Model Predictive Control of Non-Holonomic Vehicles: Beyond Differential-Drive
Mario Rosenfelder, Henrik Ebel, Jasmin Krauspenhaar, Peter Eberhard

TL;DR
This paper develops a comprehensive MPC design method for controlling non-holonomic vehicles, demonstrating its effectiveness on various vehicle types and highlighting the importance of non-quadratic cost functions for stability.
Contribution
It introduces a novel MPC design procedure based on sub-Riemannian geometry for non-holonomic vehicles, with proven stability and practical applications to complex vehicle configurations.
Findings
Controllers stabilize setpoints asymptotically with sufficient prediction horizon.
Quadratic cost functions are insufficient for reliable stabilization in these cases.
The approach is successfully applied to real hardware vehicles, including those with trailers.
Abstract
Non-holonomic vehicles are of immense practical value and increasingly subject to automation. However, controlling them accurately, e.g., when parking, is known to be challenging for automatic control methods, including model predictive control (MPC). Combining results from MPC theory and sub-Riemannian geometry in the form of homogeneous nilpotent system approximations, this paper proposes a comprehensive, ready-to-apply design procedure for MPC controllers to steer controllable, driftless non-holonomic vehicles into given setpoints. It can be ascertained that the resulting controllers nominally asymptotically stabilize the setpoint for a large-enough prediction horizon. The design procedure is exemplarily applied to four vehicles, including the kinematic car and a differentially driven mobile robot with up to two trailers. The controllers use a non-quadratic cost function tailored to…
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Taxonomy
TopicsControl and Dynamics of Mobile Robots · Vehicle Dynamics and Control Systems · Robotic Path Planning Algorithms
