Research Article  Open Access
Jianjun Yao, Zhenshuai Wan, Yue Zhao, Jie Yu, Chen Qian, Yu Fu, "Resonance Suppression for Hydraulic Servo Shaking Table Based on Adaptive Notch Filter", Shock and Vibration, vol. 2019, Article ID 9407520, 12 pages, 2019. https://doi.org/10.1155/2019/9407520
Resonance Suppression for Hydraulic Servo Shaking Table Based on Adaptive Notch Filter
Abstract
In dynamic structure test, the specimen of hydraulic servo shaking table contains not only inertia load but also elastic load. The specimen herein is simplified as a springmassdamping system, and the mathematical model of the hydraulic servo shaking table is established by theoretical analysis. The coupling between specimen’s elastic load and shaking table itself produces resonance phenomenon in the required bandwidth when the elastic load is not negligible, which deteriorates the system’s dynamic performance and even leads to the instability of the control system. Also, the timevarying resonance frequency further aggravates the control performance of the system in the shaking test. In this paper, an adaptive notch filter based on least mean square (LMS) error principle is employed to identify the resonance frequencies online and realtime adjust the parameters of the notch filter. Simulation and experiment results show the effectiveness of frequency identification and resonance mode suppression. Compared with the existing resonance suppression scheme, the proposed method can suppress the appeared resonance mode adaptively.
1. Introduction
Considering the repeatability, safety, and cost, the mechanical properties of structure and components are usually tested using dynamic laboratory test rather than field test [1–4]. As one of the most important dynamic structure test devices, shaking table is widely used in industrial fields including seismic simulation, aerospace, and infrastructure [5–8]. Compared with mechanical shaker and electrodynamic shaker, hydraulic servo shaking table has such merits as high power to weight ratio, load stiffness, response speed, and control precision, and these features make it invaluable in the dynamic structure test [9, 10]. In previous studies, the specimen of hydraulic servo shaking table is often considered as inertial load, in other words, without consideration of the effect of elastic load. However, the elastic load of the specimen in the dynamic structure test is too big to be ignored [11]. The coupling between the specimen’s elastic load and shaking table itself produces resonance phenomenon in the required bandwidth when the elastic load is not negligible, which reduces the system’s dynamic performance and even leads to the instability of the control system. Increased accuracy in dynamic structure test requires a high bandwidth. However, the bandwidth cannot be increased arbitrarily due to the appeared resonance modes. Moreover, the resonance frequency can vary during the operation of hydraulic servo shaking table because of various factors such as fabrication errors, hydraulic cylinder leakage, and hydraulic fluid compressibility [12]. This leads to that the conventional control method is difficult to achieve exact tracking response and may result in damage to the specimen.
Several methods have been studied to solve the resonance problem [13]. Schmidt and Rehm adopted fast Fourier transform (FFT) to measure resonance frequency and calculate antiresonant frequency of a dual inertia spring system [14]. Wang et al. used an adaptive notch filter with a FFT analyzer based on a sorting algorithm and nonlinear model predictive controller to suppress the loworder torsional vibration and compensate for the dynamic control performance of a helicopter system, respectively [15]. Paolo Mercorelli and Nils Werner utilized adaptive control strategy to deal with the servo valve resonance problem. Experimental results showed that adaptive resonance regulator can adaptively change in accordance with any change in the velocity of the revolution of the engine [16]. Wang et al. adopted the modified resonance frequency detection and reduction method based on an adaptive notch filter to extend the middle frequency range of an industrial servo system [17]. Wang et al. presented an improved adaptive filtered xLMS algorithm to suppress the resonance in the elastic drive system, which proves the better resonance suppression effect as well as convergence speed than the conventional method [18]. Rahman put forward a discrete time adaptive compensator based on an autotuning algorithm to suppress the timevarying resonance characteristics of a hard drive servo system [19]. Wang et al. proposed a model predictive control method to suppress mechanical resonance of a twomass servo system; in addition, amplitude limit of the shaft torque dynamic performance was improved [20]. Yan et al. proposed an addon multirate adaptive control scheme, which was based on a polynomial transformation technique and recursive leastsquares algorithm, to compensate the uncertain resonance modes beyond the Nyquist frequency in highperformance mechatronic systems, and the vibration attenuation for uncertain resonances was effectively improved [21]. Besides, fuzzy control, robust control, and neural network were also presented to suppress the resonance modes of system whose resonance frequency varies frequently [22].
From the above literature, it is easy to find that the resonance suppression methods are mainly focused on mechanical system. As an integrated industrial equipment, the hydraulic servo shaking table needs to deal with electronic signal, hydraulic signal, and mechanical signal simultaneously, so accuracy and real time are highly required. Therefore, an adaptive notch filter based on LMS error principle is employed to identify the resonance frequencies online and realtime adjust the parameters of the notch filter. The output of resonance frequency detector which estimated all the resonance frequency components is injected into the adaptive notch filter to eliminate the appeared resonance modes. Simulation and experiment results validate the effectiveness of frequency identification and resonance suppression.
The rest of the paper is organized as follows. Section 2 first presents the hydraulic servo shaking table; afterwards, its control strategy and mathematical model are discussed. Section 3 introduces the adaptive notch filter design procedure and its theoretical analysis. The resonance suppression scheme is discussed in Section 4. Then, in section 5, simulation and experimental results are presented to validate the proposed method. Finally, the main points are concluded in section 6.
2. Hydraulic Servo Shaking Table System
Figure 1 shows the hydraulic servo shaking table system, which is a realtime operation system based on online rapid prototyping control technology xPCTarget. The host computer serves as the user interface and downloads the compiled program to the target computer by Ethernet adapter. The target computer converts voltage analog signal into current drive signal and performs realtime execution of compiled program. The hydraulic pump is used to provide hydraulic energy for shaking table and controls the pressure and flow of servo valve. The signal conditioner regulates the command signal and feedback signal to accurately reproduce the reference signal [23].
Figure 2 shows the control strategy of a hydraulic servo shaking table that mainly includes an input filter, servo valve, hydraulic cylinder, shaking table, and various transducers [24]. As the most common controller for the shaking table, the three variable controller (TVC) includes TVC feedforward controller and TVC feedback controller. The TVC feedback controller comprises of displacement, velocity, and acceleration feedback signals, where the displacement feedback signal is used to improve the closedloop system stability, the velocity feedback signal is utilized to increase natural frequency of system, and the acceleration feedback signal is adopted to improve system’s damping ratio. The displacement and acceleration feedback signal are measured by displacement transducer and acceleration transducer, respectively. The velocity feedback signal is obtained from displacement feedback signal in low frequency range and acceleration feedback signal in high frequency range by signal conditioner because it is difficult to measure the three feedback signals simultaneously. The TVC feedforward controller including displacement, velocity, and acceleration feedforward signals generated by the input filter is mainly used to extend frequency bandwidth and reduce the tracking error. The error signal, which is the difference between reference signal and feedback signal, drives the hydraulic cylinder generating desired movement by servo valve [25].
The TVC control strategy is based on closedloop position design, but the shaking table requires acceleration control. So, the reference displacement, velocity, and acceleration signals are obtained by the input filter [26]. The transfer function from input acceleration signal to displacement output signal can be deduced aswhere , , is the reference acceleration, is the acceleration gain, is the stating frequency of acceleration control, and is the system’s damping ratio.
The transfer function of the TVC feedforward controller can be expressed aswhere , , and are corresponding acceleration, velocity, and displacement gain of feedforward controller. , , and represent the reference acceleration, velocity, and displacement, respectively.
The transfer function of the TVC feedback controller can be described aswhere , , and are feedback part’s acceleration, velocity, and displacement. , , and represent the measured acceleration, velocity, and displacement, respectively.
The servo valve regulates oil flow into the actuator chambers by changing the position of valve spool. The response of hydraulic oil flow to current control signal can be approximated by the following second order transfer functionwhere , , and are gain, natural angular frequency, and damping coefficient of the servo valve.
The schematic diagram of hydraulic actuator controlled by servo valve is shown in Figure 3, where the specimen is simplified into a springmassdamping system. For convenience, and are supply and return pressure, and are input and output flow, and are input and output pressure, and are input and output oil volume, and are internal leakage and external leakage coefficient, is the mass of the actuator, is the mass of the specimen, is the valve position, is the viscous damping coefficient of the specimen, is the spring stiffness of the specimen, is the effective area of the piston, is the displacement of the actuator, is the displacement of the specimen.
The flow rate equation of the servo valve can be expressed aswhere is load flow, is load pressure, and and are flow gain and flowpressure coefficient, which can be defined aswhere is flow coefficient, is the gradient of valve orifices, and is the density of hydraulic oil.
In consideration of oil elasticity, leakage, and chamber volume variation, the flow equation for each chamber can be formulated aswhere is the effective bulk modulus of hydraulic oil, , .
Combining equations (7) and (8) giveswhere is the total leakage coefficient and is the total chamber volume.
Ignoring the oil mass and coulomb friction, the force balance equation of the shaking table and specimen can be expressed as
Combining equations (5) and (9)–(11) and performing Laplace transform, the transfer function from the spool displacement to the displacement of hydraulic actuator can be derived aswhere is the total pressure flow coefficient considering leakage. is the hydraulic spring stiffness.
The specimen and shaking table itself form into resonance system due to the specimen’s elastic load. The coupling term occurring in transfer function has a great influence on the specimen in the dynamic structure test of the hydraulic servo shaking table. Therefore, the adaptive notch filter must be designed to accommodate all the frequency variation.
3. Adaptive Notch Filter
The notch filter attenuates frequency characteristic to zero at center frequency while keeping the original value unchanged at other frequencies. However, the resonance frequency can vary due to various factors such as friction, dead zone, and manufacturing tolerances. The variation of resonance frequency may deteriorate the suppression performance of the notch filter. The notch filter with wider notch can attenuate to some extent the varying resonance mode. As the notch filter becomes wider, it also induces greater magnitude and phase lag at lower frequencies resulting in a lower bandwidth system. In order to solve the above problem, the adaptive notch filter whose center frequency varies online to track the resonance frequency is proposed to online realtime suppress the appeared resonance model.
The LMS algorithm is widely used in the adaptive filter due to its computational simplicity, unbiased convergence, and stable behavior. It is an iterative gradient descent algorithm that changes each iteration based on imperfect gradient estimate to seek the optimum value on the performance surface. The LMS algorithm can be written as follows [27, 28]:where is output signal, is the reference input signal, is the updating filter weight vector, is the desired signal and is the error signal, and denotes convergence factor that controls the stability and speed of adaptation. The signal propagation process from the reference input to the system output based on the LMS algorithm is shown in Figure 4 in detail.
The sampled reference inputs are
Following the path through the weight, we obtainwhere , are rotated counterclockwise and clockwise around the unit circle through an angle.
The weights are obtained as follows:
The contribution to the output at is
Combining equation (16) into equation (17), we obtain
The term in equation (18) represents the time invariant part of the response from to . The time invariant transfer function iswhere has poles on the unit circle at , and a zero at .
The transform function from to can be expressed as
For ease of exposition, equation (20) can be rewritten as follows:
The coefficients of the above adaptive notch filter are defined as [29]where and determine the width and depth of the notch filter, respectively and is related to the center frequency .
Figure 5 illustrates the amplitudephase characteristic of the notch filter for different parameters. It is easy to see that the adaptive notch filter has larger notch width but larger phase delay as the same time. The larger phase delay may affect the system stability. Thus, the notch width should be kept as narrow as possible for the servo control system. In order to effectively suppress the variation of resonance frequency, it is necessary to online adjust the parameters of the notch filter with the varying resonance modes.
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4. Resonance Suppression Scheme
Inspired by adaptive noise reduction, which adds the sinusoidal harmonics with just the right amplitude and phase to the primary signal including a noise signal to cancel the noise component, a new resonance suppression scheme based on the adaptive notch filter is shown in Figure 6. It is generally known that the peak of the frequency response in magnitude reaches the maximum value at resonance frequency. The signal with various resonance frequency is used as excitation signal to extract the resonance frequency component. Then, the resonance frequency is estimated by resonance frequency detector. With being placed in cascade between control system and hydraulic servo shaking table, the proposed adaptive notch filter can suppress the resonance modes with the variation of resonance frequency in real time.
The resonance frequency detector consists of an excitation signal generator and FIR filter. The excitation signal can be represented aswhere denotes a group of resonance frequency candidates, which are spread around the actual resonance frequency. and are the amplitude and phase of frequency components, respectively.
The transfer function of the FIR filter is given by
The frequency response of the FIR filter can be expressed by
It is easy to see that the frequency is zero when for . The amplitude of FIR filter output reaches the minimum value when the null frequency coincides with the frequency of the component having the largest amplitude among all the frequency components.
According to the linear system theory, the estimation output of the FIR filter is derived as
The average power of the filter output signal is defined as the objective function:
Substituting equation (25) into equation (27), the objective can be rewritten as
It can be found that the objective function is a quadratic function of the filter coefficient . Taking the gradient of the objective function with respect to the filter coefficient and setting it equal to zero:
Thus, the optimal filter coefficient is found when the objective function reaches the minimization:
Suppose that is the resonance frequency. Because the resonance frequency component has much larger amplitude than the other candidate components, the optimal coefficient in equation can be approximated as follows:
The recursive algorithm for updating the filter coefficient can be described aswhere is the adaptive gain.
The optimal coefficients of the adaptive notch filter can be determined by
5. Simulation and Experiment Results
5.1. Simulation Results
In this subsection, simulation is carried out to verify the effectiveness of the proposed adaptive notch filter for resonance suppression. The transfer function for simulation model consists of three resonance modes (39 Hz, 60 Hz, and 65 Hz), which is given by
Figure 7 shows convergence of estimated frequency parameters. It can be seen that large fluctuation only occurred in initial stage, and frequency parameters quickly converge to reference value in a short time. This indicates that resonance frequency detector is able to estimate the resonance efficiently. The amplitudephase characteristic of simulation with and without the adaptive notch filter is shown in Figure 8. It is clear that the system has three resonance frequencies, which severally restricts the dynamic performance and stability performance of system. However, the three resonance frequencies are significantly attenuated by the adaptive filter notch, compared with the case of the system without the adaptive notch filter. Although the bandwidth of shaking table is sacrificed, the stability of system is well ensured, which is important for shaking table test. The detailed parameters of the adaptive notch filter are listed in Table 1. This shows that the proposed adaptive notch filter is very effective in suppressing the resonance modes.

Figure 9 shows the sine sweep frequency response with and without the adaptive notch filter. As can be seen, magnitude at the resonance frequencies obviously decreases using the proposed method. Figure 10 shows the acceleration response of excitation signal. It can be seen that the suppressed results have a good agreement with reference signal compared with unsuppressed results, which also proves that the proposed method has better performance on resonance suppression. Combined with the proposed adaptive notch filter, the final maximum tracking error is reduced from 1.8 m/s^{2} to less than 0.1 m/s^{2}.
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5.2. Experimental Results
The electrohydraulic servo shake table test used to validate the proposed resonance suppression scheme is shown in Figure 1, whose main parameters are listed in Table 2. The adaptive algorithm for the resonance frequency parameters estimation is depicted in Figure 11. Although there is an error at the beginning of the estimation process, the estimation of frequency is rapidly decreased and converges to steady state. Amplitudephase characteristic of the shaking table system without the adaptive notch filter and with the adaptive notch filter is shown in Figure 12. The parameters calculation of the adaptive notch filter is similar with the simulation, not tried in words here. As can be seen, the generated three resonance modes are significantly attenuated using the proposed scheme. The gain margin and phase margin with the adaptive notch filter are greatly improved compared to without the adaptive notch filter, which is conductive to precise control of the hydraulic shaking table. The power spectrum of the response signal with and without the adaptive notch filter is shown in Figure 13. It is clear that amplitudes of the response signal are significantly attenuated at the resonance frequency by using the proposed resonance suppression scheme.

Figure 14 shows the sine acceleration response of tracking performance and the tracking error with and without adaptive notch filter, where the excitation signal is . It is obvious that sine acceleration response with the adaptive notch filter matches well with the reference signal compared with the situation without the adaptive notch filter. The tracking error of sine acceleration response reveals that the error has been greatly reduced by the adaptive notch filter from the initial maximum 1.5 m/s^{2} to 0.3 m/s^{2}.
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Apart from sine acceleration excitation signal, a recorded earthquake wave, which happened in El Centro in southern California, is also used to validate the proposed resonance suppression scheme. The experiment results of tracking performance and the tracking error are presented in Figure 15. As can be inferred from Figure 15, the proposed resonance suppression scheme with the adaptive notch filter has better tracking accuracy than without the adaptive notch filter.
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6. Conclusions
In this paper, the operational principle and hydraulic actuator model are formulated to illustrate the resonance problem existing in the electrohydraulic servo shake table. The coupling between shaking table itself and the specimen’s elastic load deteriorates the system’s dynamic performance and even leads to the instability of the control system. Furthermore, the resonance frequency is a timevarying value during the shaking table test. In order to meet the requirements of resonance online suppressing, the adaptive notch filter based on the LMS adaptive algorithm is designed to online identify the resonance frequency and automatically adjust the filter coefficients. Simulation and experiment results demonstrate that the proposed method can be effectively used in suppressing the varying resonance mode.
Data Availability
The data used to support the findings of the study are available from the corresponding author upon request.
Conflicts of Interest
The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Acknowledgments
This project was supported by the Natural Science Foundation of Heilongjiang Province of China (grant no. E2018019) and the Fundamental Research Funds for the Central Universities (grant nos. HEUCFP201733 and HEUCFP201814).
References
 A. R. Plummer, “Special issue on control techniques for structural testing,” Proceedings of the Institution of Mechanical Engineers Part IJournal of Systems and Control Engineering, vol. 221, no. I2, pp. I–II, 2007. View at: Publisher Site  Google Scholar
 A. R. Plummer, “Modelbased motion control for multiaxis servohydraulic shaking tables,” Control Engineering Practice, vol. 53, pp. 109–122, 2016. View at: Publisher Site  Google Scholar
 J. Zhao, G. Shen, W. Zhu, C. Yang, and S. K. Agrawal, “Force tracking control of an electrohydraulic control loading system on a flight simulator using inverse model control and a damping compensator,” Transactions of the Institute of Measurement and Control, vol. 40, no. 1, pp. 135–147, 2018. View at: Publisher Site  Google Scholar
 G. Li, G. Shen, Z.C. Zhu, X. Li, and W.S. Zang, “Sine phase compensation combining an amplitude phase controller and a discrete feedforward compensator for electrohydraulic shaking tables,” Transactions of the Institute of Measurement and Control, vol. 40, no. 11, pp. 3377–3389, 2018. View at: Publisher Site  Google Scholar
 Y. Tang, Z. Zhu, G. Shen, and W. Zhang, “Real time acceleration tracking of electrohydraulic shake tables combining inverse compensation technique and neuralbased adaptive controller,” IEEE Access, vol. 5, pp. 23681–23694, 2017. View at: Publisher Site  Google Scholar
 J. J. Yao, G. L. Jiang, D. T. Di, and S. Liu, “Acceleration harmonic identification for an electrohydraulic servo shaking table based on the normalized leastmeansquare adaptive algorithm,” Journal of Vibration and Control, vol. 19, no. 1, pp. 47–55, 2013. View at: Publisher Site  Google Scholar
 A. R. Plummer, “Control techniques for structural testing: a review,” Proceedings of the Institution of Mechanical Engineers Part IJournal of Systems and Control Engineering, vol. 221, no. I2, pp. 139–169, 2007. View at: Publisher Site  Google Scholar
 W. Kim, D. Won, D. Shin, and C. C. Chung, “Output feedback nonlinear control for electrohydraulic systems,” Mechatronics, vol. 22, no. 6, pp. 766–777, 2012. View at: Publisher Site  Google Scholar
 J. J. Yao, Z. S. Wan, and Y. Fu, “Acceleration harmonic estimation in a hydraulic shaking table using water cycle algorithm,” Shock and Vibration, vol. 2018, Article ID 7278589, 12 pages, 2018. View at: Publisher Site  Google Scholar
 W. Shen, J.z. Wang, and S.k. Wang, “The control of the electrohydraulic shaking table based on dynamic surface adaptive robust control,” Transactions of the Institute of Measurement and Control, vol. 39, no. 8, pp. 1271–1280, 2017. View at: Publisher Site  Google Scholar
 R. Ghazali, Y. M. Sam, M. F. Rahmat, Z. Zulfatman, and A. W. I. M. Hashim, “Simulation and experimental studies on perfect tracking optimal control of an electrohydraulic actuator system,” Journal of Control Science and Engineering, vol. 2012, pp. 1–8, 2012. View at: Publisher Site  Google Scholar
 L. Zhang, D. Cong, Z. Yang, Y. Zhang, and J. Han, “Robust tracking and synchronization of double shaking tables based on adaptive sliding mode control with novel reaching law,” IEEE Access, vol. 4, pp. 8686–8702, 2016. View at: Publisher Site  Google Scholar
 D.H. Lee, J. H. Lee, and J.W. Ahn, “Mechanical vibration reduction control of twomass permanent magnet synchronous motor using adaptive notch filter with fast Fourier transform analysis,” IET Electric Power Applications, vol. 6, no. 7, pp. 455–461, 2012. View at: Publisher Site  Google Scholar
 P. Schmidt and T. Rehm, “Notch filter tuning for resonant frequency reduction in dual inertia systems,” in Proceedings IEEE Industry Applications Society Annual Meeting, Phoenix, AZ, USA, October 1999. View at: Google Scholar
 Y. Wang, Q. Zheng, H. Zhang, and L. Miao, “Adaptive control and predictive control for torsional vibration suppression in helicopter/engine system,” IEEE Access, vol. 6, pp. 23896–23906, 2018. View at: Publisher Site  Google Scholar
 P. Mercorelli and N. Werner, “An adaptive resonance regulator design for motion control of intake valves in camless engine systems,” IEEE Transactions on Industrial Electronics, vol. 64, no. 4, pp. 3413–3422, 2017. View at: Publisher Site  Google Scholar
 W.Y. Wang and A.W. Shen, “Detection and reduction of middlefrequency resonance for industrial servo with selftuning lowpass filter,” Journal of Control Science and Engineering, vol. 2012, pp. 1–12, 2012. View at: Publisher Site  Google Scholar
 Y.Q. Wang, F.C. Huang, and H.B. Liu, “Adaptive filtered xleast mean square algorithm with improved convergence for resonance suppression,” Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 228, no. 9, pp. 668–676, 2014. View at: Publisher Site  Google Scholar
 M. A. Rahman, A. A. Mamun, and K. Yao, “Discrete time adaptive controller for suppression of resonance in hard disk drive servo system,” International Journal of Control, Automation and Systems, vol. 13, no. 5, pp. 1161–1172, 2015. View at: Publisher Site  Google Scholar
 C. Wang, M. Yang, G. Wang, and D. G. Xu, “IEEE, mechanical resonance suppression and shaft torque limitation of twomass drive system based on model predictive control,” in Proceedings of the IECON 201440th Annual Conference of the IEEE Industrial Electronics Society, pp. 2804–2809, Dallas, TX, USA, November 2014. View at: Google Scholar
 W. Yan, C. Du, and C. K. Pang, “Multirate adaptive control of uncertain resonances beyond the Nyquist frequency in highperformance mechatronic systems,” Automatica, vol. 66, pp. 63–72, 2016. View at: Publisher Site  Google Scholar
 T. OrlowskaKowalska and K. Szabat, “Control of the drive system with stiff and elastic couplings using adaptive neurofuzzy approach,” IEEE Transactions on Industrial Electronics, vol. 54, no. 1, pp. 228–240, 2007. View at: Publisher Site  Google Scholar
 J. Yao, H. Yan, R. Xiao et al., “Sinusoidal acceleration harmonic estimation using the extended Kalman filter for an electrohydraulic servo shaking table,” Journal of Vibration and Control, vol. 21, no. 8, pp. 1566–1579, 2015. View at: Publisher Site  Google Scholar
 K. Seki, M. Iwasaki, M. Kawafuku, H. Hirai, and K. Yasuda, “Adaptive compensation for reaction force with frequency variation in shaking table systems,” IEEE Transactions on Industrial Electronics, vol. 56, no. 10, pp. 3864–3871, 2009. View at: Publisher Site  Google Scholar
 X. Li, Z. C. Zhu, G. C. Rui, D. Cheng, G. Shen, and Y. Tang, “Force loading tracking control of an electrohydraulic actuator based on a nonlinear adaptive fuzzy backstepping control scheme,” SymmetryBasel, vol. 10, no. 5, 2018. View at: Publisher Site  Google Scholar
 J. Yao, D. Di, G. Jiang, S. Gao, and H. Yan, “Identification of acceleration harmonic for an electrohydraulic servo shaking table based on Kalman filter,” Transactions of the Institute of Measurement and Control, vol. 35, no. 8, pp. 986–996, 2013. View at: Publisher Site  Google Scholar
 J. Yao, D. Di, and J. Han, “Adaptive notch filter applied to acceleration harmonic cancellation of electrohydraulic servo system,” Journal of Vibration and Control, vol. 18, no. 5, pp. 641–650, 2012. View at: Publisher Site  Google Scholar
 J. Cadzow, “Digital notch filter design procedure,” IEEE Transactions on Acoustics Speech & Signal Processing, vol. 22, no. 1, pp. 10–15, 2003. View at: Google Scholar
 C. I. Kang and C. H. Kim, “An adaptive notch filter for suppressing mechanical resonance in high track density disk drives,” Microsystem Technologies, vol. 11, no. 810, pp. 638–652, 2005. View at: Publisher Site  Google Scholar
Copyright
Copyright © 2019 Jianjun Yao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.