DATA DRIVEN INDUSTRIAL CONTROL

Syllabus

Model‐based vs data‐driven controller design. Data Collection: Sensors and IoT Devices, Big Data Infrastructure, Data storage and processing. Data Analysis and Machine learning algorithms. Data driven methods for Process Modelling. Mixed‐logical models. Adaptive controller design. Data driven Intelligent controllers. Soft sensors. Iterative feedback controller tuning. Norm based controllers. Data driven switching controller and observer schemes. Data‐driven modeling and control of large‐scale systems. Application of data driven modeling and control schemes to robotic systems and processes. Data driven control simulation.

Learning Outcomes

Aim of the course is to familiarize students with the design and implementation of data driven controllers and observers. Moreover, aim of the course is to familiarize students with the equipment required to apply these systems to industrial processes and manufacturing units. Upon successful completion of the course, the student will be able to:

• Understand the inherent differences between data-driven controllers and model-based controllers for industrial processes and manufacturing units.
• Understand the design mechanisms of data-driven controllers.
• Implement data-driven controllers, with emphasis on issues concerning sensors, IoT connections and devices for data storage and processing. 
• Develop and apply machine learning tools, with emphasis on industrial process identification.
• Design and implement adaptive controllers and observers, as well as software sensors, for industrial processes and manufacturing units. 
• Design and implement safe-switching controllers and observers for industrial systems and subsystems.
   

For the outline of the course press here.