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Executive MTech in Applied Mechatronics and Robotics

The Executive M.Tech program in Applied Mechatronics and Robotics at IIT Bhilai is designed to develop expertise in integrating mechanical, electrical, and computational systems for advanced robotics and automation solutions. The program emphasizes interdisciplinary learning and the synergic knowledge to solve real-world problems in industries such as manufacturing, aerospace, healthcare, and autonomous systems.

The flexible curriculum offers online classes, electives, and project-based learning, catering to professionals aspiring to advance their careers in robotics, automation, and mechatronics domains.

Eligibility for PhD:

Executive M.Tech. degree will be considered eligible for PhD admissions. Other MEQs will remain as per the institute norms.


Eligibility:

Minimum 2 years of relevant experience within the preceding 3 Years*.

Should have B.Tech. / B.E. / B.Sc. in relevant field (minimum 4-year program) / M.S. or M.Sc. in relevant field (minimum 2-year program) / M.C.A. (minimum 2-year program). For the candidates applying based on the M.Sc./M.S./M.C.A. degree, the undergraduate degree must be in a science or engineering field.

In the qualifying degree, at least 55% marks or a CGPA/CPI equivalent to 55% marks. In case the candidate belongs to the SC, ST, or Persons with Disability (PwD) category, this is relaxed to 50% marks or a CGPA/CPI equivalent to 50% marks. To be eligible to apply for equivalency after the third semester, the candidate must have scored the minimum marks in the qualifying degree as specified in the eligibility criteria of the regular M.Tech. program of IIT Bhilai.

*to be computed as per the last day for application submission of this round.

 

Program-specific criteria for Ex. M.Tech. admissions
  1. Relevant Experience: Industrial or Teaching experience only.
  2. Relevant field for the qualifying degree:
    • A. M.Sc./M.S. (2 years): ME / EE / ECE / CS / MT / PI / AE / IN
    • B. B.Sc. (4 years): ME / EE / ECE / CS / MT / PI / AE / IN

Duration:

The nominal duration of the Executive M.Tech. program will be 2 calendar years / 4 semesters (online).

The nominal duration of the Executive M.Tech. program with an equivalency to the regular M.Tech. will be a 5 semesters (3 online, 2 offline). Please contact IIT Bhilai for further details.

Duration:
2 years

Program Fee:

4,11,000

(Excluding Optional Fee)
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Program Objectives

  • Develop a strong understanding of mechatronics and robotics fundamentals, including sensors and actuators.
  • Gain knowledge in designing and controlling robotic systems using advanced control techniques.
  • Apply computational tools and machine learning techniques for robotics and automation systems.
  • Understand interdisciplinary integration of mechanical, electrical, and computational domains in robotics.
  • Foster innovation in designing automation solutions for industrial and research applications.
  • Build problem-solving and leadership skills for leading multidisciplinary teams in robotics projects.

Learning Outcomes

  • Demonstrate expertise in designing and analyzing mechatronic systems and robotics.
  • Apply control theory and automation techniques to create efficient robotic systems.
  • Utilize advanced AI and machine learning tools for robotics, including path planning and navigation.
  • Address real-world challenges in robotics with sustainable and ethical solutions.
  • Effectively communicate technical solutions and project outcomes to diverse stakeholders.
  • Exhibit a commitment to lifelong learning, staying updated with emerging robotics and automation trends.

Program Highlights

  • An esteemed certification, campus immersion & alumni status from IIT Bhilai
  • Learn through Virtual Instructor-Led Training (VILT)
  • Explore top-notch learning with industry experts

WHO IS THIS PROGRAM FOR?

  • The Executive MTech in Applied Mechatronics and Robotics is tailored for professionals already immersed in technology-related domains such as IT professionals, software engineers, and experts in Mechatronics and Robotics.
  • Entrepreneurs, innovators, and tech enthusiasts, as well as engineers aspiring to master the dynamic field of Applied Mechatronics and Robotics, will find this program specifically designed to meet their needs.
  • For engineers and software developers aiming to cultivate a profound understanding of Mechatronics and Robotics, the Executive MTech program offers invaluable insights and skills development.
Course Curriculum:

Semester - I (Total Credits – 9)
Course Code Course Name L T P C Category
MR01 Introduction to Mechatronics 2 0 0 2 Core Course
MR02 Mechanisms for Robotic Systems 2 0 0 2 Core Course
MR03 Modern Control Systems 2 0 0 2 Core Course
MR04 Artificial Intelligence 2 1 0 3 Core Course
Semester – II (Total Credits – 9)
Course Code Course Name L T P C Category
MR05 Robotics system 2 0 0 2 Core Course
MR06 Sensors and actuators for Robotics 2 0 0 2 Core Course
MREXX Electives in ME/EE/DSAI - - - 5 Elective
Semester – III (Total Credits – 10)
Course Code Course Name L T P C Category
MREXX Electives in MT&R - - - 6 Elective
MREXX Electives in EE/ME/DSAI - - - 4 Elective
OR
MREXX Electives in MT&R - - - 6 Elective
MRC01 Campus Immersion - - 4 2 Campus mmersion
MREXX Electives in EE/ME/DSAI - - - 2 Elective
Semester - IV (Total Credits – 12)
Course Code Course Name L T P C Category
MRP02 Capstone project 0 0 24 12 Project
OR
MRT01* Thesis x x x 12 MTech Thesis
Semester – V* (Total Credits – 14)
Course Code Course Name L T P C Category
MRT01* Thesis x x x 14 MTech thesis
Elective Courses
 
Category Sem Course Code Course Name L T P C
Electives in EE II/III MRE01 Advanced Control Theory 2 0 0 2
Electives in EE II/III MRE02 Signal Interface Circuits 2 0 0 2
Electives in EE II/III MRE03 Digital System 2 1 0 3
 
Electives in ME II/III MRE04 Intelligent Mechanical System 2 0 0 2
Electives in ME II/III MRE06 Industrial Automation 2 0 0 2
Electives in ME II/III MRE07 Smart Material and Structures 2 0 0 2
Electives in ME II/III MRE00 Soft Computing 2 0 0 2
 
Electives in DSAI II MRE08 Internet of Things 3 0 0 3
Electives in DSAI II MRE09 Machine Learning 2 0 2 3
Electives in DSAI II MRE11 Reinforcement Learning 2 0 0 2
Electives in DSAI II MRE12 Natural Language Processing for Robotics 2 1 0 3
 
Electives in MT&R III/IV MRE14 UAV Guidance & Navigation 2 1 0 3
Electives in MT&R III/IV MRE15 Industry 4.0 2 0 0 2
Electives in MT&R III/IV MRE16 Machine Vision for Robotics 3 0 0 3
Electives in MT&R III/IV MRE17 Autonomous Vehicles 2 1 0 3
Electives in MT&R III/IV MRE18 Microcontroller and Microprocessor 3 0 0 3

Additional Notes:

  • *Only for those who have opted for equivalent degree to the regular Mtech program
    • Students registered only for the Executive MTech (Online) Program:
    • Total Credits : 10 (Semester I)+ 9 (Semester II)+ 9 (Semester - III) + 12 (Semester - IV) = 40
    • Students opted for the equivalent degree to the regular MTech Program
    • Total Credits : 10 (Semester I) + 9 (Semester II) + 9 (Semester III) + 12 (Semester - IV) + 14 (Semester V) = 54
  • Campus Immersion:
    • If opted, it will be conducted offline and may also contribute to lab credit requirements.
    • (Campus immersion will run if more than 10 students opt for it.)
  • Program Equivalency:
    • This is an online Executive M.Tech. degree program, which is not equivalent to the regular M.Tech programs of IIT Bhilai. To get a degree equivalent to a regular M.Tech, the student has to opt for 26 credits offline thesis (at IIT Bhilai) after the 3 rd semester. The offline thesis will be for two semesters (semester 4 and semester 5). The selection for the equivalency program will be based on qualifying the mandatory offline written exam and an interaction conducted by IIT Bhilai.
  • Thesis Requirements:
    • To achieve equivalency with the regular offline MTech program, the candidate must complete a total of 26 thesis credits on campus starting from forth semester onwards.

Tools you'll Master


Program Structure
  • Total Credits: 40
  • Core Courses: 12-13 Credits
  • Elective Courses: 13-14 Credits (if the Campus immersion is opted), otherwise 15-16 credits.
  • Campus Immersion Program: 2 Credits (optional)
  • Project: 12 Credits (in 4th Semester)

Examination & Evaluation:

Students will be evaluated in each semester. In addition to continuous online evaluation (quiz/assignment/assessment) in the core/elective courses running in the first three semesters, the end-semester exam will be offline proctored exams.

Module :
Fundamentals of Robotics
Module 1 - Robot Kinematics
  1. History and Evolution of Robotics
  2. Types of Robots: Industrial, Service, and Special Purpose Robots
  3. Basic Components of Robots: Structure, Sensors, Actuators
  4. Overview of Robot Kinematics and Dynamics
  5. Robot Coordinate Systems and Transformations
  6. Introduction to Robotic Control Systems
Module 2 - Robot Kinematics
  1. Forward Kinematics: Denavit-Hartenberg Notation
  2. Inverse Kinematics: Problem Solving and Algorithms
  3. Workspace Analysis and Trajectory Planning
  4. Jacobians and Velocity Kinematics
  5. Singularity Analysis and Solutions
  6. Case Studies: Industrial Robot Manipulators
Module 3 - Robot Dynamics and Control
  1. Fundamentals of Robot Dynamics
  2. Equations of Motion and Dynamic Modeling
  3. Control Techniques: PID, Adaptive, and Robust Control
  4. Real-Time Control Systems and Algorithms
  5. Stability Analysis of Robotic Systems
  6. Simulation and Analysis Tools
Module 4 - Sensors and Actuators in Robotics
  1. Types of Sensors: Proximity, Vision, Force/Torque
  2. Sensor Integration and Data Fusion
  3. Types of Actuators: Electric, Hydraulic, Pneumatic
  4. Actuator Control and Motion Planning
  5. Haptic and Tactile Sensing
  6. Application Examples: Autonomous Robots, Human-Robot Interaction
Module 5 - Robotics in Industry and Future Trends
  1. Robotics in Manufacturing and Automation
  2. Mobile Robotics: AGVs and UAVs
  3. Humanoid Robots: Design and Challenges
  4. Collaborative Robotics and Safety Issues
  5. Robotics in Healthcare and Rehabilitation
  6. Future Trends: AI in Robotics, Ethical Considerations
Mechatronics Systems Design
Module 1 - Introduction to Mechatronics
  1. Overview of Mechatronics and its applications
  2. Key components: Sensors, Actuators, and Controllers
  3. System modeling and analysis
  4. Basic electronics for mechatronics
  5. Mechanical systems in mechatronics
  6. Introduction to microcontrollers and programming
Module 2 - Sensors and Actuators
  1. Types of sensors: Position, Velocity, Force, Temperature, etc.
  2. Signal conditioning and noise reduction techniques
  3. Actuators: Electrical, Hydraulic, Pneumatic, and Piezoelectric
  4. Selection criteria for sensors and actuators
  5. Integration of sensors and actuators with control systems
  6. Case studies and applications
Module 3 - Control Systems in Mechatronics
  1. Basics of control theory
  2. Open-loop and closed-loop control systems
  3. PID control and tuning
  4. Digital control techniques
  5. Implementation of control algorithms in mechatronics
  6. Control system simulation and analysis
Module 4 - Mechatronic System Design
  1. Design methodology for mechatronic systems
  2. Interfacing and integration of system components
  3. Reliability and safety in system design
  4. Rapid prototyping and testing of mechatronic systems
  5. Project management and documentation in mechatronic design
  6. Case studies of mechatronic system design
Module 5 - Advanced Topics and Applications
  1. Robotics and automation in mechatronics
  2. Smart sensors and IoT integration
  3. Advanced control strategies: Adaptive, Fuzzy, and Neural Networks
  4. Energy-efficient and sustainable mechatronic design
  5. Emerging trends and future of mechatronics
  6. Capstone project: Design and development of a mechatronic system
Control Systems
Module 1 - Introduction to Control Systems
  1. Definition and importance of control systems in engineering.
  2. Basic components of control systems: sensors, actuators, controllers.
  3. Types of control systems: open loop and closed loop.
  4. System modeling: differential equations, transfer functions.
  5. Block diagram representation of control systems.
  6. Signal flow graphs and their use in control system analysis.
Module 2 - Time Domain Analysis
  1. Standard test signals and system response.
  2. Time-domain specifications of control systems.
  3. First and second order system analysis.
  4. Stability analysis using Routh-Hurwitz criterion.
  5. Root locus technique: concept and applications.
  6. PID controllers: design and tuning methods.
Module 3 - Frequency Domain Analysis
  1. Frequency response of control systems.
  2. Bode plots: construction and interpretation.
  3. Nyquist criterion for stability analysis.
  4. Gain and phase margins.
  5. Lead, lag, and lead-lag compensators.
  6. Control system design using frequency response methods.
Module 4 - State Space Analysis
  1. State space representation of control systems.
  2. Solution of state equations.
  3. Controllability and observability concepts.
  4. State feedback controllers and observers.
  5. Introduction to optimal control.
  6. Linear Quadratic Regulator (LQR) design.
Module 5 - Advanced Topics in Control Systems
  1. Digital control systems: analysis and design.
  2. Nonlinear control systems: overview and basic techniques.
  3. Adaptive control: principles and applications.
  4. Robust control: concepts and design methods.
  5. Introduction to networked control systems.
  6. Current trends and research areas in control systems.
Sensor Technology and Instrumentation
Module 1 - Basics of Sensors and Transducers
  1. Introduction to sensors and transducers: Definitions, classifications, and characteristics.
  2. Sensor materials and fabrication techniques.
  3. Electrical and non-electrical quantity measurement: Temperature, pressure, flow, and level.
  4. Static and dynamic characteristics of sensors.
  5. Calibration and error analysis in sensor measurements.
  6. Case studies of sensor applications in industry.
Module 2 - Signal Conditioning and Processing
  1. Fundamentals of signal conditioning.
  2. Amplifiers, filters, and converters for sensor signals.
  3. Digital signal processing techniques for sensor data.
  4. Noise reduction and signal enhancement.
  5. Data acquisition systems and interfaces.
  6. Practical exercises on signal conditioning circuits.
Module 3 - Advanced Sensor Technologies
  1. Smart and intelligent sensors.
  2. MEMS (Micro-Electro-Mechanical Systems) and NEMS (Nano-Electro-Mechanical Systems) sensors.
  3. Optical and fiber optic sensors.
  4. Wireless sensor networks and IoT integration.
  5. Environmental and bio-sensors.
  6. Recent advancements in sensor technology.
Module 4 - Instrumentation Systems
  1. Principles of instrumentation design.
  2. Sensor selection criteria for instrumentation systems.
  3. Control systems and feedback mechanisms.
  4. Industrial instrumentation and automation.
  5. Reliability, safety, and maintenance of instrumentation systems.
  6. Project work: Designing a basic instrumentation system.
Module 5 - Applications and Future Trends
  1. Sensors in robotics and automation.
  2. Biomedical instrumentation and sensors.
  3. Environmental monitoring and sensing.
  4. Automotive and aerospace sensor applications.
  5. Future trends in sensor technology.
  6. Seminars on current research in sensor technology and instrumentation.
Advanced Robotics
Module 1 - Robotic Systems and Design
  1. Overview of Robotics: History, Evolution, and Current Trends
  2. Mechanical Design of Robots: Kinematics and Dynamics
  3. Actuators and Sensors in Robotics
  4. Robot End Effectors and Grippers
  5. Robotics Control Systems and Architectures
  6. Design and Simulation Tools for Robotics
Module 2 - Artificial Intelligence in Robotics
  1. Fundamentals of Artificial Intelligence and Machine Learning
  2. AI-based Perception and Vision Systems
  3. Machine Learning Algorithms for Robotics
  4. Neural Networks and Deep Learning in Robotics
  5. Natural Language Processing and Human-Robot Interaction
  6. AI in Autonomous Navigation and Path Planning
Module 3 - Advanced Control Systems
  1. Control Theory and Robotics
  2. Feedback and Feedforward Control Mechanisms
  3. Adaptive and Predictive Control in Robotics
  4. Real-Time Control Systems and Algorithms
  5. Motion Planning and Obstacle Avoidance
  6. Robotics Control Software and Programming
Module 4 - Robotics Applications and Case Studies
  1. Industrial Robotics: Automation and Manufacturing
  2. Service Robotics: Healthcare, Agriculture, and Domestic Use
  3. Field Robotics: Exploration and Environmental Monitoring
  4. Humanoid Robots: Design and Challenges
  5. Collaborative Robotics and Human-Robot Interaction
  6. Future Trends and Emerging Applications in Robotics
Module 5 - Robotics Research and Development
  1. Research Methodologies in Robotics
  2. Ethical Considerations and Impact Assessment in Robotic Applications
  3. Robotics in Extreme Environments: Space and Underwater
  4. Advanced Materials and Fabrication Techniques for Robotics
  5. Funding and Commercialization of Robotics Projects
  6. Future Directions and Challenges in Robotics Research
Humanoid Robots
Module 1 - Introduction to Humanoid Robots
  1. History and Evolution of Humanoid Robots
  2. Basic Principles and Terminology in Humanoid Robotics
  3. Mechanical Design: Structure, Joints, and Materials
  4. Power Systems and Energy Management
  5. Sensors and Perception in Humanoids
  6. Actuators and Movement Control
Module 2 - Locomotion and Stability
  1. Kinematics and Dynamics of Bipedal Walking
  2. Balance Control and Fall Prevention
  3. Motion Planning and Obstacle Navigation
  4. Gait Patterns and Adaptation to Different Terrains
  5. Simulation Tools for Humanoid Robot Locomotion
  6. Case Studies of Humanoid Robots in Locomotion
Module 3 - Manipulation and Interaction
  1. Robotic Arms and Hands: Design and Control
  2. Grasping Mechanics and Object Manipulation
  3. Human-Robot Interaction: Communication and Cooperation
  4. Facial Expressions and Gesture Recognition in Humanoids
  5. Task Planning and Execution in Dynamic Environments
  6. Safety Considerations in Humanoid Robot Interaction
Module 4 - Advanced Control Systems
  1. Control Architectures for Humanoid Robots
  2. Real-time Control and Feedback Systems
  3. Adaptive and Learning-based Control Strategies
  4. Integration of AI and Machine Learning in Humanoids
  5. Multi-robot Coordination and Teamwork
  6. Challenges and Solutions in Complex Control Systems
Module 5 - Applications and Future Trends
  1. Humanoid Robots in Healthcare and Rehabilitation
  2. Humanoids in Entertainment and Social Interaction
  3. Role of Humanoids in Industrial Applications
  4. Ethical Considerations and Social Impact of Humanoid Robots
  5. Future Directions and Emerging Technologies in Humanoid Robotics
  6. Case Studies of Successful Humanoid Robot Implementations
Robot Motion Planning and Control
Module 1 - Introduction to Robot Motion Planning
  1. Overview of Robotics and Motion Planning
  2. Configuration Space Concepts
  3. Basic Motion Planning Algorithms
  4. Graph-based Planning Methods
  5. Collision Detection and Avoidance
  6. Workspace Analysis and Path Planning
Module 2 - Robot Kinematics and Dynamics
  1. Forward and Inverse Kinematics
  2. Differential Kinematics and Jacobians
  3. Dynamics of Robotic Systems
  4. Trajectory Generation
  5. Velocity and Acceleration Analysis
  6. Force Control and Compliance
Module 3 - Motion Control Techniques
  1. PID Control in Robotics
  2. Adaptive and Robust Control Strategies
  3. Real-time Control Systems
  4. Sensor-based Control
  5. Motion Control under Uncertainty
  6. Hybrid Control Systems
Module 4 - Advanced Motion Planning
  1. Probabilistic Roadmaps
  2. Rapidly exploring Random Trees (RRT)
  3. Artificial Potential Fields
  4. Motion Planning in Dynamic Environments
  5. Multi-robot Coordination
  6. Human-Robot Interaction in Motion Planning
Module 5 - Case Studies and Applications
  1. Industrial Robotics Applications
  2. Mobile Robotics and Autonomous Vehicles
  3. Aerial Robotics Motion Planning
  4. Underwater Robotics Navigation
  5. Medical and Assistive Robotics
  6. Future Trends in Robot Motion Planning
Mobile Robotics
Module 1 - Introduction to Mobile Robotics
  1. Overview of Mobile Robotics: History and Applications
  2. Basic Components of Mobile Robots: Sensors, Actuators, and Controllers
  3. Locomotion Mechanisms and Kinematics
  4. Robotic Operating System (ROS) Basics
  5. Principles of Robot Navigation and Control
  6. Safety and Ethical Considerations in Robotics
Module 2 - Sensing and Perception
  1. Types of Robotic Sensors: Proximity, Vision, Inertial, and GPS
  2. Sensor Fusion Techniques
  3. Environmental Modeling and Feature Detection
  4. Visual Odometry and SLAM (Simultaneous Localization and Mapping)
  5. Object Recognition and Tracking
  6. Data Interpretation and Error Analysis
Module 3 - Localization and Mapping
  1. Principles of Localization: Probabilistic Methods and Filters
  2. Implementing Odometry and GPS-Based Localization
  3. Introduction to SLAM Algorithms
  4. Grid Maps and Topological Maps
  5. Path Planning Algorithms: A*, Dijkstra, and RRT
  6. Dynamic Environment Mapping
Module 4 - Navigation and Control
  1. Motion Planning and Obstacle Avoidance Strategies
  2. Feedback Control Systems: PID Controllers
  3. Behavior-Based and Hierarchical Control Architectures
  4. Autonomous Navigation in Unstructured Environments
  5. Multi-Robot Systems and Swarm Robotics
  6. Human-Robot Interaction
Module 5 - Advanced Topics and Applications
  1. Advanced SLAM Techniques and 3D Mapping
  2. Machine Learning in Mobile Robotics
  3. Autonomous Vehicles and Drone Navigation
  4. Robotics in Agriculture, Healthcare, and Industry
  5. Future Trends in Mobile Robotics
  6. Project Work and Case Studies
Robotics Vision and Image Processing
Module 1 - Introduction to Robotics Vision
  1. Overview of Robotics and Computer Vision
  2. Basics of Image Formation and Processing
  3. Cameras and Sensing in Robotics
  4. Color Image Processing Fundamentals
  5. Edge Detection and Image Segmentation
  6. Image Feature Extraction Techniques
Module 2 - Image Processing Techniques
  1. Spatial and Frequency Domain Processing
  2. Image Filtering and Enhancement
  3. Image Restoration Techniques
  4. Morphological Image Processing
  5. Image Compression and Reconstruction
  6. Introduction to Pattern Recognition
Module 3 - Advanced Vision Techniques for Robotics
  1. 3D Vision and Depth Estimation
  2. Motion Analysis and Object Tracking
  3. Machine Learning in Vision Systems
  4. Stereo Vision and Multi-View Geometry
  5. Image Registration and Fusion Techniques
  6. Real-Time Vision Processing for Robotics
Module 4 - Applications of Vision in Robotics
  1. Vision-Based Navigation and Control
  2. Object Detection and Recognition in Robotics
  3. Robotic Manipulation and Grasping
  4. Vision in Autonomous Vehicles and Drones
  5. Human-Robot Interaction and Vision
  6. Industrial Applications of Robotic Vision
Module 5 - Emerging Trends and Future Directions
  1. Deep Learning in Robotic Vision
  2. Augmented Reality and Virtual Reality in Robotics
  3. Bio-inspired Vision Systems
  4. Collaborative Robotics and Swarm Vision
  5. Ethical and Social Implications of Robotic Vision
  6. Future Trends in Robotics Vision and Image Processing
Advanced Control Systems
Module 1 - Classical Control Theory
  1. Review of Feedback Control Systems
  2. Root Locus Techniques
  3. Frequency Response Analysis
  4. Stability and Routh-Hurwitz Criterion
  5. PID Controllers and Tuning Methods
  6. Compensation Techniques
Module 2 - State-Space Analysis
  1. State-Space Representation of Systems
  2. Solution of State Equations
  3. Controllability and Observability
  4. State Feedback Control
  5. State Observers
  6. Linear Quadratic Regulator (LQR)
Module 3 - Robust Control Systems
  1. Introduction to Robust Control
  2. Uncertainty and Perturbations in Systems
  3. H-infinity Control Techniques
  4. Robust PID Control
  5. Robustness Analysis Methods
  6. Practical Applications of Robust Control
Module 4 - Adaptive and Nonlinear Control
  1. Fundamentals of Adaptive Control
  2. Model Reference Adaptive Control (MRAC)
  3. Adaptive Control Algorithms
  4. Introduction to Nonlinear Control Systems
  5. Phase Plane Analysis
  6. Lyapunov Stability Theory
Module 5 - Advanced Topics in Control Systems
  1. Neural Networks in Control Systems
  2. Fuzzy Logic Controllers
  3. Predictive Control
  4. Distributed Control Systems (DCS)
  5. Networked Control Systems
  6. Case Studies in Advanced Control Applications
Intelligent Mechatronics Systems
Module 1 - Fundamentals of Mechatronics
  1. Introduction to Mechatronics: Definition, evolution, and applications.
  2. Systems in Mechatronics: Mechanical, electrical, and control subsystems.
  3. Sensors and Actuators: Types, characteristics, and selection.
  4. Signal Conditioning and Data Acquisition Systems.
  5. Basic Electronics for Mechatronics: Microcontrollers, digital logic.
  6. Real-Time Operating Systems for Mechatronics.
Module 2 - Intelligent Systems and Robotics
  1. Overview of Intelligent Systems: AI and machine learning basics.
  2. Robotics: Kinematics, dynamics, and control.
  3. Design of Intelligent Robots: Sensors, actuators, and algorithms.
  4. Machine Vision and Image Processing.
  5. Artificial Neural Networks and Fuzzy Logic in Mechatronics.
  6. Human-Robot Interaction and Collaborative Robotics.
Module 3 - Control Systems in Mechatronics
  1. Control Theory and Systems: PID, adaptive, and robust control.
  2. Model Predictive Control in Mechatronics.
  3. System Identification and State Estimation.
  4. Advanced Control Techniques: Nonlinear and optimal control.
  5. Real-Time Control Systems: Implementation and challenges.
  6. Case Studies: Automotive, aerospace, and industrial applications.
Module 4 - Simulation and Modeling
  1. Mathematical Modeling of Mechatronic Systems.
  2. Simulation Tools and Techniques.
  3. Finite Element Analysis in Mechatronics.
  4. Multibody Dynamics for Mechatronic Systems.
  5. Co-Simulation: Mechanical and electrical system integration.
  6. Case Studies: Simulation in product development and testing.
Module 5 - Emerging Trends in Intelligent Mechatronics
  1. Internet of Things (IoT) and its Application in Mechatronics.
  2. Advanced Materials and Nanotechnology in Mechatronics.
  3. Sustainable and Green Mechatronics.
  4. Industry 4.0 and Smart Manufacturing.
  5. Wearable Mechatronics and Biomechatronics.
  6. Future Challenges and Opportunities in Intelligent Mechatronics.
Microprocessors and Microcontrollers in Mechatronics
Module 1 - Introduction to Microprocessors and Microcontrollers
  1. Overview of Microprocessors and Microcontrollers in Mechatronics
  2. Architecture of Microprocessors: Basic Concepts and Operational Principles
  3. Architecture of Microcontrollers: Specialized Features for Mechatronics
  4. Comparison between Microprocessors and Microcontrollers
  5. Basic Programming Concepts for Microprocessors and Microcontrollers
  6. Introduction to Assembly Language and High-Level Programming
Module 2 - Microprocessor and Microcontroller Programming
  1. Programming Structure and Syntax
  2. Input/Output Programming and Control
  3. Interrupts and Interrupt Service Routines
  4. Timers and Counters Programming
  5. Serial and Parallel Communication
  6. Real-Time Operating Systems for Microcontrollers
Module 3 - Interfacing Techniques
  1. Sensors and Actuators: Types and Characteristics
  2. Interfacing Sensors with Microcontrollers
  3. Interfacing Actuators with Microcontrollers
  4. Data Acquisition and Signal Processing
  5. Analog-to-Digital and Digital-to-Analog Conversion Techniques
  6. Power Supply and Signal Conditioning Circuits
Module 4 - Control Systems using Microcontrollers
  1. Control System Design Principles
  2. PID Control Implementation using Microcontrollers
  3. Motor Control: Stepper and Servo Motors
  4. Embedded Control Systems for Mechatronics
  5. Feedback Mechanisms and Closed-Loop Control
  6. Implementation of Advanced Control Algorithms
Module 5 - Applications and Case Studies
  1. Robotics and Automated Systems
  2. Automotive Electronics and Control Systems
  3. Industrial Automation using PLC and Microcontrollers
  4. Home Automation and IoT Applications
  5. Case Studies of Mechatronics Systems Using Microcontrollers
  6. Future Trends in Microprocessors and Microcontrollers in Mechatronics
Automation and Robotics
Module 1 - Fundamentals of Automation and Robotics
  1. Overview of Automation in Manufacturing
  2. History and Evolution of Robotics
  3. Types of Robots and their Applications
  4. Basic Components and Architecture of Robotic Systems
  5. Sensors and Actuators in Robotics
  6. Robotics System Integration and Human-Robot Interaction
Module 2 - Robotics Kinematics and Dynamics
  1. Forward and Inverse Kinematics
  2. Dynamics of Robotic Systems
  3. Trajectory Planning and Motion Control
  4. Robot End Effectors and Grippers
  5. Path and Motion Planning Algorithms
  6. Workspace Analysis and Optimization
Module 3 - Control Systems and Programming
  1. Basics of Control Systems in Robotics
  2. Robotics Programming Languages and Software
  3. Real-Time Control and Feedback Systems
  4. Artificial Intelligence in Robotics
  5. Robot Operating System (ROS)
  6. Advanced Programming and Simulation Tools
Module 4 - Automation Technologies and Applications
  1. Automation in Manufacturing Processes
  2. Automated Material Handling and Assembly Systems
  3. Machine Vision and Inspection Systems
  4. Industrial Internet of Things (IIoT) and Smart Factories
  5. Collaborative Robotics and Human-Centric Automation
  6. Case Studies of Automated Systems
Module 5 - Ethical, Societal, and Economic Aspects
  1. Basics of Control Systems in Robotics
  2. Robotics Programming Languages and Software
  3. Real-Time Control and Feedback Systems
  4. Artificial Intelligence in Robotics
  5. Robot Operating System (ROS)
  6. Advanced Programming and Simulation Tools
Industrial Mechatronics
Module 1 - Introduction to Mechatronics
  1. Definition and scope of mechatronics
  2. Key components: Sensors, Actuators, Controllers
  3. Systems approach to mechatronics design
  4. Interfacing mechanical and electrical components
  5. Basics of microcontrollers and PLCs
  6. Case studies of mechatronics systems
Module 2 - Sensors and Signal Conditioning
  1. Types of sensors: Position, velocity, temperature, force, etc.
  2. Signal conditioning and data acquisition
  3. Noise and filtering techniques
  4. Sensor selection and application considerations
  5. Calibration and error analysis
  6. Practical applications in industry
Module 3 - Actuators and Drives
  1. Types of actuators: Hydraulic, Pneumatic, Electric
  2. Design and operation of drive systems
  3. Stepper motors and servo motors
  4. Power electronics and motion control
  5. Integration of actuators in mechatronic systems
  6. Case studies of actuators in automation
Module 4 - Control Systems in Mechatronics
  1. Fundamentals of control theory
  2. Open-loop and closed-loop control systems
  3. PID control and advanced control strategies
  4. Real-time control system implementation
  5. Programmable Logic Controllers (PLCs)
  6. Applications in robotics and automated systems
Module 5 - Advanced Topics and Applications
  1. Industrial Internet of Things (IIoT) and smart factories
  2. Machine vision and image processing
  3. Robotics and automated guided vehicles (AGVs)
  4. AI and machine learning in mechatronics
  5. Sustainability and energy efficiency in mechatronics
  6. Emerging trends and future directions
Emerging Technologies in Mechatronics and Robotics
Module 1 - Advanced Sensors and Actuators
  1. Overview of Modern Sensors and Actuators
  2. Smart Sensors and IoT Integration
  3. Piezoelectric and Shape Memory Alloys
  4. Advanced Actuation Systems
  5. Sensor Fusion Techniques
  6. Real-time Sensing and Control
Module 2 - Robotics and Automation
  1. Evolution of Robotics Technology
  2. Collaborative Robots and Human-Robot Interaction
  3. Mobile Robotics and Autonomous Systems
  4. Industrial Automation Trends
  5. Robotics in Manufacturing and Service Industries
  6. Safety and Ethical Aspects in Robotics
Module 3 - Artificial Intelligence in Robotics
  1. Fundamentals of AI in Robotics
  2. Machine Learning Algorithms for Robotics
  3. Neural Networks and Deep Learning Applications
  4. Computer Vision and Image Processing
  5. AI for Robotic Motion and Path Planning
  6. AI in Predictive Maintenance and Fault Diagnosis
Module 4 - Control Systems and Integration
  1. Advanced Control Theories and Algorithms
  2. Real-time Control System Design
  3. Integration of Mechatronic Components
  4. Wireless and Networked Control Systems
  5. Embedded Systems in Mechatronics
  6. Simulation and Modeling of Mechatronic Systems
Module 5 - Emerging Trends and Technologies
  1. Nanotechnology in Mechatronics and Robotics
  2. 3D Printing and Additive Manufacturing
  3. Wearable Robotics and Exoskeletons
  4. Augmented Reality and Virtual Reality in Robotics
  5. Energy Harvesting and Sustainability in Robotics
  6. Future Trends and Research Directions
Machine Learning for Robotics
Module 1 - Fundamentals of Machine Learning
  1. Overview of machine learning and its application in robotics.
  2. Supervised, unsupervised, and reinforcement learning basics.
  3. Evaluation metrics and model selection.
  4. Feature extraction and data preprocessing.
  5. Basic algorithms: Decision Trees, Naïve Bayes, and KNN.
  6. Introduction to Neural Networks and Deep Learning.
Module 2 - Robotics Perception
  1. Machine learning for sensor data processing.
  2. Vision systems and image processing techniques.
  3. Object detection, recognition, and tracking.
  4. 3D point cloud processing.
  5. Audio processing and speech recognition in robotics.
  6. Sensor fusion techniques.
Module 3 - Decision Making and Control
  1. Reinforcement learning in robotics.
  2. Path planning and navigation algorithms.
  3. Predictive modeling for robotic systems.
  4. Decision-making under uncertainty.
  5. Adaptive control systems.
  6. Human-robot interaction and collaborative systems.
Module 4 - Advanced Topics in Robotic Machine Learning
  1. Deep learning for robotics.
  2. Transfer learning and domain adaptation.
  3. Generative models in robotics.
  4. Robotic learning from demonstration.
  5. Multi-agent systems and swarm intelligence.
  6. Ethical implications and responsible AI in robotics.
Module 5 - Practical Applications and Case Studies
  1. Autonomous vehicles and drones.
  2. Industrial robotics and automation.
  3. Medical robotics and assistive technologies.
  4. Search and rescue robotics.
  5. Space exploration and underwater robotics.
  6. Current research trends and future directions.
Internet of Things (IoT) in Automation
Module 1 - Introduction to IoT
  1. Definition and Evolution of IoT
  2. Key Components of IoT Systems
  3. IoT Architecture and Models
  4. IoT Protocols and Communication Technologies
  5. IoT Standards and Ecosystems
  6. Security and Privacy in IoT
Module 2 - IoT in Automation
  1. Role of IoT in Industrial and Home Automation
  2. Sensors and Actuators in IoT Automation
  3. IoT Control Systems and Interfaces
  4. Case Studies of IoT Automation Projects
  5. Data Acquisition and Management in IoT
  6. IoT and Robotics in Automation
Module 3 - IoT Connectivity and Communication
  1. Wireless Communication Technologies in IoT
  2. Wired Communication Standards in IoT
  3. Network Topologies for IoT
  4. IoT Gateways and Data Aggregation
  5. Cloud Computing in IoT
  6. Edge Computing in IoT
Module 4 - IoT Platform and Development
  1. Overview of IoT Platforms
  2. Building IoT Solutions: Tools and Techniques
  3. Programming for IoT: Languages and Frameworks
  4. IoT Device Management and Deployment
  5. IoT Analytics and Visualization
  6. Scalability and Maintenance in IoT Systems
Module 5 - Challenges and Future Trends in IoT Automation
  1. Interoperability Issues in IoT
  2. Scalability and Reliability in IoT Networks
  3. IoT in Smart Cities and Smart Grids
  4. Future Trends in IoT Technologies
  5. Ethical and Legal Considerations in IoT
  6. Industry 4.0 and IoT
Artificial Intelligence in Industrial Applications
Module 1 - Introduction to AI in Industry
  1. Overview of Artificial Intelligence
  2. History and Evolution of AI in Industrial Applications
  3. AI Techniques: Machine Learning, Deep Learning, and Robotics
  4. AI in Manufacturing and Automation
  5. Case Studies of AI Applications in Industry
  6. Challenges and Future Trends in Industrial AI
Module 2 - Machine Learning in Industry
  1. Fundamentals of Machine Learning
  2. Supervised, Unsupervised, and Reinforcement Learning
  3. Industrial Applications of Machine Learning
  4. Predictive Maintenance and Quality Control
  5. Optimization of Industrial Processes Using ML
  6. Case Studies: ML in Manufacturing, Supply Chain, etc.
Module 3 - Machine Learning in Industry
  1. Basics of Industrial Robotics
  2. Robotic Process Automation (RPA) in Industry
  3. Integration of AI with Robotics
  4. Collaborative Robots (Cobots) in Manufacturing
  5. Safety and Ethical Considerations in Industrial Robotics
  6. Case Studies in Robotics and Automation
Module 4 - Machine Learning in Industry
  1. AI in Inventory Management and Forecasting
  2. Automated Warehousing and Distribution Systems
  3. Predictive Analytics in Supply Chain Management
  4. AI for Demand Planning and Logistics Optimization
  5. Blockchain and AI in Supply Chain Transparency
  6. Case Studies: AI in Logistics and Supply Chain
Module 5 - Ethical and Legal Aspects of Industrial AI
  1. Ethical Considerations in AI Deployment
  2. AI and Employment: Impact on Workforce
  3. Legal Framework and Compliance in AI
  4. AI Governance in Industrial Applications
  5. Data Privacy and Security in AI Systems
  6. Future of AI in Industry: Challenges and Opportunities
Sustainable Energy Systems in Mechatronics
Module 1 - Sustainable Energy Systems in Mechatronics
  1. Overview of Sustainable Energy and Its Importance
  2. Energy Sources: Renewable vs Non-renewable
  3. Fundamentals of Mechatronics in Energy Systems
  4. Solar and Wind Energy Basics for Mechatronics
  5. Energy Storage and Distribution in Mechatronic Systems
  6. Environmental Impacts and Sustainability Considerations
Module 2 - Energy Efficient Design in Mechatronics
  1. Principles of Energy Efficiency in Mechatronic Design
  2. Tools and Techniques for Energy Analysis
  3. Design of Low Energy Consumption Mechanisms
  4. Integration of Energy Harvesting Devices in Mechatronics
  5. Case Studies of Energy Efficient Mechatronic Systems
  6. Simulation and Modelling for Energy Analysis
Module 3 - Renewable Energy Technologies in Mechatronics
  1. Solar Power Systems and Applications in Mechatronics
  2. Wind Energy Conversion Systems in Mechatronic Designs
  3. Hydropower and Geothermal Energy in Mechatronics
  4. Biomass Energy Utilization in Mechatronic Systems
  5. Emerging Renewable Technologies in Mechatronics
  6. Integration Challenges and Solutions in Renewable Energies
Module 4 - Control and Optimization of Energy Systems
  1. Control Strategies for Energy Systems in Mechatronics
  2. Optimization Techniques for Energy Efficiency
  3. Smart Grid Technologies in Mechatronic Systems
  4. IoT and AI Applications in Energy Management
  5. Predictive Maintenance for Energy Systems
  6. Case Studies in Control and Optimization
Module 5 - Future Trends and Innovations in Sustainable Energy
  1. Advances in Battery Technologies and Energy Storage
  2. Innovations in Solar and Wind Energy Conversion
  3. Emerging Trends in Green Energy and Mechatronics
  4. Sustainability and Lifecycle Analysis in Mechatronics
  5. Regulatory and Policy Frameworks for Sustainable Energy
  6. Future Prospects and Challenges in Sustainable Mechatronics
Program Fee :

The total fee for the online Executive M.Tech. program in various disciplines for the 4 semesters (excluding optional fees, improvement/compartment examination fee and re-enrollment fee, and any expense for food and lodging during stay on the campus) is as follows:


Executive MTech in Applied Mechatronics and Robotics
Application Fee (Non Refundable ) 5,000
Admission Fee Semester 1 Semester 2 Semester 3 Semester 4
1,01,500 1,01,500 1,01,500 1,01,500
Examination/Assessment Fee 4,000 4,000 4,000 4,000
Total Fee (Excluding Optional Fee) 4,11,000
Optional :

Executive MTech in Applied Mechatronics and Robotics Sem 1 Sem 2 Sem 3 Sem 4
Optional Campus Immersion Fee - 10,000 - 10,000
Optional Institute Alumni Fee - - - 6,000
Total Optional Fee 26,000

Refund Policy:

Click Here to check the refund policy

Admission Process


Selection process will be scheduled post-counseling & application process, depending on the number of eligible applications
as per seat availability for the program. This entire process will be online.

Program Certificate


Executive M.Tech in Data Science & Data Analytics

Executive MTech in Applied Mechatronics and Robotics

Complete the program successfully to obtain this valuable certificate.