L1-Adaptive MPPI Architecture for Robust and Agile Control of Multirotors
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Abstract (arXiv)
This paper presents a multirotor control architecture, where Model Predictive Path Integral Control (MPPI) and L1 adaptive control are combined to achieve both fast model predictive trajectory planning and robust trajectory tracking. MPPI provides a framework to solve nonlinear MPC with complex cost functions in real-time. However, it often lacks robustness, especially when the simulated dynamics are different from the true dynamics. We show that the L1 adaptive controller robustifies the architecture, allowing the overall system to behave similar to the nominal system simulated with MPPI. The architecture is validated in a simulated multirotor racing environment.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Mar 2020.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 3; 2025: 3.
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- 2022Neural-Fly Enables Rapid Learning for Agile Flight in Strong Windscited by 80
- 2022Learning a Single Near-hover Position Controller for Vastly Different Quadcopterscited by 16
- 2022Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Controlcited by 13
- 2023DATT: Deep Adaptive Trajectory Tracking for Quadrotor Controlcited by 11
- 2023Deep Model Predictive Optimizationcited by 7
- 2022Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertaintycited by 3
- 2024CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Designcited by 3
- 2024Neural Internal Model Control: Learning a Robust Control Policy via Predictive Error Feedbackcited by 2
- 2024Robust Model Based Reinforcement Learning Using L1 Adaptive Controlcited by 1
- 2021Robustifying Reinforcement Learning Policies with L1 Adaptive Controlcited by 0
- 2023Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentationcited by 0
- 2025Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functionscited by 0
- 2025A Robust Task-Level Control Architecture for Learned Dynamical Systems12 Nov 2025
- 2025Safety-Aware Imitation Learning via MPC-Guided Disturbance Injection5 Aug 2025
- 2025Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functions20 Feb 2025
- 2024Neural Internal Model Control: Learning a Robust Control Policy via Predictive Error Feedback20 Nov 2024
- 2024Robust Model Based Reinforcement Learning Using L1 Adaptive Control21 Mar 2024
- 2024CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design14 Jan 2024
- 2023DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control13 Oct 2023
- 2023Deep Model Predictive Optimization6 Oct 2023
- 2023Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation28 Mar 2023
- 2022Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control23 Oct 2022
- 2022Learning a Single Near-hover Position Controller for Vastly Different Quadcopters19 Sep 2022
- 2022Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds13 May 2022
- 2022Neural-Fly Enables Rapid Learning for Agile Flight in Strong Windscited by 80
- 2022Learning a Single Near-hover Position Controller for Vastly Different Quadcopterscited by 16
- 2022Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Controlcited by 13
- 2023DATT: Deep Adaptive Trajectory Tracking for Quadrotor Controlcited by 11
- 2023Deep Model Predictive Optimizationcited by 7
- 2022Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertaintycited by 3
- 2024CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Designcited by 3
- 2024Neural Internal Model Control: Learning a Robust Control Policy via Predictive Error Feedbackcited by 2
- 2024Robust Model Based Reinforcement Learning Using L1 Adaptive Controlcited by 1
- 2021Robustifying Reinforcement Learning Policies with L1 Adaptive Controlcited by 0
- 2023Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentationcited by 0
- 2025Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functionscited by 0
- 2025Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functions20 Feb 2025
- 2024Neural Internal Model Control: Learning a Robust Control Policy via Predictive Error Feedback20 Nov 2024
- 2024Robust Model Based Reinforcement Learning Using L1 Adaptive Control21 Mar 2024
- 2024CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design14 Jan 2024
- 2023DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control13 Oct 2023
- 2023Deep Model Predictive Optimization6 Oct 2023
- 2023Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation28 Mar 2023
- 2022Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control23 Oct 2022
- 2022Learning a Single Near-hover Position Controller for Vastly Different Quadcopters19 Sep 2022
- 2022Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds13 May 2022
- 2022Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty1 Mar 2022
- 2021Robustifying Reinforcement Learning Policies with L1 Adaptive Control4 Jun 2021