LLA-MPC on Embedded Hardware: Rapid Adaptive Control with Thousands of Parallel Models
Authors: Henry Z. Liao, Maitham F. AL-Sunni, John M. Dolan
Organizations: Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA · Department of Electrical & Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
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.
Figures & tables
Fig. 1: Nominal model tracking shown to the left. LLA-MPC shown on the right.
Fig. 2: Model parameter estimation, longitudinal velocity, and H -step prediction error for run with LLA-MPC .
Fig. 3: Nominal model tracking shown to the left. LLA-MPC shown on the right.
Fig. 4: Model parameter estimation, longitudinal velocity, and H -step prediction error for run with LLA-MPC .
Fig. 5: Nominal model tracking shown on the top two images. LLA-MPC shown on the bottom two.
Fig. 6: Model parameter estimation, longitudinal velocity, and H -step prediction error for run with LLA-MPC .
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
Hassan Jardali, Ihab S. Mohamed, Durgakant Pushp +1
Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408 USA
Real-time whole-body controllers for legged robots typically plan through a fixed nominal model and degrade when the deployed dynamics change. Adaptive methods typically require a model structure that contact dynamics do not provide, or they need offline training for each anticipated condition. We present Look-back and Look-ahead Adaptive Model Predictive Path Integral control (LLA-MPPI). The method converts whole-body adaptation into selection over a bank of GPU-batched contact simulators with different physical or structural parameters. Windowed prediction errors select the simulator that best explains recent motion. A whole-body MPPI planner optimizes controls through the selected model. The framework requires no offline training, and its selected hypotheses are physically interpretable. Across four simulated tasks, it achieves 97.5% success while the strongest baseline reaches 74% and an oracle with the true model reaches 98.5%. Hardware validation on a Unitree Go2 shows the robot walking under a payload added mid-run, walking after one leg is disabled, and pushing a box to its goal while increasing its mass on the fly. Code, videos, and project details are available at: https://lla-control.github.io
Sebin Jung, Maitham F. AL-Sunni, Juan Alvarez-Padilla +3
Carnegie Mellon University, Pittsburgh, PA, USA · Massachusetts Institute of Technology, Cambridge, MA, USA
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.