Learn how to build your own data-driven models using classical and machine learning based methods.
To acquire skills for
- Identification of models for systems with unknown structure based on measured input-output data
- Estimate the state of dynamic systems from input-output measurements
Introduction to system identification and state estimation
- classes of models
- linear and nonlinear methods
Practical system identification
- design of excitation signals
- residual-based validation: metrics and correlation methods
- model structure selection
Linear system identification
- batch and
- recursive least squares
Nonlinear system identification
- benchmark problems
- polinomial NARMAX and
- machine learning based approaches
- Kalman filtering
- Receding-horizon methods for linear, nonlinear and hybrid systems
- Introduction: motivation, application, computational platforms, evaluation criteria;
- Review of signals and systems: continuous and discrete-time;
- Linear system identification: least squares methods;
- Design of excitation signals and model validation methods;
- Adaptive system identification;
- Introduction to state estimation: Kalman filter applied to system identification and state estimation, moving-horizon state estimation;
- Nonlinear system identification: higher-order polynomials and machine learning methods;
- Practical hands-on sessions assisted by the lecturer.
Computational exercises, oral exams, and final project.
At the end of the course the students should choose a challenging problem and solve it using the methods learned throughout the course.
I think this is a good opportunity to get in touch with system identification in a controlled environment and also publicize your own work, besides participating in congresses and symposia.
Such evaluation exercises also important aspects of research methodology, meanwhile applying fundamental concepts of estimation and identification.
I strongly encourage students to then submit their work for peer review with my help. Below I list published works that were recently developed by former students:
- Improved Feature Extraction of Guided Wave Signals for Defect Detection in Welded Thermoplastic Composite Joints
- A Comparison of Feature Extraction Methods for Crack and Ice Monitoring in Wind Turbine Blades: System Identification and Matrix Decomposition
- Three-axle vehicle lateral dynamics identification using double lane change maneuvers data
- Nonlinear Grey-box Identification of a Landing Gear based on Drop Test Data
- Comparison of Nonlinear Receding-Horizon and Extended Kalman Filter Strategies for Ground Vehicles Longitudinal Slip Estimation
- Damage Detection in Composite Plates with Ultrasonic Guided-waves and Nonlinear System Identification
- Slip Estimation with Receding-horizon Strategy for Off-road Vehicles with Nonlinear Tire Interactions
- Evaluation of Nonlinear System Identification to Model Piezoacoustic Transmission
- Deep Learning Applied to Data-driven Dynamic Characterization of Hysteretic Piezoelectric Micromanipulators
- An R library for nonlinear black-box system identification (this is the course library I co-developed some years back)
- Genetic Algorithm for Topology Optimization of an Artificial Neural Network Applied to Aircraft Turbojet Engine Identification
- Comparison of friction models for gray-box identification of an electromechanical positioning system
AGUIRRE, L. A. Introdução à identificação de sistemas – técnicas lineares e não-lineares aplicadas a sistemas reais. 4th ed. Belo Horizonte, Brazil: Editora UFMG, 2015 [in Portuguese].
BAR-SHALOM, Y.; LI, X. R.; KIRUBARAJAN, T. Estimation with applications to tracking and navigation: theory algorithms and software. John Wiley & Sons, 2004.
BILLINGS, S. A. Nonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains. West Sussex, United Kingdom: John Wiley & Sons Ltd., 2013.
LJUNG, L. System identification: theory for the user. 2nd. ed. Upper Saddle River, NJ: PTR Prentice Hall, 1999.
PINTELON, R.; SCHOUKENS, J. System identification: a frequency domain approach. 2nd ed. Hoboken, NJ: John Wiley & Sons, 2012.
RAWLINGS, J. B.; MAYNE, D. Q.; DIEHL, M. Model Predictive Control: Theory, Computation, and Design. Madison, WI: Nob Hill Publishing, 2017.
SCHOUKENS, J.; PINTELON, R.; ROLAIN, Y. Mastering system identification in 100 exercises. John Wiley & Sons, 2012.