From Simulation to Reality: Assessing the Efficacy of Pure Pursuit, MPC, and MPCC on the F1Tenth Platform

Simulation serves as the primary proving ground where autonomous algorithms take shape. Yet, a significant disconnect exists between a perfect digital environment and the physical laws of reality, a phenomenon known as the “sim-to-real” gap. 

This engineering challenge forms the core of Xiangyiming Kong’s research, which utilised the F1Tenth racing platform to test the limits of modern control algorithms.

The Contenders: Three Approaches to Control

The study examined three distinct control algorithms, selected for their varying levels of computational complexity and theoretical sophistication:

1. Pure Pursuit: The simplest of the three, this geometric path-tracking algorithm calculates a steering angle based on a “pursuit point” at a fixed distance ahead of the vehicle. It remains computationally efficient and stable, functioning much like a driver looking a few meters down the road to adjust the steering wheel.
2. Model Predictive Control (MPC): A more sophisticated optimization-based algorithm. MPC uses a dynamic model of the vehicle to predict future states over a finite horizon. It solves an optimization problem at every time step to minimize tracking errors and control effort, effectively planning ahead to balance safety and efficiency.

Model Predictive Contouring Control (MPCC): The most complex of the trio. Unlike standard MPC, which strictly follows a pre-determined path, MPCC combines predictive modeling with contouring objectives. It balances the trade-off between path accuracy and progress, allowing the vehicle to deviate slightly from the centerline if it means carrying more speed through a corner, mimicking the behavior of a professional racing driver finding the fastest line.