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locomotion. Key Responsibilities: The successful candidate will: develop locomotion and local motion control algorithms for humanoid robots, implement learning-based and/or model-based control methods (e.g
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modelling, microgrid technologies, and simulation-based research. Key Responsibilities Participate in and manage the research project with the PI, Co-PI, and team to design a robust mobile DC microgrid
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algorithms for dynamic master selection, coordinating BESS, PV, diesel generators, and other sources. Implement predictive, rule-based, or optimisation-based control strategies using MATLAB/Simulink, Python
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feedback linearization, enabling control of nonlinear systems under uncertainty and partial model knowledge, Learning dynamics within control loops, integrating adaptive and optimization-based updates (e.g
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model for a motion controller. This strategy was also adopted in robotized medical surgery for instance. -- Challenges -- • Formulate control algorithms that take advantage of the interactive simulation
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progress, current wearable robotic systems face fundamental limitations. Model-based controllers ensure interpretability and safety but often struggle to accommodate human variability and complex real-world
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of different natures (e.g., physically-based vs. simplified or data-driven models) and different levels of precision, in order to support multi-fidelity approaches. Methods — The work will first focus on
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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI INGEGNERIA DELL'ENERGIA ELETTRICA E DELL'INFORMAZIONE "GUGLIELMO MARCONI" | Italy | 10 days ago
automation systems, with semi-automated energy-consistent models and digital twins to generate reliable synthetic data. - Control and diagnostic techniques based on hybrid approaches integrating advanced
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sensitivity (by controlling field amplitude, frequency, and penetration depth). _ Implement MRP measurements under controlled stress (uniaxial and residual). _ Validate the model with experimental MRP data
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. Over the past decade, Sorin Olaru and collaborators developed MPC-based congestion management and distributed control tools that account for storage and curtailment [1, 2, 3]. These works motivate