# Learning How to Autonomously Race a Car: a Predictive Control Approach

## TL;DR

This paper introduces a Learning Model Predictive Controller for autonomous racing that iteratively improves lap times by updating control strategies based on previous laps, reducing computational load and using system identification.

## Contribution

It proposes a novel LMPC strategy that minimizes computational complexity and introduces a system identification method for autonomous racing control.

## Key findings

- Reduced computational burden in LMPC
- Successful experimental validation on Berkeley Autonomous Race Car
- Improved lap times through iterative learning

## Abstract

In this paper we present a Learning Model Predictive Controller (LMPC) for autonomous racing. We model the autonomous racing problem as a minimum time iterative control task, where an iteration corresponds to a lap. In the proposed approach at each lap the race time does not increase compared to the previous lap. The system trajectory and input sequence of each lap are stored and used to systematically update the controller for the next lap. The first contribution of the paper is to propose a LMPC strategy which reduces the computational burden associated with existing LMPC strategies. In particular, we show how to construct a safe set and an approximation to the value function, using a subset of the stored data. The second contribution is to present a system identification strategy for the autonomous racing iterative control task. We use data from previous iterations and the vehicle's kinematics equations to build an affine time-varying prediction model. The effectiveness of the proposed strategy is demonstrated by experimental results on the Berkeley Autonomous Race Car (BARC) platform.