Model predictive control (MPC) is applied with success in industry for automating constrained multivariable dynamical systems in an optimized way. However, some crucial aspects of MPC design largely remain to be addressed to unleash the full potential of MPC in applications: the efforts required to collect experimental data, identify the prediction model, and calibrate the controller, must be reduced considerably; the controller must self-adapt seamlessly to cope with unforeseen changes and not require excessively demanding computer hardware for deployment. This project aims to address methodologically such aspects and establish a theoretical and algorithmic framework for designing the next generation of nonlinear adaptive embedded MPC systems from data. Firstly, to reduce data-collection efforts significantly, we will develop tools that enable the design of experiments based on novel active-learning approaches to nonlinear system identification, coupled with robust MPC schemes to ensure safe data collection. Secondly, to cut calibration efforts down drastically, we will devise innovative preference-based methods that can learn from calibrators' assessments and automatically detect critical closed-loop scenarios. Thirdly, we will develop methods for seemingly adapting the prediction model at runtime to cope with uncertainties and model mismatches not seen during the design, as well as methods for approximating the control law with different tradeoffs between the amount of required online computations and the obtained closed-loop performance. To demonstrate the potential industrial use of the methodologies and algorithms developed in the project, we will formulate and solve laboratory benchmark problems on an experimental robotic platform, a challenging system for data-driven control due to its highly nonlinear, multi-input/multi-output, and fast-sampling dynamics.

Cracks in silicon solar cells composing photovoltaic (PV) modules are induced during production (soldering of busbars onto solar cells, other defects), transportation, installation and exposure to the environment. The economic impact of cracking in PV modules has been assessed in about 6 Euro/(kWp year) due to the cost of repair/substitution and the missing production while cracks are not yet observable with the naked eye. This has a clear huge technological and economic impact on the market that can be estimated in 180 MEuro/year of losses, by considering a conservative amount of 30 GWp of new installations in the World per year. If cracking cannot be avoided due to the brittleness of Silicon, the proposed idea to be taken to proof of concept is to limit its effect as much as possible. A new generation of PV modules displaying a superior resistance against cracking is proposed, starting from the fundamental discovery within the CA2PVM ERC StG project that residual thermo-mechanical compressive stresses in Silicon cells are beneficial to induce crack face contact and electric recovery. An innovative pre-stressing technique will be designed to increase the residual compressive stresses in Silicon and achieve the crack closure state for any crack and therefore avoid electrical power-losses. An exploitation strategy based on patenting of the technical solution, writing of a business plan, and founding a spin-off/start-up company with a team with interdisciplinary skills will be implemented. This will allow for fund raising and exploitation of the idea also based on the already established industrial contacts.

Sleep and wakefulness have traditionally been regarded as two mutually exclusive states characterized by differences in consciousness and responsiveness to the environment. However, the last two decades of research have demonstrated that sleep is actually a locally regulated phenomenon and that cortical islands of sleep- and wake-like activity can often coexist across distinct brain areas. Intriguingly, this mosaic of activity is also directly related to the presence and content of mental activity during sleep. In line with this, many sleep disorders, including insomnia and arousal disorders, are associated with significant local alterations in the balance between wake- and sleep-like activity. In spite of these considerations, the classical view of sleep as a uniform global state is still dominant in both basic and clinical research. Moreover, it remains unclear whether the occurrence of local wake-like activity is related to specific physiological functions of sleep. The objective of this project is to progress towards a deep understanding of the mechanisms that regulate sleep at a local level through the exploitation of known properties of the thalamocortical system. At the core of the proposal is the idea that particular sensory-stimulation protocols may allow to directly modulate sleep intensity in a local, region-specific manner. Such approaches could be used to non-invasively perturbate regional sleep-related brain activity, thus allowing to investigate the causal consequences on sleep mentation, subjective sleep quality and sleep-related functions, including learning and memory. Of note, the same approaches could also find application in counteracting alterations of local sleep regulation in pathological conditions. Knowledge gathered within the project could yield potential breakthroughs in numerous key applications of tremendous clinical, social and economic interest including treatment of sleep disorders and prevention of sleepiness-related accidents.