight is still a challenge because it requires miniaturization, energy efficiency, and control of nonlinear flight dynamics. This ongoing project consists of developing a flying, vision-based micro-robot, a bio-inspired controller composed of adaptive spiking neurons directly mapped into digital microcontrollers, and a method to evolve such a neural controller without human intervention. This article describes the motivation and methodology used to reach our goal as well as the results of a number of preliminary experiments on vision-based wheeled and flying robots.

Action Potentials↗
PubMed2001

Nonlinear and extra-classical receptive field properties and the statistics of natural scenes.

The neural mechanisms of early vision can be explained in terms of an information-theoretic optimization of the neural processing with respect to the statistical properties of the natural environment. Recent applications of this approach have been successful in the prediction of the linear filtering properties of ganglion cells and simple cells, but the relations between the environmental statistics and cortical nonlinearities, like those of end-stopped or complex cells, are not yet fully understood. Here we present extensions of our previous investigations of the exploitation of higher-order statistics by nonlinear neurons. We use multivariate wavelet statistics to demonstrate that a strictly linear processing would inevitably leave substantial statistical dependencies between the outputs of the units. We then consider how the basic nonlinearities of cortical neurons--gain control and ON/OFF half-wave rectification--can exploit these higher-order statistical dependencies. We first show that gain control provides an adaptation to the polar separability of the multivariate probability density function (PDF), and, together with an output nonlinearity, enables an overcomplete sparse coding. We then consider how the remaining higher-order dependencies between different units can be exploited by a combination of basic ON/OFF point nonlinearities and subsequent weighted linear combinations. We consider two statistical optimization schemes for the computation of the optimal weights: principal component analysis (PCA) and independent component analysis (ICA). Since the intermediate nonlinearities transform some of the higher-order dependencies into second-order dependencies even the basic PCA approach is able to exploit part of the redundancies. ICA ignores this second-order structure, but can exploit higher-order dependencies. Both schemes yield a variety of nonlinear units which comprise the typical nonlinear processing properties, such as end-stopping, side-stopping, complex-cell properties and extra-classical receptive field properties, but the 'ideal' complex cells seem only to occur with PCA. Thus, a combination of ON/OFF nonlinearities with an integrated PCA-ICA strategy seems necessary to exploit the statistical properties of natural images.

Algorithms↗
PubMed1990

Simulation of movement detection by direction-selective ganglion cells in the rabbit and squirrel retina.

A veto-gate model of movement detection by direction-selective ganglion cells in the vertebrate retina, first proposed by Barlow and Levick (1965), provides the basis for a model described in this study. The model is a simple network consisting basically of (1) two subunits that have receptive fields with a center-surround organization and an adaptational gain control, (2) a lateral inhibitory pathway, (3) a site of nonlinear interaction, followed by (4) a leaky temporal integrator. The model is tested by comparing its basic properties to those reported in the physiological literature on rabbit and squirrel direction-selective retinal ganglion cells. It is shown that the physiological findings on sensitivity to flashes, moving spots or slits, and phi-movement stimuli, can be mimicked quite well by our model. Similarities between the component processes of the subunits and known retinal processes are pointed out. The simulation studies shed a new light on some of the known properties and suggest several new, more revealing, physiological experiments. Such experiments are necessary to develop a full specification of this type of model and to fix more parameter values than is possible at present. Results of some critical experiments are predicted to enable physiologists to falsify or corroborate the model. The simulation studies also help to distinguish use from abuse of this type of model in explanations of psychophysical findings. For example, neither the most complete Barlow-Levick detector nor any stripped-down versions that retain a temporally extended lateral inhibition (which is essential to mimick the physiological findings), respond well to moving random-pixel arrays.

Animals↗
PubMed1999

Central representation of time during motor learning.

This study stemmed from the observation that the brain of human as well as nonhuman primates is capable of forming and memorizing remarkably accurate internal representations of the dynamics of the arm. These dynamics establish a functional relation between applied force and ensuing arm motion, a relation that generally is quite complex and nonlinear. Current evidence shows that the motor control system is capable of adapting to perturbing forces that depend on motion variables such as position, velocity, and acceleration. The experiments we report here were aimed at establishing whether or not the motor system also may adapt to forces that depend explicitly on time rather than on motion variables. Surprisingly, the experiments suggest a negative answer. When asked to compensate for a predictable and repeated time-varying pattern of disturbing forces, subjects learned to counteract the disturbance by producing forces that did not depend on time but on the velocity and the position of the arm. We conclude from this evidence that time and time-dependent dynamics are not explicitly represented within the neural structures that are responsible for motor adaptation. Although our findings are not sufficient to rule out the presence of a timing structure within the central nervous system, they are consistent with other investigations that conspicuously failed to find evidence for such a central clock.

Adaptation, Physiological↗
PubMed1994

Predictive control of nonlinear systems based on identification by backpropagation networks.

Using the property of universal approximation of multilayer perceptron neural network, a class of discrete nonlinear dynamical systems are modeled by a perceptron with two hidden layers. A backpropagation algorithm is then used to train the model to identify the nonlinear systems to a desired level of accuracy. Based on the identified model, a one-step-ahead predictive control scheme is proposed in which the future control inputs are obtained through some nonlinear optimization process. Making use of the online learning properties of neural networks, the predictive control scheme is further developed into an adaptive one which is robust to the incompleteness of identification. Simulation results show that this neural control scheme works well even for some very complicated nonlinear systems.

Mathematics↗
PubMed2004

An adaptive, self-organizing dynamical system for hierarchical control of bio-inspired locomotion.

In this paper, dynamical systems made up of locally coupled nonlinear units are used to control the locomotion of bio-inspired robots and, in particular, a simulation of an insect-like hexapod robot. These controllers are inspired by the biological paradigm of central pattern generators and are responsible for generating a locomotion gait. A general structure, which is able to change the locomotion gait according to environmental conditions, is introduced. This structure is based on an adaptive system, implemented by motor maps, and is able to learn the correct locomotion gait on the basis of a reward function. The proposed control system is validated by a large number of simulations carried out in a dynamic environment for simulating legged robots.

Adaptation, Physiological↗
PubMed2003

Self-tuning adaptive control for an industrial weigh belt feeder.

An industrial weigh belt feeder is used to transport solid materials into a manufacturing process at a constant feedrate. It exhibits nonlinear behavior because of motor friction, saturation, and quantization noise in the measurement sensors. To overcome the nonlinearities, a simple yet effective method of controller autotuning, an indirect self-tuning regulator, was designed and implemented for an industrial weigh belt feeder. Implementation issues are discussed and experimental results show the effectiveness of the adaptive controller for several different reference inputs. Also, the performance of the indirect self-tuning regulator is compared with that of a fuzzy logic controller for the same application.

Journal Article↗
PubMed2005

An intelligent adaptive control scheme for postsurgical blood pressure regulation.

This paper presents an adaptive modeling and control scheme for drug delivery systems based on a generalized fuzzy neural network (G-FNN). The proposed G-FNN is a novel intelligent modeling tool, which can model unknown nonlinearities of complex drug delivery systems and adapt to changes and uncertainties in these systems online. It offers salient features, such as dynamic fuzzy neural topology, fast online learning ability and adaptability. System approximation formulated by the G-FNN is employed in the adaptive controller design for drug infusion in intensive care environment. In particular, this paper investigates automated regulation of mean arterial pressure (MAP) through intravenous infusion of sodium nitroprusside (SNP), which is one attractive application in automation of drug delivery. Simulation studies demonstrate the capability of the proposed approach in estimating the drug's effect and regulating blood pressure at a prescribed level.

Artificial Intelligence↗
PubMed2004

Fuzzy dynamic output feedback control with adaptive rotor imbalance compensation for magnetic bearing systems.

This paper presents a dynamic output feedback control with adaptive rotor-imbalance compensation based on an analytical Takagi-Sugeno fuzzy model for complex nonlinear magnetic bearing systems with rotor eccentricity. The rotor mass-imbalance effect is considered with a linear in the parameter approximator. Through the robust analysis for disturbance rejection, the control law can be synthesized in terms of linear matrix inequalities. Based on the suggested fuzzy output feedback design, the controller may be much easier to implement than conventional nonlinear controllers. Simulation validations show that the proposed robust fuzzy control law can suppress the rotor imbalance-induced vibration and has excellent capability for high-speed tracking and levitation control.

Journal Article↗
PubMed1996

Saturation revealed by clamping the gain of the retinal light response.

The saturation nonlinearity of the retinal light response in human was measured by a psychophysical technique in which the adaptive gain control mechanism was clamped by the presence of a fixed surround in a small (7') foveal test field. Gain clamping was established by showing that the normal variation in temporal summation properties with test intensity was abolished in the gain clamping paradigm. The static saturation function constructed from the increment/decrement asymmetries around a range of base intensities was shown to conform more closely to the Naka-Rushton hyperbolic saturation equation than to three other candidate nonlinearities.

Adaptation, Ocular↗
PubMedDate not supplied

An adaptive strategy for controlling chaotic system.

This paper presents an adaptive strategy for controlling chaotic systems. By employing the phase space reconstruction technique in nonlinear dynamical systems theory, the proposed strategy transforms the nonlinear system into canonical form, and employs a nonlinear observer to estimate the uncertainties and disturbances of the nonlinear system, and then establishes a state-error-like feedback law. The developed control scheme allows chaos control in spite of modeling errors and parametric variations. The effectiveness of the proposed approach has been demonstrated through its applications to two well-known chaotic systems: Duffing oscillator and Rössler chaos.

Computer Simulation↗
PubMed2002

Joint stiffness of the ankle and the knee in running.

The spring-mass model is a valid fundament to understand global dynamics of fast legged locomotion under gravity. The underlying concept of elasticity, implying leg stiffness as a crucial parameter, is also found on lower motor control levels, i.e. in muscle-reflex and muscle-tendon systems. Therefore, it seems reasonable that global leg stiffness emerges from local elasticity established by appropriate joint torques. A recently published model of an elastically operating, segmented leg predicts that proper adjustment of joint elasticities to the leg geometry and initial conditions of ground contact provides internal leg stability. Another recent study suggests that in turn the leg segmentation and the initial conditions may be a consequence of metabolic and bone stress constraints. In this study, the theoretical predictions were verified experimentally with respect to initial conditions and elastic joint characteristics in human running. Kinematics and kinetics were measured and the joint torques were estimated by inverse dynamics. Stiffnesses and elastic nonlinearities describing the resulting joint characteristics were extracted from parameter fits. Our results clearly support the theoretical predictions: the knee joint is always stiffer and more extended than the ankle joint. Moreover, the knee torque characteristic on the average shows the higher nonlinearity. According to literature, the leg geometry is a consequence of metabolic and material stress limitations. Adapted to this given geometry, the initial joint angle conditions in fast locomotion are a compromise between metabolic and control effort minimisation. Based on this adaptation, an appropriate joint stiffness ratio between ankle and knee passively safeguards the internal leg stability. The identified joint nonlinearities contribute to the linearisation of the leg spring.

Adult↗
PubMed2005

A global bioheat model with self-tuning optimal regulation of body temperature using Hebbian feedback covariance learning.

In the lower brain, body temperature is continually being regulated almost flawlessly despite huge fluctuations in ambient and physiological conditions that constantly threaten the well-being of the body. The underlying control problem defining thermal homeostasis is one of great enormity: Many systems and sub-systems are involved in temperature regulation and physiological processes are intrinsically complex and intertwined. Thus the defining control system has to take into account the complications of nonlinearities, system uncertainties, delayed feedback loops as well as internal and external disturbances. In this paper, we propose a self-tuning adaptive thermal controller based upon Hebbian feedback covariance learning where the system is to be regulated continually to best suit its environment. This hypothesis is supported in part by postulations of the presence of adaptive optimization behavior in biological systems of certain organisms which face limited resources vital for survival. We demonstrate the use of Hebbian feedback covariance learning as a possible self-adaptive controller in body temperature regulation. The model postulates an important role of Hebbian covariance adaptation as a means of reinforcement learning in the thermal controller. The passive system is based on a simplified 2-node core and shell representation of the body, where global responses are captured. Model predictions are consistent with observed thermoregulatory responses to conditions of exercise and rest, and heat and cold stress. An important implication of the model is that optimal physiological behaviors arising from self-tuning adaptive regulation in the thermal controller may be responsible for the departure from homeostasis in abnormal states, e.g., fever. This was previously unexplained using the conventional "set-point" control theory.

Adaptation, Physiological↗
PubMed2004

A new view on visuomotor channels: the case of the disappearing dynamics.

A considerable body of kinematic data supports the proposal that independent visuomotor channels are involved in the control of the transport and grip components of reach and grasp. These channels are seen as having separate perceptual inputs, outputs and internal processing and are thought by some to correspond to independent neuroanatomical pathways. The idea that different groups of muscles and biomechanical structures can be controlled independently is attractive, but this kinematically-inspired hypothesis fails to take into account the complexity of the dynamic relationships and their interactions within the neuromusculoskeletal system. Inertial, viscous, centrifugal, coriolis, gravitational and reflex cross couplings exist between efferent drives to muscles and resulting body movements. Rotation at even a single joint generates a complex set of dynamic reaction forces and requires coordinated activation of many muscles throughout the body to maintain posture and balance. In this theoretical paper we present a new view of independent visuomotor channels in the form of an adaptive neural controller that can compensate for the above interactions and decouple the relationships between efferent drives to muscles and resulting body movements. At the same time, the neural controller renders all the dynamics (linear and nonlinear), other than time delays, of the neuromusculoskeletal system, unobservable in the visuomotor relationships. Using the geometry of nonlinear dynamical systems we show that, providing certain constraints on the structure of time delays within the system are satisfied, there exists a neural controller that can render all the dynamics of the neuromusculoskeletal system (except for time delays) unobservable in the responses. The controller simultaneously decouples all the interactive dynamics so that each of the m independent inputs controls one and only one degree of freedom of the response. This means that each degree of freedom in a multi-joint response can be controlled by an independent component of the visual input, a behaviour that has long been observed in visual tracking experiments. The controller effectively establishes m independent visuomotor channels. However, rather than reflecting separate neuroanatomical pathways, the independent channels result from a neural controller with convergent and divergent connections to compensate for the interactive nonlinear dynamics within the neuromusculoskeletal system. This new view of visuomotor channels has implications for neural control processes involved in the acquisition and adaptability of skilled perceptual-motor behaviour in general, as well as for the design of robotic controllers.

Biomechanical Phenomena↗
PubMed2002

Effortful control: factor structure and relation to externalizing and internalizing behaviors.

Effortful control, a temperamentally based ability to inhibit a dominant response and activate a subdominant response, was assessed on 3 occasions using a comprehensive, age-appropriate behavioral battery in this 4-year longitudinal study of children (N = 103) from toddlerhood to early school age. The focus was twofold: to explore the structure of effortful control in a normally developing sample and to examine the relations between effortful control and adaptive functioning. Exploratory factor analyses supported the contention that effortful control is a complex, multidimensional construct with longitudinally stable factors. Effortful control was also significantly associated with mother-reported total behavior problems in a nonlinear fashion, with lower and higher levels of effortful control contributing to higher total problem scores. These findings have implications for the assessment of temperament in children and, most importantly, for the study of nonlinear contributions of temperament to early childhood behaviors.

Adaptation, Psychological↗