Experimental Results on Adaptive Output Feedback Control Using a Laboratory Model Helicopter

Kutay, Ali Türker; Calise, Anthony J.; Idan, Moshe; Hovakimyan, Naira

doi:10.2514/6.2002-4921

Cited by 25 publications

(40 citation statements)

References 2 publications

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“…Dynamical sliding-mode control approach has been often adopted for plant subjects to uncertainties [8,9]. Adaptive theory is also applied to overcome an uncertainty problem [10][11][12]. In [9][10][11][12], experimental results of laboratory helicopters have been presented.…”

Section: Introductionmentioning

confidence: 99%

Robust backstepping decentralized tracking control for a 3-DOF helicopter

Yao

Sun

et al. 2015

Nonlinear Dyn

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A three-degree-of-freedom helicopter attitude tracking control system can be described as a multi-input multi-output strict-feedback form system with unknown parameters, bounded disturbances, and nonlinear uncertain cross-couplings. Both normbounded and nonlinear uncertainties are discussed. The whole nonlinear system is divided into a nominal disturbance-free system and an equivalent disturbance part. A robust control method based on signal compensation technique and backstepping decentralized control is proposed. Robust practical tracking property of closed-loop system is proven, and the tracking error can be made as small as desired with expected convergence rate. Experimental results demonstrate the improved tracking performance of the attitude tracking control system with different types of reference signals, such as step signals, ramp signals, and nonstationary sinusoidal signals.

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Section: Introductionmentioning

confidence: 99%

Robust backstepping decentralized tracking control for a 3-DOF helicopter

Yao

Sun

et al. 2015

Nonlinear Dyn

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“…Nevertheless, we cannot neglect the fact that fuzzy controllers depend heavily on expert experience. A neural network-based adaptive output feedback control method is proposed since a neural network algorithm is capable of selfadaption, online learning and fault tolerance [7]. However, a neural network controller needs considerable training time and can easily fall into local minimum.…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Integral Backstepping Control of a 3-DOF Helicopter

Fang

Gao

Zhang

2012

International Journal of Advanced Robotic Systems

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Unmanned aerial vehicles have enormous potential applications in military and civil fields. A Quanser's 3-DOF helicopter is a simplified and benchmark experimental model for validating the effectiveness of various flight control algorithms. The attitude control of the 3-DOF helicopter is a challenging task since the helicopter is an under-actuated system with strong coupling and model uncertainty characteristics. In this paper, an adaptive integral backstepping algorithm is proposed to realize robust control of the 3-DOF helicopter. The proposed control algorithm can estimate model uncertainties online and improve the robustness of the control system. Simulation and experiment results demonstrate that the proposed algorithm performs well in tracking and under model uncertainties.

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“…Because this helicopter has nonlinear and unstable dynamics as well as significant cross-coupling between its control channels, the control of this MIMO system is a challenging task. Many researchers have investigated the control of 3-DOF helicopters [15][16][17][18][19][20][21][22][23][24][25][26][27]. In [15], Fradkov et al presented a PID control law for a 3-DOF helicopter with a scheme for state estimation.…”

Section: Introductionmentioning

confidence: 99%

“…It consisted of an inversion-based feedforward controller for trajectory tracking and a feedback controller for the trajectory error dynamics. In [21], Kutay et al introduced an adaptive output feedback control method based on model inversion with feedback linearization and linearly parameterized neural networks to cancel modeling errors. Other control approaches can be found in the literature, such as fuzzy logic control [22], fuzzy-sliding mode control [23], predictive control [24], H ∞ control [25], neural networks control [26], and adaptive control [27].…”

Section: Introductionmentioning

confidence: 99%

Model-free controller with an observer applied in real-time to a 3-DOF helicopter

Boubakir¹,

Labiod²,

Boudjema³

et al. 2014

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, a model-free controller with an observer is presented for a class of uncertain continuous-time multiinput-multioutput nonlinear dynamic systems. The proposed model-free control law consists of 2 parts: the first part is a linear control term used to specify the dynamics of the closed loop system, and the second part is a compensator of the effects of uncertainties and external disturbances. The compensator is synthesized from an estimator of the effects of uncertainties and disturbances based on the Lyapunov approach. In order to estimate the unavailable states of the controlled system, a linear state observer is designed. All of the signals in the closed-loop system are proved to be uniformly ultimately bounded using the Lyapunov stability theory. The effectiveness and feasibility of the proposed control strategy are examined in a real-time application for a helicopter with 3 degrees of freedom.

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Experimental Results on Adaptive Output Feedback Control Using a Laboratory Model Helicopter

Cited by 25 publications

References 2 publications

Robust backstepping decentralized tracking control for a 3-DOF helicopter

Robust backstepping decentralized tracking control for a 3-DOF helicopter

Robust Adaptive Integral Backstepping Control of a 3-DOF Helicopter

Model-free controller with an observer applied in real-time to a 3-DOF helicopter

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