Software and teaching material
The goal of this course is to provide the background for advanced modelling and data analysis techniques. The course has both a theoretical and a practical flavour. This course is the natural extension of the course “Model Identification and Data Analysis” .
The course focuses on the following topics:
- Recursive and adaptive methods for parameter identification: Recursive Least Squares (RLS), Extended Least Squares (ELS), Recursive Maximum Likelihood (RML). Forgetting factor. Adaptive and predictive control: self-tuning control.
- Adaptive filtering and active noise control applications: Adaptive digital filtering. Digital filter structures: FIR, IIR. Standard and modified LMS algorithm. The active noise control setting. Broadband feedforward control algorithms: FXLMS, leaky FXLMS, Filtered-U Recursive LMS. Narrowband feedforward control. Periodic disturbance rejection. Disturbance frequency estimation.
- State space methods: Estimation, prediction and filtering based on the Kalman Filter. Using the Kalman filter for parameter estimation (gray-box identification). Extended Kalman Filter for non-linear systems. Virtual sensors. Subspace identification methods.
- Nonlinear model identification: block-oriented models, NARX models, neural networks, Radial Basis Functions. Model structure selection.
The final test examination is structured in two parts: a written test and a project.
- Course Presentation
- Recursive Least Squares
- Recursive Maximum Likelihood
- Adaptive control
- Adaptive filtering
- Active Noise Control: Introduction and applications
- Broadband feedforward ANC: The FxLMS algorithm
- Broadband feedforward ANC: The FuLMS algorithm
- Narrowband feedforward ANC
- Adaptive notch filtering with frequency tracking
- On-line secondary path modeling techniques
- Kalman filtering: Introduction
- Kalman filtering: The steady state predictor
- Kalman filtering: Comparison with the Kolmogorov-Wiener approach
- Particle filtering
- Multivariable model identification
- Nonlinear model identification
- Identification of polynomial NARX models
- Generalized frequency response functions
- Artificial neural networks
Contact me by e-mail to obtain lecture notes not available anymore.
A (provisional) list of suggested projects is available at this link. Contact me for project assignments and further details. Personal project proposals are most welcome.
Written test exams (PDF)
- Academic Year 2015-16
- Academic Year 2016-17
- Academic Year 2017-18
Software e materiale didattico