Modelling and Simulation

Objectives

Upon successful completion of this course unit, students should be able to derive models of systems that arise from the interaction among subsystems of various types (mechanical, electrical, chemical, biological, electronic, communications, computational, andother) and validate, analyze, and simulate them using appropriate computational tools

Program

Introduction to modeling and simulation.
I. Time-driven systems. Models of economic, physical, electromechanical, electronic, energy, cell biology, and dynamic population systems. Distributed parameter systems. Parameter identification. Differential and difference equations. Dynamic linear and nonlinear state models. Linearization. Equilibrium points and stability. Computation of solutions.
II. Simulation tools. Numerical solution of systems of equations and ordinary and partial differential equations. Euler and Runge-Kutta methods. Fixed step and variable step methods. Languages and computational environments.
III. Discrete event systems. Automata. Markov chains. Queing theory. Monte Carlo Methods. Exs: manufacturing systems, computer networks, communications.
IV. Introduction to hybrid systems. Agent-based modeling and simulation. Exs: Biological Systems, Autonomous Vehicles, AirTraffic.

Teaching Methodologies

50% continuous evaluation / 50% non-continuous evaluation

Bibliography

1.Introduction to Discrete Event Systems, The Kluwer International Serie, Cassandras, C.G., Lafortune, 1999, Academic Publishers
2. Modeling and Simulation for Automatic Control, O. Egeland and J.T Gravdahl, , 2002, Marine Cybernetics