Autonomous racing has attracted significant attention recently, presenting challenges in selecting an optimal controller that operates within the onboard system's computational limits and meets operational constraints such as limited track time and high costs. This paper introduces a Linear Parameter-Varying Model Predictive Controller (LPV-MPC) for lateral control. Implemented on an IAC AV-24, the controller achieved stable performance at speeds exceeding 160 mph (71.5 m/s). We detail the controller design, the methodology for extracting model parameters, and key system-level and implementation considerations. Additionally, we report results from our final race run, providing a comprehensive analysis of both vehicle dynamics and controller performance. A Python implementation of the framework is available at: https://tinyurl.com/LPV-MPC-acados
Fig. 2 : Overview of the single-track bicycle model, highlighting relevant forces, angles, and lateral errors in their positive directions.
Tires
Bp
Cp
Dp
Ep
Clinear
Front
34.59
1.81
2100
-1.0
132k
Rear
35.04
1.96
3036
-0.26
209k
TABLE I : Initial Pacejka Parameters values using IMS data. Dp is in Newtons (N), and Clinear is in N/rad.
Fig. 3 : Measured cornering forces versus slip angles with the corresponding fitted Pacejka curve, obtained from the final IMS run at a longitudinal velocity of 56ms−1 . Note that the vertical offset in the Pacejka model (typically denoted as shy ) was not accounted for.
Fig. 4 : High-level software architecture of our autonomous racing system for a single-car format [ 3 ] .
Fig. 5 : Experimental results for path tracking during our highest speed lap. The highlighted areas mark the start and finish of the lap.
Fig. 6 : Tracking errors vs. velocity during the complete run.
Fig. 7 : Measured cornering forces Fy versus slip angles, αf and αr , with the corresponding fitted Pacejka curve, obtained from the final run at a longitudinal velocity of 72ms−1 . The Linear Force curve depicts the estimated force computed using the cornering stiffness parameters that were determined during the practice runs and employed during the final run.
Tires
Bp
Cp
Dp
Ep
Clinear
Front
22.30
2.00
3885.85
-1.00
173k
Rear
26.08
2.00
5342.89
-1.00
278k
TABLE II : Pacejka parameter values obtained via EM optimization using our final run data. Dp is in Newtons [N], and Clinear is in [N/rad]. Note that these parameters have not been tested with the proposed controller.
Fig. 8 : Lateral vs. longitudinal acceleration (g-g diagram) during the complete run. Accelerations were measured using the on-board IMU.
Fig. 9 : Lateral model error vs. velocity during the final run.
Fig. 10 : Computation time tcomp. of the control loop during the high-speed lap.
This paper presents Adaptive Differentiable Model Predictive Contouring Control (AD-MPCC), a framework for autonomous racing that integrates differentiable MPCC with online parameter estimation to handle varying road-surface conditions. For online parameter estimation, we leverage a parameterized Pacejka Magic Formula together with a regularized moving-horizon estimation scheme with exponentially decaying weights to capture road interactions and update parameters in real time. Furthermore, we propose a differentiable MPCC (Diff-MPCC) framework that enables optimal adjustment of objective weights based on predefined long-horizon performance costs. To implement Diff-MPCC for online objective weight adaptation, we propose a Pacejka-informed machine learning model that is trained in a supervised manner using data generated by Diff-MPCC to tune the objective weights. Simulation results demonstrate that AD-MPCC reliably ensures safety and achieves faster lap times compared to baseline controllers in both single-surface and multiple-surface scenarios.
Nam T. Nguyen, Binh Nguyen, Ahmad Amine +3
Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA. · Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA. · CTI Lab4EV, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam.
We present a generalized implementation of Look-Back and Look-Ahead Adaptive Model Predictive Control (LLA-MPC), a learning-free framework for real-time, rapid adaptive system identification and control. The original formulation was demonstrated only in simulation, for autonomous racing with a fixed model structure. Our implementation has modular dynamics and integrators, and we validate it on the F1TENTH platform under constrained computation and noisy state estimation. The system identifies tire parameters online by evaluating thousands of candidate models in real time on an embedded computer. Experiments with low-friction tires across changing surfaces show that LLA-MPC completes high-speed tracking tasks where a fixed nominal model fails. Code, videos, and our related work are available at: https://lla-control.github.io.
Henry Z. Liao, Maitham F. AL-Sunni, John M. Dolan
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA · Department of Electrical & Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
As autonomous vehicles (AVs) operate in increasingly dynamic traffic conditions, lateral control must be performed while longitudinal speed and acceleration vary. Yet many existing lateral controllers rely on constant-speed or operating-point-based assumptions, which can degrade performance during transient longitudinal maneuvers. Moreover, most methods assume precisely known vehicle parameters, despite real-world parametric uncertainties. To address these limitations, this paper presents a longitudinal-motion-aware robust nonlinear lateral control framework for AVs. It first derives a tracking error model that depends on varying longitudinal speed and acceleration. Using this model, feedback linearization is employed to obtain a linear input-output relation for lateral error tracking while embedding longitudinal motion into the control law. The resulting internal dynamics are then analyzed to ensure overall system stability. To address parameter uncertainty, two robust control designs with distinct implementation trade-offs are proposed: (i) a Lyapunov redesign (LR) approach inspired by sliding mode control, and (ii) an incremental nonlinear dynamic inversion (INDI) method. Both are rigorously analyzed and proven to ensure ultimate boundedness, with key robustness-tuning parameters explicitly identified. Simulations demonstrate enhanced tracking accuracy, consistent performance across varying speeds and accelerations, and robustness to model uncertainties, while also examining the effects of the robustness-related parameters. Real-vehicle tests further confirm real-time implementation and practical path-tracking performance on actual hardware.
Sixu Li, Nitesh Kumar, Reyshwanth Ganeshan +3
Texas A&M University, College Station, TX 77843, USA