Comprehensive Course Breakdown Across All 8 Semesters

Advanced Departmental Elective Courses

Reinforcement Learning for Control Systems: This course explores how reinforcement learning algorithms can be integrated with traditional control methods to solve complex dynamic optimization problems. Students will learn about Q-learning, policy gradients, and actor-critic methods in the context of control system design. Real-world applications include autonomous vehicles, robotics, and process control.

Advanced Cyber-Physical Systems: Focuses on the integration of computational algorithms with physical systems, emphasizing safety, security, and reliability aspects. Topics include distributed control, sensor fusion, real-time operating systems, and industrial IoT architectures.

Biomedical Signal Processing & Control: Applies signal processing techniques to analyze physiological signals such as ECG, EEG, and EMG. Students will design control systems for medical devices including pacemakers, prosthetics, and diagnostic equipment.

Smart Grid Integration and Energy Management: Covers the control of power distribution networks, renewable energy integration, demand response programs, and microgrid operations. Emphasis is placed on stability analysis, load forecasting, and grid optimization using advanced control strategies.

Industrial Robotics & Automation: Introduces industrial robotics with focus on motion planning, trajectory control, safety protocols, and integration with existing manufacturing systems. Practical sessions include programming ABB, KUKA, and Fanuc robots.

State Space Control Methods: Builds upon classical control theory to explore advanced state-space techniques for modeling and controlling multi-input multi-output systems. Includes controllability, observability, Kalman filtering, and observer design.

Robust Control Systems: Examines techniques for designing controllers that remain stable and performant under uncertainty and disturbances. Concepts include H-infinity control, μ-synthesis, and parameter-dependent controllers.

Optimization in Control Applications: Covers mathematical optimization methods specifically tailored for control system design, including convex optimization, nonlinear programming, and heuristic algorithms for large-scale systems.

Digital Signal Processing for Control Systems: Combines digital signal processing theory with practical implementation in feedback control. Topics include discrete-time filtering, spectral analysis, digital PID controllers, and FPGA-based implementations.

Quantitative Finance Engineering: Applies control theory to financial modeling, including derivative pricing, portfolio optimization, risk management, and algorithmic trading strategies using stochastic control methods.

Project-Based Learning Philosophy

The department emphasizes project-based learning as a core pedagogical strategy. Students begin with small group projects in the second year, progressing to increasingly complex individual or team-based capstone initiatives in the final year. Mini-projects are assigned every semester, allowing students to apply theoretical knowledge in practical settings.

Projects are selected from industry partnerships, research grants, and faculty-led initiatives. Each project undergoes rigorous evaluation using predefined criteria including innovation, technical merit, documentation quality, and presentation skills. Students receive mentorship from faculty members throughout the project lifecycle.

The final-year thesis/capstone project is a culmination of all learned concepts, requiring students to propose, implement, and evaluate a significant control system solution. Projects often result in publications, patents, or commercial applications, with many students presenting their work at national conferences.