Objectives and competences
Basic knowledge of mathematical modelling, linear algebra and linear control theory is recomended.
Content (Syllabus outline)
Introduction: general aspects, dynamic systems, model building, model, basic approaches to modelling, evaluation of models.
Modelling and simulating of dynamic systems with Matlab: The Physical Model, Time Domain Simulation, Model Conversion, Frequency Domain Simulation, Block Diagrams, Model Building.
Mathematical Modelling of Engineering Systems: Simple Mechanical Systems, Simple Fluid Systems, Simple Thermal Systems; Introduction and Physical Description, Model Description, Model Development, Analysis and Conclusions.
Model simplification: transfer behaviour of linear systems, simplification of linear systems, generalisation to nonlinear systems.
Identification of simple models from step responses: types of models, approximation of lag systems with aperiodicaly damped behaviour, systems without compensation, oscillating systems, systems with inverse reaction.
Parameter identification: basic idea, mathematical description of sampled systems, ARX- simple least squares estimation, alternative identification approaches.
Modelling using nonlinear black box models: motivation, perception neural nets, quality of models, evaluation.
Quality of models: sources of errors, limits of accuracy, model performance and controller performance.
Learning and teaching methods
• lectures, seminar, tutorials, lab work, homework assignments.
Intended learning outcomes - knowledge and understanding
• describe and understand dynamic behaviour of physical processes writing the conservation equations that describe the physical phenomena of interest and putting these into the appropriate form suitable for analysis and simulation,
• construct technical report of accomplished seminar work,
• select and apply procedure, engineering methods and tools for modelling and identification of practical cases,
• identify static and dynamic behaviour of linear and nonlinear processes based on measurement data,
• use acquired knowledge for implementation of similar engineering examples,
• analyse models in simulations with Matlab or Simulink,
• simplify and linearize the obtained models
Intended learning outcomes - transferable/key skills and other attributes
• Communication skills: oral lab work defence, manner of expression at lab exercises, seminar reports, tests and at written examination.
• Use of information technology: use of software tools for engineering processes simulation and analysis.
• Calculation skills: calculation of numerical exercises at tutorials, seminars, homework assignments and at written examination.
• Problem solving: modelling, identification and s analysis of simple engineering systems.
Readings
Rihard Karba: Modeliranje procesov, Univerza v Ljubljani, Fakulteta za elektrotehniko in Fakulteta za računalništvo in informatiko, Ljubljana, 1999.
Maja Atanasijević-Kunc: Modeliranje procesov, Zbirka primerov z ilustracijami v Matlab-Simulinku, Univerza v Ljubljani, Fakulteta za elektrotehniko in Fakulteta za računalništvo in informatiko, Ljubljana, 2005.
D. Matko: Identifikacije, Univerza v Ljubljani, Fakulteta za elektrotehniko in Fakulteta za računalništvo in informatiko, Ljubljana, 1992.
B. Tovornik: Izbrana poglavja iz parametričnih identifikacij procesov, Univerza v Mariboru, Fakulteta za elektrotehniko, računalništvo in informatiko, Maribor, 1992.
R. Isermann: Uberwachung und fehlerdiagnose, VDI Verlag, 1994.
Prerequisits
Basic knowledge of mathematical modelling, linear algebra and linear control theory.
Additional information on implementation and assessment The written exam may be substituted with continuous knowledge testing and assessment.
