Model Predictive Path Integral Control with Smoothness-Oriented Extensions
Experimental Validation on a High-Speed Autonomous Vehicle
M.S.T. Lam (TU Delft - Mechanical Engineering)
R. Ferrari – Mentor (TU Delft - Team Riccardo Ferrari)
W. Martens – Mentor (TU Delft - Team Riccardo Ferrari)
Javier Alonso-Mora – Graduation committee member (TU Delft - Learning & Autonomous Control)
A. Dabiri – Graduation committee member (TU Delft - Team Azita Dabiri)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Autonomous driving near the handling limits of a vehicle places stringent demands on the control layer, where nonlinear dynamics, actuator delay, and model mismatch dominate closedloop behavior. Sampling-based optimal control methods, and in particular Model Predictive Path Integral (MPPI), offer an attractive alternative to deterministic receding-horizon control through stochastic rollouts and importance weighting. While MPPI has demonstrated strong performance in simulation, its real-world behavior at high speed remains insufficiently characterized.
This thesis investigates the deployment of baseline MPPI and three smoothness-oriented extensions, Dynamic Covariance MPPI, Smooth MPPI (SMPPI), and Low-pass Filtered Sampling MPPI (LFS-MPPI), on a small-scale autonomous racing platform. Experiments focus on high-speed trajectory tracking under tight lane constraints and obstacle interactions, where steering bandwidth limitations and actuation delay significantly influence stability. Results show that baseline MPPI achieves competitive speeds and low nominal tracking error, but generates excessive high-frequency steering activity that induces sustained oscillations on hardware. Stability improvements are consistently associated with reduced steering-rate excitation. Among the evaluated extensions, LFS-MPPI provides the most favorable tradeoff between smoothness, tracking accuracy, and robustness by shaping the sampling process rather than post-processing the control signal.
The experiments further reveal a fundamental coupling between model fidelity and sampling efficiency under strict real-time constraints. By increasing the proportion of dynamically plausible rollouts at reduced sample counts, LFS-MPPI enables stable deployment of a dynamic bicycle prediction model and reliable tracking up to 3.5 m/s on the physical racetrack. These findings demonstrate that sampling structure is a decisive factor in achieving stable, high-performance, real-world MPPI control.