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 from safe set.
In this talk, I will first provide an overview of the Learning Model Predictive Control strategy 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.
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