Direct NMPC for Post-Stall Motion Planning with Fixed-Wing UAVs

Basescu, Max; Moore, J. Russell

doi:10.1109/icra40945.2020.9196724

Cited by 24 publications

(9 citation statements)

References 22 publications

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“…This phenomenon will hinder the completion of the concatenate loop. Therefore, we considered limiting the speed to [20,0,0] of the drone at the end of the first rolling primitive, although this will increase the entire maneuver time to some extent.…”

Section: Half Cuban Eight and Cuban Eight Maneuvermentioning

confidence: 99%

“…However, for fixed-wing UAV flight maneuvers, it has to be stressed that those approaches did not take the complicated physical models with complex aerodynamic features into consideration. Therefore, the non-differential flatness property of fixed-wing UAV [20] hinders the application of the aforementioned methods. Solving the optimal control problems is also an effective maneuver planning method and has attracted growing attention.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid-Driven Optimization Framework for Fixed-Wing UAV Maneuvering Flight Planning

et al. 2021

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Performing autonomous maneuvering flight planning and optimization remains a challenge for unmanned aerial vehicles (UAVs), especially for fixed-wing UAVs due to its high maneuverability and model complexity. A novel hybrid-driven fixed-wing UAV maneuver optimization framework, inspired by apprenticeship learning and nonlinear programing approaches, is proposed in this paper. The work consists of two main aspects: (1) Identifying the model parameters for a certain fixed-wing UAV based on the demonstrated flight data performed by human pilot. Then, the features of the maneuvers can be described by the positional/attitude/compound key-frames. Eventually, each of the maneuvers can be decomposed into several motion primitives. (2) Formulating the maneuver planning issue into a minimum-time optimization problem, a novel nonlinear programming algorithm was developed, which was unnecessary to determine the exact time for the UAV to pass by the key-frames. The simulation results illustrate the effectiveness of the proposed framework in several scenarios, as both the preservation of geometric features and the minimization of maneuver times were ensured.

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Section: Half Cuban Eight and Cuban Eight Maneuvermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid-Driven Optimization Framework for Fixed-Wing UAV Maneuvering Flight Planning

et al. 2021

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show abstract

“…Nonlinear model predictive control (NMPC) has proved to be a powerful approach for controlling high-dimensional, complex robotic systems (e.g., [1], [2], [3], [4]). Nevertheless, although these methods can handle large state spaces, nonlinear dynamics, and system constraints, their performance can be adversely affected by the presence of uncertainty even in the context of real-time replanning [5].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, although these methods can handle large state spaces, nonlinear dynamics, and system constraints, their performance can be adversely affected by the presence of uncertainty even in the context of real-time replanning [5]. A number of approaches have been proposed to compensate for this marginal robustness, the simplest of which is to generate a feedback policy to track the current recedinghorizon plan (e.g., [2]). These approaches, however, do not account for uncertainty or closed-loop performance during the planning process.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, our approach leverages recently derived sample complexity bounds [10] to provide a probabilistic guarantee on system performance. More specifically, our algorithm 1 Johns Hopkins University Applied Physics Lab {Adam.Polevoy,Joseph.Moore}@jhuapl.edu 2 Johns Hopkins University mkobila1@jhu.edu builds on Probably Approximately Correct Robust Policy Search (PROPS) [11] to directly optimize an upper confidence bound on the expected cost and the probability of constraint violation for a receding-horizon feedback policy. Not only does this approach encourage robust policy generation, but the minimized bounds themselves provide a statistical guarantee for each planning interval.…”

Section: Introductionmentioning

confidence: 99%

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Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC)

Polevoy¹,

Kobilarov²,

Moore³

2022

Preprint

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Approaches for stochastic nonlinear model predictive control (SNMPC) typically make restrictive assumptions about the system dynamics and rely on approximations to characterize the evolution of the underlying uncertainty distributions. For this reason, they are often unable to capture more complex distributions (e.g., non-Gaussian or multi-modal) and cannot provide accurate guarantees of performance. In this paper, we present a sampling-based SNMPC approach that leverages recently derived sample complexity bounds to certify the performance of a feedback policy without making assumptions about the system dynamics or underlying uncertainty distributions. By parallelizing our approach, we are able to demonstrate real-time receding-horizon SNMPC with statistical safety guarantees in simulation on a 24-inch wingspan fixedwing UAV and on hardware using a 1/10 th scale rally car.

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Incremental adaptive fixed‐wing unmanned aerial vehicle maneuver control with nonlinear disturbance observer: A finite‐time approach

Cao

Wang

2023

Asian Journal of Control

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This article proposes a versatile controller based on the incremental strategy to explore a finite‐time (FT) solution of fixed‐wing unmanned aerial vehicle (UAV) maneuver control considering the lumped disturbances such as unmodeled dynamics or exogenous perturbations. First, a finite‐time nonlinear disturbance observer augmented with a neural network item is developed to attenuate the adverse effect of the disturbances mentioned above. Then considering the estimated lumped disturbance, a finite‐time incremental controller in the quaternion form is proposed. Rigorous proof shows that the nonlinear disturbance observer and controller achieve finite‐time convergence. In the numerical simulation, the superiority of the proposed method compared to several advanced nonlinear control approaches is illustrated, despite the mismatches in aerodynamic coefficients, wind gusts, and actuator failures. Furthermore, based on a novel model identification approach, a hardware‐in‐the‐loop (HITL) experiment is performed in a high‐fidelity environment, wherein our method's feasibility and practicability are verified.

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Direct NMPC for Post-Stall Motion Planning with Fixed-Wing UAVs

Cited by 24 publications

References 22 publications

A Hybrid-Driven Optimization Framework for Fixed-Wing UAV Maneuvering Flight Planning

A Hybrid-Driven Optimization Framework for Fixed-Wing UAV Maneuvering Flight Planning

Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC)

Incremental adaptive fixed‐wing unmanned aerial vehicle maneuver control with nonlinear disturbance observer: A finite‐time approach

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