The main research activity is devoted to control, modeling and identification of systems.
- Modeling of electro-mechanical systems with application to Automotive systems
- Robotic Manipulation, Planning and Control for Collaborative Robots, Human-Robot Interaction
- Autonomous systems, Autonomous Driving, Model Predictive Control, Networked Control Systems
- Identification, Filtering, Localization and Navigation, Assistive Technology
Modeling of electro-mechanical systems with application to Automotive systems
The accurate modeling of physical systems represents a fundamental starting point for understanding the systems dynamics, determining the system efficiency, performing parameters estimation and for establishing an effective and good control. An effective tool for modeling physical systems is the Power-Oriented Graphs (POG) modeling technique (Ref1), which allows to build blocks schemes that are directly implementable in the Simulink environment using standard libraries. Examples of physical systems modeled using the POG technique: Planetary Gear Sets (Ref2), Gearboxes (Ref3), Transmission Systems (Ref4) Permanent Magnet Synchronous Motors (PMSMs) (Ref5), Multilevel Flying-Capacitor Converters (Ref6).
The energy efficiency analysis of some physical systems in the automotive field is addressed in Ref7 and Ref8. The problem of physical systems parameters estimation is addressed in Ref9 for linear and non-linear systems. For estimation of PMSM parameters have been developed in Ref10.
Planetary Gear Sets, Gearboxes, Transmission Systems, PMSMs and Multilevel Converters are among the main physical elements that are typically found in Hybrid Electric Vehicles (HEVs). An HEVs can be seen as a complex physical system given by the combination of several physical subsystems interacting with each other through energetic ports. Thanks to the accurate modeling of the physical subsystems, the energy management of different types of HEVs architecture can be easily obtained: series (Ref11), parallel (Ref12) and Power-Split (Ref13 and Ref14).
Robotic Manipulation, Planning and Control for Collaborative Robots, Human-Robot Interaction
Collaborative Robotics refers to…
Autonomous systems, Autonomous Driving, Model Predictive Control, Networked Control Systems
Autonomous Driving is takled …X
Identification, Filtering, Localization and Navigation, Assistive Technology
Identification is devoted to the determination of a model (Linear, Nonlinear or LPV) for a system starting from data, offline and online. It can be based on a Bayesian approach or Least Square approximation. See recent papers on the topic: giarre1; giarre2, giarre3
Filtering is referred to application of Kalman Filtering for autonomous system or networked systems, both in decentralized and centralized implementation. Kalman filtering is used also for sensor fusion in application of localization and navigation. See recent papers or presentation on the topic: giarre4, giarre5
Navigation refers to the Determination of the position and velocity of a moving body with respect to a known reference point while
Localization to the planning and maintenance of a course from one location to another, avoiding obstacles and collisions. see giarre6
Assistive Technology is referred to the development of specific technology for people with needs, such as visually impared or older people, to help them autonomously moving in unfamiliar environments or to autonously guide wheelchairs; different interfaces and sensors have been designed to communicate with them: giarre7, giarre8