This conference paper presents a comparison study between different charging techniques for energy storage systems. The work presents the application of charging methods in two different types of models, which are a dynamic nonlinear electrochemical and the well-known equivalent circuit model. For both cases, a controller is designed in order to analyze its performance, using the classical PID implemented in the vast majority of industry controllers. In order to validate its implementation, the case of an emerging technology in terms of energy storage has been considered, as is the vanadium redox flow battery. The models have been calibrated for later validation, using a particle swarm optimizer and a real dataset found in the literature. The controllers have been developed separately, considering the variables and characteristics of each model. Finally, a comparison of both controlled systems is presented.

Energy storage systems can provide a solution for the current challenges derived from the increasing penetration of renewable energies. Each energy storage system has different characteristics so their combination can be the best solution to achieve the requirements of a given scenario. To achieve the maximum potential of the Energy storage system they must be supplied with an optimal control strategy. Traditional control strategies only focus on increasing self consumption and do not take into consideration future generation and load. Model predictive control can use load and generation forecasts to provide a multi-objective solution which takes into consideration energy storage system degradation, grid congestion and self consumption between others. Neural networks are used to obtain the generation and load forecast, trained with empirical data from real households. An online model based predictive controller implemented for a grid composed by one lithium-ion battery, one vanadium redox flow battery, photovoltaic generation and electric consumption of 14 households. Finally the results of the classical method of maximizing self consumption, the ideal predictive controller considering perfect forecast and the real predictive controller are shown and discussed.

This paper proposes a new adaptive estimation approach to online estimate the model parameters of a piezoelectric cantilever beam. The beam behavior is firstly modeled using partial differential equations (PDE) considering the Kelvin-Voigt damping. To facilitate the estimation of unknown model parameters, the Galerkin’s method is introduced to extract desired vibration modes by separating the time and space variables of the PDE. Then, considering two major vibration modes, the corresponding system model can be represented by a fourth-order ordinary differential equation (ODE). Finally, by using measured input and output information, a novel adaptive parameter estimation strategy is introduced to estimate the unknown parameters of the derived ODE model in real time. For the purpose of driving the parameter updating law, the estimation error is extracted by using an auxiliary variable and a time-varying gain. Consequently, the convergence of the parameter estimation error is rigorously proved based on the Lyapunov theory. Simulations and experimental results show the validity and practicability of the proposed estimation method.

This conference paper presents a comparison between a$H_{\infty}$control technique and a classical PID, applied on a redox flow battery system. The study presents a dynamic nonlinear electrochemical model that considers the effect of over-potentials losses in the computation of the voltage measurement. The controller is designed to regulate the output voltage of the battery. The$H_{\infty}$controller is designed using the classical weighting function approach while the PID controller is tuned using a particle swarm optimizer. Finally, a comparison between the designed$H_{\infty}$controller, and the classic PID is presented.

Recently, new alternatives to conventional energy sources such as fossil fuels are arising due to global problems related to climate change effect and energy shortage. In this context, fuel cells and combined heat and power technologies appear as a possible solution due to their ability to provide both electrical and thermal energy more efficiently compared to traditional methods. Related to this, high-temperature proton exchange membrane fuel cells offer the possibility of implementing combined heat and power systems, and they are also considered an efficient technology that emits less greenhouse gases. In this article a model predictive control based energy management system for a specific house is presented. Simulation and control models of the system are presented, together with dimensions and energy profiles used. Finally, control objectives and the proposed control algorithm are detailed, and the results when trying to match residential heat and power demands are discussed.

This paper presents an alternative adaptive parameter estimation framework for nonlinear systems with time-varying parameters. Unlike existing techniques that rely on the polynomial approximation of time-varying parameters, the proposed method can directly estimate the unknown time-varying parameters. Moreover, this paper proposes several new adaptive laws driven by the derived information of parameter estimation errors, which achieve faster convergence rate than conventional gradient descent algorithms. In particular, the exponential error convergence can be rigorously proved under the well-recognized persistent excitation condition. The robustness of the developed adaptive estimation schemes against bounded disturbances is also studied. Comparative simulation results reveal that the proposed approaches can achieve better estimation performance than several other estimation algorithms. Finally, the proposed parameter estimation methods are verified by conducting experiments based on a roto-magnet plant.

This document presents a project to develop freely redistributable materials to conduct educational lab projects with MATLAB, Simulink, Arduino and low-cost plants. This work materials introduce the fundamentals of Control Engineering through exercises and videos. Along with all this, the most important steps and issues appeared in the project are explained, so anyone interested on doing a project can have a starting point instead of starting a project from scratch, which most of times this results hard to implement.

Non-deterministic generation from renewable sources have resulted in the incorporation energy storage systems in modern grids. Management of energy between different storage elements need to done optimally to ensure efficient operation of the grid. The intraday energy management problem is addressed in this work through an online model predictive control using multi objective optimisation. This work analyses the energy interaction among different storages when penalty weights in a multi objective optimisation problem is varied, in order to find an optimal scenario in terms of weight distribution. Different scenarios are identified and performance indices are proposed to achieve the same. The work also addresses implicitly the objective of minimising rate of degradation batteries. Simulation results are presented to aid in the analysis.

In this paper, we propose real-time adaptive parameter estimation methods for a polymer electrolyte membrane fuel cell (PEMFC) to facilitate the modeling and the subsequent control synthesis. Specifically, the electrochemical model of this fuel cell is in a nonlinearly parametric formulation. Hence, most of existing parameter estimation techniques for PEMFC mainly rely on the optimization approaches, requiring heavy computational costs or even offline implementation. In comparison to those methods, real-time adaptive parameter estimation methods for nonlinearly parametric system are developed in this paper. First, the nonlinearly parametric function is linearized by using the Taylor series expansion. Then, adaptive parameter estimation methods are proposed for estimating the constant or time-varying parameters of PEMFC. Different from the well-recognized adaptive parameter estimation methods, the proposed adaptive laws are driven by the extracted estimation errors, so that exponential convergence of the parameter estimation error can be guaranteed, without using any predictors or observers. Finally, practical experiments in a H-100 PEMFC system are conducted, which illustrate satisfactory performances of the presented parameter estimation methods under different operation scenarios

In this work the dynamic model of a domestic heating system is described. The heating system is based on a High Temperature PEM Fuel Cell. The model corresponds to a home placed in the city of Barcelona. The set of equations describing its behavior, the implementation in MATLAB/Simulink and some preliminary results for the control system are described.

The tracking/rejection of periodic disturbances in linear systems is achievable via internal model principle-based techniques, such as repetitive control. However, when the frequency of the signal to track/reject is uncertain or time-varying, the performance of these controllers decays dramatically. This article discusses the basics of Model Reference Adaptive Control with Minimal Controller Synthesis, and assesses its applicability to this problem from a comparative study of the performance of this type of controllers and a repetitive controller, both designed to regulate the speed of a DC motor subject to periodic disturbances. The study is supported by experimental results.

As other Internal Model Principle based strategies, digital repetitive control uses an internal model which provides infinite gain at specific frequencies. In systems subject to actuator saturation, a controller with this characteristics is highly prone to windup effect. Since linear design does not consider the actuator saturation this non linear behaviour may compromise the performance and even the stability of the system. One way to deal with this problem is to avoid saturation by selecting an actuator with larger capacity but it increases the implementation cost. In other cases, an anti-windup scheme is necessary. The main goal of the anti-windup strategy is threefold: to obtain a faster recovery of the system after saturation, to achieve less performance degradation and to preserve stability. In this paper three different anti-windup schemes have been selected from the available literature to address the windup problem in digital repetitive control. The design and implementation issues are discussed. A simulation example compares the results when saturation is reached either during the transient response or in steady state.

In the current context of increasing global energy needs and the depletion of traditional energy sources are particularly relevant management processes and the energy use improving. To contribute to the dissemination of these processes our institution has developed a project that aims to development a set of virtual laboratories to introduce these processes to engineering students. This paper presents an environment designed to illustrate the energy management ideas used in a metropolitan transportation system.

One of the career areas included in the field of biomedical engineering is the application of engineering system analysis: physiological modelling, simulation and control. This paper describes a virtual laboratory for the analysis and the study of human circulatory system. The virtual laboratory is based on the compilation of several mathematical models described in the literature. Presented application has been build using MATLAB/Simulink and EJS, so it combines good computation capabilities and it is completely interactive. The virtual laboratory is designed in order to understand the operation of the circulatory system under normal conditions, and to predict circulatory variables at different levels of stimuli and conditions.

This paper presents a discrete control design for time-delay systems subjected to the periodical command signal or exogenous disturbances. Unlike other dead-time compensators (DTC), we take profit of the system components to construct an internal model. In addition, a novel disturbance observer is developed to compensate the effect of disturbances, and thus to achieve tracking and disturbance rejection simultaneously. The possible fractional delay from discretization is also handled by using a fractional delay filter. The stability conditions and robustness analysis under model uncertainties are provided. Two numerical examples including a supply chain management (SCM) is provided to illustrate the feasibility of the results.

Digital repetitive control is a technique which allows to track periodic references and/or reject periodic disturbances. Repetitive controllers are usually designed assuming a fixed frequency for the signals to be tracked/rejected, its main drawback being a dramatic performance decay when this frequency varies. A usual approach to overcome the problem consists of an adaptive change of the sampling time according to the reference/disturbance period variation. However, this sampling period adaptation implies parametric changes affecting the closed-loop system behavior, that may compromise the system stability. This article presents a design strategy which allows to compensate for the parametric changes caused by sampling period adjustment. Stability of the digital repetitive controller working under time-varying sampling period is analyzed. Theoretical developments are illustrated with experimental results.

In this work a new methodology which allows to discretize linear continuous-time passivity based controller is presented. This methodology is based on choosing a proper output, which preserves the passivity structure, while keeping the continuous-time energy function. Analytic formulation and a numerical example are provided in the paper