The Ecclesiology of Archimandrite Sophrony (Sakharov)

The present thesis is a critical study of Archimandrite Sophrony Sakharov's ecclesiology. Its central claim is that Archim. Sophrony, a twentieth century Russian ascetic and theologian, understands the Church as a created-uncreated Being, which is hypostatizable, soborny, and sophiological. Archim Sophrony’s theology stems from the idea of theosis, understood as the ontological meeting ‘ground’ between God and Man, which was the primary concern of most Russian theologians of the time. However, the differences of perspective among these theologians led to a variety of ways in which theosis is approached and defined. For Archim Sophrony, a theology of theosis needs to look first at the question regarding the simultaneous difference and identity between Divinity and Man. This exclusive concern with the ontological in-between, where God and Man become One Being, is the common concern of a series of other contemporary Russian theologians, most notably Fr Sergii Bulgakov, whose formative influence on Archim. Sophrony's thought will also be looked at in the present thesis. Archim. Sophrony addresses the question of theosis by developing a highly creative system of interpretations around the concept of Divine image, founded on the theologies of St Gregory Palamas and Fr Sergii Bulgakov. Thus, he distinguishes between three moments of human existence: essence, energy and hypostaticity, which reflect the three Divine modes of existence. Consequently, Archim. Sophrony makes three central ecclesiological statements: (1) that the Being of the Church is hypostatical; (2) that it is soborny; and (3) that it enters a special ontological relationship with the Divine Being which allows for the simultaneous absolute distinction and absolute identity of the two Beings. These three ecclesiological statements represent the three main claims of our research, and also generate the structure of the present thesis

On the design of Robust tube-based MPC for tracking

17th IFAC World Congress (IFAC'08)Seoul, Korea, July 6-11This paper deals with the design procedure of the recently presented robust MPC for tracking of constrained linear systems with additive disturbances. This controller is based on nominal predictions and it is capable to steer the nominal predicted trajectory to any target admissible steady state, that is retaining feasibility under any set point change. By means of the notion of tube of trajectories, robust stability and convergence is achieved. The controller formulation has some parameters which provides extra degrees of freedom to the design procedure of the predictive controller. These allow to deal with control objectives such as disturbance rejection, output offset prioritization or enlargement of the domain of attraction. In this paper, output prioritization method, LMI based design procedures and algorithms for the calculation of invariant sets are presented. The proposed enhanced design of the MPC is demonstrated by an illustrative example

MPC for tracking of piece-wise constant referente for constrained linear systems

16th IFAC World Congress. Praga (República Checa) 03/07/2005Model predictive control (MPC) is one of the few techniques which is able to handle with constraints on both state and input of the plant. The admissible evolution and asymptotically convergence of the closed loop system is ensured by means of a suitable choice of the terminal cost and terminal contraint. However, most of the existing results on MPC are designed for a regulation problem. If the desired steady state changes, the MPC controller must be redesigned to guarantee the feasibility of the optimization problem, the admissible evolution as well as the asymptotic stability. In this paper a novel formulation of the MPC is proposed to track varying references. This controller ensures the feasibility of the optimization problem, constraint satisfaction and asymptotic evolution of the system to any admissible steady-state. Hence, the proposed MPC controller ensures the offset free tracking of any sequence of piece-wise constant admissible set points. Moreover this controller requires the solution of a single QP at each sample time, it is not a switching controller and improves the performance of the closed loop system

Cooperative distributed MPC for tracking

This paper proposes a cooperative distributed linear model predictive control (MPC) strategy for tracking changing setpoints, applicable to any finite number of subsystems. The proposed controller is able to drive the whole system to any admissible setpoint in an admissible way, ensuring feasibility under any change of setpoint. It also provides a larger domain of attraction than standard distributed MPC for regulation, due to the particular terminal constraint. Moreover, the controller ensures convergence to the centralized optimum, even in the case of coupled constraints. This is possible thanks to the warm start used to initialize the optimization Algorithm, and to the design of the cost function, which integrates a Steady-State Target Optimizer (SSTO). The controller is applied to a real four-tank plant

Control of Solar Power Systems: a survey

9th International Symposium on Dynamics and Controlof Process Systems (DYCOPS 2010)Leuven, Belgium, July 5-7, 20109This paper deals with the main control problems found in solar power systems and the solutions proposed in literature. The paper first describes the main solar power technologies, its development status and then describes the main challenges encountered when controlling solar power systems.Ministerio de Ciencia y Tecnología DPI2008-05818Ministerio de Ciencia y Tecnología DPI2007-66718-C04-04Junta de Andalucía P07-TEP-0272

Robust control of the distributed solar collector field ACUREX using MPC for tracking

17th IFAC World Congress 2008. Seoul (Korea). 06/07/2008This paper presents the application of a robust model predictive control for tracking of piece-wise constant references (RMPCT) to a distributed collector field, ACUREX, at the solar power plant of PSA (Solar Plant of Almería). The main characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong disturbances in the process. The real plant is assumed to be modeled as a linear system with additive bounded uncertainties on the states. Under mild assumptions, the proposed RMPCT can steer the uncertain system in an admissible evolution to any admissible steady state, that is, under any change of the set point. This allows us to reject constant disturbances compensating the effect of then changing the setpoint

An Educational plant based on the Quadruple-tank process

7TH IFAC SYMPOSIUM ON ADVANCES IN CONTROL. 21/06/2006. MADRIDThis paper presents an experimental tank system developed at the University ofSeville for process control education. This plant is based on the well known quadruple-tank process and some modifications have been done in order to obtain a wide rangeof applications. The quadruple tank process is a multivariable laboratory plant of inter-connected tanks that can be easily configured to exhibit the effect of multivariable zero(minimum and non-minimum phase) on the system behavior, as well as the effect of nonlinear dynamics, saturation, constraints, etc.In the real plant implementation, the original structure of the process has been modifiedto offer a wide variety of uses for both educational and research purposes.Thus, differentplants can be configured such as one single tank, two or three cascaded tanks, a mixtureprocess and hybrid dynamics. Moreover the dynamics parameters of each tank can be setup by tuning the cross-section of the outlet hole of the tank. Furthermore, the real plant hasbeen implemented using industrial instrumentation and a PLC for the low level control.Supervision and control of the plant is carried out in a computer by means of OPC (Olefor Process Control) which allows one to connect the plant with a wide range of controlprograms such as LabView, Matlab or industrial SCADA.Ministerio de Ciencia y Tecnología DPI 2004-0744

Detección de trampas en el videojuego CS:GO utilizando técnicas de Inteligencia Artificial

En el presente proyecto de fin de grado se ha desarrollado un sistema de detección de trampas o, en términos internacionales, cheats, en el videojuego de disparos en primera persona llamado Counter-Strike: Global Offensive (CS:GO). El uso de trampas en los videojuegos arruina la experiencia de los jugadores que no las utilizan, ya que los que sí lo hacen, obtienen una ventaja visual o de ayuda de asistencia en el control del ratón sobre los jugadores que únicamente utilizan su habilidad. Además, el presente videojuego está dotado de una gran importancia a nivel internacional, siendo uno de los más presentes en competiciones de deportes electrónicos. En dichas competiciones están presentes cheats realizados por programadores profesionales que son capaces de saltarse, en gran medida, la seguridad impuesta por las mismas mediante los sistemas anti-cheat. Cabe destacar que la suma total de dinero a repartir en las competiciones anuales, superó los 11 millones de dólares en 2018, de ahí la importancia de mantener una escena competitiva limpia, donde los jugadores ganen de forma lícita el dinero. Este proyecto, enfoca su objetivo en crear un sistema capaz de detectar mediante inteligencia artificial, y, concretamente, aprendizaje supervisado, los cheats de asistente de puntería, o, en términos internacionales, aimbot. Dicho asistente de puntería permite al jugador que lo utilice obtener una ayuda en el control de movimiento del ratón. Esto se produce mediante operaciones de lectura y escritura de memoria, en las que se obtiene información del videojuego que se almacena de forma dinámica en la memoria del computador, y se sobrescribe dicha información con la nueva, aquella que otorga ventaja al jugador. Para ello, se crean y se utilizan programas que captan la información más relevante de una partida, y convierten dicha información relevante en un formato legible para su tratamiento en programas de generación de modelos de aprendizaje supervisado. Finalmente, se consiguen varios modelos que consiguen clasificar más de un 95% de las instancias correctamente.This project is developed using the videogame CS:GO (Counter Strike: Global Offensive) as its base. This is a first person shooter (FPS) videogame developed by Valve Corporation, which is the owner of the most important digital distribution platform in the world right now, Steam. It is the forth videogame of the series Counter Strike, being launched officially at August 21st, 2012. This series, along with other series like Call of Duty or Battlefield, is the most important in the world of FPS videogames, since it is the one with the highest number of online players. There are competitions in a lot of videogames, where the players can prove who is better, but, precisely, in this one, the amount of money invested is very high. For example, in 2018, there were at least more than 11 million of dollars invested (information collected from the most important competitions). However, such amount attracts players, and not all of them are honest and honoured. There are players who use cheats (self-made or bought ones from professional coders) called cheaters, to have an advantage over the skilled players. These cheats, sometimes are good enough to get over the anticheat security which the videogame or the competitions provide. There are cases of professional players being banned by the anticheat after winning prizes in important competitions, and the money is not taken back. This is one of the problems that has to be solved. There are different types of cheats as well, depending on the help they provide. The ones that are more common in competitions are aim-assistant ones (called aimbot), since they do not show anything in the player’s screen. Also, they are used in the competitive mode of the game. Because of this, the main goal of this project is to create a system which is capable of distinguish aimbot from humanized aiming. Also, this system will have to respect the privacy of the players and not be intrusive. This will be the second goal. For constructing the system, Artificial Intelligence will be used, and more precisely, supervised learning. In supervised learning, the system will build a function from training data, which will be data with pre-assigned classes. Final model will be chosen after doing many tests, where the algorithm used and the dataset will be changed. It will be the model with the highest percentage of correct classified instances and has less false positives. The different models will be generated with Weka and RapidMiner, so .arff files will need to be generated.Ingeniería Informátic

Implementation of model predictive control for tracking in embedded systems using a sparse extended ADMM algorithm

This article presents a sparse, low-memory footprint optimization algorithm for the implementation of model predictive control (MPC) for tracking formulation in embedded systems. This MPC formulation has several advantages over standard MPC formulations, such as an increased domain of attraction and guaranteed recursive feasibility even in the event of a sudden reference change. However, this comes at the expense of the addition of a small amount of decision variables to the MPC's optimization problem that complicates the structure of its matrices. We propose a sparse optimization algorithm, based on an extension of the alternating direction method of multipliers, that exploits the structure of this particular MPC formulation. We describe the controller formulation and detail how its structure is exploited by means of the aforementioned optimization algorithm. We show closed-loop simulations comparing the proposed solver against other solvers and approaches from the literature

Tractable robust MPC design based on nominal predictions

Many popular approaches in the field of robust model predictive control (MPC) are based on nominal predictions. This paper presents a novel formulation of this class of controller with proven input-to-state stability and robust constraint satisfaction. Its advantages are: (i) the design of its main ingredients are tractable for medium to large-sized systems, (ii) the terminal set does not need to be robust with respect to all the possible system uncertainties, but only for a reduced set that can be made arbitrarily small, thus facilitating its design and implementation, (iii) under certain conditions the terminal set can be taken as a positive invariant set of the nominal system, allowing us to use a terminal equality constraint, which facilitates its application to large-scale systems, and (iv) the complexity of its optimization problem is comparable to the non-robust MPC variant. We show numerical closed-loop results of its application to a multivariable chemical plant and compare it against other robust MPC formulations.Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación PID2019-106212 RB-C41Junta de Andalucía - Fondo Europeo de Desarrollo Regional P20_00546Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación PDC2021-121120-C2