Flight control system design via lmi-approach

Komnaska, Marta M.

doi:10.18372/1990-5548.37.6350

Cited by 2 publications

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“…The flight control system explored in this paper consists of a Successive Loop Closure (SLC) baseline controller [2] which can be obtained conveniently by solving linear matrix inequalities (LMI) [3] - [7], [10] with reduced computational complexity. In order to obtain numerical results we consider lateral motion control including roll angle stabilization as the inner loop, heading angle stabilization as the intermediate loop, and the reference track stabilization in the horizontal plane as the outer loop.…”

Section: Introductionmentioning

confidence: 99%

Robust gain-scheduled control for unmanned aerial vehicles

Nadsadna¹

2016

ECS

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The flight dynamics of a modern aircraft are highly nonlinear and vary over the wide flight envelope of the aircraft. This fact makes it difficult for a single controller to achieve the desired closedloop specifications. This paper considers gain-scheduling successive loop control design for linear parameter-varying system in terms of linear matrix inequalities. The obtained controller guarantees an efficient unmanned aerial vehicle's control under external disturbances within the flight envelope. The design procedure is illustrated by a case study of unmanned aerial vehicle lateral channel control.Index terms-Gain scheduling; linear matrix inequalities; robustness; unmanned aerial vehicle.I.

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Section: Introductionmentioning

confidence: 99%

Robust gain-scheduled control for unmanned aerial vehicles

Nadsadna¹

2016

ECS

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“…Therefore, the problem of flight control system (FCS) design for small UAVs that possesses with desired level of performance and remains insensible to various perturbations remains a challenge for an engineer. A great number of works related to FCS design can be found in literature; some of them propose to use mixture of observer and linear quadratic regulator (LQR), where the desired level of performance and robustness of the developed FSC is achieved through the  H H 2 -optimization technique [4][5][6]; Linear Matrix Inequalities approach also proposes alternative method of FCS design based on Lyapunov theory [7][8][9][10][11][12][13][14][15].…”

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confidence: 99%

Intelligent Flight Control System Design for the Small UAV Based on the Adaptive Neuro-Fuzzy Inference System

Komnatska¹,

Bondarenko²

2017

Upr. sist. maš.

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Intelligent Flight Control System Design for the Small UAV Based on the Adaptive Neuro-Fuzzy Inference System Рассмотрена задача синтеза системы управления полетом малого беспилотного летательного аппарата с элементами искусственного интеллекта. Процедура синтеза показана на примере управления продольным движением беспилотного летательного аппарата с использованием адаптивной нейро-нечеткой модели. Ключевые слова: система управления полетом, нечеткое управление, адаптивная нейро-нечеткая система логического вывода, метод градиентного спуска, процедура обратного распространения ошибки. Розглянуто задачу синтезу системи керування польотом малого безпілотного літального апарату з елементами штучного інтелекту. Процедуру синтезу показано на прикладі керування повздовжнім рухом безпілотного літального апарату з використанням адаптивної нейро-нечіткої моделі. Ключові слова: система управління польотом; нечітке управління; адаптивна нейро-нечітка система логічного виводу; метод градієнтного спуску; процедура зворотного розповсюдження помилки. The paper considers a problem of flight control system design for small unmanned aerial vehicle with elements of intelligent control. The design procedure is illustrated by an example of unmanned aerial vehicle longitudinal control with application of adaptive neurofuzzy inference model.

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Flight control system design via lmi-approach

Cited by 2 publications

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Robust gain-scheduled control for unmanned aerial vehicles

Robust gain-scheduled control for unmanned aerial vehicles

Intelligent Flight Control System Design for the Small UAV Based on the Adaptive Neuro-Fuzzy Inference System

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