A reduced-order-model-based multiple-in multiple-out gust alleviation control law design method in transonic flow

Chen, Gang; Xian, Wang; Li, Yueming

doi:10.1007/s11431-013-5416-x

Cited by 14 publications

(4 citation statements)

References 30 publications

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“…Their CFD numerical results indicate that using this new active flow control strategy, the amplitude of the lift could be reduced by 17.67% with the steady blowing method. Chen, et al (218) proposed a gust alleviation control law design method based Proper Orthogonal Decomposition (POD)/ROM for flexible aircraft in the transonic flow regime. Using this method, they showed that the gust-induced lift and moment can be reduced by more than 50% for both the discrete 1-minus-cosine gust and a sequence of 1-minus-cosine and sine gust.…”

Section: Analytical Investigationsmentioning

confidence: 99%

Gust loads on aircraft

Cao

Ismail

2019

Aeronaut. j.

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An important prerequisite for the design, assessment and certification of aircraft and their associated control systems is a quantitative specification of the environment in which the aircraft is intended to operate, for example, atmospheric gust. Gust loads on aircraft may induce detrimental influences such as increased aerodynamic and structural loads, structural deformation and decreased flight dynamic performance. This paper presents a systematic and comprehensive overview of important concepts and applications of gust loads on aircraft. This overview includes a brief research background, concepts, research techniques, influences and load alleviation measures of gust. Finally, we summarise some potential improvements in the future work. It is also recommended to learn from previous experiences to avoid aviation accidents due to flight through atmospheric gusts and turbulence.

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Section: Analytical Investigationsmentioning

confidence: 99%

Gust loads on aircraft

Cao

Ismail

2019

Aeronaut. j.

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“…The second branch is based on the eigenmodes of the flow field, including the proper orthogonal decomposition (POD), which projects the governing equations onto these eigenmodes to obtain a low-order dynamic model [10][11][12]. Due to the good performance in the accuracy and efficiency, the POD technique has become an active area of the ROM research and has been widely used in optimal design [13,14], control design [15,16], aeroelastic analysis [17][18][19], etc. However, due to the influence of inner product, simplification of the original governing equations, and the lack of dissipation in numerical schemes, the conventional POD ROMs are usually unstable and additional stabilization strategies have to be adopted to solve these problems [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Unsteady Aerodynamic Modeling Based on POD-ARX

Wang

Zhang

et al. 2018

International Journal of Aerospace Engineering

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The lack of stability is a problem encountered when applying the classical POD-Galerkin method to problems of unsteady compressible flows around a moving structure. To solve this problem, a hybrid reduced-order model named POD-ARX is constructed in this paper. The construction of this model involves two steps, including first extracting the fluid modes with the POD technique and then identifying the modal coefficients with the ARX model. The POD modes with the block of all modified primitive variables are extracted from the system response to the training signal. Once the POD modes are obtained, the snapshots are projected on these modes to determine the time history of modal coefficients and the resulting modal coefficients are used to identify the parameters of ARX model. Then, the ARX model is used to predict the modal coefficients of the system response to the validation signal. Sample two-dimensional aerodynamic force calculations are conducted to demonstrate this method. Results show that this method can produce a stable and accurate prediction to the aerodynamic response with significant improvement of computational efficiency for linear and even some nonlinear aerodynamic problems. In addition, this method also shows good wide-band characteristics by using the “3211” multistep signal as the training signal.

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“…The wind field is an important influence factor for aircraft design and control (Chen et al, 2014). Plugging wind fields into the trajectory planning model can obtain a more reliable planning result.…”

Section: Introductionmentioning

confidence: 99%

Trajectory planning for mini unmanned helicopter in obstacle and windy environments

Xiang

Tong

2018

AEAT

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Purpose Obstacle and wind field are common environmental factors for mini unmanned helicopter (MUH) flight. This paper aims to develop a trajectory planning approach guiding MUH to avoid static and dynamic obstacles and to fly in steady uniform or boundary-layer wind field. Design/methodology/approach An optimal control model including a nonlinear flight dynamics model and a cubic obstacle model is established for MUH trajectory planning. Radau pseudospectral method is used to generate the optimal trajectory. Findings The approach can plan reasonable obstacle-avoiding trajectories in obstacle and windy environments. The simulation results show that high-speed wind fields increase the flight time and fluctuation of control inputs. If boundary-layer wind field exists, the trajectory deforms significantly and gets closer to the ground to escape from the strong wind. Originality/value The key innovations in this paper include a cubic obstacle model which is straightforward and practical for trajectory planning and MUH trajectory planning in steady uniform wind field and boundary-layer wind field. This study provides an efficient solution to the trajectory planning for MUH in obstacle and windy environments.

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A reduced-order-model-based multiple-in multiple-out gust alleviation control law design method in transonic flow

Cited by 14 publications

References 30 publications

Gust loads on aircraft

Gust loads on aircraft

Unsteady Aerodynamic Modeling Based on POD-ARX

Trajectory planning for mini unmanned helicopter in obstacle and windy environments

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