The main research activity is devoted to control, modeling and identification of systems.

Modeling of electro-mechanical systems with application to Automotive systems

The accurate modeling of physical systems represents a fundamental starting point in order to understand the system dynamics, determine the system efficiency, perform parameters estimation and to establish an effective and good control. An effective tool to model physical systems is the Power-Oriented Graphs (POG) modeling technique (Ref1), which allows to build block schemes that are directly implementable in the Simulink environment using standard libraries. Some examples of physical systems modeled using the POG technique are reported in the following: Planetary Gear Sets (Ref2 and Ref3), Gearboxes (Ref4), Transmission Systems (Ref5) Permanent Magnet Synchronous Motors (PMSMs) (Ref6), Multilevel Flying-Capacitor Converters (Ref7).

The energy efficiency analysis of some physical systems in the automotive field is addressed in Ref8 and Ref9. The problem of physical systems parameters estimation is addressed in Ref10 for linear and non-linear systems. As far as PMSMs parameters estimation is concerned, a dedicated Matlab App is proposed in Ref6.

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 HEV 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 architectures can be effectively addressed: series (Ref5), parallel (Ref12) and Power-Split (Ref13 and Ref14).

Robotic Manipulation, Planning and Control for Collaborative Robots, Human-Robot Interaction

Human robot cooperation is a non-trivial challenge for future generation of domestic and industrial robot applications, where the latter are required to operate in non-structured environments together with humans.
Learning by demonstration (LbD) is becoming a standard way for enabling robots to perform desired tasks. This field of research enables to move from purely preprogrammed robots to very flexible user-based interfaces by imitation learning techniques, which may improve human-robot collaboration as their interaction becomes more natural, intuitive and efficient.
LbD combines trajectory and symbolic domain to encode the key features out of a dataset of samples from demonstrated tasks (Ref15). Successful implementation at trajectory level involved task representations with B_spline and Dynamic Motion Primitives (DMP) for rehabilitation and assistance purposes (Ref16 and Ref17). Here, adaptive control algorithms use patient’s feedback in real time, leading the robot to actuate the desired exercise and tailor it based on user’s needs.

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