Control Design for Nonlinear Flexible Wings of a Robotic Aircraft

He, Wei; Zhang, Shuang

doi:10.1109/tcst.2016.2536708

Cited by 277 publications

(125 citation statements)

References 38 publications

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“…According to the above stability analysis for the suspension cable system of a helicopter, the following conclusions can be drawn. (15) and (16), considering the auxiliary systems (27) and (28) and the control laws (29) and (30)…”

Section: Lemma 6 For the Given Lyapunov Function (34) Its Derivativmentioning

confidence: 99%

“…The parameters are listed for the suspension cable system of a helicopter in Table 1 Under the developed adaptive laws (31), (32), and (33), considering the auxiliary systems (27) and (28), the simulation results are given by Figures 3 to 8 with robust adaptive controllers (29) and (30). Figures 3 to 8 are the responses for the suspension cable system of a helicopter, which is described by Equation 14 under boundary conditions (15) and (16).…”

Section: Numerical Simulationmentioning

confidence: 99%

“…[21][22][23][24][25][26][27] The control problem of a string system modeled by a PDE was considered in the work of Qu,24 in which a robust adaptive boundary controller was designed. A new idea of the sliding mode control design was raised for the boundary control problem of a distributed parameter system governed by a parabolic PDE in the work of Cheng et al 25 In order to restrain the vibration, a boundary controller was proposed by utilizing the Lyapunov direct method for a riser system in the work of He et al 26 In the work of He and Zhang, 27 by applying boundary control schemes, the boundary control problem of flexible wings was studied for a robotic aircraft system, which was represented by a set of distributed parameter systems. In addition, since actuator physical constraints can severely degrade the closed-loop system performance, the control design with actuator constraints presents a tremendous challenge.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust adaptive constrained boundary control for a suspension cable system of a helicopter

Ren

Shi

2017

Adaptive Control & Signal

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SummaryIn this article, the problems of modeling and controlling are investigated for a suspension cable system of a helicopter with input saturation, system parameter uncertainties, and external disturbances by using the boundary control method.In accordance with the Hamilton's principle, the model of the suspension cable system of a helicopter is established by using a set of partial differential and ordinary differential equations. Considering nonsymmetric saturation constraint, the auxiliary systems are designed to handle with the effect of input saturation. Considering Lyapunov's direct method and the designed auxiliary systems, two robust adaptive boundary controllers are provided by the actuators at the helicopter and the box. Under the proposed controllers, the error between the bottom payload and the target location and the vibration range are uniformly ultimately bounded. Moreover, they will converge to a small neighborhood of zero by selecting the suitable parameters. Meanwhile, to guarantee the validity of the proposed adaptive boundary control laws, some sufficient conditions are raised. Simulation results are provided to verify that the effectiveness of the designed controllers in this paper.

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Section: Lemma 6 For the Given Lyapunov Function (34) Its Derivativmentioning

confidence: 99%

Section: Numerical Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust adaptive constrained boundary control for a suspension cable system of a helicopter

Ren

Shi

2017

Adaptive Control & Signal

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“…In this regard, it is necessary to develop alternative sensing technology. It is noted that a lot of research works are based on the imitation of biological systems, e.g., inspired by bats and birds, a robotic aircraft with flexible wings was modeled and controlled in [5]. Inspired by tactile sensing of humans, we aim to develop an approach of haptic identification for robots.…”

Section: Introductionmentioning

confidence: 99%

Haptic Identification by ELM-Controlled Uncertain Manipulator

Yang

Huang

Cheng

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

146

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Abstract-This paper presents an extreme learning machine (ELM) based control scheme for uncertain robot manipulators to perform haptic identification. ELM is used to compensate for the unknown nonlinearity in the manipulator dynamics. The ELM enhanced controller ensures that the closed-loop controlled manipulator follows a specified reference model, in which the reference point as well as the feedforward force is adjusted after each trial for haptic identification of geometry and stiffness of an unknown object. A neural learning law is designed to ensure finite-time convergence of the neural weight learning, such that exact matching with the reference model can be achieved after the initial iteration. The usefulness of the proposed method is tested and demonstrated by extensive simulation studies.

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“…Aerial Vehicles (AVs) have been used more and more widely in surveillance, search and rescue mission, aerial mapping, cartography, border patrol, inspection, agricultural imaging, and other related areas [1][2][3]. During these flight missions, the AVs usually fly in low altitudes, which makes them be subject to collision with static obstacles (such as buildings, trees, mountains, etc.)…”

Section: Introductionmentioning

confidence: 99%

Spatial Trajectory Tracking Control of a Fully Actuated Helicopter in Known Static Environment

Zhao

et al. 2016

J Intell Robot Syst

Self Cite

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In this paper, we consider the control problem of tracking a 3D spatial trajectory for a fully actuated helicopter in static known environment, which is predefined to avoid obstacles and collisions considering the distance, fuel consumption and other related constraints. For this purpose, a nonlinear controller using the radial basis function neural network (RBFNN) is designed. Based on Lyapunov analysis, the proposed adaptive neural network control succeeds in tracking the desired trajectory robustly to a small neighborhood of zero, and guarantees the boundedness of all the closed-loop signals at the same time.Extensive numerical results are given to illustrate the effectiveness of the designed controller.

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Control Design for Nonlinear Flexible Wings of a Robotic Aircraft

Cited by 277 publications

References 38 publications

Robust adaptive constrained boundary control for a suspension cable system of a helicopter

Robust adaptive constrained boundary control for a suspension cable system of a helicopter

Haptic Identification by ELM-Controlled Uncertain Manipulator

Spatial Trajectory Tracking Control of a Fully Actuated Helicopter in Known Static Environment

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