cs.ROOct 2, 2026

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

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Abstract

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 HH -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 HH -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 HH -step prediction error for run with LLA-MPC .

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