A5044298415- Advanced Control Systems Optimization
- Iterative Learning Control Systems
- Bipolar Disorder and Treatment
- Functional Brain Connectivity Studies
- Additive Manufacturing Materials and Processes
- Fault Detection and Control Systems
- Schizophrenia research and treatment
- Catalytic Processes in Materials Science
- Control Systems and Identification
- Genetics and Neurodevelopmental Disorders
- Advanced Neuroimaging Techniques and Applications
- Genomic variations and chromosomal abnormalities
- Soybean genetics and cultivation
- Psychiatric care and mental health services
- Neuroscience and Music Perception
- High Entropy Alloys Studies
- Educational Environments and Student Outcomes
- Genetic Associations and Epidemiology
- Phosphodiesterase function and regulation
- Ion channel regulation and function
- Psychosomatic Disorders and Their Treatments
- Neuroscience and Neuropharmacology Research
- Educational and Social Studies
- Human Health and Disease
- Italian Literature and Culture
ETH Zurich
2022-2024
University of Udine
2009-2017
Royal Edinburgh Hospital
2009-2011
University of Edinburgh
2009-2011
Background Prior imaging studies have shown structural, functional and biochemical impairments in patients with generalized anxiety disorder (GAD), particularly the right hemisphere. In this study we investigated, for first time to best of our knowledge, white-matter microstructure organization GAD. Method A total 12 DSM-IV GAD 15 matched healthy controls underwent a magnetic resonance session diffusion weighted imaging, exploring water molecules by means apparent coefficients (ADCs)....
Background: Variation in the G72 (DAOA) gene is understood to convey susceptibility for bipolar disorder through an uncertain mechanism. Little known about structural brain phenotypes associated with this gene. We hypothesised that reductions temporal lobe and amygdala gray matter would be variation at two loci which evidence of genetic linkage has been repeatedly demonstrated. Methods: examined associations risk variants M23 M24 5′ end encoding 81 controls 38 people disorder. Results:...
Selective laser melting is a promising additive manufacturing technology enabling the fabrication of highly customizable products. A major challenge in selective ensuring quality produced parts, which influenced greatly by thermal history printed layers. We propose Batch-Model Predictive Control technique based on combination model predictive control and iterative learning control. This approach succeeds rejecting both repetitive non-repetitive disturbances thus achieves improved tracking...
This work introduces the Data-Enabled Predictive iteRative Control (DeePRC) algorithm, a direct data-driven approach for iterative LTI systems. The DeePRC learns from previous iterations to improve its performance and achieves optimal cost. By utilizing tube-based variation of scheme, we propose two-stage that enables safe active exploration using left-kernel-based input disturbance design. method generates informative trajectories enrich historical data, which extends maximum achievable...
Model mismatch and process noise are two frequently occurring phenomena that can drastically affect the performance of model predictive control (MPC) in practical applications. We propose a principled way to tune cost function constraints linear MPC schemes achieve good robust constraint satisfaction on uncertain nonlinear dynamics with additive noise. The tuning is performed using novel algorithm based backpropagation developed our earlier work. Using scenario approach, we provide...
This work introduces the Data-Enabled Predictive Repetitive Control (DeePRC) algorithm, a direct data-driven approach for repetitive LTI systems. The DeePRC learns from previous iterations to improve its performance and achieves optimal cost. By utilizing tube-based variation of scheme, we propose two-stage that enables safe active exploration using left-kernel-based input disturbance design. method generates informative trajectories enrich historical data, which extends maximum achievable...
Model predictive control (MPC) is pervasive in research and industry. However, designing the cost function constraints of MPC to maximize closed-loop performance remains an open problem. To achieve optimal tuning, we propose a backpropagation scheme that solves policy optimization problem with nonlinear system dynamics policies. We enforce using linearization allow contain elements depend on current state past solutions. Moreover, simple extension can deal losses feasibility. Our approach,...
We analyze the convergence properties of a robust adaptive model predictive control algorithm used to an unknown nonlinear system. show that by employing standard quadratic stabilizing cost function, and recursively updating nominal through kinky inference, resulting controller ensures true system origin, despite presence uncertainty. illustrate our theoretical findings numerical simulation.
This paper details the design solution awarded at 2017 international call for ideas and implementation of fifty “innovative schools” launched by Italian Ministry Education Research (MIUR). The project expands an ongoing personal research, focusing on class layout in relation to educational curriculum proposed inspired principles Social Constructivism with final aim providing continuity among nursery, infant primary schools. “School Tomorrow” designed MIUR has no traditional desk, but modular...
Selective laser melting is a promising additive manufacturing technology enabling the fabrication of highly customizable products. A major challenge in selective ensuring quality produced parts, which influenced greatly by thermal history printed layers. We propose Batch-Model Predictive Control technique based on combination model predictive control and iterative learning control. This approach succeeds rejecting both repetitive non-repetitive disturbances thus achieves improved tracking...