Keywords: Discrete Event Dynamic Systems, Control Design, Networked Systems
Abstract: We study the concept of systems synchronisation in Max-Plus algebra. We show that the problem of synchronising Max-Plus linear systems is twofold. It can be stated in terms of controlled invariance, and of coreachability. The controlled invariance is the property of a set for which a suitable control exists, that maintains the trajectory in the set. We recall some properties of controlled invariance, in particular the existence of a maximal controlled invariant set included into any given set. Beyond the determination of the admissible controlled invariant set, we define and study a problem of coreachability. The controlled invariant set is the target and we study the existence of a control law that brings the trajectory to the target from the initial state within a specific number of steps. We illustrate the study with an example from the literature.

Keywords: Discrete Event Dynamic Systems, Control Design
Abstract: Linear systems over the max-plus algebra can model discrete event systems where synchronization, without competition, is involved. The lack of competition can be partly circumvented by considering multiple linear models, each representing a possible choice in resource allocation, and a switching mechanism, thus obtaining a switching linear max-plus system. We propose a formulation of the model matching problem for systems of such kind. The aim is to force a given plant to match exactly the output of a given model. A sufficient condition for the solvability of the problem is obtained by extending the geometric approach to switching systems over the max-plus algebra.

Keywords: Discrete Event Dynamic Systems
Abstract: A wide range of Discrete Event Systems (DES) such as manufacturing systems, telecommunications networks, transportation networks, and parallel computing, etc., can be modelled as max-plus systems in which maximization and addition are the main operations. In this paper, we use a nonlinear approach to deal with the error-estimation of nondeterministic max-plus systems with bounded random variables. This estimation is carried out following the ideas of the stochastic filtering theory for classical time-driven dynamic systems. The probability densities for the entries of the system matrices are assumed to be known, and a prediction-correction filtering scheme is used to compute the estimated state. The filtering algorithm is based on the minimization of a criterion, which is capable to evaluate the estimation-error, and to deal with the trade-off between prediction and measurement.

Keywords: Discrete Event Dynamic Systems, Manufacturing Modelling for Management and Control
Abstract: P-time event graphs (P-TEGs) are event graphs where the residence time of tokens in places is bounded by specified time windows. In this paper, we define a new property of P-TEGs, called weak consistency. In weakly consistent P-TEGs, the number of times a transition can fire before the first violation of a time constraint can be made as large as desired. We show the practical implications of this property and, based on previous results in graph theory, we formulate an algorithm of strongly polynomial time complexity that verifies it. From this algorithm, it is possible to determine, in pseudo-polynomial time, the maximum number of firings before the first constraint violation in a P-TEG.

Keywords: Linear Control Systems, Networked Systems
Abstract: This work deals with the problem of designing state observers in the presence of unknown inputs for switching linear structured systems - i.e., dynamical systems which consist of a finite indexed family of linear structured systems and a switching signal indicating the active system at each time instant. Switching linear structured systems lend themselves to be described both by families of parametric state space models and by families of directed graphs, in addition to the signal ruling the switching from one mode to another. Hence, the approach adopted herein is blended. It leverages on structural notions stemmed from the geometric approach and it exploits interpretations grounded on the graph theory. The notions of switching conditioned invariant subset and switching essential output injection play a key role in the derivation of the main result, a constructive necessary and sufficient condition for solvability of the unknown-input state observation problem. The methodological discussion is illustrated by two examples.

Keywords: Control Design, Non Linear Control Systems
Abstract: This paper deals with the study of discrete-time switched affine systems. While generally the literature is focused on the global (practical) stabilization problem, there are classes of switched affine systems that can be stabilized only locally. In this paper we deal with such a class of switched affine systems. More precisely, we consider switched affine systems with decoupled switching in the state matrix and the affine input term. For this particular class of systems, a switching law design is proposed and conditions ensuring the system's local practical stability are provided. A qualitative approach is provided based on the existence of a stabilizing linear state feedback. These results are illustrated using a numerical example.

Keywords: Modelling, Identification and Signal Processing, Non Linear Control Systems, Distributed Parameter Systems
Abstract: For complex processes whose evolution is described by partial differential equations, the identification of failures is often difficult. In thermal systems, for example, when one or more heating sources fail, the sensors placed at a distance perceive the effect with delay. From these noisy observations, it is then necessary to determine with precision the moment of failure and the possible restart of the heating. As the inverse heat conduction problems are ill-posed, a regularisation method has been developed and implemented for non-linear parabolic systems. It allows to detect failures and restarts of one or more sources. In the following, the whole problematic and the method of resolution is presented. Particular attention is paid to the effect of measurement noise on fault identification.

Keywords: Optimal Control, Robust Control, Modelling and Control of Environmental Systems
Abstract: Optimal sensor and actuator placement is performed for active vibration control. The components are selected to minimize the vibration impact on the strip by H2-norm minimization. Then, active vibration control is based on a PI-observer design allowing, not only to estimate the state, but also the disturbances. Some numerical results illustrate the performance of the proposed active vibration control.

Keywords: Distributed Parameter Systems, Linear Control Systems, Computers for Control
Abstract: Heat exchangers are generally described by two coupled partial differential equations (PDEs), and belong to the class of systems of conservation laws. In this paper we propose a result on the regulation of heat exchangers, in which we are choosing a proportional-integral (PI)-controller implemented on an output feedback from a boundary condition. First, a study on the existence and uniqueness of solutions of uncontrolled system is carried out, and we give a condition on the parameters of the system that ensures that the control-observation problem is well-posedness. Secondly we successively study the stability of the system controlled by a proportional and integral action, when the gains are adjusted following respectively a classic Lyapunov approach, and using a recent result that combines Lyapunov's approach and operator perturbation theory.

Keywords: Modelling, Identification and Signal Processing, Distributed Parameter Systems
Abstract: This contribution presents an application of the computational fluid dynamics (CFD) method in control-oriented modeling of a draft tube baffle (DTB) crystallizer's hydrodynamics. First, a CFD simulation of the overall system is performed. In a second step, the results of this simulation serve as basis for the comparison of partial DTB configurations with parts of our developed model, which is based on transport and transport-like partial differential equations (PDEs). Comparing the results of the PDE models with those of the transient CFD simulations allows an improvement of the developed hydrodynamic model and an assessment of the approximation quality. It is shown that the developed PDE models can represent the complex flow inside the DTB sufficiently well.

Keywords: Networked Systems, Optimal Control, Economic and Business Systems
Abstract: Geopolitical instability, climate change and black swan events disrupt the trade and logistics of resources around the globe. Events such as the unforeseen closure of the Suez Canal or the cessation of trade between some players due to wars or embargos are some examples of this. The problem of predicting local price change under modification of an underlying transport network unites elements of game theory, network theory and transport theory. The micro-economic Cournot oligopoly game involves modelling economic actors as rational players attempting to maximise profit. Under fixed transport conditions, analytical results can be found on the equilibria. Similarly, the transport layer can be analytically solved using techniques for selfish routing. Where trade and transport layers are linked together there is inter-layer feedback wherein players attempt to maximise their utility. We looked at the nature of the approach towards a new equilibrium under this instantaneous network change. In this respect our findings indicate that players benefit significantly from taking advantage of the non-simultaneous responses to the market rather than from moving to a new equilibrium utility. We found that when uncoupled, both the upper and lower layers have concave utility curves meaning there exist unique and stable equilibria in both cases. This leads to the multilayer model having non-unique stable equilibria for which general solutions exist.

Keywords: Modelling and Control of Biomedical Systems, Modelling, Identification and Signal Processing
Abstract: Thyroid cancer is a type of disease that affects the thyroid gland. The primary diagnosis of thyroid tumors based on histopathological patterns can be ambiguous in some cases, so the use of machine learning techniques might improve the classification of thyroid diseases. Moreover, the lack of high sampled datasets makes the classification issue even more complex. This paper proposes a comparative evaluation of two classical machine learning techniques and one Bayesian network framework. We use Exploratory Data Analysis techniques and oversampling methods for data preprocessing and overfitting reduction. Results show that the use of Bayesian network frameworks can help in integrating prior expertise knowledge in the classification problem and build new hypotheses about features interaction.

Keywords: Linear Control Systems, Safeprocess, Robust Control
Abstract: In this work, we consider a discrete-time switched linear system. A novel approach is introduced to estimate its state in a finite fixed time which can be arbitrarily chosen and is independent of the initial state. For cases where the additive disturbance and measurement noise are known, we provide an exact estimation. Otherwise, a finite-time interval observer is designed. The crucial idea is based on using past values of the input and output of the studied system and a minimal dwell time condition. Simulation results are provided to illustrate the effectiveness of the proposed techniques in different scenarios of finite time choices.

Keywords: Control Design, Linear Control Systems
Abstract: This paper focuses on the study of the behavior of critical roots when a dynamical system is stabilized by a PD-controller, for which the derivative action has been approximated by using two commensurate delays. The use of such an approximation leads to a characteristic quasipolynomial whose coefficients depend explicitly on the delay parameter. The aim of the paper is to address the way the delay parameter may affect the location of the roots of the corresponding characteristic function, and in particular the cases when ``small'' delays induce instability in the closed-loop systems. Such an analysis is performed by expressing the critical solution of the system as a delay-dependent power series. Illustrative examples complete the presentation.

Keywords: Large Scale Complex Systems, Control Design, Optimal Control
Abstract: In this paper, a new approach to distributed MPC of linear interconnected time-delay systems is proposed. The general case of time-delay in both the states and the control inputs is considered. The approach includes representation of the interconnected time-delay dynamics in the augmented state space and reformulation of the centralized MPC problem into a Quadratic Programming framework. The latter this is solved distributedly by using the dual gradient method for optimization. The suggested approach would be appropriate for embedded distributed MPC and is illustrated on a simulation example of two interconnected time-delay systems.

Keywords: Modelling and Control of Biomedical Systems, Linear Control Systems, Control Design
Abstract: In this work, we investigate the human quiet standing regulation problem using a single-link inverted pendulum model in the sagittal plane via the ankle joint’s passive/active torques actions. The active torque consists of ankle muscle contractions that are activated by the delayed action of the Central Nervous System (neural controller). The passive torque is related to the intrinsic mechanical properties of the muscle-tendon-ligament component. The failure of the human quiet standing is then directly related to the failure of one or both types of torques. We propose to model the neural controller as a delayed Proportional-Derivative-Acceleration controller acting on the ankle joint angular position. By using the multiplicity-induced-dominancy property, the critical time delay of the motor control and the critical ankle-joint stiffness are both investigated.

Keywords: Power and Energy Systems, Stochastic Systems, Modelling, Identification and Signal Processing
Abstract: Typical equivalent circuit models for Lithium-ion batteries are represented in the form of the so-called Thévenin form. These models comprise a state of charge (SOC) controlled voltage source as well as lumped elements such as series resistances as well as at least one RC subnetwork to describe dynamic effects by the terminal current and voltage as input and output of the battery. In classical state estimation approaches, these characteristics are assumed to be identified beforehand, while aging of battery cells is related to a loss of the total capacity, changes in the charging/discharging efficiency, an increase of the Ohmic cell resistance, and/or changes of the time constants of the above-mentioned RC networks. Such variations can be estimated by means of additional parameters that are included in the system's state vector. However, the typically applied approaches do not allow for a direct identification of the nonlinear dependencies of the circuit elements on the SOC and other influence factors such as currents or the cell temperature. Therefore, this paper proposes a real-time capable, two-stage identification of these dependencies on the example of the open-circuit voltage by means of an Unscented Kalman Filter (UKF) approach. In the first stage, the state variables of the dynamic system model are estimated together with a lumped disturbance term (an additive correction of the open-circuit voltage). In the second stage, this disturbance term is used to correct the a-priori knowledge of the open-circuit voltage and to simultaneously express the identification quality in terms of covariances for the coefficients of its piecewise polynomial approximation in each temporal discretization step. The efficiency of the proposed methodology is demonstrated in simulations for an experimentally validated system model.

Keywords: Modelling, Identification and Signal Processing, Power and Energy Systems, Distributed Parameter Systems
Abstract: Complex systems require complex models to have high accuracy and a wide observation of internal dynamics. However, those models are not suitable for fast simulations or on-line applications. The modelling of Lithium ions batteries has been explored for years where model-based approaches and data-driven approaches have been developed. New techniques are being created to come up with simple enough models with high precision. A reduced order electrochemical model based on Dynamic Mode Decomposition with Control (DMDc) is developed here to achieve high prediction accuracy for state-of-charge (SOC) estimation without the need of a high-dimensional model. The performance is illustrated using numerical simulations and comparison with standard equivalent-circuit modeling with Coulomb counting.

Keywords: Modelling and Control of Biomedical Systems, Control Design, Non Linear Control Systems
Abstract: In this paper, the plasma glucose regulation problem for Type 2 diabetic patients by subcutaneous insulin infusion is studied. A nonlinear time-delay model of the glucose-insulin regulatory system, which takes into account the subcutaneous infusion of insulin, is exploited for the design of a quantized sampled--data static state feedback glucose controller. Quantization in both input/output channels is considered. Spline interpolation methodologies are used in order to obtain an approximation of suitable needed past values of the system state that cannot be always available from the buffer. The stabilization in the sample--and--hold sense is used in order to prove that the proposed digital glucose regulator ensures the semi--global practical stability of the related closed--loop tracking error system, with arbitrarily small final target ball. The proposed digital glucose regulator is validated through simulations.

Keywords: Adaptive and Learning Systems, Modelling and Control of Biomedical Systems
Abstract: Reinforcement learning, thanks to the observation-action approach, represents a useful control tool, in particular when the dynamics are characterized by strong non-linearity and complexity. In this sense, reinforcement learning has a natural application in the biological systems field where the complexity of the dynamics makes the automatic control particularly challenging. This paper presents a combined application of neural networks and reinforcement learning, in the so-called field of deep reinforcement learning, for the glucose regulation problem in patients with diabetes mellitus. The glucose control problem is solved through the Deep Deterministic Policy Gradient (DDPG) and the Soft Actor-Critic (SAC) algorithms, where the environment exploited for the agent's interactions is represented by a glucose model that is completely unknown to agents. Preliminary results show that the DDPG and SAC agents can suitably control the glucose dynamics, making the proposed approach promising for further investigations. The comparison between the two agents shows a better behaviour of SAC algorithm.

Keywords: Modelling and Control of Biomedical Systems, Stochastic Systems
Abstract: This note investigates how noise propagates in cascades of metabolic transformations. Motivation stems from recent single cell experiments that have shown that noise generated in gene expression and enzymes fluctuations propagates to growth rate through metabolic fluxes. A stochastic approach based on Continuous-Time Markov Chains (CTMC) is exploited to model all reactions, with a special interest in the substrate production, assumed to happen in bursts. Different noise features are dealt with, including correlation of intermediate players, noise impact on the end-product and the role of a feedback from the end-product that may control the substrate production. Most results are given in terms of analytical solutions of the CTMC, in some cases exploiting linear approximations; in all these cases, the findings are validated via Monte Carlo stochastic simulations. The proposed results highlight how substrate production in bursts, cascade length and distance among species affect fluctuations and correlations, with the feedback possibly playing a crucial role in favor of noise propagation.

Keywords: Non Linear Control Systems, Modelling and Control of Biomedical Systems
Abstract: Within cells, transcription factors (TFs) bind to a wide range of nonspecific genomic sites in addition to their target sites. Binding to such high affinity "decoys" has been shown to qualitatively alter the dynamics of gene regulatory circuits. Analyzing simple gene expression models with decoy binding we derive formulas for the TF response time as a function of the number of decoys, binding affinity, and stability of the decoy-bound TF. Our results show that while on one hand, decoys make the response sluggish whenever decoy binding stabilizes the TF, on the other hand, decoys can accelerate responses by destabilizing the bound TF. We apply these results in the context of a genetic oscillator based on an activator-repressor motif, where sustained oscillations result from a rapid activator-mediated positive feedback working in conjunction with a slow repressor-mediated negative feedback. Consistent with our response time analysis, we find that activator binding to decoy sites can destroy oscillations in the case of a stable decoy-activator complex that functions to slow down the positive feedback. In contrast, an unstable decoy-activator complex can expand the oscillatory parameter regime. In conclusion, our response time analysis provides intuitive insights into the emergence of sustained oscillations.

Keywords: Modelling and Control of Biomedical Systems, Stochastic Systems, Discrete Event Dynamic Systems
Abstract: In nature, cells face changes in environmental conditions that can modify their growth rate. In these dynamic environments, recent experiments found changes in cell size regulation. Currently, there are few clues about the origin of these changes in cell size. In this work, we model cell division as a stochastic process that occurs at a rate proportional to the size. We propose that this rate is zero if the cell is smaller than a minimum size. We show how this model predicts some of the properties found in cell size regulation. For example, among our predictions, we found that the mean cell size is an exponential function of the growth rate under steady conditions. We predict that cells become smaller and how the division strategy changes during dynamic nutrient depletion. Finally, we use the model to predict cell regulation in an arbitrary complex dynamic environment.

Keywords: Modelling and Control of Biomedical Systems, Non Linear Control Systems
Abstract: In vitro simulators give the chance to mimic specific human physiological/unphysiological conditions to test medical devices, accelerating innovation cycles, and rapidly exploring new effective solutions. Specifically, in this work, we consider a Pulse Duplicator in use at the Healing Research Laboratory, at the University of Padova, Italy. It allows assessing the performance of prosthetic heart valves under simulated cardiac conditions by generating a controlled pulsatile flow obtained by setting specific system characteristics such as the peripheral resistance and the compliance. In order to conduct effective prosthetic heart valve tests, the Pulse Duplicator has to be manually tuned to provide suitable hemodynamics waveforms (i.e. flow and pressure waves). To assist this time-consuming and not trivial task, we present a Phase Plane Analysis of a particular signal (i.e., the left ventricular pressure) in order to capture additional hemodynamic characteristics of clinical interest that can serve system tuning purposes.

Keywords: Economic and Business Systems, Stochastic Systems, Computers for Control
Abstract: Our paper is devoted to an applications of the feedback control methodology to the modern Algorithmic Trading (AT). The controller we propose performs a specific four term control design consisting of proportional, integral, derivative, and second order derivative terms (PIDD). The designed PIDD based trading strategy also incorporates a switched (dynamic) structure. We develop a specific switching mechanism that involves a backtesting driven profit maximization procedure on the historical data. We also study here the resulting model-free PIDD trading strategy in the frequency domain. Additionally, we give in this section a rigorous formal estimation of the win rate for the trading algorithm under consideration. This abstract probabilistic win rate helps to justify the conceptual applicability of the proposed trading scheme. We finally apply the obtained AT strategy to a real-world stock market, namely, to the Binance Bitcoin/USD price dynamics. The considered practical example illustrate implementability of the proposed AT approach.

Keywords: Stochastic Systems, Non Linear Control Systems
Abstract: In this paper, we investigate the influence of different numerical solutions methods on the chaoticity of fractional chaotic systems. In particular, fractional Chen system is discussed and approximated from both analytical and numerical point of view applying different calculation methods. The impacts of the chosen methods to the chaotic behavior of the fractional system are compared and analyzed.

Keywords: Optimal Control, Economic and Business Systems, Large Scale Complex Systems
Abstract: One of the central tasks of macroeconomic analysis is forecasting and control of economic agents' expectations for key macroeconomic indicators such as inflation, gross domestic product (GDP), prices and others, etc., so that to achieve maximum level of their welfare through monetary and fiscal policy. This problem is solved within the framework of the optimal control theory using New Keynesian (NK) models that can demonstrate complex behavior, including periodic and chaotic dynamics, even in conditions of dynamical economic equilibrium. Revealing the economic mechanism for the emergence of such irregular dynamics can improve forecasting behavior of the model of the economy and stabilize undesirable dynamics.

We consider this complex mechanism from two sides. First, we analyze how various monetary and fiscal policy regimes, active as well as passive, stimulate the emergence of different types of dynamics, both locally determinate and explosive ones, using the NK model with government debt as an example. Second, we clarify how government debt affects optimal monetary policy. A non-trivial backward integration algorithm is implemented to identify the initial state of the model, which leads to optimal dynamic equilibrium and the achievement of targeted values of key macroeconomic indicators.

Keywords: Non Linear Control Systems, Optimal Control, Power and Energy Systems
Abstract: The pulse and glide (PnG) strategy was proven to be a fuel-economic speed strategy in car-following and free-driving scenarios. Using this strategy, the powertrain periodically switches between pulse and glide modes while the vehicle repeatedly accelerates and decelerates. The switching of the operating modes and complexities of the powertrain components lead to difficulties in system modelling and control. Furthermore, the road slope influences whether the PnG strategy is more fuel-economic than the constant speed (CS) strategy. However, the precise upper and lower limits of the road slope for PnG to be fuel-economic are yet to be studied. In this study, for widespread utilization, the problem is simplified to avoid online optimization calculation. The optimal control of a powertrain, a nonlinear hybrid system with a typical modern engine, is considered. The results demonstrated that when the road slope r∈(−1.8%, 4.3%), the PnG strategy is more fuel-economic than CS strategy at the same average speed. The optimal engine power, initial speed, and pulse mode duration for fuel economy are obtained for given road slopes. The results indicate that when r≤−1.8%, no driving power is necessary regardless of the strategy employed; when r≥4.3%, the speed profiles of both strategies are similar. Therefore, the results suggest that PnG strategy is more fuel-economic than CS strategy only when the road slope is limited in a range, i.e., (−1.8%, 4.3%). Furthermore, the poor fuel-saving performance of PnG strategy in other road slopes is investigated by precise analysis of the calculation results.

Keywords: Control Design, Robust Control
Abstract: This paper presents robust control design for a mobile tracking antenna using the CRONE approach. The antenna is modeled by two coupled mechanical axes used to track azimuth and elevation angles. The parametric uncertainties of the model and the axes mechanical coupling are taken into account in the design of the position loop controller. For comparison purposes, the same control specifications are applied for a PID controller. Simulation results show the effectiveness of the CRONE controller in terms of robustness and stability, as compared to the PID.

Keywords: Robust Control, Discrete Event Dynamic Systems, Stochastic Systems
Abstract: The min-sup type Robust Kalman Filter (RKF) introduced in Azhmyakov [2002] guarantees a robust estimate in uncertain linear dynamic systems under relatively weak assumptions related to the state and observation noises. In particular, it is supposed that the system and observation noises have some unknown probability distribution functions from some classes of centered distributions with bounded covariances with the known upper bound matrices. In our paper, we address the identification problem for upper-bound matrices in RKF, in the case of scalar observations. We use a novel Penalized Uncoverage (PU) function and an advanced optimization technique for this purpose. The novel PU-RKF methodology we develop in this paper is applied to robust state estimation in the stationary autoregressive model. We finally compare computationally our new PU-RKF algorithm with a classical approach involving a combination of the maximum-likelihood estimation and Kalman Filter (ML-KF) for Gaussian and some non-Gaussian noises.

Keywords: Control Design, Optimal Control, Non Linear Control Systems
Abstract: The current paper proposes to compare the performances of a proportional-integral (PI) controller whose parameters are determined in a model-based way using a metaheuristic search algorithm, with the performances of a PI determined in a model-free way by using virtual reference feedback tuning (VRFT) algorithm and using iterative feedback tuning (IFT) algorithm. The PI controller is included in a control system structure to control the position of a shape memory alloy (SMA) laboratory equipment.

Keywords: Adaptive and Learning Systems, Manufacturing Modelling for Management and Control
Abstract: In this paper, an early time-series classification (ETSC) algorithm is applied to support fault diagnosis in a complex hydraulic system (HS) with several interconnected components. The proposed technique aims at early classifying the state of the system while keeping the loss of classification inaccuracy at the minimum level. In contrast to baseline models that detect the eventual faults at the end of each working cycle, the ETSC model can diagnose any fault type of the HS components before observing the entire working cycle. Indeed, the early classification model successfully achieves a trade-off between the accuracy and the earliness criterion. Experimental results on a realistic HS dataset from the related literature show that the ETSC method can effectively identify different fault types with a higher accuracy and earlier compared to baseline methodologies.

Keywords: Adaptive and Learning Systems, Modelling, Identification and Signal Processing, Stochastic Systems
Abstract: An indirect data-driven state observer design approach for the inertia wheel pendulum considering static friction of the actuated inertia disc is presented. The frictional forces occurring in a real laboratory setup are characterized by the Stribeck effect as well as the transition between two different dynamic behaviors, sticking and non-sticking. These switching nonlinear dynamics are identified with various machine learning methodologies in a data-driven manner, i.e., the unsupervised separation and feature clustering of measured state trajectories into two dynamic classes, and the supervised classification of a state-dependent switching condition. The identified system with the interior switching-structure of two dynamics is combined with a moving horizon estimator.

Keywords: Adaptive and Learning Systems, Non Linear Control Systems, Modelling, Identification and Signal Processing
Abstract: Model Predictive Control is an industry-standard technique used to drive systems based on their internal dynamics. When not all states are available for feedback, a state estimator, such as an Extended Kalman Filter, is employed to achieve control over the complete system state. Nevertheless, when the system under control is nonlinear, these two combined methods can result in a computationally heavy control strategy, raising significantly the cost of implementing it online. In this paper, a data-driven strategy based on the Koopman Operator theory is presented to identify and replicate the dynamics of the Kalman Filter plus Model Predictive Controller pair in a resource-efficient scheme. First, a closed-loop operation data-set is generated from a pre-calibrated reference controller; then, a finite-dimensional approximation is derived for the Koopman Operator of the filter plus controller dynamics in the lifted space of observables; finally, the stability of the identified controller is evaluated through closed-loop simulations; in case the desired response has not been achieved, the identification process is performed iteratively with a progressively increasing regularization coefficient. A simulated example applied to the Van der Pol oscillator is presented to illustrate the effectiveness of the approach.

Keywords: Modelling, Identification and Signal Processing, Adaptive and Learning Systems, Power and Energy Systems
Abstract: Within the concept of a smart grid, aggregators have the task of coordinating the behavior of large sets of Distributed Energy Resources, each of them offering small power/energy capacities, which help to balance the power grid and can serve as providers of services. Adequate coordination strategies are required to optimally exploit these resources in the ancillary services market. However, deriving model-based control policies for them is complex due to the heterogeneity and uncertainty related to the large set of associated agents. Then, a data-driven model is an adequate solution for this sort of situation. This paper presents the application of the Youla–Kucera Data-Driven Control strategy for the development of an aggregator to regulate the power consumption of a set of thermoelectric refrigerators, avoiding the modeling process and directly designing a controller from data. A detailed simulation framework was executed to verify the validity of the proposed methodology. It is shown that the derived aggregator is able to offer frequency containment reserves service, achieving the required settling time of 30 seconds and with a tracking error below 4.7%.

Keywords: Adaptive and Learning Systems, Networked Systems, Stochastic Systems
Abstract: With the imminent advent of quantum communications head-on and its landing in what is going to be called the Quantum Internet, it is essential to research different ways of how it could be done in its most efficient way. In this work, we propose a game theory based self-adaptive system that, by accumulating reward, manages to learn how to minimize the latency of packets that are sent in a congested quantum network. To begin with, we study under which circumstances the quantum protocol outperforms its classical equivalent. First, analyzing the minimum number of packets moving through the network as a function of the size of the network necessary for guaranteeing an improvement in the quantum protocol. Then, studying the minimum size of the network based on the number of nodes needed to ensure the aforementioned advantage. Furthermore, taking advantage of the concepts of non-cooperative games and reinforcement learning, a protocol is designed and the behavior of a network with quantum nodes is evaluated. In order to analyze this protocol, different algorithms and scenarios are tested by measuring their efficiency and adaptability.