Introduction

Cyber-Physical Systems (CPS) integrate computing, networking, and physical processes to enable smart, autonomous, and real-time applications across industries. From smart grids and autonomous vehicles to Industry 4.0 and healthcare automation, CPS is revolutionizing modern infrastructure. This course covers the fundamental principles, architecture, AI-driven control, security challenges, and future trends in CPS.

Course Objectives

By the end of this course, participants will be able to:

• Understand the architecture and components of Cyber-Physical Systems.
• Design and analyze real-time embedded and networked control systems.
• Implement AI and machine learning for smart decision-making in CPS.
• Address security, privacy, and safety challenges in CPS applications.
• Explore autonomous systems, smart grids, and industrial automation.
• Investigate emerging trends in digital twins, blockchain for CPS, and quantum computing.

Who Should Attend?

This course is ideal for:

• Electrical, electronics, and computer engineers.
• IoT, AI, and embedded systems developers.
• Researchers and professionals in autonomous systems, smart cities, and Industry 4.0.
• Security specialists in critical infrastructure and cyber-physical security.
• Entrepreneurs and innovators in intelligent automation and CPS applications.

5-Day Course Outline

Day 1: Fundamentals of Cyber-Physical Systems (CPS)

• Introduction to CPS: Definition, characteristics, and real-world applications.
• CPS Architecture and Design: Sensors, actuators, control loops, and real-time processing.
• Embedded Systems in CPS: Hardware-software co-design and energy-efficient computing.
• IoT and Edge Computing in CPS: Data acquisition, fog computing, and latency challenges.
• Hands-On Session: Building a simple CPS prototype using IoT sensors and embedded controllers.

Day 2: Communication, AI, and Control in CPS

• Cyber-Physical Communication Networks: 5G, TSN (Time-Sensitive Networking), and LPWANs.
• Real-Time and AI-Driven Control Systems: Model predictive control (MPC) and reinforcement learning.
• Digital Twins and Simulation of CPS: Virtual models for predictive maintenance and optimization.
• AI for CPS Decision-Making: Machine learning for anomaly detection and automation.
• Hands-On Session: Implementing AI-based anomaly detection in a CPS environment.

Day 3: Security, Privacy, and Safety in CPS

• Cybersecurity Challenges in CPS: Threat modeling, attack vectors, and defense mechanisms.
• Privacy and Data Protection: Secure data sharing, encryption, and access control.
• Resilient CPS Design: Fault tolerance, redundancy, and fail-safe mechanisms.
• Blockchain for CPS Security: Distributed ledger for secure transactions and authentication.
• Hands-On Session: Simulating a cybersecurity attack and mitigation strategy in a CPS system.

Day 4: Industrial CPS, Smart Cities, and Autonomous Systems

• Industrial CPS and Industry 4.0: Smart factories, robotics, and predictive maintenance.
• Autonomous Vehicles and Transportation CPS: V2X communication, LiDAR integration, and ADAS.
• Smart Grids and Energy CPS: Grid automation, distributed energy resources, and demand response.
• Healthcare and Wearable CPS: Remote monitoring, AI-driven diagnostics, and smart implants.
• Hands-On Session: Developing a smart grid CPS application using real-time simulation.

Day 5: Future Trends, Challenges, and Final Project

• Emerging Technologies in CPS: Quantum computing, neuromorphic computing, and photonic processors.
• Regulatory and Ethical Considerations: Compliance, safety standards, and policy challenges.
• Interoperability and Scalability in CPS: Standardization and cross-domain integration.
• Final Project: Designing and presenting a CPS prototype for a real-world application.
• Q&A and Certification: Course recap, discussions, and participant presentations.