This paper presents a comparative study between flatness-based control and functional observer-based feedback control, with a focus on how each method addresses control tasks in systems with complex dynamics. Both control strategies are widely utilized in applications such as robotics, aerospace, and automotive systems, where efficient and reliable control is essential. The paper explores the key principles, advantages, and limitations of each approach, providing a detailed comparison of their applicability, design complexity and robustness. Our paper builds on our previous work [11] which focused exclusively on flatness-based control for the Ball Balancing Table (BBT) and its simulation results. We now extend this by comparing it with a functional observer-based feedback controller for the same system.

This paper introduces the dynamic mode decomposition (DMD) for the prediction of the air quality and the precipitation in the presence of high rain as an application of environment prediction tasks by forecasting the appropriate index parameters for each field. The forecasting procedure is based on the use of two real data bases containing the pollutant concentration, and the precipitation for the flooding forecasting. Moreover, the temporal evolution of the DMD modes, can be used to reconstruct the desired components and perform forecasting at the same time using the eigenvalues and eigenvectors. This task of the DMD is already known by the literature, the new in this paper is the application of the DMD for forecasting tasks on the environment issues which present appreciated results that are discussed and well analysed in this paper using different performance indexes to prove its efficiency.

Comfort conditions in buildings are no more a luxury than a necessity. Architects and designers are focussing their work on providing the best conditions of comfort in a building depending on the activity. The university gives students and teachers a space where they can share knowledge and experience. Besides, it should offer the best conditions for this process to work properly. That is why a lot of effort is put into bringing university occupants a proper space for working and learning. Despite all this effort, an evaluation of the comfort conditions in classrooms is necessary to ensure that indoor conditions can be adapted to occupant activity. One way to do this is to perform in situ measurements during teaching hours and compare the results to local standards. Besides the measures, we need occupants review regarding the Indoor Environment Quality (IEQ) trough survey. These data give us late feedback regarding the IEQ. therefore, with the different set of data acquired we intend to predict comfort conditions using different machine learning algorithms. A comparison between these algorithms will help us choose an adequate algorithm to estimate perceived comfort

The increasing use of renewable energies, in particular photovoltaics in recent years, leads researchers to study this type of energy in depth. Photovoltaic panels have a major problem that is when their temperature increases, their efficiency decreases. For this reason, many research works are interested to solve this problem in order to increase the electrical efficiency. The best solution used is the PV/T hybrid systems which consists of a photovoltaic panel and a thermal system co-combining in a single unit. In this paper, a presentation of different methods to increase the efficiency of PV/T hybrid systems and a mathematical model of a PV/T water system is developed. A simulation and an experimental validation showing the influence of the water flow variation on the efficiency of the real PV/T hybrid system, is tested.

In this paper, the direct approach which was introduced for the first time by [Rotella and Zambettakis, 2011] in designing minimal functional state observers is extended to deal with discrete-time systems. One of the benefits of this approach is that it does not require solving the Sylvester matrix equation that appears in other observer design procedures. Both stable observers and arbitrary fixed poles observers problems are considered for minimality. A numerical example and simulation results explain the effectiveness and the benefits of the proposed algorithm.

This paper deals with the modelling, simulation and control of a wave power plant installed in the northern cost of Spain. This plant is an onshore Oscillating Water Column (OWC) composed of a Wells turbine coupled to a Doubly Fed Induction Generator (DFIG). The power generated by the OWC is limited by the stalling behavior, a feature of the Wells turbine; therefore an airflow control is proposed in order to avoid the stalling phenomenon. This is achieved by regulating the airflow in the turbine duct by means of the throttle valve. The proposed control is based on a Proportional Integral (PI) controller. The results shows that the proposed control strategy provides a significant improvement of the generated power.

In this paper, a Fuzzy-based Model Predictive Control (FMPC) for a class of nonlinear systems subject to faults and uncertainties is proposed. Our contribution comes from the combination of Parallel Distributed Compensation (PDC) and Model Predictive Control (MPC) for nonlinear systems described by Takagi-Sugeno (T-S) fuzzy model. The objective is to design a stable, robust and efficient Fault Tolerant Controller (FTC). The validity of the proposed FTC strategy and its application to faults tolerance is illustrated through an application to a given automotive Cyber-Physical System (CPS).