Prof. Sergio Matteo Savaresi

Argomenti del programma: The program of the course is as follows. Discrete-time linear dynamical systems: definition and analysis of model classes for Time Series and Input/Output models (ARMA, ARMAX) Linear optimal prediction Black-box identification from measured data-sets of Input/Output models (algorithm, analysis, validation) Black-box identification from measured data-sets of state-space models Variable estimation: Kalman Filter and…

Argomenti del programma: The course consists of several case studies of real, significant problems related to automation in transport systems and vehicles: - Control of the vertical, roll and pitch dynamics through “intelligent” electronic suspensions systems (load-levelling, active semi-active suspensions, etc.), with emphasis on electric cars - Control of the longitudinal dynamics during braking and acceleration (ABS, traction control,…

Argomenti del programma: The program of the course is as follows. Non-parametric system identification: the subspace-based state-space approach Frequency domain parametric identification of models from data Kalman Filter: prediction, virtual-sensing and gray-box system identification Analysis and design of closed-loop systems using the minimum-variance approach Non-linear system-identification and SW-sensing: parametric nonlinear fitting;…

Argomenti del programma: The program of the course is as follows. Non-parametric system identification: the subspace-based state-space approach Frequency domain parametric identification of models from data Kalman Filter: prediction, virtual-sensing and gray-box system identification Analysis and design of closed-loop systems using the minimum-variance approach Non-linear system-identification and SW-sensing: parametric nonlinear fitting;…

Argomenti del programma: The program of the course is as follows. Non-parametric system identification: the subspace-based state-space approach Frequency domain parametric identification of models from data Kalman Filter: prediction, virtual-sensing and gray-box system identification Analysis and design of closed-loop systems using the minimum-variance approach Non-linear system-identification and SW-sensing: parametric nonlinear fitting;…

Argomenti del programma: The course consists of several case studies of real, significant problems related to automation in transport systems and vehicles: - Control of the vertical, roll and pitch dynamics through “intelligent” electronic suspensions systems (load-levelling, active semi-active suspensions, etc.), with emphasis on electric cars - Control of the longitudinal dynamics during braking and acceleration (ABS, traction control,…

Argomenti del programma: The program of the course is as follows. Discrete-time linear dynamical systems: definition and analysis of model classes for Time Series and Input/Output models (ARMA, ARMAX) Linear optimal prediction Black-box identification from measured data-sets of Input/Output models (algorithm, analysis, validation) Black-box identification from measured data-sets of state-space models Variable estimation: Kalman Filter and…

Argomenti del programma: The program of the course is as follows. Non-parametric system identification: the subspace-based state-space approach Frequency domain parametric identification of models from data Kalman Filter: prediction, virtual-sensing and gray-box system identification Analysis and design of closed-loop systems using the minimum-variance approach Non-linear system-identification and SW-sensing: parametric nonlinear fitting;…

Argomenti del programma: The program of the course is as follows. Discrete-time linear dynamical systems: definition and analysis of model classes for Time Series and Input/Output models (ARMA, ARMAX) Linear optimal prediction Black-box identification from measured data-sets of Input/Output models (algorithm, analysis, validation) Black-box identification from measured data-sets of state-space models Variable estimation: Kalman Filter and…

Argomenti del programma: The program of the course is as follows. Non-parametric system identification: the subspace-based state-space approach Frequency domain parametric identification of models from data Kalman Filter: prediction, virtual-sensing and gray-box system identification Analysis and design of closed-loop systems using the minimum-variance approach Non-linear system-identification and SW-sensing: parametric nonlinear fitting;…