Machine Learning in Control and Automation

There is no doubt that Machine learning is having a huge impact in many fields of society, science, and engineering, and control engineering is not an exception. This has been possible due to the widespread availability of massive amounts of data, increased computing power, and the development of efficient software implementations. Thanks to the recent advances in Machine Learning, academics and practitioners alike are exploring new ways to tackle key problems in control and automation, and developing brand-new control strategies with direct application in process industry, robotics, automotive industry, aeronautics, food processing industry, etc.

In this seminar series, we bring together researchers that are interested in, or conducting research on Machine Learning for control and automation purposes. A variety of techniques and their applications to real-world systems will be covered, ranging from traditional machine learning techniques such as System Identification, State Estimation and Model Predictive Control (MPC) to Deep Neural Networks (DNN)-based approaches and Reinforcement Learning. We offer a varied program of national and international speakers.