Vibration suppression of wind turbine nacelle with active electromagnetic mass damper systems using adaptive backstepping control

Many structural systems, such as wind turbines, are exposed to high levels of stress during operation. This is mainly because of the flow-induced vibrations caused by the wind load encountered in every tall structure. Preventing the flow-induced vibration has been an important research area. In this study, an active electromagnetic mass damper system was used to eliminate the vibrations. The position of the stabilizer mass in the active electromagnetic mass damper system was determined according to the displacement information read on the system without using any spring element, unlike any conventional system. The proposed system in this study has a structure that can be implemented as a vibration suppressor in many intelligent structural systems. Two opposing electromagnets were used to determine the instant displacement of the stabilizer mass. The control currents to be given to these electromagnets are determined by using an adaptive backstepping control design. The adaptive controller algorithm can predict the wind load used in the controller design without prior knowledge of the actual wind load. It was observed that the designed active electromagnetic mass damper structure is successful in suppressing system vibrations. As a result, the proposed active electromagnetic mass damper system has been shown to be suitable for structural systems in flow-induced vibration damping.

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Accelerated Adaptive Backstepping Control of the Chaotic MEMS Gyroscope by Using the Type-2 Sequential FNN

Ingeniería e Investigación ◽

10.15446/ing.investig.v41n1.85825 ◽

2021 ◽

Vol 41 (1) ◽

pp. e85825

Author(s):

Le Zhao ◽

Shao hua Luo ◽

Guan ci Yang ◽

Jun yang Li

Keyword(s):

Controller Design ◽

Chaotic Oscillation ◽

Backstepping Control ◽

System Stability ◽

Adaptive Backstepping ◽

Mems Gyroscope ◽

Adaptive Backstepping Control ◽

Stability Performance ◽

Chaos Suppression ◽

Complex Term

In this paper, we propose an accelerated adaptive backstepping control algorithm based on the type2 sequential fuzzy neural network (T2SFNN) for the microelectromechanical system (MEMS) gyroscope with deadzone and constraints. Firstly, the mathematical model of the MEMS gyroscope is established to perform dynamical analyses and controller design. Then, the phase diagrams and Lyapunov exponents are presented to reveal its chaotic oscillation, which is harmful to system stability. In order to suppress oscillations derived from chaos and deadzone, an accelerated adaptive backstepping controller is proposed wherein an adaptive auxiliary is established to compensate the influence of nonsymmetric deadzone on stability performance, along with the T2SFNN designed to approximate unknown functions of dynamic systems. Furthermore, the speed function is introduced to accelerate convergence speed of the control system, and the problem of complex term explosion in traditional backstepping is successfully solved by a secondorder tracking differentiator. Finally, simulation results show that the proposed control scheme can guarantee asymptotic convergence of all signals in the closedloop system, as well as satisfying states constraints and fulfilling the purposes of chaos suppression and accelerated convergence.

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ADAPTIVE BACKSTEPPING CONTROL OF A PIEZO-POSITIONING MECHANISM WITH HYSTERESIS

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2007-0006 ◽

2007 ◽

Vol 31 (1) ◽

pp. 97-110 ◽

Cited By ~ 8

Author(s):

Jing Zhou ◽

Changyun Wen ◽

Chengjin Zhang

Keyword(s):

Controller Design ◽

Backstepping Control ◽

Hysteretic Behavior ◽

Nonlinear Observer ◽

Design Parameters ◽

Adaptive Backstepping ◽

Lugre Model ◽

Unknown Parameters ◽

Adaptive Backstepping Control ◽

Robust Adaptive

Piezo-positioning mechanisms are often used in high-precision positioning applications. Due to their materials, nonlinear hysteretic behavior is commonly observed in such mechanisms and can be described by a LuGre model. In this paper, we develop two robust adaptive backstepping control algorithms for piezo-positioning mechanisms. In the first scheme, we take the structure of the LuGre model into account in the controller design, if the parameters of the model are known. A nonlinear observer is designed to estimate the hysteresis force. In the second scheme, there is no apriori information required from these parameters and thus they can be allowed totally uncertain. In this case, the LuGre model is divided into two parts. While the unknown parameters of one part are incorporated with unknown system parameters for estimation, the effect of the other part is treated as a bounded disturbance. An update law is used to estimate the bound involving this partial hysteresis effect and the external load. For both schemes, it is shown that not only global stability is guaranteed by the proposed controller, but also both transient and asymptotic performances are quantified as explicit functions of the design parameters so that designers can tune the design parameters in an explicit way to obtain the required closed loop behavior.

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