Modeling and Model Predictive Control of an Industrial Hydraulic System

This thesis aims to illustrate the construction of a mathematical model of a hydraulic system, oriented to the design of a model predictive control (MPC) algorithm. The modeling procedure starts with the basic formulation of a piston-servovalve system. The latter is a complex non linear system with some unknown and not measurable effects that constitute a challenging problem for the modeling procedure. The first level of approximation for system parameters is obtained basing on datasheet informations, provided workbench tests and other data from the company. Then, to validate and refine the model, open-loop simulations have been made for data matching with the characteristics obtained from real acquisitions. The final developed set of ODEs captures all the main peculiarities of the system despite some characteristics due to highly varying and unknown hydraulic effects, like the unmodeled resistive elements of the pipes. After an accurate analysis, since the model presents many internal complexities, a simplified version is presented. The latter is used to linearize and discretize correctly the non linear model. Basing on that, a MPC algorithm for reference tracking with linear constraints is implemented. The results obtained show the potential of MPC in this kind of industrial applications, thus a high quality tracking performances while satisfying state and input constraints. The increased robustness and flexibility are evident with respect to the standard control techniques, such as PID controllers, adopted for these systems. The simulations for model validation and the controlled system have been carried out in a Python code environment.

Seismic mitigation of existing masonry structures by means of added damping and stiffness

In this work seismic upgrading of existing masonry structures by means of hysteretic ADAS dampers is treated. ADAS are installed on external concrete walls, which are built parallel to the building, and then linked to the building's slab by means of steel rod connection system. In order to assess the effectiveness of the intervention, a parametric study considering variation of damper main features has been conducted. To this aim, the concepts of equivalent linear system (ELS) or equivalent viscous damping are deepen. Simplified equivalent linear model results are then checked respect results of the yielding structures. Two alternative displacement based methods for damper design are herein proposed. Both methods have been validated through non linear time history analyses with spectrum compatible accelerograms. Finally ADAS arrangement for the non conventional implementation is proposed.

Towards the circularity of outdoor lighting systems

In recent years, there has been increasing attention to lighting energy efficiency, due to economics - lower energy costs - and environmental reasons - maninduced climate change. Driven by strict energy-efficiency requirements, the lighting industry started to replace the traditional lamps with LED lighting solutions, ignoring the limits of their maintenance and recycling. Faced with an increasing global population, rising resource consumption and associated negative environmental impacts, shifting from a traditional economic linear model to a more sustainable paradigm of growth is now becoming increasingly urgent. Whereas the topic of circular economy has been widely investigated in literature in the past, little attention has been reserved for the different evaluation tools to assess and improve product circularity and how companies can become more resource-efficient. Hence, the present thesis investigates the implementation of a circular economy in the lighting industry through the use of circularity indicators and ecodesign strategies. Concerning the real luminaire products, the role of the luminaire in the circular economy and recycling industry is explored, highlighting the limits of their End-of-life process. The main conclusions of the thesis reveal the significance of initial product development, reuse, remanufacturing and repair strategies in a transition towards a circular economy.

Bayesian Linear Regression for estimation of the Fractal Dimension of the Cerebral Cortex

The cerebral cortex presents self-similarity in a proper interval of spatial scales, a property typical of natural objects exhibiting fractal geometry. Its complexity therefore can be characterized by the value of its fractal dimension (FD). In the computation of this metric, it has usually been employed a frequentist approach to probability, with point estimator methods yielding only the optimal values of the FD. In our study, we aimed at retrieving a more complete evaluation of the FD by utilizing a Bayesian model for the linear regression analysis of the box-counting algorithm. We used T1-weighted MRI data of 86 healthy subjects (age 44.2 ± 17.1 years, mean ± standard deviation, 48% males) in order to gain insights into the confidence of our measure and investigate the relationship between mean Bayesian FD and age. Our approach yielded a stronger and significant (P < .001) correlation between mean Bayesian FD and age as compared to the previous implementation. Thus, our results make us suppose that the Bayesian FD is a more truthful estimation for the fractal dimension of the cerebral cortex compared to the frequentist FD.