Modeling of Dynamic Systems
Written by a recognized authority in the field of identification and control, this book draws together into a single volume the important aspects of system identification AND physical modelling. KEY TOPICS: Explores techniques used to construct mathematical models of systems based on knowledge from physics, chemistry, biology, etc. (e.g., techniques with so called bond-graphs, as well those which use computer algebra for the modeling work). Explains system identification techniques used to infer knowledge about the behavior of dynamic systems based on observations of the various input and output signals that are available for measurement. Shows how both types of techniques need to be applied in any given practical modeling situation. Considers applications, primarily simulation. For practicing engineers who are faced with problems of modeling.
Why Read This Book
You should read this book if you need a rigorous, practice-oriented bridge between physics-based modeling and data-driven system identification — it shows how to build, validate and refine dynamic models that underpin DSP, communications and control systems. You will learn to combine physical insight (bond-graphs, computer-algebra modeling) with statistical identification techniques to get reliable models for filters, spectral estimators, adaptive algorithms and real-world signal-processing applications.
Who Will Benefit
Practicing engineers, graduate students, and researchers with some background in signals & systems who need to develop accurate dynamic models for control, DSP, radar, audio, or communications applications.
Level: Advanced — Prerequisites: Undergraduate-level signals & systems and linear algebra, basic probability & statistics, differential equations and exposure to state-space methods; familiarity with MATLAB or similar numerical tools is highly recommended.
Key Takeaways
- Construct physics-based state-space and bond-graph models for mechanical, electrical, chemical and electromechanical systems
- Apply parametric and nonparametric system identification methods to estimate dynamic models from time- and frequency-domain data
- Use spectral analysis and FFT-based techniques for model structure selection and noise characterization
- Design and validate digital filters and adaptive estimators informed by identified models
- Combine physical modeling and statistical identification to improve robustness in radar, communications and audio/speech signal-processing tasks
Topics Covered
- 1. Introduction: Modeling vs. Identification — aims and workflows
- 2. Mathematical representations of dynamic systems: transfer functions and state-space
- 3. Physical modeling techniques: bond-graphs and component-based models
- 4. Computer-algebra and symbolic modeling methods
- 5. Parametric identification: prediction error methods and ML approaches
- 6. Nonparametric and frequency-domain identification: spectral methods and FFT
- 7. State-space identification and subspace methods
- 8. Nonlinear system identification and model structures
- 9. Adaptive filtering and recursive estimation
- 10. Experiment design, noise modeling and statistical validation
- 11. Practical aspects: software tools, implementation and numerical issues
- 12. Case studies: control systems, communications, radar and audio applications
- 13. Model reduction, model updating and interpretation
Languages, Platforms & Tools
How It Compares
Overlaps with Ljung's own System Identification texts but places stronger emphasis on combining physics-based modeling (bond-graphs, symbolic methods) with identification for practical engineering problems; compared with Söderström & Stoica, it is more focused on the interplay between modeling and identification rather than purely frequency-domain estimation theory.
