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Mechanical and Civil Engineering Seminar

Thursday, October 15, 2020
11:00am to 12:00pm
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Online Event
Learning MPC and its Applications to Robotic Systems
Ugo Rosolia, Postdoctoral Scholar, Mechanical and Civil Engineering, California Institute of Technology,

*Connection details for this online presentation will be posted when available

Leveraging historical data to iteratively improve the performance of predictive controllers has been an active theme of research in the past few decades. The key idea is to use recorded state-input pairs in order to compute at least one of the following three components: i) a model which describes the evolution of the system, ii) a safe set of states (and an associated control policy) from which the control task can be safely executed and iii) a value function which represents the cumulative closed-loop cost from a given state of the safe set. 

In this talk, I will first provide an overview of the theory of Learning Model Predictive Control that I have developed during my PhD. In particular, I will show how historical data can be used in the control design to guarantee safety, exploration and performance improvement. In the second part of the talk, I will show the effectiveness of the proposed methodology on an autonomous racing example and a manipulator task example.

For more information, please contact Mikaela Laite by phone at (626) 395-4128 or by email at mlaite@caltech.edu.