Metodología de selección y dimensionado de un SSSC para el control del flujo de potencia en la red eléctrica de transporte

147 p. El contenido del capítulo 6 está sujeto a confidencialidad.

An observer-based vaccination control law for an SEIR epidemic model based on feedback linearization techniques for nonlinear systems

This paper presents a vaccination strategy for fighting against the propagation of epidemic diseases. The disease propagation is described by an SEIR (susceptible plus infected plus infectious plus removed populations) epidemic model. The model takes into account the total population amounts as a refrain for the illness transmission since its increase makes the contacts among susceptible and infected more difficult. The vaccination strategy is based on a continuous-time nonlinear control law synthesised via an exact feedback input-output linearization approach. An observer is incorporated into the control scheme to provide online estimates for the susceptible and infected populations in the case when their values are not available from online measurement but they are necessary to implement the control law. The vaccination control is generated based on the information provided by the observer. The control objective is to asymptotically eradicate the infection from the population so that the removed-by-immunity population asymptotically tracks the whole one without precise knowledge of the partial populations. The model positivity, the eradication of the infection under feedback vaccination laws and the stability properties as well as the asymptotic convergence of the estimation errors to zero as time tends to infinity are investigated.

Testing the performance and feasibility of Bluetooth communications in pervasive systems

Smart and mobile environments require seamless connections. However, due to the frequent process of ''discovery'' and disconnection of mobile devices while data interchange is happening, wireless connections are often interrupted. To minimize this drawback, a protocol that enables an easy and fast synchronization is crucial. Bearing this in mind, Bluetooth technology appears to be a suitable solution to carry on such connections due to the discovery and pairing capabilities it provides. Nonetheless, the time and energy spent when several devices are being discovered and used at the same time still needs to be managed properly. It is essential that this process of discovery takes as little time and energy as possible. In addition to this, it is believed that the performance of the communications is not constant when the transmission speeds and throughput increase, but this has not been proved formally. Therefore, the purpose of this project is twofold: Firstly, to design and build a framework-system capable of performing controlled Bluetooth device discovery, pairing and communications. Secondly, to analyze and test the scalability and performance of the \emph{classic} Bluetooth standard under different scenarios and with various sensors and devices using the framework developed. To achieve the first goal, a generic Bluetooth platform will be used to control the test conditions and to form a ubiquitous wireless system connected to an Android Smartphone. For the latter goal, various stress-tests will be carried on to measure the consumption rate of battery life as well as the quality of the communications between the devices involved.

A Data Dropout Compensation Algorithm Based on the Iterative Learning Control Methodology for Discrete-Time Systems

This paper deals with the convergence of a remote iterative learning control system subject to data dropouts. The system is composed by a set of discrete-time multiple input-multiple output linear models, each one with its corresponding actuator device and its sensor. Each actuator applies the input signals vector to its corresponding model at the sampling instants and the sensor measures the output signals vector. The iterative learning law is processed in a controller located far away of the models so the control signals vector has to be transmitted from the controller to the actuators through transmission channels. Such a law uses the measurements of each model to generate the input vector to be applied to its subsequent model so the measurements of the models have to be transmitted from the sensors to the controller. All transmissions are subject to failures which are described as a binary sequence taking value 1 or 0. A compensation dropout technique is used to replace the lost data in the transmission processes. The convergence to zero of the errors between the output signals vector and a reference one is achieved as the number of models tends to infinity.

Depression of Serotonin Synaptic Transmission by the Dopamine Precursor L-DOPA

Imbalance between the dopamine and serotonin (5-HT) neurotransmitter systems has been implicated in the comorbidity of Parkinson's disease (PD) and psychiatric disorders. L-DOPA, the leading treatment of PD, facilitates the production and release of dopamine. This study assessed the action of L-DOPA on monoamine synaptic transmission in mouse brain slices. Application of L-DOPA augmented the D2-receptor-mediated inhibitory postsynaptic current (IPSC) in dopamine neurons of the substantia nigra. This augmentation was largely due to dopamine release from 5-HT terminals. Selective optogenetic stimulation of 5-HT terminals evoked dopamine release, producing D2-receptor-mediated IPSCs following treatment with L-DOPA. In the dorsal raphe, L-DOPA produced a long-lasting depression of the 5-HT1A-receptor-mediated IPSC in 5-HT neurons. When D2 receptors were expressed in the dorsal raphe, application of L-DOPA resulted in a D2-receptor-mediated IPSC. Thus, treatment with L-DOPA caused ectopic dopamine release from 5-HT terminals and a loss of 5-HT-mediated synaptic transmission.