The area focuses on the fields of mathematical modeling, identification, optimization and control of dynamical systems. The aim is to propose original theoretical results and novel algorithms to address fundamental problems in the context of data-driven modeling and control, system identification, distributed/online learning for sparse optimization, predictive and robust control. Particular attention is also devoted to the application of the proposed algorithms to challenging real-world engineering problems.
Specific research topics in this area are:
- Set-membership estimation theory
- Set-membership identification of linear and nonlinear dynamical systems
- Direct and indirect data-driven controller design
- Robust control of dynamical systems affected by structured and unstructured uncertainty
- Robust Internal model control in the presence of input saturation constraints
- Model predictive control theory and algorithms
- Optimization algorithms for efficient training of recurrent neural networks
- Sparse system identification
- Sparse learning with concave regularization
- Distributed online learning for sparse optimization
- Identification and control of automotive systems, with particular focus on modeling, identification, and control of the longitudinal, lateral, and vertical dynamics of the vehicle
- Predictive and robust control strategies for optimal energy management in electric vehicle
- PE1_10 ODE and dynamical systems
- PE1_20 Control theory, optimisation and operational research
- PE1_21 Application of mathematics in sciences
- PE1_22 Application of mathematics in industry and society
- PE6_2 Distributed systems, parallel computing, sensor networks, cyber-physical systems
- PE6_11 Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)
- PE7_1 Control engineering
- PE7_3 Simulation engineering and modelling
- PE7_8 Networks, e.g. communication networks and nodes, Internet of Things, sensor networks, networks of robots