While the potential of learning-based control paradigms is being demonstrated in numerous applications, the supporting theory addressing questions related to their safety, reliability, performance or also their computational properties is still in its infancy.

Model predictive control offers a promising framework to approach this problem. It directly facilitates online learning to improve the description of any problem component, i.e. the model, cost or constraints, with the goal of enhancing closed-loop performance. Despite the opportunities, however, the MPC approach is often challenged by complex and long-term tasks. In this talk, I will discuss different flavours of learning-based MPC as well as tools for their analysis, and also address how MPC principles can be used to enrich the theoretical properties of other learning-based control techniques. Throughout the talk, the ideas will be highlighted using demonstrations of autonomous vehicles and robotics.

The Role of Learning in MPC and Vice Versa


<br>**Session Chair:** Sorin Olaru


**Author:** Zeilinger Melanie N.   
**Paper link:** Please find my paper [**HERE**](https://drive.google.com/file/d/1yYMjcwO811e9_zTfHfJPZFSwv87I9DSv/view?usp=sharing).

![](https://assets.underline.io/uploads/markdown_image/1/image/a6845e72e0091c6bfd87c7645b9ce224.jpeg)

While the potential of learning-based control paradigms is being demonstrated in numerous applications, the supporting theory addressing questions related to their safety, reliability, performance or also their computational properties is still in its infancy.

Model predictive control offers a promising framework to approach this problem. It directly facilitates online learning to improve the description of any problem component, i.e. the model, cost or constraints, with the goal of enhancing closed-loop performance. Despite the opportunities, however, the MPC approach is often challenged by complex and long-term tasks. In this talk, I will discuss different flavours of learning-based MPC as well as tools for their analysis, and also address how MPC principles can be used to enrich the theoretical properties of other learning-based control techniques. Throughout the talk, the ideas will be highlighted using demonstrations of autonomous vehicles and robotics.

Plenary Session 6: The Role of Learning in MPC and Vice Versa  

invited talk

The IFAC Conference on Nonlinear Model Predictive Control (NMPC 2021) aims to bring together researchers interested in and work in the MPC field from both academia and industry. This allows us to reflect and establish the current state-of-the-art and focus on the future development of the MPC field towards relevant directions.

Model predictive control (MPC) is indisputably one of the advanced control techniques that have significantly affected control engineering practice with thousands of controllers implemented in various fields, spanning from the process industry to automotive and robotics. This is due to its unique ability to handle constraints, take preview information about disturbances and setpoint changes into account, and optimize performance. Furthermore, MPC allows the consideration of various types of dynamics, from linear to nonlinear, discrete to continuous, as well as an online adaptation of models and parameters. The research community on MPC is very active, with hundreds of papers published every year. Over the past decades, many theoretical, as well as implementation results, have been achieved.

### **All content presented at NMPC2021, including videos, papers and slides will be available only to registered (regular/student) participants. The content can be accessed until August 31st, 2021.**



![](https://assets.underline.io/uploads/markdown_image/1/image/946cb7978767129748bc1bb6270cb362.png)

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Nonlinear Model Predictive Control Conference 2021

The IFAC Conference on Nonlinear Model Predictive Control (NMPC 2021) aims to bring together researchers interested in and work in the MPC field from both academia and industry. This allows us to reflect and establish the current state-of-the-art and focus on the future development of the MPC field towards relevant directions.

Melanie Zeilinger

1

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The Role of Learning in MPC and Vice Versa

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