Semester-wise syllabus for an M.Tech in Control Systems Engineering

 

Semester 1:

Core Foundations 

Courses: 

1. Advanced Control Theory 

   - State-space analysis, multivariable control, Lyapunov stability, and pole placement. 

2. Digital Control Systems 

   - Z-transform, discrete-time systems, PID tuning, and real-time implementation (FPGA/microcontrollers). 

3. Modern Control Techniques 

   - Optimal control (LQR, LQG), robust control (H∞, µ-synthesis), and adaptive control. 

4. Modeling and Simulation of Dynamic Systems

   - Bond graphs, nonlinear system modeling, and simulation tools (MATLAB/Simulink). 

5. Research Methodology 

   - Technical writing, data analysis (Python/MATLAB), and experimental design. 

Labs:

- Control Systems Simulation Lab (MATLAB/Simulink, LabVIEW) 

- Embedded Systems Lab (Arduino, Raspberry Pi for real-time control) 

 

Semester 2:

Specialization & Electives 

Core Courses: 

1. Nonlinear Control Systems

   - Sliding mode control, feedback linearization, chaos control, and bifurcation analysis. 

2. Optimal and Predictive Control

   - Model Predictive Control (MPC), dynamic programming, and trajectory optimization. 

 

Electives (Examples): 

- Robotics and Autonomous Systems (kinematics, SLAM, path planning) 

- Industrial Automation (PLC, SCADA, DCS) 

- Adaptive and Intelligent Control (neural networks, fuzzy logic) 

- Networked Control Systems (time-delay systems, IoT integration) 

- Control of Power Electronics (motor drives, grid-tied inverters) 

Labs: 

- Robotics Lab (ROS, Gazebo for robot control simulations) 

- PLC/SCADA Lab (Siemens TIA Portal, Allen-Bradley) 

 

Semester 3:

Advanced Electives & Project Work 

Electives (Examples):

- AI/ML in Control Systems (reinforcement learning, deep learning for control) 

- Fault Detection and Diagnosis 

- Biomedical Control Systems(prosthetics, physiological system modeling) 

- Quantum Control Systems (basics of quantum feedback control) 

- Advanced Mechatronics (sensor fusion, actuator control) 

Project/Dissertation:

- Phase 1: Topic selection (e.g., drone stabilization, smart grid control, robotic surgery systems), literature review, and proposal submission. 

- Seminars: Presentations on emerging trends like edge AI for control, cyber-physical systems, or ethical AI in automation. 

 

Semester 4:

Thesis/Project Completion

Thesis/Project: 

- Full-time focus on hardware/software implementation (e.g., autonomous vehicle control, industrial process optimization). 

- Final documentation, viva voce defense, and potential industry collaboration. 

Additional Components:

- Industrial Internship (optional, with automation firms like Siemens, ABB, or robotics startups). 

- Workshops: Training in tools like Simulink Real-Time, dSPACE, or ROS 2. 

 

Elective Tracks (Specializations):

1. Robotics and Autonomous Systems 

   - Autonomous navigation, swarm robotics, human-robot interaction. 

2. Industrial Automation 

   - Industry 4.0, digital twins, smart manufacturing. 

3. AI/ML in Control 

   - Neural network-based controllers, reinforcement learning for dynamic systems. 

4. Automotive Control Systems 

   - ADAS, electric vehicle power train control, battery management systems.