L1-Adaptive MPPI Architecture for Robust and Agile Control of Multirotors

Pravitra, Jintasit; Ackerman, Kasey A.; Cao, Chengyu; Hovakimyan, Naira; Theodorou, Evangelos A.

doi:10.48550/arxiv.2004.00152

Cited by 4 publications

(9 citation statements)

References 19 publications

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“…In (Nakka et al, 2020) the learned dynamics is projected into a finite-dimensional space using generalized polynomial chaos, and the trajectory planning problem is written as a convex optimization problem based on the approximated dynamics. Pravitra et al (2020) used model predictive path integral control (MPPI) for motion planning, and used L1 adaptive control for handling the potential mismatch between the nominal and true dynamics. Koller et al (2018) and Wabersich and Zeilinger (2020a,b) propose learning-based model predictive control (MPC) schemes that provide high-probability safety guarantees throughout the learning process using GPs.…”

Section: Related Workmentioning

confidence: 99%

Uncertainty-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric

Sun

Khojasteh

Shekhar

et al. 2021

Preprint

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In this paper, we consider the problem of using a robot to explore an environment with an unknown, state-dependent disturbance function while avoiding some forbidden areas. The goal of the robot is to safely collect observations of the disturbance and construct an accurate estimate of the underlying disturbance function. We use Gaussian Process (GP) to get an estimate of the disturbance from data with a high-confidence bound on the regression error. Furthermore, we use neural Contraction Metrics to derive a tracking controller and the corresponding high-confidence uncertainty tube around the nominal trajectory planned for the robot, based on the estimate of the disturbance. From the robustness of the Contraction Metric, error bound can be pre-computed and used by the motion planner such that the actual trajectory is guaranteed to be safe. As the robot collects more and more observations along its trajectory, the estimate of the disturbance becomes more and more accurate, which in turn improves the performance of the tracking controller and enlarges the free space that the robot can safely explore. We evaluate the proposed method using a carefully designed environment with a ground vehicle. Results show that with the proposed method the robot can thoroughly explore the environment safely and quickly.

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Section: Related Workmentioning

confidence: 99%

Uncertainty-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric

Sun

Khojasteh

Shekhar

et al. 2021

Preprint

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“…Similarly, in [34] a Model Predictive Path Integral (MPPI) controller was coupled with a nonlinear reference model L 1 adaptive controller for agile quadrotor flight, however the authors have not shown feasibility of the proposed method on real hardware. No analsis of the adaptive control signal is provided, however video footage released of the simulation performance of the proposed L 1 -MPPI architecture indicates highly oscillatory control performance in the first-person camera view 1 .…”

Section: Related Workmentioning

confidence: 99%

“…We implement the L 1 adaptive controller using a nonlinear reference model [40], which estimates both matched and unmatched uncertainties using a piecewise constant adaptation law [41,42]. The derivation is similar to [34], however we account for the uncertainties directly at the rotor thrust level. First, define R I B = e B x , e B y , e B z as the rotation matrix from the body frame to the inertial frame.…”

Section: L 1 -Adaptive Augmentationmentioning

confidence: 99%

“…We begin by implementing several state of the art controllers in simulation including a Single Rotor Thrust NMPC (SRT-NMPC), a data-driven NMPC (GP-MPC) from [9], MPPI with Baseline Control from [34], INDI-NMPC from [16], and our proposed L 1 -NMPC with and without an aerodynamic model included in the underlying NMPC. All baseline controllers have an aerodynamic model enabled by default.…”

Section: A Simulationmentioning

confidence: 99%

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Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotors

Hanover,

Foehn,

Sun

et al. 2021

Preprint

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Agile quadrotor flight in challenging environments has the potential to revolutionize shipping, transportation, and search and rescue applications. Nonlinear model predictive control (NMPC) has recently shown promising results for agile quadrotor control, but relies on highly accurate models for maximum performance. Hence, model uncertainties in the form of unmodeled complex aerodynamic effects, varying payloads and parameter mismatch will degrade overall system performance. In this paper, we propose L1-NMPC, a novel hybrid adaptive NMPC to learn model uncertainties online and immediately compensate for them, drastically improving performance over the non-adaptive baseline with minimal computational overhead. Our proposed architecture generalizes to many different environments from which we evaluate wind, unknown payloads, and highly agile flight conditions. The proposed method demonstrates immense flexibility and robustness, with more than 90% tracking error reduction over non-adaptive NMPC under large unknown disturbances and without any gain tuning. In addition, the same controller with identical gains can accurately fly highly agile racing trajectories exhibiting top speeds of 70 km/h, offering tracking performance improvements of around 50% relative to the non-adaptive NMPC baseline. We will release our code fully open-sourced upon acceptance.

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“…To guide the sampling distribution safely in case of external disturbances while keeping the control proactive, a sampling-based Tube-MPC method is augmented in [4] with an iterative linear quadratic Gaussian controller for sampling distribution guidance and disturbance rejection. Model predictive path integral control [6] is integrated with L1 adaptive control in [13] to achieve both fast model predictive trajectory planning and robust trajectory tracking. Besides, the minimum intervention principle has shown an increasing interest in the safe control domain of semi-autonomous vehicles.…”

Section: Introductionmentioning

confidence: 99%

Safe Learning-based Gradient-free Model Predictive Control Based on Cross-entropy Method

Zheng,

Yang,

et al. 2021

Preprint

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In this paper, a safe and learning-based control framework for model predictive control (MPC) is proposed to optimize nonlinear systems with a gradient-free objective function under uncertain environmental disturbances. The control framework integrates a learning-based MPC with an auxiliary controller in a way of minimal intervention. The learning-based MPC augments the prior nominal model with incremental Gaussian Processes to learn the uncertain disturbances. The cross-entropy method (CEM) is utilized as the sampling-based optimizer for the MPC with a gradient-free objective function. A minimal intervention controller is devised with a control Lyapunov function and a control barrier function to guide the sampling process and endow the system with high probabilistic safety. The proposed algorithm shows a safe and adaptive control performance on a simulated quadrotor in the tasks of trajectory tracking and obstacle avoidance under uncertain wind disturbances.

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L1-Adaptive MPPI Architecture for Robust and Agile Control of Multirotors

Cited by 4 publications

References 19 publications

Uncertainty-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric

Uncertainty-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric

Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotors

Safe Learning-based Gradient-free Model Predictive Control Based on Cross-entropy Method

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