Currently, Computer Science in the mandatory levels of the education system are acquiring a significantly important role. Therefore, it is a priority to work on the production of educational resources in the area. This article presents an unplugged educational resource, built within the framework of participatory design, aimed at promoting the teaching of concepts related to Turing Machines.

. Este trabajo aborda el problema de diagnóstico de fallos en sistemas lineales e invariantes en el tiempo bajo la presencia de perturbaciones gaussianas, a través de observadores basados en modelo de tipo proporcional-integral con ganancias predefinidas. Para ello, se propone un diseño conjunto de los generadores y evaluadores de residuos de fallo teniendo en cuenta el compromiso entre parámetros físicos como la tasa de falsas alarmas, los mínimos fallos detectables, y la integral del cuadrado del error de los residuos ante fallos escalón. También se aborda el problema de aislamiento dinámico de fallos. Para resolver este diseño se presentan dos enfoques distintos: el primero basado en el filtro de Kalman de régimen permanente y el segundo basado en técnicas de optimización convexa.

En este trabajo se desarrolla un sistema de evaluación automatizada implementado en Moodle y mediante formularios de Google para asignaturas de control automático. El objetivo es fomentar el trabajo individual previo para preparar las sesiones de prácticas de laboratorio en la asignatura de Sistemas Automáticos, de tercer curso de las ingenierías de la rama industrial. El sistema desarrollado permite al estudiante tener una realimentación inmediata de sus competencias para resolver problemas de ingeniería de control. Se han probado dos formas de implementación: mediante un formulario de Google, y adaptando la herramienta de cuestionario de Moodle. En ambos casos la idea ha sido incluir problemas de control personalizados, incluyendo problemas de modelado, identificación y ajuste de controladores, tanto por métodos experimentales como métodos basados en modelo. Para garantizar el trabajo individual y evitar copias se han generado automáticamente diferentes enunciados de problemas, escalando las ganancias y las constantes de tiempo.

Remote state estimation problems in the presence of eavesdroppers have recently been investigated in the literature. For unstable systems, it has been shown that it is possible to keep the expected estimation error covariance bounded, while the expected eavesdropper error covariance becomes unbounded in the infinite horizon. In this note, we consider an alternative notion of security based on the amount of information revealed to the eavesdropper. Upper and lower bounds on the information revealed are derived. In particular, in the infinite horizon, it is shown that with unstable systems, any transmission policy (within the class of stationary deterministic policies where the sensor at each time step can either transmit its local state estimate or not) which keeps the expected estimation error covariance bounded must always reveal a non-zero expected amount of information to the eavesdropper.

This paper studies transmission scheduling for remote state estimation in the presence of an eavesdropper. A sensor transmits local state estimates over a packet dropping link to a remote estimator, while an eavesdropper can successfully overhear each sensor transmission with a certain probability. The objective is to determine when the sensor should transmit, in order to minimize the estimation error covariance at the remote estimator, while trying to keep the eavesdropper error covariance above a certain level. This is done by solving an optimization problem that minimizes a linear combination of the expected estimation error covariance and the negative of the expected eavesdropper error covariance. Structural results on the optimal transmission policy are derived, and shown to exhibit thresholding behavior in the estimation error covariances. In the infinite horizon situation, it is shown that with unstable systems one can keep the expected estimation error covariance bounded while the expected eavesdropper error covariance becomes unbounded, for all eavesdropping probabilities strictly less than one.

This paper studies a remote state estimation problem in the presence of an eavesdropper. A sensor transmits local state estimates over a packet dropping link to a remote estimator, which at the same time can be overheard by an eavesdropper with a certain probability. The objective is to determine when the sensor should transmit, in order to minimize the estimation error covariance at the remote estimator, while trying to keep the eavesdropper error covariance above a certain level. This is done by solving an optimization problem that minimizes a linear combination of the expected estimation error covariance and the negative of the expected eavesdropper error covariance. Structural results on the optimal transmission policy are derived, and shown to exhibit thresholding behaviour in the estimation error covariances. In the infinite horizon situation, it is shown that with unstable systems one can keep the expected estimation error covariance bounded while the expected eavesdropper error covariance becomes unbounded.

We study transmission power budget minimization of battery-powered nodes in a remote state estimation problem over multi-hop wireless networks. Communication links between nodes are subject to fading, thereby generating random dropouts. Relay nodes help to transmit measurements from distributed sensors to an estimator node. Hopping through each relay node introduces a unit delay. Motivated by the need for estimators with low computational and implementation cost, we propose a jump estimator whose modes depend on a Markovian parameter that describes measurement transmission outcomes over a finite interval. It is well known that transmission power helps to increase the reliability of measurement transmissions, at the expense of reducing the life-time of the nodes' battery. Motivated by this, we derive a tractable iterative procedure, based on semi-definite programming, to design a finite set of filter gains, and associated power control laws to minimize the energy budget while guaranteeing an estimation performance level. This procedure allows us to tradeoff the complexity of the filter implementation with performance and energy use.

SummaryIn this work, we address the observer‐based control problem for networked control systems with an unknown time‐varying packet arrival rate (PAR) and under root mean square‐norm bounded disturbances. We assume packetized transmissions of both measurement and control input through a communication network with successful delivery acknowledgement. Using the measurement reception state and the control transmission acknowledgement, we derive a filter to estimate the PAR. We consider that the PAR changes sporadically from a constant value to another one; that is, it has two different behaviours: transient and steady state. While the observer only updates the state estimation using the current received measurements, the controller computes the control action employing the current state estimation and the previous applied control input. We propose to schedule both the observer and controller with rational functions of the PAR estimation. We show that the separation principle applies, and then, seeking higher performance accuracy, we develop an optimization observer and controller design procedure that considers the two possible behaviours of the PAR. This optimization procedure attempts to maximize the estimation and control performances for each of the possible constant values of the PAR while offering robustness against PAR estimation errors and variations of the PAR. By exploiting sum‐of‐squares decomposition techniques, the design procedure involves an optimization problem over polynomials. A numerical example illustrates the effectiveness of the proposed approach. Copyright © 2017 John Wiley & Sons, Ltd.

This note addresses the problem of feedback control with a constrained number of active inputs. This problem is known as sparse control. Specifically, we describe a novel quadratic model predictive control strategy that guarantees sparsity by bounding directly the $\ell _0$ -norm of the control input vector at each control horizon instant. Besides this sparsity constraint, bounded constraints are also imposed on both control input and system state. Under this scenario, we provide sufficient conditions for guaranteeing practical stability of the closed-loop. We transform the combinatorial optimization problem into an equivalent optimization problem that does not consider relaxation in the cardinality constraints. The equivalent optimization problem can be solved utilizing standard nonlinear programming toolboxes that provides the input control sequence corresponding to the global optimum.

This paper studies remote state estimation in the presence of an eavesdropper. A sensor transmits local state estimates over a packet dropping link to a remote estimator, while an eavesdropper can successfully overhear each sensor transmission with a certain probability. The objective is to determine when the sensor should transmit, in order to minimize the estimation error covariance at the remote estimator, while trying to keep the eavesdropper error covariance above a certain level. This is done by solving an optimization problem that minimizes a linear combination of the expected estimation error covariance and the negative of the expected eavesdropper error covariance. Structural results on the optimal transmission policy are derived, and shown to exhibit thresholding behaviour in the estimation error covariances. In the infinite horizon situation, it is shown that with unstable systems one can keep the expected estimation error covariance bounded while the expected eavesdropper error covariance becomes unbounded. An alternative measure of security, constraining the amount of information revealed to the eavesdropper, is also considered, and similar structural results on the optimal transmission policy are derived. In the infinite horizon situation with unstable systems, it is now shown that for any transmission policy which keeps the expected estimation error covariance bounded, the expected amount of information revealed to the eavesdropper is always lower bounded away from zero. An extension of our results to the transmission of measurements is also presented.

This work addresses the fault diagnosis problem for LTI systems under the presence of Gaussian noises through model-based proportional-integral observers with predefined gains. We propose an integrated design of residual generators and evaluators which takes into account the trade-off between physically meaningful parameters such as the false alarm rate, the minimum isolable faults and the integral squared error of the residuals under step faults. Dynamical fault isolation is also taken into account. In order to solve this design problem, we present two different approaches: one based on the steady-state Kalman filter and another based on convex optimization techniques.

This work presents a strategy to minimise the network usage and the energy consumption of wireless battery-powered sensors in the observer problem over networks. The sensor nodes implement a periodic send-on-delta approach, sending new measurements when a measure deviates considerably from the previous sent one. The estimator node implements a jump observer whose gains are computed offline and depend on the combination of available new measurements. We bound the estimator performance as a function of the sending policies and then state the design procedure of the observer under fixed sending thresholds as a semidefinite programming problem. We address this problem first in a deterministic way and, to reduce conservativeness, in a stochastic one after obtaining bounds on the probabilities of having new measurements and applying robust optimisation problem over the possible probabilities using sum of squares decomposition. We relate the network usage with the sending thresholds and propose an iterative procedure for the design of those thresholds, minimising the network usage while guaranteeing a prescribed estimation performance. Simulation results and experimental analysis show the validity of the proposal and the reduction of network resources that can be achieved with the stochastic approach.

In this work we aim to develop auto-assessment computer based tools integrated in virtual educational platforms such as Moodle, for control systems subjects. The goal is to promote individual practice in the laboratory sessions of the subject of Automatic Control Systems which is taught in the third academic year of the engineering degrees. This tool allows the student to have an immediate feedback of his aptitudes on solving control engineering related problems in a previous work that must be done prior to the experimental practices. Moreover, it alleviates time and place constraints which favour the predisposition of the student to undertake the given problems. For these purposes, we have adapted the already existing quiz tool in Moodle to include explicitly parameterized control problems such as system modelling, system identification, and control tuning with both experimental and model based techniques. With this, we have automatically generated different problem proposals by means of time and gain shifts, which allows us to assure self-study.

In this work, we present a fault detection strategy applicable to the blade and pitch system in offshore wind turbines. First, we model the system and possible faults and propose a PI observer to identify the faults. Then, the observer is designed accounting the sensors measurement noise, and addressing a trade off between the needs of false alarm rate, minimum detectable fault and detection time. By means of a well known benchmark, several simulations show the goodness of the approach and its flexibility to explicitly fix the fault detector performance.

This paper addresses the estimation problem for discrete-time systems where both measurements and control commands are sent to a central station through a lossy network without delivery acknowledgment. The central unit implements the estimation and control algorithms. We propose a jump observer that uses the expected value of the unknown control input at the actuator to run an open loop estimation. Then, the absence of acknowledgment in the control input transmission is dealt with through the introduction of an unknown disturbance. The observer update is performed by means of jumping gains when there are available measurements. We employ an statistic of the control error (new disturbance), i.e., the difference between the control inputs at the plant and at the observer, to schedule the observer gains in real time. The observer is designed to minimize the H-infinity norm from disturbances, measurement noises and control input errors, to estimation error. The infinite-dimensional design problem is turn into an optimization problem over polynomials using sum-of-squares decomposition techniques. Benefits of the proposal are shown in a simulation example.

In this paper, we address the fault diagnosis problem for discrete-time multi-sensor systems over communication networks with measurement dropouts. We use the measurement outcomes to model the measurement reception scenarios. Based on this, we propose the use of a jump observer to diagnose multiple faults. We model the faults as slow time-varying signals and introduce this dynamic in the observer to estimate the faults and to generate a residual. The fault detection is assured by comparing the residual signal with a prescribed threshold. We design the jump observer, the residual and the threshold to attain disturbance attenuation, fault tracking and detection conditions and a given false alarm rate. The false alarm rate is upper bounded by means of Markov's inequality. We explore the tradeoffs between the minimum detectable faults, the false alarm rate and the response time to faults of the fault diagnoser. By imposing the disturbances and measurement noises to be Gaussian, we tighten the false alarm rate bound which improves the time needed to detect a fault. A numerical example is provided to illustrate the effectiveness of the theory developed in the paper.