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SYSTEM DYNAMICS AND
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2720313
Code: 2720313
Credits: 5
Semester 2
IACI
System Dynamics and Modelling
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Follow on Instagram for UpdatesSystem Dynamics and Modelling Syllabus — Unit-wise Content
The official GTU curriculum for System Dynamics and Modelling (subject code 2720313) as prescribed for IACI · Semester 2 divides the course into 7 units, covering 106 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (2720313)Unit 1 Introduction
- An Example of Process Control
- Steady-State Process Control
- Dynamical Properties of the Process
- Feedback Process Control
- Transient Performance of Feedback Control
- Block Diagram
- Feedforward Control
- Development of Process Control 10 10-20%
Unit 2 Mathematical Modelling of Processes
- General Principles of Modeling
- Examples of Dynamic Mathematical Models
- Liquid Storage Systems
- Heat Transfer Processes
- Mass Transfer Processes
- Chemical and Biochemical Reactors
- General Process Models
- Classification of Systems. 04 10-20%
Unit 3 Analysis of Process Models
- The Laplace Transform
- Definition of the Laplace Transform
- Laplace Transforms of Common Functions
- Properties of the Laplace Transform
- Inverse Laplace Transform
- Solution of Linear Differential Equations by Laplace Transform Techniques
- State-Space Process Models
- Concept of State
- Solution of State-Space Equations
- Canonical Transformation
- Controllability, and Observabilityof Continuous-Time Systems
- Canonical Decomposition Input-Output Process Models
- SISO Continuous Systems with Constant Coefficients
- Transfer Functions of Systems with Time Delays
- Algebra of Transfer Functions for SISO Systems
- Input Output Models of MIMO Systems – Matrix Input Output Models of MIMO Systems – Matrix of Transfer Functions
- BIBO Stability
- Transformation of I/O Models into State- Space Models
- I/O Models of MIMO Systems - Matrix Fraction 05 25-30%
Unit 4 Dynamical Behaviour of Processes
- Time Responses of Linear Systems to Unit Impulse and Unit Step
- Unit Impulse Response
- Unit Step Response
- Computer Simulations
- The Euler Method
- The Runge-Kutta method
- Runge-Kutta Method for a System of 04 5-10 % Differential Equations
- Time Responses of Liquid Storage Systems
- Time Responses of CSTR
- Frequency Analysis
- Response of the Heat Exchanger to Sinusoidal Input Signal
- Definition of Frequency Responses
- Frequency Characteristics of a First Order System
- Frequency Characteristics of a Second Order System
- Frequency Characteristics of an Integrator
- Frequency Characteristics of Systems in a Series
- Statistical Characteristics of Dynamic Systems
- Fundamentals of Probability Theory
- Random Variables
- Stochastic Processes
- White Noise
- Response of a Linear System to Stochastic Input
- Frequency Domain Analysis of a Linear System With Stochastic Input
Unit 5 Discrete-Time Process Models
- Computer Controlled and Sampled Data Systems
- Discrete- Time Transfer Functions
- Input-Output Discrete-Time Models – Difference Equations
- Direct Digital Control
- State-Space Discrete-Time Models
- Properties of Discrete-Time Systems
- Discrete-Time Feedback Systems – Control Performance Discrete-Time Model of Two Tanks in Series e, Examples of Discrete-Time Process Models
- Discrete-Time Tank Model
- Discrete-Time Model of Two Tanks in Series
- Steady-State Discrete-Time Model of Heat Exchangers in Series. 07 25-30%
Unit 6 Process Identification
- Models of Linear Dynamic Systems
- Identification from Step Responses
- First Order System
- Underdamped Second Order System
- System of a Higher Order
- Least Squares Methods
- Recursive Least Squares Method
- Modifications of Recursive Least Squares
- Identification of a Continuous-time Transfer Function. 05 5-10 %
Unit 7 System Identification
- Least squares Estimation
- Linear model for Least squares Estimation
- Least squares Problem :Formulation and solution
- covariance in stationary
- ergodic processes
- white noise
- detection of periodicity through ACF, detection of transmission delays using ACF, covariance of zero mean processes through convolution
- ARMA Processes
- mixed notation
- what is ARMA Processes?
- moving average processes
- is unique estimation possible?
- auto regressive processes
- auto regressive moving average processes
- Nonparametric models
- covariance between signals of LTI systems
- frequency response of LTI systems excited by white noise
- prediction error models
- one step ahead prediction error model
- finite impulse response modular Input model
- ARMAX input model
- ARIMAX input model
- output error model
- box- jenkins model
- case study : Drifting Noise model
- Revisiting Least Squares Estimation
- Statistical Properties of Least squares Estimate
- Recursive Least Squares
- Weight Selection For Iterative Calculations 10 20-25 %
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