Frequency-domain subspace identification of nonlinear mechanical systems - Application to a solar array structure

The present paper addresses the experimental identification of a simplified realisation of a solar array structure in folded configuration. To this end, a nonlinear subspace identification technique formulated in the frequency domain, referred to as the FNSI method, is exploited. The frequency response functions of the underlying linear structure and the nonlinear coefficients are estimated by this approach. Nonlinearity is caused by impacts between adjacent panels and friction and gaps appearing in their clamping interfaces. This application is challenging for several reasons, which include high modal density and the complicated nature of the involved nonlinear mechanisms

Timing and placing samplings to optimally calibrate a reactive transport model: exploring the potential for <i>Escherichia coli</i> in the Scheldt estuary

For the calibration of any model, measurements are necessary. As measurements are expensive, it is of interest to determine beforehand which kind of samples will provide the maximum of information. Using a criterion related to the Fisher information matrix, it is possible to design a sampling scheme that will enable the most precise model parameter estimates. This approach was applied to a reactive transport model (based on SLIM) of Escherichia coli in the Scheldt Estuary. As this estuary is highly influenced by the tide, it is expected that careful timing of the samples with respect to the tidal cycle will have an effect on the quality of the data. The timing and also the positioning of samples were optimised according to the proposed criterion. In the investigated case studies the precision of the estimated parameters could be improved by up to a factor of ten, confirming the usefulness of this approach to maximize the amount of information that can be retrieved from a fixed number of samples

Resilient Parameter-Invariant Control With Application to Vehicle Cruise Control

This work addresses the general problem of resilient control of unknown stochastic linear time-invariant (LTI) systems in the presence of sensor attacks. Motivated by a vehicle cruise control application, this work considers a first order system with multiple measurements, of which a bounded subset may be corrupted. A frequency-domain-designed resilient parameter-invariant controller is introduced that simultaneously minimizes the effect of corrupted sensors, while maintaining a desired closed-loop performance, invariant to unknown model parameters. Simulated results illustrate that the resilient parameter-invariant controller is capable of stabilizing unknown state disturbances and can perform state trajectory tracking

Intermittent control models of human standing: similarities and differences

Two architectures of intermittent control are compared and contrasted in the context of the single inverted pendulum model often used for describing standing in humans. The architectures are similar insofar as they use periods of open-loop control punctuated by switching events when crossing a switching surface to keep the system state trajectories close to trajectories leading to equilibrium. The architectures differ in two significant ways. Firstly, in one case, the open-loop control trajectory is generated by a system-matched hold, and in the other case, the open-loop control signal is zero. Secondly, prediction is used in one case but not the other. The former difference is examined in this paper. The zero control alternative leads to periodic oscillations associated with limit cycles; whereas the system-matched control alternative gives trajectories (including homoclinic orbits) which contain the equilibrium point and do not have oscillatory behaviour. Despite this difference in behaviour, it is further shown that behaviour can appear similar when either the system is perturbed by additive noise or the system-matched trajectory generation is perturbed. The purpose of the research is to come to a common approach for understanding the theoretical properties of the two alternatives with the twin aims of choosing which provides the best explanation of current experimental data (which may not, by itself, distinguish beween the two alternatives) and suggesting future experiments to distinguish between the two alternatives

A rigorous model of reflex function indicates that position and force feedback are flexibly tuned to position and force tasks

This study aims to quantify the separate contributions of muscle force feedback, muscle spindle activity and co-contraction to the performance of voluntary tasks (“reduce the influence of perturbations on maintained force or position”). Most human motion control studies either isolate only one contributor, or assume that relevant reflexive feedback pathways during voluntary disturbance rejection tasks originate mainly from the muscle spindle. Human ankle-control experiments were performed, using three task instructions and three perturbation characteristics to evoke a wide range of responses to force perturbations. During position tasks, subjects (n = 10) resisted the perturbations, becoming more stiff than when being relaxed (i.e., the relax task). During force tasks, subjects were instructed to minimize force changes and actively gave way to imposed forces, thus becoming more compliant than during relax tasks. Subsequently, linear physiological models were fitted to the experimental data. Inhibitory, as well as excitatory force feedback, was needed to account for the full range of measured experimental behaviors. In conclusion, force feedback plays an important role in the studied motion control tasks (excitatory during position tasks and inhibitory during force tasks), implying that spindle-mediated feedback is not the only significant adaptive system that contributes to the maintenance of posture or force

Analysis of reflex modulation with a biologically realistic neural network

In this study, a neuromusculoskeletal model was built to give insight into the mechanisms behind the modulation of reflexive feedback strength as experimentally identified in the human shoulder joint. The model is an integration of a biologically realistic neural network consisting of motoneurons and interneurons, modeling 12 populations of spinal neurons, and a one degree-of-freedom musculoskeletal model, including proprioceptors. The model could mimic the findings of human postural experiments, using presynaptic inhibition of the Ia afferents to modulate the feedback gains. In a pathological case, disabling one specific neural connection between the inhibitory interneurons and the motoneurons could mimic the experimental findings in complex regional pain syndrome patients. It is concluded that the model is a valuable tool to gain insight into the spinal contributions to human motor control. Applications lay in the fields of human motor control and neurological disorders, where hypotheses on motor dysfunction can be tested, like spasticity, clonus, and tremor

Relating reflex gain modulation in posture control to underlying neural network properties using a neuromusculoskeletal model

During posture control, reflexive feedback allows humans to efficiently compensate for unpredictable mechanical disturbances. Although reflexes are involuntary, humans can adapt their reflexive settings to the characteristics of the disturbances. Reflex modulation is commonly studied by determining reflex gains: a set of parameters that quantify the contributions of Ia, Ib and II afferents to mechanical joint behavior. Many mechanisms, like presynaptic inhibition and fusimotor drive, can account for reflex gain modulations. The goal of this study was to investigate the effects of underlying neural and sensory mechanisms on mechanical joint behavior. A neuromusculoskeletal model was built, in which a pair of muscles actuated a limb, while being controlled by a model of 2,298 spiking neurons in six pairs of spinal populations. Identical to experiments, the endpoint of the limb was disturbed with force perturbations. System identification was used to quantify the control behavior with reflex gains. A sensitivity analysis was then performed on the neuromusculoskeletal model, determining the influence of the neural, sensory and synaptic parameters on the joint dynamics. The results showed that the lumped reflex gains positively correlate to their most direct neural substrates: the velocity gain with Ia afferent velocity feedback, the positional gain with muscle stretch over II afferents and the force feedback gain with Ib afferent feedback. However, position feedback and force feedback gains show strong interactions with other neural and sensory properties. These results give important insights in the effects of neural properties on joint dynamics and in the identifiability of reflex gains in experiments

Centralized Inverted Decoupling Control

This paper presents a new methodology of multivariable centralized control based on the structure of inverted decoupling. The method is presented for general n×n processes, obtaining very simple general expressions for the controller elements with a complexity independent of the system size. The possible configurations and realizability conditions are stated. Then, the specification of performance requirements is carried out from simple open loop transfer functions for three common cases. As a particular case, it is shown that the resulting controller elements have PI structure or filtered derivative action plus a time delay when the process elements are given by first order plus time delay systems. Comparisons with other works demonstrate the effectiveness of this methodology through the use of several simulation examples and an experimental lab process

The intermuscular 3–7 Hz drive is not affected by distal proprioceptive input in myoclonus-dystonia

In dystonia, both sensory malfunctioning and an abnormal intermuscular low-frequency drive of 3–7 Hz have been found, although cause and effect are unknown. It is hypothesized that sensory processing is primarily disturbed and induces this drive. Accordingly, experimenter-controlled sensory input should be able to influence the frequency of the drive. In six genetically confirmed myoclonus-dystonia (MD) patients and six matched controls, the low-frequency drive was studied with intermuscular coherence analysis. External perturbations were applied mechanically to the wrist joint in small frequency bands (0–4, 4–8 and 8–12 Hz; ‘angle protocol) and at single frequencies (1, 5, 7 and 9 Hz; ‘torque’ protocol). The low-frequency drive was found in the neck muscles of 4 MD patients. In these patients, its frequency did not shift due to the perturbation. In the torque protocol, the externally applied frequencies could be detected in all controls and in the two patients without the common drive. The common low-frequency drive was not be affected by external perturbations in MD patients. Furthermore, the torque protocol did not induce intermuscular coherences at the applied frequencies in these patients, as was the case in healthy controls and in patients without the drive. This suggests that the dystonic 3–7 Hz drive is caused by a sensory-independent motor drive and sensory malfunctioning in MD might rather be a consequence than a cause of dystonia