Smart Automation and Robotics

Qualification aims

The module facilitates the design and implementation of advanced control strategies, integration of IoT devices into automation systems, and development of autonomous robotic systems by applying control theory, communication protocols, machine learning, and considering societal implications, with the aim to optimize system operations and enhance robot autonomy.

Students can

• design and implement advanced control strategies
• integrate IoT devices into automation systems
• reflect specific properties of industrial automation systems and choose the right technology for the respective problem
• apply state-of-the-art automation technologies and be able to assess their advantages and limitations
• design, construct and program autonomous and connected robotic systems
• integrate AI algorithms to enhance the autonomy of robots
• evaluate the ethical implications of deploying smart automation and advanced robotics

by

• understanding and applying linear, nonlinear, and model predictive control theory
• employing wired and wireless communication protocols
• utilizing principles of mechanics, electronics, and computing
• utilizing machine learning, computer vision and sensor fusion techniques
• reflecting on societal, environmental, and ethical considerations
• using “state of the art” analysis and design software
• summarizing results in reports
• presenting results in oral presentations

to

• optimize the operation of control systems
• enhance real-time monitoring and control capabilities
• address complex industrial challenges
• enhance reliability and autonomy of collaborative robots
• ensure responsible use of technology

Module Content

Advanced Control Engineering

• Advanced PID control (override control, etc.)
• Industrial PID controllers
• Matrix norms
• State space approach
• Interconnected systems and feedback
• Stability, Ljapunow stability and I/O stability
• Reachability, Observability and Controllability
• State feedback and output feedback
• Observers
• Multivariable poles and zeros
• Structural characteristics of non-linear systems
• Model-based predictive control systems
• Internal model control and Smith predictor
• Linear model predictive control (MPC)
• Nonlinear model predictive control (NMPC)

Smart Automation and IoT

• Introduction into Industrial IoT and ‘Industry 4.0’
• Designating factors of industrial IoT applications
• IIoT connectivity, interfaces and protocols, such as MQTT, OPC UA
• Interfacing systems via OPC UA
• Architecture of vertical and horizontal IIoT applications
• IoT platforms and cloud-based systems
• IIoT Semantics and their implementation, e.g. via OPC UA
• Digital twins
• Handling of data
• Principles and terminology of MES (ISA-95)
• Industrial implementation examples, focus on OPC UA and MQTT
• Modern automation approaches such as VPLC
• Modern programming approaches in automation

Advanced Robotics

• Introduction to robotics and Python
• ROS2
• Machine Vision and OpenCV
• Servos with Arduinos
• Microcontrollers and micro-ROS2
• Odometry
• Sensors: Ultrasound and IMU
• Monte Carlo and Kalman Filters
• Robot Localization
• Robot Navigation
• Autonomous Driving
• AI in Autonomous Driving
• Connected Driving

Bibliography

• Astrom, K.J., Hagglund, T.: Advanced PID Control, ISA, Research Triangle Park, 2006
• William, R.L., Lawrence, D.A.: Linear State-Space Control Systems. Wiley, 2007
• Liebermann, N.P.: Troubleshooting Process Plant Control. Wiley, 2008
• Meyer, H., Fuchs, F., Thiel, K.: Manufacturing Execution Systems: Optimal Design, Planning, and Deployment. Mcgraw Hill Book Co, 2009.
• Kletti, H.(Editor): Manufacturing Execution System MES. Springer Berlin Heidelberg, 2010.
• Schleipen: Praxishandbuch OPC UA, ISBN 978-3-8343-3413-8
• Lea: Internet of Things for Architects, ISBN 978-1-78847-059-9
• http://mqtt.org/
• https://www.amqp.org/
• IEC 62443 international norm
• www.ros.org
• Akai, N. „Reliable Monte Carlo localization for mobile robots”, Journal of Field Robotics, Vol. 40, Issue 3, pp. 595-613, 2023
• Urrea C., Agramonte, R., “Kalman Filter; Historical Overview and Review of Its Use in Robotics 60 Years after Its Creation”, Journal of Sensors, vol. 2021, Article ID 9674015, 21 pages, 2021
• Yasuda, Y, Martins L.E., Cappablanco F., “Autonomous Visual Navigation for Mobile Robots: A Systematic Literature Review”, ACM Computing Surveys, Vol. 53, No. 1, Issue 1, Article 13, pp 1-34, 2020
• https://academy.nvidia.com/en/
• www.opencv.org
• www.arduino.cc
• https://ubuntu.com/tutorials