Course Structure Overview
The PLC program is structured over eight semesters with a balanced mix of foundational courses, core engineering subjects, departmental electives, science electives, and laboratory experiences. Each semester carries a credit load that reflects the increasing complexity and specialization of the curriculum.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | PHYS101 | Physics for Engineers | 3-0-0-3 | - |
| 1 | MATH101 | Mathematics I | 4-0-0-4 | - |
| 1 | EC101 | Basic Electronics | 3-0-0-3 | - |
| 1 | CSE101 | Introduction to Computing | 2-0-0-2 | - |
| 1 | ENGL101 | English for Communication | 3-0-0-3 | - |
| 1 | PHYSLAB101 | Physics Laboratory | 0-0-2-2 | - |
| 1 | EC102 | Electronics Laboratory | 0-0-2-2 | - |
| 1 | CSE102 | Computing Lab | 0-0-2-2 | - |
| 2 | MATH201 | Mathematics II | 4-0-0-4 | MATH101 |
| 2 | PHYS201 | Thermodynamics and Statistical Mechanics | 3-0-0-3 | PHYS101 |
| 2 | EC201 | Network Analysis and Synthesis | 3-0-0-3 | EC101 |
| 2 | CSE201 | Programming in C | 2-0-0-2 | CSE101 |
| 2 | MECH201 | Engineering Mechanics | 3-0-0-3 | - |
| 2 | EC202 | Electronic Devices and Circuits | 3-0-0-3 | EC101 |
| 2 | CSE202 | Lab: Programming in C | 0-0-2-2 | - |
| 3 | MATH301 | Mathematics III | 4-0-0-4 | MATH201 |
| 3 | EC301 | Digital Electronics | 3-0-0-3 | EC201 |
| 3 | MECH301 | Mechanics of Materials | 3-0-0-3 | MECH201 |
| 3 | CSE301 | Data Structures and Algorithms | 3-0-0-3 | CSE201 |
| 3 | EC302 | Analog Electronics | 3-0-0-3 | EC202 |
| 3 | CSE302 | Lab: Data Structures and Algorithms | 0-0-2-2 | - |
| 4 | MATH401 | Mathematics IV | 4-0-0-4 | MATH301 |
| 4 | EC401 | Control Systems Theory | 3-0-0-3 | EC301 |
| 4 | CSE401 | Database Management Systems | 3-0-0-3 | CSE301 |
| 4 | MECH401 | Fluid Mechanics | 3-0-0-3 | MECH301 |
| 4 | EC402 | Signals and Systems | 3-0-0-3 | EC302 |
| 4 | CSE402 | Lab: DBMS | 0-0-2-2 | - |
| 5 | EC501 | Instrumentation Systems | 3-0-0-3 | EC401 |
| 5 | CSE501 | Computer Architecture | 3-0-0-3 | CSE401 |
| 5 | EC502 | Process Dynamics and Control | 3-0-0-3 | EC401 |
| 5 | MECH501 | Heat Transfer | 3-0-0-3 | MECH401 |
| 5 | CSE502 | Operating Systems | 3-0-0-3 | CSE401 |
| 5 | EC503 | Lab: Instrumentation | 0-0-2-2 | - |
| 6 | EC601 | Industrial Communication Networks | 3-0-0-3 | EC501 |
| 6 | CSE601 | Embedded Systems Programming | 3-0-0-3 | CSE501 |
| 6 | EC602 | Advanced Process Control | 3-0-0-3 | EC502 |
| 6 | MECH601 | Manufacturing Technology | 3-0-0-3 | MECH501 |
| 6 | CSE602 | Lab: Embedded Systems | 0-0-2-2 | - |
| 7 | EC701 | Smart Manufacturing | 3-0-0-3 | EC601 |
| 7 | CSE701 | Machine Learning for Control Systems | 3-0-0-3 | CSE601 |
| 7 | EC702 | Cybersecurity in Industrial Environments | 3-0-0-3 | EC602 |
| 7 | MECH701 | Energy Management | 3-0-0-3 | MECH601 |
| 7 | CSE702 | Project Planning and Management | 3-0-0-3 | - |
| 8 | EC801 | Capstone Project in PLC | 6-0-0-6 | All previous courses |
| 8 | CSE801 | Advanced Topics in Automation | 3-0-0-3 | CSE701 |
| 8 | EC802 | Lab: Capstone Project | 0-0-4-4 | - |
Detailed Course Descriptions
Here are detailed descriptions of key departmental elective courses:
Industrial Communication Networks: This course introduces students to various industrial communication protocols such as Modbus, Ethernet/IP, Profinet, CAN, and OPC UA. Students learn how to configure network topologies, troubleshoot communication issues, and integrate field devices into larger automation systems. Practical labs involve setting up networks using real hardware platforms.
Embedded Systems Programming: Designed to equip students with skills in designing embedded control systems using microcontrollers like ARM Cortex-M series. Topics include real-time operating systems (RTOS), device drivers, memory management, and debugging techniques. Labs provide hands-on experience with development boards and tools like Keil, IAR Embedded Workbench, and STM32CubeMX.
Advanced Process Control: This course explores advanced control strategies beyond classical PID control, including state-space methods, optimal control, robust control, and model predictive control. Students implement these techniques in simulation environments using MATLAB/Simulink and apply them to complex industrial processes like distillation columns or heat exchangers.
Smart Manufacturing: The course covers concepts of Industry 4.0 including digital twins, edge computing, cloud integration, and data analytics for manufacturing optimization. Students work on projects involving smart factory simulations, predictive maintenance systems, and real-time production monitoring using IoT sensors and cloud platforms.
Cybersecurity in Industrial Environments: This course focuses on securing industrial control systems against cyber threats. Topics include threat modeling, secure network design, access control mechanisms, vulnerability assessment, and incident response strategies. Practical labs involve simulating attacks on PLC-based systems and implementing defense measures.
Machine Learning for Control Systems: Students learn how to apply machine learning algorithms to enhance control performance in industrial settings. Emphasis is placed on supervised and unsupervised learning techniques for fault detection, system identification, and adaptive control. Projects involve training neural networks to predict process behavior or optimize control parameters.
Energy Management: The course covers energy auditing, renewable energy integration, power quality analysis, and efficiency improvement strategies in industrial plants. Students analyze real plant data, propose energy-saving solutions, and model energy consumption using software tools like MATLAB and EnerCalc.
Project Planning and Management: This course prepares students for managing large-scale automation projects. It covers project lifecycle phases, risk assessment, resource planning, scheduling techniques (Gantt charts, PERT), and quality control methodologies. Case studies from real industrial environments are used to reinforce learning outcomes.
Project-Based Learning Philosophy
The department believes that theoretical knowledge must be complemented with practical application through project-based learning. From the second year onwards, students engage in mandatory mini-projects that allow them to apply concepts learned in class to real-world problems. These projects are designed to foster creativity, problem-solving, and teamwork skills.
Each student selects a project topic from a list provided by faculty members or proposes their own idea after consultation with advisors. The selection process ensures alignment with current industry needs and personal interest areas. Projects typically span two semesters and culminate in presentations, reports, and demonstrations.
The final-year capstone project is an intensive, multi-disciplinary endeavor that integrates all learned knowledge. Students collaborate closely with faculty mentors and often work alongside industry partners to address genuine challenges faced by organizations. The project is evaluated based on technical innovation, feasibility, documentation quality, and presentation skills.