Davide Carminati
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Davide Carminati has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Elisa Capello
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
1 shared publication - Enza Incoronata Trombetta
Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, Torino, Italy
1 shared publication
Latest Publications
- Open Access
- 19 November 2022
- Published by MDPI AG in Applied Sciences
- Vol. 12 (22) , 11769
Nonlinear UGV Identification Methods via the Gaussian Process Regression Model for Control System Design
In this paper, two identification methods are proposed for a ground robotic system. A Gaussian process regression (GPR) model is presented and adopted for a system identification framework. Its performance and features were compared with a wavelet-based nonlinear autoregressive exogenous (NARX) model. Both algorithms were compared and experimentally validated for a small ground robot. Moreover, data were collected throughout the onboard sensors. The results show better prediction performance in the case of the GPR method, as an estimation algorithm and in providing a measure of uncertainty.
- 19 November 2022
- Published by MDPI AG in Applied Sciences
- Vol. 12 (22) , 11769
Open AccessNonlinear UGV Identification Methods via the Gaussian Process Regression Model for Control System Design
In this paper, two identification methods are proposed for a ground robotic system. A Gaussian process regression (GPR) model is presented and adopted for a system identification framework. Its performance and features were compared with a wavelet-based nonlinear autoregressive exogenous (NARX) model. Both algorithms were compared and experimentally validated for a small ground robot. Moreover, data were collected throughout the onboard sensors. The results show better prediction performance in the case of the GPR method, as an estimation algorithm and in providing a measure of uncertainty.