Optimized under-actuated control of blade vibration system under wind uncertainty

This paper studies the problem of optimal under-actuated control of blade vibration system (BVS) under wind uncertainty. The nonlinear BVS model is established to reveal limit cycle oscillations in flapwise, edgewise and torsional vibration motions. The difficulty of under-actuated vibration control...

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Bibliographic Details
Published in:Journal of sound and vibration 2020-02, Vol.467, p.115070, Article 115070
Main Authors: Li, Nailu, Mu, Anle, Yang, Hua, Magar, Kaman T.
Format: Article
Language:English
Subjects:
Actuators
Blade vibration system
Computer simulation
Control systems design
Controllers
Degrees of freedom
Evolutionary algorithms
Evolutionary optimization
Limit cycle oscillations
Linear quadratic regulator
Optimization
Oscillators
Partial state feedback
State feedback
Torsional vibration
Uncertainty
Under-actuated vibration control
Vibration
Vibration control
Wind uncertainty
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Summary:This paper studies the problem of optimal under-actuated control of blade vibration system (BVS) under wind uncertainty. The nonlinear BVS model is established to reveal limit cycle oscillations in flapwise, edgewise and torsional vibration motions. The difficulty of under-actuated vibration control of BVS lies in manipulating multiple degree of freedom (DOF) motions by single pitch input, and wind uncertainty could further threaten vibration reductions. A novel partial state feedback vibration control scheme is proposed based on modified linear quadratic regulator and linearized BVS. To deal with wind uncertainty, the problem of designing optimal vibration controller is converted into the problem of parameters optimization. A combined correlated differential evolutionary (CC-DE) algorithm is designed to ensure efficient optimization solution. Simulation results show that proposed method has significantly improved vibration reductions in multiple DOF motions, enhanced efficiency in actuator utilization and good performance under diverse wind uncertainties, compared to other conventional methods.
ISSN:0022-460X
1095-8568
DOI:10.1016/j.jsv.2019.115070