Dubins path‐based dynamic soaring trajectory planning and tracking control in a gradient wind field

Gao, Chen; Liu, Hugh H. T.

doi:10.1002/oca.2248

Cited by 18 publications

(15 citation statements)

References 24 publications

(44 reference statements)

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“…So, as the optimization criterion, it is proposed to use the minimum energy consumption [15], operation time [16], finding the shortest path in a graph [17] or technical and economic indicator [18].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of the method of quasi-optimal robust control for periodic operational processes

Lutsenko

Fomovskaya

Koval

et al. 2017

EEJET

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Пошук супремуму оптимальної траєк-торії управління практично недосяжний в реальних умовах. Це пов'язано з великою кількістю ступенів свободи керованої сис-теми, коливанням попиту, вхідних і вихід-них цін, істотною нелінійністю внутріш-ньосистемних процесів. Виходом в такій ситуації може бути розробка робастних методів квазіоптимального управління. Пропонований підхід заснований на встанов-ленні взаємозв'язку управління перетворю-вальної системи з поточним рівнем виробни-чих запасів Ключові слова: оптимізація пов'язаних систем, квазіоптимальний управління, ро-бастна оптимізація, квазіоптимальна трає-кторія управління Поиск супремума оптимальной траекто-рии управления практически недостижим в реальных условиях. Это связано с большим количеством степеней свободы управляе-мой системы, колебанием спроса, входных и выходных цен, существенной нелинейно-стью внутрисистемных процессов. Выходом в такой ситуации может быть разработ-ка робастных методов квазиоптимального управления. Предлагаемый подход основан на установлении взаимосвязи управления преобразовательной системы с текущим уровнем производственных запасов Ключевые слова: оптимизация связан-ных систем, квазиоптимальное управление, робастная оптимизация, квазиоптимальная траектория управления UDC 007.5

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of the method of quasi-optimal robust control for periodic operational processes

Lutsenko

Fomovskaya

Koval

et al. 2017

EEJET

View full text Add to dashboard Cite

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“…There is a maximum rate of the mechanical energy acceleration in the spindle. In addition, Equation (38) can be rewritten as There is a maximum rate of the mechanical energy acceleration in the spindle. In addition, Equation (38) can be rewritten as…”

Section: Energy Perspectivementioning

confidence: 99%

“…This involved the division of the Rayleigh cycle into four sections, the characteristics of which were determined by simple equations. Gao and Liu [38] also developed a dynamic soaring plan in which the Dubins path was used to improve the computational efficiency. They showed that dynamic soaring could be achieved using modern technology.…”

Section: Introductionmentioning

confidence: 99%

Albatross-Like Utilization of Wind Gradient for Unpowered Flight of Fixed-Wing Aircraft

2017

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Abstract:The endurance of an aircraft can be considerably extended by its exploitation of the hidden energy of a wind gradient, as an albatross does. The process is referred to as dynamic soaring and there are two methods for its implementation, namely, sustainable climbing and the Rayleigh cycle. In this study, the criterion for sustainable climbing was determined, and a bio-inspired method for implementing the Rayleigh cycle in a shear wind was developed. The determined sustainable climbing criterion promises to facilitate the development of an unpowered aircraft and the choice of a more appropriate soaring environment, as was demonstrated in this study. The criterion consists of three factors, namely, the environment, aerodynamics, and wing loading. We develop an intuitive explanation of the Raleigh cycle and analyze the energy mechanics of utilizing a wind gradient in unpowered flight. The energy harvest boundary and extreme power point were determined and used to design a simple bio-inspired guidance strategy for implementing the Rayleigh cycle. The proposed strategy, which involves the tuning of a single parameter, can be easily implemented in real-time applications. In the results and discussions, the effects of each factor on climbing performance are examined and the sensitivity of the aircraft factor is discussed using five examples. Experimental MATLAB simulations of the proposed strategy and the comparison of the results with those of Gauss Pseudospectral Optimization Software confirm the feasibility of the proposed strategy.

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“…The extensive computational resources required to perform the large number of calculations have primarily seen the use of trajectory optimization for exploring the theoretical feasibility of soaring maneuvers [5,6]. Even so, through continued research efforts, real-time optimization techniques have now largely bypassed the problem of timely trajectory generation [7][8][9]. As for the issue of aircraft control, a significant body of research has explored the popular approach of explicitly tracking a calculated trajectory [10,11] in a category of methods known as tracking control.…”

Section: Introductionmentioning

confidence: 99%

Neuroevolutionary Control for Autonomous Soaring

Kim

Perez

2021

Aerospace

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The energy efficiency and flight endurance of small unmanned aerial vehicles (SUAVs) can be improved through the implementation of autonomous soaring strategies. Biologically inspired flight techniques such as dynamic and thermal soaring offer significant energy savings through the exploitation of naturally occurring wind phenomena for thrustless flight. Recent interest in the application of artificial intelligence algorithms for autonomous soaring has been motivated by the pursuit of instilling generalized behavior in control systems, centered around the use of neural networks. However, the topology of such networks is usually predetermined, restricting the search space of potential solutions, while often resulting in complex neural networks that can pose implementation challenges for the limited hardware onboard small-scale autonomous vehicles. In exploring a novel method of generating neurocontrollers, this paper presents a neural network-based soaring strategy to extend flight times and advance the potential operational capability of SUAVs. In this study, the Neuroevolution of Augmenting Topologies (NEAT) algorithm is used to train efficient and effective neurocontrollers that can control a simulated aircraft along sustained dynamic and thermal soaring trajectories. The proposed approach evolves interpretable neural networks in a way that preserves simplicity while maximizing performance without requiring extensive training datasets. As a result, the combined trajectory planning and aircraft control strategy is suitable for real-time implementation on SUAV platforms.

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Dubins path‐based dynamic soaring trajectory planning and tracking control in a gradient wind field

Cited by 18 publications

References 24 publications

Development of the method of quasi-optimal robust control for periodic operational processes

Development of the method of quasi-optimal robust control for periodic operational processes

Albatross-Like Utilization of Wind Gradient for Unpowered Flight of Fixed-Wing Aircraft

Neuroevolutionary Control for Autonomous Soaring

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