Modeling and Identification

Process modeling. Fundamental and empirical models. Classification of systems. Modeling of systems: analytical modeling versus identification from measured data. Static systems: linear, quadratic and nonlinear programming. Linear continuous and discrete time analytical models: transfer and state-space models. Obtaining of these models from principal schemes of mechanical, electrical, hydraulic and thermal systems. Similarity of models of conservative systems: Kirchhoff rules. Simplification of models: linearization, approximation, partial realization. Robust models: Kharitonov theorems, stability margin. Identification as a process: estimation of parameters. Parametric models: method of least squares. Nonparametric models: correlation and spectral analysis. On-line identification, recursive algorithms. Identification in closed loop.

NB! This course will take place in spring semester 2024/2025 which starts on 3rd of February and ends on 16th of June (you can find that information under Start date section). The real course start and end dates will be announced at the beginning of February at the latest.

• Knows and understands basic models of systems, methods of modeling and identification and their practical applications; • Can describe a dynamical system in different forms, analyze and compare them; • Can analytically find mathematical models of real systems (mechanical, electrical, hydraulic, thermal systems); • Can optimize and simplify models; • Knows and can use fractional-order models; • Can use MATLAB environment (Identification and Optimization Toolboxes) for system modeling, identification and optimization problems.

Final assessment can consist of one test/assignment or several smaller assignments completed during the whole course. After declaring a course the student can re-sit the exam/assessment once. Assessment can be graded or non-graded. For specific information about the assessment process please get in touch with the contact person of this course. For specific information about grade transfer please contact your home university

Basic knowledge of Linear Algebra, Mathematical Analysis and Differential Equations.

• 1. L. Ljung, T. Glad "Modeling of Dynamic Systems"
• 2. K. Ogata "Modern Control Engineering"
• 3. I. Cochin "Analysis and Design of Dynamic Systems"
• 4. W.J. Palm III "Modeling, Analysis and Control of Dynamic Systems"
• 5. C. Close, D. Frederick "Modeling and Analysis of Dynamic Systems
• 6. T. Söderström, P. Stoica "System Identification"
• 7. L. Ljung "System Identification"

lectures, practices