Performance-oriented model learning for data-driven MPC design

Model Predictive Control (MPC) is an enabling technology in applications requiring controlling physical processes in an optimized way under constraints on inputs and outputs. However, in MPC closed-loop performance is pushed to the limits only if the plant under control is accurately modeled; otherwise, robust architectures need to be employed, at the price of reduced performance due to worst-case conservative assumptions. In this paper, instead of adapting the controller to handle uncertainty, we adapt the learning procedure so that the prediction model is selected to provide the best closed-loop performance. More specifically, we apply for the first time the above "identification for control" rationale to hierarchical MPC using data-driven methods and Bayesian optimization.Comment: Accepted for publication in the IEEE Control Systems Letters (L-CSS

Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification

Effective quantification of uncertainty is an essential and still missing step towards a greater adoption of deep-learning approaches in different applications, including mission-critical ones. In particular, investigations on the predictive uncertainty of deep-learning models describing non-linear dynamical systems are very limited to date. This paper is aimed at filling this gap and presents preliminary results on uncertainty quantification for system identification with neural state-space models. We frame the learning problem in a Bayesian probabilistic setting and obtain posterior distributions for the neural network's weights and outputs through approximate inference techniques. Based on the posterior, we construct credible intervals on the outputs and define a surprise index which can effectively diagnose usage of the model in a potentially dangerous out-of-distribution regime, where predictions cannot be trusted

Causation as Constraints in Causal Set Theory

Many approaches to quantum gravity -the theory that should account for quantum and gravitational phenomena under the same theoretical umbrella- seem to point at some form of spacetime emergence, i.e., the fact that spacetime is not a fundamental entity of our physical world. This tenet has sparked many philosophical discussions: from the so-called empirical incoherence problem to different accounts of emergence and mechanisms thereof. In this contribution, I focus on the partial order relation of causal set theory and argue that causation can be characterized as an a-temporal constraint over the kinematic space defined by the theory. The relation constrains the growth of a new element/event with respect to the other elements/events of a given set. Therefrom, the flow of time emerges from the collection of the possible growths of the given set, where each possibility is characterized by a classical probability assigned by the dynamics of the theory

Experimental qualification of new instrumentation for lead-Lithium eutectic in IELLLO facility

The experimental facility IELLLO was installed in ENEA Brasimone R.C. in 2007, aiming to support the design of liquid Test Blanket Modules that will be installed in ITER and to contribute to the development of Lead-Lithium Eutectic (LLE) technologies. IELLLO has been recently upgraded by installing instrumentation relevant for ITER application. Differential pressure transducers, a Coriolis and a thermal mass flow meters were installed in the facility. An experimental campaign was planned, setting two objectives. The first objective was to qualify the instrumentation for flowing LLE The installation of a differential pressure transducer across each flow meter made also possible to characterize the pressure drops across these instruments. The second objective of this activity was to improve the knowledge on the performances of the main components of the loop at lower mass flow rates (namely 0.5-1.2 kg/s) and to quantify their pressure drops. The investigated flow rates were chosen to be relevant for the LLE loop of the WCLL TBS (Water Cooled Lead-Lithium Test Blanket System). This work presents the results of the experimental campaign, paying particular attention to underline the lessons learned on how to correctly operate instrumentation for LLE

Experimental campaign on the upgraded He-FUS3 facility

An extensive thermal-hydraulic experimental campaign was conducted on He-FUS3 helium loop facility to support the conceptual design of HCLL and HCPB Test Blanket System. The experiments were divided into three distinct phases. The first one was dedicated to the evaluation of the new ATEKO Turbo Circulator (TC) performances, identifying its operating limits in terms of supplied helium mass flow as a function of rotational speed, cold by-pass opening and loop pressure. The outcomes were compared with the manufacturer theoretical performance map and with a RELAP5-3D pre-test computation. In the second phase, experiments were carried out to analyze the facility dynamic response in hot conditions and to characterize its main components (TC, heaters, economizer, cooling system and valves). The wide amount of collected data will serve for the development and validation of a numerical model of the facility at TBS conditions. For the third phase, the tests were designed to investigate He-FUS3 behavior in accidental conditions representative of LOFAs and LOCAs scenarios

Technical efficiency and corporate structure of Italian private hospitals: evidence from one-step Stochastic Frontier Analysis

This paper aims to identify the relationship between the output-oriented technical efficiency of Italian private hospitals and their ownership structure. Using the one-step Stochastic Frontier Analysis technique, we explain technical efficiency throughout a set of variables capturing the firm’s shareholder activity, ownership concentration, and managerial ownership. Results suggest that (1) technical efficiency is positively affected by managerial ownership, and (2) private hospitals are more efficient when ownership concentration is low