Feedback linearization and linear observer for a quadrotor unmanned aerial vehicle

Mokhtari, A.; M'Sirdi, Kouider; Meghriche, K.; Belaïdi, A.

doi:10.1163/156855306775275495

Cited by 78 publications

(39 citation statements)

References 5 publications

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“…In [13,25], this objective desires R < 0 or V Π < 0 in the guidance. Substituting (1c) into (1b) yields 2 [23], a polynomial controller was proposed for a quadrotor unmanned aerial vehicle (UAV) combining with a linear observer to deal with bounded uncertainties and disturbances. Therefore, a linear observer is still a useful tool in coping with nonlinear control problems.…”

Section: 2mentioning

confidence: 99%

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Comparative investigations of nonlinear and linear observers for a highly manoeuvrable target in sliding mode guidance

Wang¹,

Sun²,

Du³

et al. 2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. Target manoeuvre is one of the key factors affecting guidance accuracy. To intercept highly maneuverable targets, a second-order sliding-mode guidance law, which is based on the super-twisting algorithm, is designed without depending on any information about the target motion. In the designed guidance system, the target estimator plays an essential role. Besides the existing higher-order sliding-mode observer (HOSMO), a first-order linear observer (FOLO) is also proposed to estimate the target manoeuvre, and this is the major contribution of this paper. The closed-loop guidance system can be guaranteed to be uniformly ultimately bounded (UUB) in the presence of the FOLO. The comparative simulations are carried out to investigate the overall performance resulting from these two categories of observers. The results show that the guidance law with the proposed linear observer can achieve better comprehensive criteria for the amplitude of normalised acceleration and elevator deflection requirements. The reasons for the different levels of performance of these two observer-based methods are thoroughly investigated.Key words: guidance law, second-order sliding mode, super-twisting algorithm, linear observer, target manoeuvre. matched disturbances. However, the chattering phenomenon is the major obstacle for the implementation of SMC in practice, and a number of methods have been proposed to reduce the chattering. Levant first proposed the higher-order sliding mode (HOSM) control method to attenuate the chattering [9][10][11][12]. The second-order sliding mode control is the most widely used HOSM method in which the super-twisting algorithm is utilised to attenuate the differentiable disturbance and ensure the finite-time stability [13,14]. To intercept highly manoeuvrable targets, the second-order sliding mode methods were utilised in [13] and [14], based on the nonlinear observers, which are used to check the uncertainty caused by target manoeuvres. However, in many simulations, it is readily observed that the guidance commands (acceleration) generated by the proposed guidance laws of [13] and [14] exhibit the properties of oscillation with a large amplitude. Comparing the fundamental guidance component with the observer component, the latter contributes to this phenomenon much more. Hence, an effective estimation approach is urgently needed to deal with highly manoeuvrable targets.Han proposed a novel philosophy, namely the active disturbance rejection control (ADRC) algorithm [15,16], which has attracted wide attention in the past decade. The core of ADRC is an extended state observer (ESO) that can accurately observe the total disturbance. Han originally presented the ESO in a nonlinear form [15], which is too complicated to tune, and is difficult to be implemented. In [16], a linear ESO (LESO) was proposed in terms of frequency bandwidth, which provides a useful design approach in practical applications. Although this

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Section: 2mentioning

confidence: 99%

“…Since 1 x is available, a reduced-order observer can be employed to , (23) which can be described in a state-space form as …”

Section: Target Motion Estimation By Using Folomentioning

confidence: 99%

Comparative investigations of nonlinear and linear observers for a highly manoeuvrable target in sliding mode guidance

Wang¹,

Sun²,

Du³

et al. 2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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“…and considering the dynamic equations (5)(6)(7)(8), the state space model of the quadrotor will be a multi-variable system composed of six 2 nd order nonlinear subsystems and it takes this form:…”

Section: B Quadrotor State Space Modelmentioning

confidence: 99%

“…The work reported in the literature is by now quite vast and addresses different approaches for miniature rotor craft stabilization including linear control [5], robust control [8], nonlinear control [3], [7] among others. Many researchers have proposed different control algorithms based on nonlinear model of the quadrotor but these algorithms work based on simplifying the nonlinear model and supposing some decoupling conditions [4].…”

Section: Introductionmentioning

confidence: 99%

Different linearization control techniques for a quadrotor system

Belkheiri¹,

Rabhi²,

Pégard³

2012

Ccca12

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In this paper, we propose different linearization control algorithms to solve the stabilization problem of the quadrotor. First we introduce the nonlinear model of the quadrotor. Then using tangent linearization method, a linear model is generated of the system where decentralized and centralized LQR control methods are applied. The second strategy is based on exact feedback linearization of the nonlinear model of the quadrotor. The comparison between these methods is highlighted by simulations to show effectiveness of the proposed methods.

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“…Some researchers, presented attitude estimation algorithms based on Kalman filter such as SAKF [3] and RAKF [4], for UAV problems when noise statistical characteristics are unknown, and time-varying vibrations are the main disturbance source, also for problems against sensor/actuator fault of the system. Others papers presented the sliding mode and high-order sliding mode respectively like an observer [5,6] in order to estimate the unmeasured states and the effects of the external disturbances such as wind and noise. In [7], the unknown parameter of the quadrotor are identified using state estimation method with the implementation of Unscented Kalman Filter (UKF).…”

mentioning

confidence: 99%

Modeling and Predictive Control of Nonlinear Coupled and Underactuated Dynamics of a Hexacopter

Ibrahim¹

2017

Vestn. IžGTU im. M.T. Kalašnikova

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Рассматривается применение фильтра Калмана (KF) для идентификации беспилотного летательного аппарата (БЛА). В целях разработки математической модели летательного аппарата для числовой симуляции рассматриваются его динамические характеристики, учитывающие 3 степени свободы (DOF), нелинейность, сопряженность и взаимозависимость параметров. Представлен анализ симуляции, позволяющий оценить неизвестные параметры динамической модели гексакоптера при наличии шумов в сигналах обратной связи. Метод оценки пространства состояний посредством анализа главных компонент был использован для идентификации точной нелинейной модели на основе фильтра Калмана для оценки и прогнозирования высоты гексакоптера. Результаты сравниваются с результатами, полученными без использования метода прогнозирования.

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Feedback linearization and linear observer for a quadrotor unmanned aerial vehicle

Cited by 78 publications

References 5 publications

Comparative investigations of nonlinear and linear observers for a highly manoeuvrable target in sliding mode guidance

Comparative investigations of nonlinear and linear observers for a highly manoeuvrable target in sliding mode guidance

Different linearization control techniques for a quadrotor system

Modeling and Predictive Control of Nonlinear Coupled and Underactuated Dynamics of a Hexacopter

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