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 coordination 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: discrete-time state-space models, controllability and observability, state feedback and state estimation, observer-based control.
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 Modern Control Theory: Evolution of control systems, feedback control concept,
state-space representation, transfer function and frequency domain analysis.
State-Space Modeling: State variables and state equations, transfer function to state-space
conversion, controllability and observability, state-space realization, and canonical forms, stability analysis in state-space.
Control Stability analysis: Stability concepts, asymptotic stability, s-domain stability criterion, PID
control via empirical and Ziegler-Nichols (Z-N) oscillation method, frequency domain Techniques,
stability margins as gain margin and phase margin.
Predictive Control: Model predictive control, generalized prediction, control of time delayed system, Smith predictor, case study.
Introduction to Non-Linear Systems: Definition and characteristics of non-linear systems, comparison with linear systems, examples of non-linear systems in engineering and other fields.
Nonlinear System Modeling: Nonlinear equations and state-space representation, non-linearities in systems: static, dynamic, and mixed, linearization techniques for non-linear systems.
Stability Analysis: Lyapunov stability theory for non-linear systems, stability criteria: lyapunov's
direct method, indirect method, and lasalle's invariance principle, input-output stability analysis for non-linear systems.
Nonlinear Control Design: Feedback linearization, sliding mode control, adaptive control methods.
State observers for non-linear systems: Nonlinear observers: extended Kalman filter, sliding mode observers, non-linear system identification and parameter estimation.
Nonlinear Control Applications: Nonlinear control in robotics and autonomous systems, non-linear control in power systems and renewable energy.
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 Optimal Control Systems: Overview of control systems, introduction to optimization and optimal control concepts, comparison between classical and optimal control approaches, review of differential equations and linear algebra, optimization theory fundamentals.
Dynamic Programming and Calculus of Variations: Introduction to dynamic programming,
Bellman's principle of optimality, Hamilton-Jacobi-bellman equation, Euler LaGrange equations and variational calculus.
Continuous-Time Optimal Control: Optimal control problems formulation, state and control constraints, solution techniques: maximum principle, Hamiltonian approaches, case study.
Discrete-Time Optimal Control: Optimal control problems in discrete-time, dynamic programming algorithms: value iteration, policy iteration, computational aspects and numerical implementation, case studies and practical examples.
Linear Quadratic Regulator Control: LQR problem formulation and solution, Riccati equation and optimal control synthesis, stability analysis and robustness considerations, applications in aerospace, robotics, and control engineering.
Model Predictive Control and Advanced Optimal Control: Introduction to model predictive control - MPC, formulation and solution of MPC problems, realtime optimization and receding horizon control, constraints handling and stability analysis, optimal estimation, filtering and advanced optimal control strategies.
Introduction to Intelligent Control Theory: Overview of intelligent control and its applications, evolution of intelligent control systems, relationship between intelligent control and traditional control, Challenges and opportunities in intelligent control.
Fuzzy Logic Control: Fuzzy set theory and fuzzy logic principles, fuzzy rule-based systems, fuzzy inference methods, Design and tuning of fuzzy controllers.
Neural Network Control: Basics of artificial neural networks (ANN), Multilayer perceptron (MLP) networks, Backpropagation algorithm for training ANNs, Neural network-based control architectures.
Reinforcement Learning in Control: Introduction to reinforcement learning (RL), Markov decision processes and Q-learning, Temporal difference methods, RL for control and decision-making tasks.
Embedded System Architecture: Microcontrollers and microprocessors, memory systems: ROM, RAM, flash, input/output (I/Os) interfaces and peripherals, real-time operating systems (RTOS) for embedded systems.
Embedded Programming Languages and Tools: C and C++ programming for embedded systems, assembly language programming, integrated development environments (IDES), debugging and simulation tools.
Real-Time Systems and Scheduling and Device Drivers: Real-time requirements and constraints, task scheduling algorithms, priority inversion and priority inheritance, real-time operating system (RTOS) features and services. Device driver development for embedded systems, interrupt handling and interrupt service routines (ISRS).
Communication Protocols for Embedded Systems: Serial communication protocols (UART, SPI, I2C), network protocols (Ethernet, Wi-Fi, Bluetooth), Wireless communication standards (Zigbee, RFID, NFC), CAN and LIN bus protocols for automotive applications.
Embedded System Interfacing, Security and Reliability: Sensor and actuator interfacing, Analog-todigital and digital-to-analog conversion, display and user interface interfacing, Interfacing with external memory and storage devices. Security threats, secure boot and firmware updates, error detection and error handling techniques, fault tolerance and redundancy in embedded systems.
Sensors and actuators: Classification of sensors and actuators, role of sensors and actuators in measurement and control systems, sensor characteristics, sensor types, sensor principles, sensor calibration and compensation techniques, actuator types, actuation principles, actuator performance parameters, analog sensors, digital sensors.
Sensor Interfaces, Signal Processing and Communication: Sensor interface circuits, sensor signal conditioning techniques, analog-to-digital conversion and digital-to-analog conversion, Sensor networks and wireless sensor networks, Sensor network topologies and communication protocols, power management and energy harvesting in wireless sensor networks.
Actuator Control and Applications: Actuator control systems: open-loop and closed-loop control, Actuator drive circuits and power electronics, Actuator control algorithms: PID control, feedback control, Applications of actuators in robotics, automation, and mechatronics.
Emerging Trends in Sensors and Actuators: MEMS and nanotechnology-based sensors and actuators, Smart sensors and wireless sensor networks, Internet of Things (IoT) and sensor integration, Energy-efficient and sustainable sensor and actuator technologies.
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.
Differential Equations: First-order ordinary differential equations, second-order ordinary differential equations, dynamic mathematical modelling, Laplace transforms and their applications.
Linear Algebra: Introduction of matrices, vectors, inverse matrix, eigenvalues and eigenvectors of matrices, inear vector spaces, state space models and solutions, characteristic value problem, definite forms, coordinate transform, maxima and minima, LaGrange multiplier, variation of dynamic systems, variation of deformable bodies. Linear systems of equations.
Complex Functions: Complex functions and notation, mapping by elementary functions, conformal mapping, Probability: Statistical estimation and hypothetical testing, regression and correlation, analysis of variance, statistics and sampling distributions.
Fourier Analysis: Fourier series representation, Fourier transform and its properties, Applications of Fourier analysis in control systems.
Control Systems Design: Transfer functions and frequency response, PID controllers and controller tuning, Stability proof and theorems and performance analysis in control systems.
Probability: Introduction to set theory, experiments, and sample spaces (discrete and continuous), joint and conditional probability, total probability, Bayes’ Theorem, independent events, and Bernoulli’s trials.
Random Variables: Definition and conditions for a function to be a random variable. Coverage includes discrete, continuous, and mixed random variables.
Distribution and Density Functions: Overview of distribution and density functions with properties. Focus on Binomial, Poisson, Uniform, Exponential, Gaussian, Rayleigh distributions, conditional distributions, conditional density functions, and operations on one random variable.
Multiple Random Variables: Concepts of vector random variables, joint and marginal distribution and density functions, conditional distributions, statistical independence, and operations on sums of random variables. Central Limit Theorem for unequal and equal distributions.
Stochastic Processes - Temporal Characteristics: Classification of stochastic processes, distribution and density functions, statistical independence, and stationarity. Analysis of system responses through mean, mean-squared value, autocorrelation, and cross-correlation functions.
Stochastic Processes - Spectral Characteristics: Power spectrum and its relationship with the autocorrelation function, cross-power density spectrum, and the response of linear systems with spectral characteristics of input-output relationships.
Advanced Process Control: Model-based control strategies, predictive control techniques and control of complex processes.
Process Dynamics and Modeling: Mathematical modeling of industrial processes, system
identification methods and simulation and analysis of process models.
Control Systems Engineering: Advanced control system design, multivariable control techniques and robust control and adaptive control.
Instrumentation and Measurement: Advanced sensors and instrumentation, calibration and measurement uncertainty and real-time data acquisition and analysis.
Process Optimization: Optimization techniques for industrial processes, optimization under constraints and process integration and energy optimization.
Safety and Reliability in Process Control: Safety instrumented systems (SIS), fault detection and diagnostics and reliability analysis and risk assessment.
Process Instrumentation and Control: Specialized courses focusing on control systems and processes in specific industries such as chemical, petrochemical, power generation, or manufacturing.
Robot Kinematics and Dynamics: Forward and inverse kinematics, robot manipulator modeling, robot dynamics and control, computer/robot vision. joint types, assembly and mechanisms.
Sensors, Actuators & Mechanisms: Types of sensors and actuators used in robotics, mechanical design considerations, robot mechanisms and mobility, end effectors and grippers.
Robot Programming and Design Process: Motion planning and control, path planning algorithms, virtual environments, conceptualization, design principles and methodologies, cad modeling for robotics, prototyping and iterative design.
Fabrication and Assembly: Materials selection, fabrication techniques (e.g., 3D printing, CNC machining), assembly and integration of robot components, testing and validation.
Advanced Topics: Advanced robotics concepts (e.g., swarm robotics, bio-inspired robotics), emerging trends in robot design and fabrication.
Discrete-time Signals and Systems: Sampling, Aliasing, Quantization, Convolution, Correlation Properties of Discrete-time Signals and Systems, Modeling discrete systems, Conversion of differential equations into difference equations, Solution of difference equations.
Fourier Analysis for Discrete Signals: Discrete Time Fourier Series, Discrete Fourier Transform (DFT), Inverse DFT, Windowing and DFT Leakage, Direct Computation of DFT, Fast Fourier Transform (FFT), Divide and Conquer, Radix algorithms, Inverse FFT, Applications.
Discrete-time Systems Implementation: Overview of z-transform, Analysis of discrete systems, Structures of Discrete-time systems, Fixed and Floating number types, Quantization effects.
Design of Digital Filters: General Considerations for filer design, Finite Impulse Response (FIR) Filters, Infinite Impulse Response (IIR) Filters, Techniques of FIR and IIR filter Design, HR and FIR, spectral estimation, adaptive filters, multi-rate signal processing, Wavelets and joint time-frequency analysis, and real-time signal processing.
Multirate Signal Processing and Adaptive Filters: Down sampling and Up sampling, Decimation and Interpolation, Least Mean Squares (LMS) Algorithm, Recursive Least Squares (RLS) Algorithm.
Modern Neural Networks in DSP: Neural Network Architectures for DSP, Signal Classification and Processing: Leveraging deep learning for signal tasks.
Probability: Introduction to set theory, experiments, and sample spaces (discrete and continuous), joint and conditional probability, total probability, Bayes’ Theorem, independent events, and Bernoulli’s trials.
Random Variables: Definition and conditions for a function to be a random variable. Coverage includes discrete, continuous, and mixed random variables.
Distribution and Density Functions: Overview of distribution and density functions with properties.
Focus on Binomial, Poisson, Uniform, Exponential, Gaussian, Rayleigh distributions, conditional distributions, conditional density functions, and operations on one random variable.
Multiple Random Variables: Concepts of vector random variables, joint and marginal distribution and density functions, conditional distributions, statistical independence, and operations on sums of random variables. Central Limit Theorem for unequal and equal distributions.
Stochastic Processes - Temporal Characteristics: Classification of stochastic processes, distribution and density functions, statistical independence, and stationarity. Analysis of system responses through mean, mean-squared value, autocorrelation, and cross-correlation functions.
Stochastic Processes - Spectral Characteristics: Power spectrum and its relationship with the autocorrelation function, cross-power density spectrum, and the response of linear systems with spectral characteristics of input-output relationships.
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.