CourseSpecifications
Valid as from the academic year 2024-2025

Modelling and Simulation of Dynamical Systems (E005722)

Course size
Credits 6.0(nominal values; actual values may depend on programme)
Study time 180 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 2)
EnglishGent

Lecturers in academic year 2024-2025
Crevecoeur, GuillaumelectureseminarTW08

lecturer-in-charge

Offered in the following programmes in 2024-2025
crdts
Bridging Programme Master of Science in Electromechanical Engineering(main subject6Control Engineering and Automation)
Master of Science in Electromechanical Engineering(main subject Control Engineering and 6Automation)
Master of Science in Electromechanical Engineering(main subject Electrical Power6Engineering)
Master of Science in Electromechanical Engineering(main subject Maritime Engineering)
6
Master of Science in Electromechanical Engineering(main subject Mechanical
6Construction)
Master of Science in Electromechanical Engineering(main subject Mechanical Energy
6Engineering)
Master of Science in Chemical Engineering
6
Master of Science in Chemical Engineering
6offering
AA
AA
AA
AA

Teaching languages
EnglishKeywords
dynamic systems, modelling, simulation, dynamic behavior, optimization, optimal control.
Position of the course
This course is about the modelling and simulation of dynamical systems to analyse thebehaviour and optimizing the performance of electromechanical systems. These systems
become progressively more integrated with interactions of physical phenomena stemming fromdifferent engineering domains: mechanics, fluid flow, electronics, etc. Additionally, the ubiquityof data arising from advancements in sensors allows to learn from data and extract behavioural
patterns. To advance upon the design of dynamical systems one can rely on apprehending andpredicting their behaviour. To optimize the operational performance, model based control
approaches possess the capability to find feedforward optimal control actions.
Model based system engineering provides a means to tackle the above-mentioned challenges
in the engineering of physical dynamical systems. Various computational approaches arestudied that are key in system engineering. This course offers:(1) tools to model multi-domain engineering systems. Systematic approaches are provided
that formulate practical mathematical models of a physical process. To that end, physics-basedmodels that are based on energetic formalisms, i.e. Lagrangian and Hamiltonian equations ofmotion, graph-based models and data-driven models are presented.
(2) tools to simulate and analyse system behaviour. The accuracy of the time-based
simulations requires in depth knowledge of the algorithms to calculate the future state ofsystems.
(3) tools to optimally control nonlinear systems and optimize their performance. Deterministicand stochastic optimization techniques that underpin model based control are provided. Bothcontinuous time and discrete time nonlinear optimal control approaches are presented.
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Finally, hybrid system consisting of continuous time dynamics and discrete event-driven
dynamics are studied with automata and Petri net formalisms.
The presented methodologies are applied on mechatronic and robotic applications such as thedouble pendulum, electromechanical drivetrains, car suspensions, wind turbines and robot
arms.
Contents
1. Introduction and general scope: modelling of process, model types, concept of control
2. Physics based modelling: calculus of variations, Euler-Lagrange equation, Lagrangian
equations of motion, Hamiltonian equations of motion
3. Graph based modelling: efforts and flows, power flows, bondgraph model, bondgraphnetworks
4. Data driven modelling: least-squares regression, maximum likelihood estimation, Bayesian
approach, multivariate models, neural networks and machine learning, classification, modelorder reduction
5. Simulation of dynamical systems for system engineering: initial value problems: existence
and uniqueness, Lyapunov functional, implicit and explicit time stepping, single step, multi-step,
variable step; rootfinders; boundary value problems: shooting methods, parametric boundary
value problem for trajectory generation
6. Optimization for model based system engineering design: problem definition, optimality
conditions, gradient based methods, sequential quadratic programming, stochastic gradientdescent, stochastic population based and distribution based methods
7. Model based control: continuous time and discrete time nonlinear optimal control
8. Hybrid systems: discrete state systems, languages and automata, Petri nets
Project: implementation of computational algorithms for the modelling and simulation ofdynamical systems with the finality to assess the behavior of systems and optimize theirperformance.
Initial competences
Mathematical analysis (differential equations, linear algebra, Taylor series and convergence),physics (mechanics, electromagnetism, power and energy), basics of probability and statistics.
Final competences
1 Insight in how to model a complicated system: simplest possible accurate system model.
2 Use of compositionality, abstraction, hierarchy to develop mathematical models of systems,
1 to be able to implement for simulation, to reduce and to validate.
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3 Use of Euler-Lagrange and Hamiltonian methods.
4 To be able to implement mathematical models of continuous systems for simulation
1 purposes using numerical integration routines.
5 Use of data driven models for regression and classification.
6 Design model based controllers for continuous time and discrete time system models.
7 Use of computer platforms for the implementation of simulation programs.
8 Develop system models for systems having asynchronous events using automata and Petri1 nets.
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
Seminar, Lecture
Study material
Type: Syllabus
Name: online, free of charge, through the online learning environment
Indicative price: Free or paid by faculty
Optional: noReferences• Karnopp, Marcolis and Rosenberg: System Dynamics: Modelling and simulation of• mechatronic systems, Wiley, 2000
• P.P. Varaiya: Notes on Optimization, Van Nostrand Reinhold 1972
• A. van der Schaft en H. Schumacher: An introduction to Hybrid Dynamical Systems,(Approved)2• Springer, 2000
• T. Braunl: Embedded Robotics, Springer, 2003
Course content-related study coaching
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Oral assessment
Examination methods in case of periodic assessment during the second examination period
Oral assessment
Examination methods in case of permanent assessment
Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible in modified form
Extra information on the examination methods
During examination period: oral closed-book exam, written preparation. During semester:
graded project reports.
Calculation of the examination markStudents must successfully complete the project during the semester, and pass the oral, closedbook exam; score consists of 30% for oral presentation and written report on project work
during the semester, and for 70% for oral exam.

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