# Adaptive Model Predictive Control for High-Accuracy Trajectory Tracking in Changing Conditions

arXiv: https://arxiv.org/abs/1807.05290 · HTML: https://mlsanity.com/paper/1807.05290/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2018.

## Built on by (most cited first, 7 of 7)

- [Learning a Single Near-hover Position Controller for Vastly Different Quadcopters](https://mlsanity.com/paper/2209.09232/) (2022)
- [Learning Based NMPC Adaptation for Autonomous Driving using Parallelized Digital Twin](https://mlsanity.com/paper/2402.16645/) (2024)
- [Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation](https://mlsanity.com/paper/2111.06742/) (2021)
- [Adaptive Model Predictive Control of Wheeled Mobile Robots](https://mlsanity.com/paper/2201.00863/) (2022)
- [A Reinforcement Learning Approach for Robust Supervisory Control of UAVs Under Disturbances](https://mlsanity.com/paper/2305.12543/) (2023)
- [Dynamics-Invariant Quadrotor Control using Scale-Aware Deep Reinforcement Learning](https://mlsanity.com/paper/2503.09622/) (2025)
- [Same World, Different Knowledge: When Isolated Audits Misjudge World-Model Repairs](https://mlsanity.com/paper/2609.21155/) (2026)

## Abstract (arXiv)

Robots and automated systems are increasingly being introduced to unknown and dynamic environments where they are required to handle disturbances, unmodeled dynamics, and parametric uncertainties. Robust and adaptive control strategies are required to achieve high performance in these dynamic environments. In this paper, we propose a novel adaptive model predictive controller that combines model predictive control (MPC) with an underlying L1 adaptive controller to improve trajectory tracking of a system subject to unknown and changing disturbances. The L1 adaptive controller forces the system to behave in a predefined way, as specified by a reference model. A higher-level model predictive controller then uses this reference model to calculate the optimal reference input based on a cost function, while taking into account input and state constraints. We focus on the experimental validation of the proposed approach and demonstrate its effectiveness in experiments on a quadrotor. We show that the proposed approach has a lower trajectory tracking error compared to non-predictive, adaptive approaches and a predictive, non-adaptive approach, even when external wind disturbances are applied.