Phase Motion Control (Italy)

Nowadays, industrial servopositioning systems are used in many applications that demand high positioning accuracy, while requiring at the same time high dynamic movements. However, these systems often suffer from mechanical non-idealities, primarily the elasticity induced by the trasmission, which leads to load oscillation when using a standard collocated controller approach. While load side position sensors can be added to allow the use of control strategies that reduce the vibrations, this solution is often impractical in industrial environment. In this paper, we propose the use of the Acceleration Aided Kalman Filter (AAKF), which requires a low-cost load side MEMS accelerometer, to obtain accurate and robust estimates of load variables. Using load side information, two different control approaches are implemented, a PID with Active Damping and a Model Predictive Controller (MPC). The experimental results confirm that both control algorithms work as intended, considerably reducing load side vibration while using high dynamic movements, therefore reducing the cycle time of the positioning. Thus, the AAKF is validated as an effective alternative to the standard load position sensor in industrial environment.

This paper presents an innovative chathot architecture designed to support decision-making in the context of mobility data, leveraging recent advancements in Large Language Models (LLMs). As transportation systems and location based services produce more data, understanding human mobility patterns and providing relevant insights becomes critical for effective decision-making. The chatbot aims to offer a user-friendly tool that allows users to interact with mobility datasets through natural language, preventing the end user from writing complex SQL queries and allowing them to create data visualization on the fly.

Sensorless algorithms for Permanent Magnet Synchronous Motor (PMSM) are generally divided in methods for the low speed region and methods for the medium-high speed region. Algorithms based on high frequency injection show a good behavior for low motor speed, nevertheless voltage injection is associated to loud acoustic noise and additional losses. On the contrary, methods based on back-electromotive force and on rotor flux observer are passive methods (i.e. they do not require additional voltage injection); however they guarantees high performance only in the medium-high speed region. In this paper, rotor flux observer based sensorless algorithm performance is analyzed, specially focusing on behavior at low speed. No-load, linear torque, quadratic torque and compressor-type torque load is experimentally tested.