Real-time Nonlinear MPC Strategy with Full Vehicle Validation for Autonomous Driving

Abstract

In this paper, we present the development and deployment of an embedded optimal control strategy for autonomous driving applications on a Ford Focus road vehicle. Non-linear model predictive control (NMPC) is designed and deployed on a system with hard real-time constraints. We show the properties of sequential quadratic programming (SQP) optimization solvers that are suitable for driving tasks. Importantly, the designed algorithms are validated based on a standard automotive development cycle: model-in-the-loop (MiL) with high fidelity vehicle dynamics, hardware-in-theloop (HiL) with vehicle actuation and embedded platform, and vehicle-hardware-in-the-loop (VeHiL) testing using a full vehicle. The autonomous driving environment contains both virtual simulation and physical proving ground tracks. Throughout the process, NMPC algorithms and optimal control problem (OCP) formulation are fine-tuned using a deployable C code via code generation compatible with the target embedded toolchains. Finally, the developed systems are applied to autonomous collision avoidance, trajectory tracking and lane change at high speed on city/highway and low speed at a parking environment.

Integration Stability Analysis Of Nonlinear Model Predictive Motion Control For Autonomous Vehicles

“…The RK4 method is used in the NPMC Control based strategy for the stabilization of an electric vehicle proposed by Siampis [14]. Allamaa and Listov et al present a real-time NPMC strategy with the RK4 method as a discretization method for AVs [15]. The same approach is used in the torquevectoring controller [16] proposed by Parra and Tavernini.…”

“…Owaki and Yuno adopt a similar approach [25]. Parra and Tavernini implemented a 25Hz nonlinear MPC on a real autonomous vehicle [15]. These works show more details, but they do not discuss the integration stability at low speed.…”