Master in Electrical Engineering (Control and Robotics)
Compulsory Courses
Introduction to Advanced Control Systems: Overview of control systems, different dynamic systems, review of classical control theory, introduction to advanced control theory, state variables and state equations, transfer function to state-space conversion.
Design via Root Locus: Improving steady state error via cascade compensation, improving transient response via cascade compensation, improving steady-state error and transient response, feedback compensation.
Frequency Response Techniques: Asymptotic approximations, Bode plots, Nyquist plot, stability margins, case study.
Design via Frequency Response: Transient response via gain adjustment, compensator design, case study.
Nonlinear Control Systems: Introduction to nonlinear control, nonlinear models and nonlinear phenomena, common nonlinearities, multiple equilibria, limit cycles, phase portraits, Lyapunov stability theory, feedback linearization, sliding mode control
Introduction to Robot Modelling: Mathematical modeling and dynamics of robots, configuration space, workspace, classification of robotic manipulators, accuracy and repeatability, wrists and end effectors, common kinematic arrangements of manipulators, lagrange’s equations of motion for robot anipulators, constrained motion, closed-chain mechanisms.
Rigid Motions and Homogeneous Transformations: Representing positions and rotations, transformations, parameterizations, rigid motions, homogeneous transformations.
Robot Kinematics: Kinematic chains, forward kinematics: the Denavit- Hartenberg convention, inverse kinematics, kinematic decoupling and approaches.
Force and Compliance Control and motion planning: Force control principles, force sensing and measurement techniques, compliance control for robot operation, dynamic motion planning techniques, collision detection and avoidance algorithms, realtime motion planning, task-level planning and oordination of multiple robots.
Discrete-Time Systems: Discrete-time signals and systems, sampling and reconstruction of signals, zero-order hold and first-order hold techniques, signal reconstruction using interpolation, ztransform
and its properties, transfer functions in the z-domain.
Discrete-Time Modeling: Discretization of continuous-time models, difference equations and state-space representation, system identification techniques.
Digital Controller Design: Design specifications and performance criteria discrete-time PID control pole placement techniques (state-feedback) optimal control (LQR and LQG)
Stability Analysis: Stability mapping of analog and digital regions, stability in the z-domain, Jury's stability criterion, Routh- Hurwitz stability criterion.
Frequency Domain Analysis: Frequency response of discrete-time systems, Bode plots and Nyquist stability criteria.
Digital Filter Design: Finite impulse response (FIR) filters, infinite impulse response (IIR) filters design methods (windowing, frequency sampling, etc.) filter implementation techniques.
State-space analysis and design: discretetime state-space models, controllability and observability, state feedback and
Overview of adaptive control systems, motivation and advantages of adaptive control, comparison with conventional control techniques.
Model Reference Adaptive Control (MRAC): Concept, architecture and design, parameter adaptation algorithms: gradient-based, least squares, recursive estimation, stability analysis and robustness considerations.
Direct and Indirect Adaptive Control: Direct model reference adaptive control, self-tuning regulators (STR), gain scheduling and adaptive PID control, adaptive pole placement control. Indirect adaptive control architecture, adaptive observers and parameter estimation techniques, model and system identification methods.
Stability and Convergence Analysis: Lyapunov stability theory, robustness analysis, adaptive control with bounded parameter errors, convergence analysis of parameter adaptation algorithms.
Advanced Adaptive Control of Nonlinear Systems: Nonlinear system modeling techniques, adaptive
backstepping control, sliding mode control, time-varying system identification, recursive identification
algorithms, adaptive control with time varying parameters, tracking and regulation in time-varying systems, adaptive control with fuzzy logic systems & neural networks, adaptive control with reinforcement learning, adaptive control in networked systems.
Industrial automation: Industrial automation and its importance in manufacturing, PID control and advanced control techniques and distributed control systems (DCS), Communication protocols (e.g., Modbus, OPC) and industrial ethernet and field buses. Sensors and transducers used in industrial applications, signal conditioning and data acquisition and measurement and control of variables such as temperature, pressure, and flow.
Robotics in Manufacturing: Robotic kinematics and dynamics, robot programming and control and robotic applications in manufacturing, including pick-and-place, welding, and assembly.
Manufacturing Processes and Systems: manufacturing processes of machining, welding, casting, lean manufacturing principles and integration of automation into manufacturing systems.
Advanced Automation Technologies: Advanced control strategies, industrial robotics and automation in industry 4.0/5.0 and smart factories and IoT (Internet of Things) applications in industrial automation.
Quality Control and Safety: Quality control techniques, Statistical Process Control (SPC), non- destructive testing methods and automated inspection system, safety standards and regulations (e.g., ISO 13849, IEC 61508), hazard analysis and risk assessment and safety interlock systems and emergency shutdown procedures.
Elective Courses
Introduction to AC/DC Motors: Types, characteristics and applications of AC/DC motors covering synchronous and induction motor, permanent magnet motor, brushless DC motor, servo motor, universal motor.
Power Electronics and Power Conversion: Power semiconductor devices, AC/DC, DC/DC conversion techniques Motor Drive Topologies: AC/DC motor drive topologies, Drive circuits and components, Control techniques for motor drives, motor starting techniques.
Motor Control and Operation Techniques: Speed control, Torque control, Sensor-less control techniques, VFDs, four quadrant operation of DC drives, Control of DC motors by DC choppers.
Motor Protection: Motor protection techniques and devices, Overload, overvoltage, and undervoltage protection, Ground fault protection.
PLC and HMI for Motor Control: Principles of PLC and HMI, Programming techniques for motor control, HMI design and integration Maintenance and Troubleshooting of Motor Drives: Preventive maintenance techniques, Troubleshooting techniques for motor drives, Failure analysis and repair of motor drives.
ntroduction to Machine Learning: Overview of machine learning, Types of machine learning algorithms, Machine learning process and workflow, Supervised Learning and Linear Regression Decision Tree and Random Forest, Naïve Bayes and Support Vector Machine: Unsupervised Learning: Unsupervised learning, Clustering techniques, Dimensionality reduction, Evaluation of unsupervised learning results, Natural Language Processing and Text Mining Deep Learning: Basics of artificial neural networks (ANN), Feedforward neural networks, Activation functions and backpropagation, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Introduction to TensorFlow, Keras, PyTorch.
Time Series Analysis: Time series data and its characteristics, Time series visualization and decomposition, Evaluating time series models, Handling seasonality and trend.
Introduction to Estimation and Detection Theory: Overview of estimation and detection problems, key concepts: parameter estimation and hypothesis testing, relationship to statistical inference and decision theory. Applications in control, signal processing, communication, and identification.
Probability and Random Variables: Review of probability theory and random variables, probability density functions and cumulative/probability distribution functions, moment generating and characteristic functions.
Parameter Estimation: Method of moments and maximum likelihood estimation, Bayesian estimation and posterior probability, Cramer Rao lower bound and efficiency of estimators, least squares estimation and linear regression.
Adaptive Estimation and Detection: Adaptive estimation in changing environments, recursive estimation algorithms: recursive least squares (RLS) and Kalman filter, adaptive detection algorithms: LMS and RLS, performance analysis of adaptive algorithms.
Estimation and Detection in Gaussian & Non- Gaussian Noise: Estimation of mean and variance in gaussian noise, detection in gaussian noise: matched filter, correlation, and optimal detectors, MMSE estimation, detection and estimation in colored gaussian noise. estimation and detection in heavy-tailed and non-gaussian noise, robust estimation techniques.
Introduction to Haptics, Human-Robotics, and Metaverse, human-robotics interaction with virtual and real environment, overview of the metaverse and its applications.
Principles of Haptic Feedback: Understanding the sense of touch and haptic perception, haptic devices and technologies, haptic feedback generation and control.
Haptics in Human-Robot Interaction: Haptic interfaces for teleoperation and telepresence, haptic communication and collaboration between humans and robots, haptic feedback is applications such as prosthetics and rehabilitation
Human-Robotics Integration: Sensor technologies for human-robot interaction, motion planning and control in human-robot collaboration, ethical and social considerations in human-robotics integration.
Metaverse Concepts and Applications: Definition and characteristics of the metaverse, virtual reality (vr) and augmented reality (ar) in the metaverse, metaverse applications in gaming, education, healthcare, and more.
Haptics and Metaverse Integration: Haptic feedback in virtual environments, haptic interactions and immersion in the metaverse, haptic-enabled virtual reality and augmented reality systems.
Introduction to Data Analytics: Overview of data analytics and their applications in engineering, Types of data and data sources, Data preprocessing techniques, Data visualization techniques, Outlier detection and removal.
Statistical Analysis Techniques: Probability and probability distributions, Time Series Analysis, Hypothesis testing, Regression analysis.
Data Mining Techniques: Association rules, Clustering, Decision trees, Random forests Optimization Techniques: Linear and Nonlinear programming, Genetic algorithms, Simulated annealing by using the tools Excel, Tableau, python and others.
Applications and Case Studies: Applications in respective field, Grid Monitoring and State Estimation, False
Data Injection Attacks against State Estimation, State Estimation, MMSE State Estimation and Generalized Likelihood Ratio Test, Demand Response, Communications and Sensing.