Course

Syllabus

Foundations of Intelligent Control Systems: Basic understanding of control systems. Evolution of intelligent control in engineering applications. Definition and scope of intelligent control. Comparison between intelligent control and classical control systems. Smart control technologies in automation, robotics, and IoT. Present developments and international policies in intelligent control. Overview of stakeholders in intelligent control systems.

Features of intelligent control – Fuzzy Logic Systems (FLS), Neural Networks (NN), Evolutionary Algorithms (EA). Hybrid systems – Neuro-Fuzzy Systems, GA-NN integration. Sensors and devices – Intelligent Electronics Devices (IED), IoT-enabled controllers. Communication standards and protocols for intelligent systems.

Advanced Techniques in Intelligent Control: Reinforcement Learning (RL) – Markov Decision Processes (MDP), Q-Learning, Deep Q-Networks (DQN). Adaptive and Predictive Control – Model Predictive Control (MPC), Self-tuning controllers. Edge AI and embedded systems – Implementation of intelligent control on microcontrollers (e.g., Raspberry Pi, ESP32). Cyber-Physical Systems (CPS) – Role of intelligent control in CPS, security, and robustness.

Applications and Future Trends in Intelligent Control: Industrial applications – Smart manufacturing, Industry 4.0, drones, autonomous vehicles, and robotics. Energy management systems. Emerging technologies – Quantum-inspired intelligent control.

Pre-requisite: Nil

Course Objectives

• To understand the principles and applications of intelligent control systems.
• To familiarize students with soft computing techniques such as fuzzy logic, neural networks, and evolutionary algorithms.

Course Outcomes

CO1: Understand the foundational concepts and evolution of intelligent control systems.

CO2: Comprehend soft computing techniques and their application in control systems.

CO3: Analyze advanced techniques used in intelligent control mechanism.

CO4: Apply intelligent control strategies to real-world problems in IoT and edge computing.

CO-PO Mapping

1. Nazmul Siddique, “Intelligent Control: A Hybrid Approach Based on Fuzzy Logic, Neural Networks, and Genetic Algorithms”, Springer, 2020.
2. Richard S. Sutton, Andrew G. Barto, “Reinforcement Learning: An Introduction”, MIT Press, 2018.
3. Charu C. Aggarwal, “Neural Networks and Deep Learning: A Textbook”, Springer, 2018.
4. Timothy J. Ross, “Fuzzy Logic with Engineering Applications”, Wiley, 2019.
5. James B. Rawlings, David Q. Mayne, Moritz Diehl, “Model Predictive Control: Theory, Computation, and Design”, Nob Hill Publishing, 2017.
6. Xiaofei Wang, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan, Xu Chen “Edge AI: Convergence of Edge Computing and Artificial Intelligence”, Springer, 2021.