Linear System Analysis & Design

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Linear System Analysis & Design

This course offers participants a platform to grasp the essentials of linear and modern control theory. It includes training in design and simulation within the MATLAB environment, alongside practical experience with industry-standard equipment. By the course’s conclusion, participants should have enhanced knowledge and proficiency in this field.

Learning Outcomes

On completion, students will be able to:

Analyse time domain and frequency domain performance characteristics of a linear time invariant (LTI) system.
Interpret the stability of an LTI system using graphical methods.
Develop MATLAB programs to analyse performance characteristics and stability.
Design Lag and Lead compensator for given specifications.
Design PID controllers for DC motors and Oven Heater

Course Contents (Academic schedule)

Time Domain Specifications, Stability Analysis using Root Locus, Frequency Domain Specifications, Stability using Bode Plots and Nyquist plots, State Space Analysis, Pole placement techniques, Speed Control and Position Control of DC motor, PID controller design for temperature control, PID Characteristics, Lag and Lead Compensator Design.

Essential linear algebra for deep learning
Fundamentals of linear classification: weights, bias, scores, and loss functions
Calculus for the gradient descent algorithm
Forward and backward propagation with regularization
Batch processing for large datasets
Linear to nonlinear classification via activation functions
Computational setup of a shallow neural network
Tuning neural network performance
Pre-processing data and batch normalization
Cross-validation for validating model performance
Extending the computational setup from a shallow to a deep neural network
Introduction to the TensorFlow library
Application projects: implementing shallow and deep neural network models using TensorFlow; implementing machine learning models on edge devices using Edge Impulse.