Professor Dario Richiedei was born in Asolo (TV) on 18/09/1978.
In April 2003 he graduated in Management Engineering at the University of Padua with a score of 110/110 cum laude. In March 2008 he obtained a PhD in Industrial Engineering, specialising in Mechatronics and Industrial Systems. In October 2008 he took up his position as University Researcher at the University of Padua, attached to the Department of Industrial Systems Engineering and Management (DTG - Vicenza Branch). In October 2014 he takes service as Associate Professor, and in November 2021 as Full Professor, in the same department.
Since the AA 2008-2009 he has been holding, on a tenure or tenure track basis, several courses within the disciplinary sector IIND-02/A (ex ING-IND 13), Applied Mechanics of Machines) at the University of Padua.
Most of the research activity carried out by Prof. Dario Richiedei concerns studies of a theoretical, numerical and experimental nature concerning machine mechanics, robotics and mechanical vibrations, and the control of mechanical systems. This activity is framed in national and local research programmes or in collaborations with private entities.
The main lines of research in which Ing. Dario Richiedei is currently involved are:

1. MODELLING OF MULTIBODY AND VIBRATING SYSTEMS:
* dynamic modelling of multibody systems, including mechanisms and robots with deformable members
* model reduction in vibrating systems for real-time and model-based design applications
* synthesis of state estimators
* numerical methods for simulation
* development of digital twins

2. ACTIVE AND PASSIVE CONTROL OF VIBRATING SYSTEMS
* dynamic structural modification in vibrating systems
* active control of deformable systems
* Hybrid control (active+passive) for "eigenstructure assignment
* Motion planning in vibrating systems
* Model-based filters and sensor fusion in load cell measurements
* Control and modelling of systems with flexible elements
* Control of oscillation of liquids in containers

3 DESIGN OF HIGH-PERFORMANCE MECHATRONIC SYSTEMS
* Energy-optimal motion planning
* Model-based design of mechatronic systems for integrated optimal component selection

4 ARTIFICIAL INTELLIGENCE APPLICATIONS
* Failure detection in mechanical and/or mechatronic and/or robotic systems
* Reinforcement learning for motion control

5. MOTION PLANNING AND CONTROL IN CABLE ROBOTS
* Digital modelling
* Prototyping
* Advanced motion control and planning techniques

Prof. Richiedei is currently the author of about 150 papers, published in international journals or presented at international conferences.

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