Data-driven distributionally robust MPC for systems with uncertain dynamics

We present a novel data-driven distributionally robust Model Predictive Control formulation for unknown discrete-time linear time-invariant systems affected by unknown and possibly unbounded additive uncertainties. We use off-line collected data and an approximate model of the dynamics to formulate a finite-horizon optimization problem. To account for both the uncertainty related to the dynamics and the disturbance acting on the system, we resort to a distributionally robust formulation that optimizes the cost expectation while satisfying Conditional Value-at-Risk constraints with respect to the worst-case probability distributions of the uncertainties within an ambiguity set defined using the Wasserstein metric. Using results from the distributionally robust optimization literature we derive a tractable finite-dimensional convex optimization problem with finite-sample guarantees for the class of convex piecewise affine cost and constraint functions. The performance of the proposed algorithm is demonstrated in closed-loop simulation on a simple numerical example

Stochastic MPC for energy hubs using data driven demand forecasting

Energy hubs convert and distribute energy resources by combining different energy inputs through multiple conversion and storage components. The optimal operation of the energy hub exploits its flexibility to increase the energy efficiency and reduce the operational costs. However, uncertainties in the demand present challenges to energy hub optimization. In this paper, we propose a stochastic MPC controller to minimize energy costs using chance constraints for the uncertain electricity and thermal demands. Historical data is used to build a demand prediction model based on Gaussian processes to generate a forecast of the future electricity and heat demands. The stochastic optimization problem is solved via the Scenario Approach by sampling multi-step demand trajectories from the derived prediction model. The performance of the proposed predictor and of the stochastic controller is verified on a simulated energy hub model and demand data from a real building.Comment: 6 pages, 5 figures. Submitted to IFAC World Congress 202

Brain-Computer Interfaces: Investigating the Transition from Visually Evoked to Purely Imagined Steady-State Potentials

Brain-Computer Interfaces (BCIs) based on Steady State Visually Evoked Potentials (SSVEPs) have proven effective and provide significant accuracy and information-transfer rates. This family of strategies, however, requires external devices that provide the frequency stimuli required by the technique. This limits the scenarios in which they can be applied, especially when compared to other BCI approaches. In this work, we have investigated the possibility of obtaining frequency responses in the EEG output based on the pure visual imagination of SSVEP-eliciting stimuli. Our results show that not only that EEG signals present frequency-specific peaks related to the frequency the user is focusing on, but also that promising classification accuracy can be achieved, paving the way for a robust and reliable visual imagery BCI modality. Clinical relevance-Brain computer interfaces play a fundamental role in enhancing the quality of life of patients with severe motor impairments. Strategies based on purely imagined stimuli, like the one presented here, are particularly impacting, especially in the most severe cases

Changes in body composition and psychological profile when overcoming four Everesting bike challenges

Problem Statement: During ultra-endurance races, given the long duration of the competitions, athletes can experience variations in body composition and moods. These elements can greatly affect the athlete's performance. Purpose: To evaluate the effects of an ultra-endurance race (4 consecutive Everesting Bike Challenges) on the body composition and moods of an adult athlete. Material and Methods: A well-trained amateur cyclist (male; 46 years; 64 kg; 1.69 cm; BMI 22.4 kg/m2) was monitored during the 4 Everesting Bike Challenges. This test is an ultra-endurance challenge that involves overcoming 8848 meters by climbing a single peak several times. The changes in body composition and hydration, calculated by bio-impedentiometry, and the changes in moods, obtained by administering the Profile of Mood States (POMS), in addition to Rating of Perceived Exertion (RPE) and Visual Analogic Scale, were measured at the beginning, during or at the end of each Everesting passed. Results: The resting heart rate was 42 beats per minute. The estimated theoretical maximal heart rate was 174 bpm. The monitored athlete overcame the 4 Everesting Bike Challenges covering a total of 904.79 km. The time taken to complete the race was 113 hours and 18 minutes. The total height difference exceeded was 35395 m. During the race the athlete pedaled with an average heart rate of 97 bpm. Body mass dropped from 64.0 to 63.1 kg between the start and end of the test. Wide variations in the athlete's Vigor (T0=16:T5=6), fatigue (T0=0:T5=6) and Sleep quality (T0=100:T5= ≈0) were found during the competition. Regarding the Rating of Perceived Exertion scale, the results obtained indicate a medium-low value (RP=3). Conclusion: The results of this study showed negligible reduction in body mass in the athlete who performed an ultra-endurance challenge. During and at the end of the climbing challenge, a significant reduction in Vigor and an important increase in Fatigue levels was highlighted, as well as a very evident reduction in Sleep quality. From the analysis of the RPE scale, medium-low values emerge at the end of each EB

A Control Theory Approach for Thermal Balancing of MPSoC

Thermal balancing and reducing hot-spots are two important challenges facing the MPSoC designers. In this work, we model the thermal behavior of a MPSoC as a control theory problem which enables the design of an optimum frequency controller without depending on the thermal profile of the chip. The optimization performed by the controller is targeted to achieve thermal balancing on the MPSoC thermal profile to avoid hotspots and improve its reliability. The proposed system is able to perform an on-line minimization of chip thermal gradients based on both scheduler requirements and the chip thermal profile. We compare this with state of the art thermal management approaches. Our comparison shows that the proposed system offers a better both thermal profile (temperature differences higher than 4 degrees C have been reduced from 27.9% to 0.45%) and performance (up to 32% task waiting time reduction)

ASSESSMENT OF THE UNCERTAINTY BUDGET FOR AN INTEGRATED SYSTEM IN CLAY PIGEON SHOOTING DISCIPLINE

The purpose of this study was a part of a wider project to characterize the athletic gesture in the Olympic shooting disciplines, expanding the scientific knowledge in clay pigeon shooting. This project aimed to design and develop an integrated measurement system able to acquire and analyse the shooters‘ physical parameters. An uncertainty budget was assessed, identifying the main sources of uncertainty for their measurement, being a preparatory step for the validation of a measurement system, which should be able to identify the most relevant parameters affecting the shooting performance

Convex-Based Thermal Management for 3D MPSoCs Using DVFS and Variable-Flow Liquid Cooling

In this work, we propose a novel online thermal management approach based on model predictive control for 3D multi-processors system on chip (MPSoCs) using microfluidic cooling. The controller uses dynamic voltage and frequency scaling (DVFS) for the computational cores and adjusts the liquid flow rate to meet the desired performance requirements and to minimize the overall MPSoC energy consumption (MPSoC power consumption+cooling power consumption). Our experimental results illustrate that our policy satisfies performance requirements and maintains the temperature below the specified threshold, while reducing cooling energy by up to 50% compared with traditional state-of-the-art liquid cooling techniques. The proposed policy also keeps the thermal profile up to 18°C lower compared with state of the art 3D thermal management using variable-flow liquid cooling

Cumulative Human Impacts on Mediterranean and Black Sea Marine Ecosystems: Assessing Current Pressures and Opportunities

Management of marine ecosystems requires spatial information on current impacts. In several marine regions, including the Mediterranean and Black Sea, legal mandates and agreements to implement ecosystem-based management and spatial plans provide new opportunities to balance uses and protection of marine ecosystems. Analyses of the intensity and distribution of cumulative impacts of human activities directly connected to the ecological goals of these policy efforts are critically needed. Quantification and mapping of the cumulative impact of 22 drivers to 17 marine ecosystems reveals that 20% of the entire basin and 60-99% of the territorial waters of EU member states are heavily impacted, with high human impact occurring in all ecoregions and territorial waters. Less than 1% of these regions are relatively unaffected. This high impact results from multiple drivers, rather than one individual use or stressor, with climatic drivers (increasing temperature and UV, and acidification), demersal fishing, ship traffic, and, in coastal areas, pollution from land, accounting for a majority of cumulative impacts. These results show that coordinated management of key areas and activities could significantly improve the condition of these marine ecosystems.JRC.H.1-Water Resource

Multicore Thermal Management with Model Predictive Control

The goal of thermal management is to meet maximum operating temperature constraints, while at the same time tracking timevarying performance requirements. Current approaches avoid thermal violations by forcing abrupt operating points changes (e.g. processor shutdown), which cause sharp performance degradation. In this paper we aim at achieving a smooth thermal control action, that minimizes the variance of performance tracking error. We formulate this problem as a discrete-time optimal control problem, which can be solved using the theory and computational tools developed in the field of model-predictive control. Our optimization process considers the thermal profile of the system, its evolution over time, and time-varying workload requirements. Experimental results show that the proposed approach offers significant thermal balancing improvements over previous methods