Robust Sliding Control of SEIR Epidemic Models

This paper is aimed at designing a robust vaccination strategy capable of eradicating an infectious disease from a population regardless of the potential uncertainty in the parameters defining the disease. For this purpose, a control theoretic approach based on a sliding-mode control law is used. Initially, the controller is designed assuming certain knowledge of an upper-bound of the uncertainty signal. Afterwards, this condition is removed while an adaptive sliding control system is designed. The closed-loop properties are proved mathematically in the nonadaptive and adaptive cases. Furthermore, the usual sign function appearing in the sliding-mode control is substituted by the saturation function in order to prevent chattering. In addition, the properties achieved by the closed-loop system under this variation are also stated and proved analytically. The closed-loop system is able to attain the control objective regardless of the parametric uncertainties of the model and the lack of a priori knowledge on the system.

Effect of Neonatal Asphyxia on the Impairment of the Auditory Pathway by Recording Auditory Brainstem Responses in Newborn Piglets: A New Experimentation Model to Study the Perinatal Hypoxic-Ischemic Damage on the Auditory Syste

Introduction Hypoxia-ischemia (HI) is a major perinatal problem that results in severe damage to the brain impairing the normal development of the auditory system. The purpose of the present study is to study the effect of perinatal asphyxia on the auditory pathway by recording auditory brain responses in a novel animal experimentation model in newborn piglets. Method Hypoxia-ischemia was induced to 1.3 day-old piglets by clamping 30 minutes both carotid arteries by vascular occluders and lowering the fraction of inspired oxygen. We compared the Auditory Brain Responses (ABRs) of newborn piglets exposed to acute hypoxia/ischemia (n = 6) and a control group with no such exposure (n = 10). ABRs were recorded for both ears before the start of the experiment (baseline), after 30 minutes of HI injury, and every 30 minutes during 6 h after the HI injury. Results Auditory brain responses were altered during the hypoxic-ischemic insult but recovered 30-60 minutes later. Hypoxia/ischemia seemed to induce auditory functional damage by increasing I-V latencies and decreasing wave I, III and V amplitudes, although differences were not significant. Conclusion The described experimental model of hypoxia-ischemia in newborn piglets may be useful for studying the effect of perinatal asphyxia on the impairment of the auditory pathway.

Eurobalise-Train communication modelling to assess interferences in railway control signalling systems

The evolution of the railway sector depends, to a great extent, on the deployment of advanced railway signalling systems. These signalling systems are based on communication architectures that must cope with complex electromagnetical environments. This paper is outlined in the context of developing the necessary tools to allow the quick deployment of these signalling systems by contributing to an easier analysis of their behaviour under the effect of electromagnetical interferences. Specifically, this paper presents the modelling of the Eurobalise-train communication flow in a general purpose simulation tool. It is critical to guarantee this communication link since any lack of communication may lead to a stop of the train and availability problems. In order to model precisely this communication link we used real measurements done in a laboratory equipped with elements defined in the suitable subsets. Through the simulation study carried out, we obtained performance indicators of the physical layer such as the received power, SNR and BER. The modelling presented in this paper is a required step to be able to provide quality of service indicators related to perturbed scenarios.

Optimal control predicts human performance on objects with internal degrees of freedom.

On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.

Risk-sensitive optimal feedback control accounts for sensorimotor behavior under uncertainty.

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models.

Nonautonomous elementary net systems and their application to programmable logic control

In this paper, a novel approach to Petri net modeling of programmable logic controller (PLC) programs is presented. The modeling approach is a simple extension of elementary net systems, and a graphical design tool that supports the use of this modeling approach is provided. A key characteristic of the model is that the binary sensory inputs and binary actuation outputs of the PLC are explicitly represented. This leads to the following two improvements: outputs are unambiguous, and interaction patterns are more clearly represented in the graphical form. The use of this modeling approach produces programs that are simple, lightweight, and portable. The approach is demonstrated by applying it to the development of a control module for a MonTech Positioning Station. © 2008 IEEE.

Modelling and predictive control: Enabling technologies for reconfiguration

An approach to reconfiguring control systems in the event of major failures is advocated. The approach relies on the convergence of several technologies which are currently emerging: Constrained predictive control, High-fidelity modelling of complex systems, Fault detection and identification, and Model approximation and simplification. Much work is needed, both theoretical and algorithmic, to make this approach practical, but we believe that there is enough evidence, especially from existing industrial practice, for the scheme to be considered realistic. After outlining the problem and proposed solution, the paper briefly reviews constrained predictive control and object-oriented modelling, which are the essential ingredients for practical implementation. The prospects for automatic model simplification are also reviewed briefly. The paper emphasizes some emerging trends in industrial practice, especially as regards modelling and control of complex systems. Examples from process control and flight control are used to illustrate some of the ideas.

Modelling of equipment loaded panels for robust control investigations

This paper develops a modelling technique for equipment load panels which directly produces (adequate) models of the underlying dynamics on which to base robust controller design/evaluations. This technique is based on the use of the Lagrange's equations of motion and the resulting models are verified against those produced by a finite Element Method Model.