# Learning Model Predictive Control for Connected Autonomous Vehicles

## TL;DR

This paper introduces a Learning Model Predictive Controller tailored for connected autonomous vehicles, capable of handling dynamic environments and data-driven decision variables, with proven effectiveness through simulations.

## Contribution

It extends nonlinear LMPC architecture to accommodate data-driven variables and dynamic environments in CAV platooning applications.

## Key findings

- Effective control logic demonstrated in simulations
- Converges to optimal strategies over model and data-driven variables
- Handles dynamic environments in connected autonomous vehicle scenarios

## Abstract

A Learning Model Predictive Controller (LMPC) is presented and tailored to platooning and Connected Autonomous Vehicles (CAVs) applications. The proposed controller builds on previous work on nonlinear LMPC, adapting its architecture and extending its capability to (a) handle dynamic environments and (b) account for data-driven decision variables that derive from an unknown or unknowable function. The paper presents the control design approach, and shows how to recursively construct an outer loop candidate trajectory and an inner iterative LMPC controller that converges to an optimal strategy over both model-driven and data-driven variables. Simulation results show the effectiveness of the proposed control logic.