Rejection of Periodic Wind Disturbances on a Smart Rotor Test Section Using Lifted Repetitive Control

Houtzager, Ivo; Wingerden, J. van; Verhaegen, Michel

doi:10.1109/tcst.2011.2181171

Cited by 57 publications

(31 citation statements)

References 26 publications

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“…A closely related approach is repetitive control and there has been research on the application of this design to wind turbine problems, e.g., Houtzager, van Wingerden, and Verhaegen (2013). Once both approaches are sufficiently mature, an obvious area for future research is an in-depth comparative performance evaluation to aid the commissioning engineer.…”

Section: Discussionmentioning

confidence: 99%

Iterative learning control applied to a non-linear vortex panel model for improved aerodynamic load performance of wind turbines with smart rotors

Blackwell

Tutty

Rogers

et al. 2015

International Journal of Control

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The inclusion of smart devices in wind turbine rotor blades could, in conjunction with collective and individual pitch control, improve the aerodynamic performance of the rotors. This is currently an active area of research with the primary objective of reducing the fatigue loads but mitigating the effects of extreme loads is also of interest. The aerodynamic loads on a wind turbine blade contain periodic and non-periodic components and one approach is to consider the application of iterative learning control algorithms. In this paper, the control design is based on a simple, in relative terms, computational fluid dynamics model that uses non-linear wake effects to represent flow past an airfoil. A representation for the actuator dynamics is included to undertake a detailed investigation into the level of control possible and on how performance can be effectively measured.

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Section: Discussionmentioning

confidence: 99%

Iterative learning control applied to a non-linear vortex panel model for improved aerodynamic load performance of wind turbines with smart rotors

Blackwell

Tutty

Rogers

et al. 2015

International Journal of Control

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“…Because of the convexity of S, for Â 2 S and Â 2 ıS we have Q Â T rg D .Â Â / T rg > 0, then V p 6 0. In addition, from (22), it follows that whenever Â.t/ 2 ıS, we have P Â T rg 6 0, which implies that P Â.t/ points either inside S or along the tangent plane of ıS at point Â.t/. Because Â.0/ 2 S, it follows that Â.t/ 2 S, 8t > 0.…”

Section: Proofmentioning

confidence: 94%

“…Consider the closed-loop system (1), (8) with the robust adaptive law (22) designed for the parametric model (17) and assume that the condition given in Lemma 1 is satisfied and N > 2 N n max . For all plant uncertainties m .s/ that satisfy the inequality…”

Section: Theoremmentioning

confidence: 99%

“…The limitations of internal-model-based rejection of disturbances with known time-varying frequencies have been studied in [21]. A feedback control scheme referred to as repetitive control has been designed in [22] for rejection of periodic wind disturbances in wind turbines. In [23], an adaptive scheme has been proposed for unknown disturbance attenuation for multi-channel discrete-time systems in the absence of plant modeling uncertainties.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rejection of unknown periodic disturbances for continuous‐time MIMO systems with dynamic uncertainties

Jafari

Ioannou

2016

Adaptive Control & Signal

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Rejection of unknown periodic disturbances in multi-channel systems has several industrial applications that include aerospace, consumer electronics, and many other industries. This paper presents a design and analysis of an output-feedback robust adaptive controller for multi-input multi-output continuous-time systems in the presence of modeling errors and broadband output noise. The trade-off between robust stability and performance improvement as well as practical design considerations for performance improvements are presented. It is demonstrated that proper shaping of the open-loop plant singular values as well as over-parameterizing the controller parametric model can significantly improve performance. Numerical simulations are performed to demonstrate the effectiveness of the proposed scheme.

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“…It is well known that repetitive control is effective for control of industrial robotic manipulators [3] and disc drives [18]. In addition, repetitive control systems have been recently applied to DC-AC converters in microgrids [29], shunt active power filters [10], wind turbines [15], and so on.…”

Section: Example 52: (Application To Repetitive Control Systems)mentioning

confidence: 99%

Tangential Nevanlinna-Pick interpolation for strong stabilization of MIMO distributed parameter systems

Wakaiki

Yamämoto

Özbay

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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We study the problem of finding stable controllers that stabilize a multi-input multi-output distributed parameter system while simultaneously reducing the sensitivity of the system. The plants we consider have finitely many unstable transmission zeros, but they can possess infinitely many unstable poles. Using the tangential Nevanlinna-Pick interpolation with boundary conditions, we obtain both upper and lower bounds of the minimum sensitivity that can be achieved by stable controllers. We also derive a method to find stable controllers for sensitivity reduction. In addition, we apply the proposed method to a repetitive control system. © 2012 IEEE

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Rejection of Periodic Wind Disturbances on a Smart Rotor Test Section Using Lifted Repetitive Control

Cited by 57 publications

References 26 publications

Iterative learning control applied to a non-linear vortex panel model for improved aerodynamic load performance of wind turbines with smart rotors

Iterative learning control applied to a non-linear vortex panel model for improved aerodynamic load performance of wind turbines with smart rotors

Rejection of unknown periodic disturbances for continuous‐time MIMO systems with dynamic uncertainties

Tangential Nevanlinna-Pick interpolation for strong stabilization of MIMO distributed parameter systems

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