Active Vibration Control of Helicopter Fuselage with Large Dynamic Modeling Errors

Function-weighted frequency response function sensitivity method for analytical model updating

Considering the number of applications, and the quantity of research conducted over the past few decades, it would not be an overstatement to label the piezoelectric materials as the cream of the crop of the smart materials. Among the various smart materials, the piezoelectric materials have emerged as the most researched material for practical applications. They owe it to a few key factors like low cost, large frequency bandwidth of operation, availability in many forms, and the simplicity offered in handling and implementation. For piezoelectric materials, from an application standpoint, the area of active control of vibration, noise, and flow, stands, alongside energy harvesting, as the most researched field. Over the past three decades, several authors have used piezoelectric materials as sensors and actuators, to (i) actively control structural vibrations, noise and aeroelastic flutter, (ii) actively reduce buffeting, and (iii) regulate the separation of flows. These studies are spread over several engineering disciplines—starting from large space structures, to civil structures, to helicopters and airplanes, to computer hard disk drives. This review is an attempt to concise the progress made in all these fields by exclusively highlighting the application of the piezoelectric material. The research carried out in the past five years, in the areas of modeling, and optimal positioning of piezoelectric actuators/sensors, for active vibration control (AVC), are covered. Along with this, investigations into different control algorithms, for the piezoelectric based AVC, are also reviewed. Studies reporting the use of piezoelectric modal filtering and self sensing actuators, for AVC, are also surveyed. Additionally, research on semi-AVC techniques like the synchronized switched damping (on elements like resistor, inductor, voltage source, negative capacitor) has also been covered.

Non-contact optical actuators driven by high-energy lights are investigated and applications to active vibration control of plates are demonstrated in this study. An optical servo control system coupled with a rectangular plate is proposed. Active vibration control and modal control effectiveness of the rectangular plate are evaluated. The proportional velocity feedback is compared with the Lyapunov control. Experimentally calibrated simulation results show that the optical actuators are effective in vibration control and the highest frequency of controllable vibration can achieve several hundred Hertz. The time histories also indicate that the Lyapunov control is more effective as compared with the proportional feedback control.

Based on the system reserve energy index as objective optimization function of the structure vibration system, sets up the multi-objective optimal disposition mathematical model which refer to the system control gain and the sensors and actuators placements, quantities, lengths of the active vibration control system, adopts the multi-island genetic algorithm (MIGA) as optimization strategy. Experiments with intelligent cantilever beam as an example, the vibration control effect is very distinct. Hence, approves the method correctness and feasibility.

A narrowband active vibration control algorithm with frequency estimator based on lattice notch filter is proposed to suppress the primary signal with multi line spectra. A adaptive iir (infinite impulse response) lattice gradient notch filter was adapted to estimated the frequency of the multi-harmonics buried in the primary signal, then the reference signal is filtered by the adaptive band pass filter. With the reference signal filtered by the adaptive band pass filter, active control algorithm is implemented. Simulations of the frequency estimation of the multi-sinusoidal signal are conducted and simulation results confirm that the proposed algorithm could achieve effective performance when the frequency of the primary disturbance is unknown. Active control of vibration has finds a lot application such as fans, engines, and air conditioners in nowadays. The basic concept of active vibration control is to suppress the primary disturbance with the secondary force which is of same amplitude and opposite phase. Signals generated by the rotating machines such as fans, diesel engines, and air conditioners is characterized with discreet frequency which cause damage to operator in the working environment. Narrowband active vibration control (NAVC) is an effective method to suppress such vibration with multi-line spectra. The application of NAVC has been implemented taken into applications since 1970's. A lot of algorithms have been proposed for suppress the primary disturbance with multi-spectral. There exists two families of algorithm in AVC system, which composed of filtered-X least mean square (FXLMS) algorithm and filtered-X recursive least square algorithm (FXRLS). Due to its simplicity and implementation efficiency, FXLMS algorithm is the most popular adaptive algorithm to suppress the primary disturbance compared to FXRLS algorithms. In the application of NAVC systems, the performance of NAVC systems is directly correlated with the reference signal and the primary signal. NAVC algorithm could not achieve effective performance if the correlation between the primary signal and the reference signal is weak. Frequency estimation of the primary signal is an effective method to adapt to improve the relativity between the reference signal and the primary signal. To estimate the frequency of a sinusoidal signal, a number of algorithm has been proposed to exploit the predictable nature of sinusoidal primary disturbance and characterize the frequency of the sinusoidal primary disturbance, such the Kalman algorithm, music, FFT (fast Fourier transform), etc. such method could be capable of tracking nonstationary or slowly-varyin g primary signals. However, they have some shortcomings in the application of NAVC systems. a lot of Kalman-based algorithms have been proposed for the frequency estimation of the sinusoidal signal in many applications such as the electrical power system, however, it requires much more computation burdens which is much more complicated compared the other algorithms. FFT methods have been proposed in the past to estimated the frequency of the primary signal, but it is has been found that the estimated result could not be accessed effectively in real-time of AVC system. Adaptive notch filter has been proposed for the frequency estimation of the sinusoidal signal in a lot of applications. Adaptive lattice filter shows much more stability and performance compared to the bilinear adaptive notch filter. In this paper, the adaptive lattice notch filter is introduced into an adaptive vibration control system to estimate the frequency of synthesized desired signal. Then, the reference signal is generated to suppress the primary vibration in the active vibration control system.

Commercial active noise and vibration control systems for vehicles are gradually developing, mainly in Japan, for both active noise control of interior sound and active control engine mounts [1]. There have been four Japanese commercial applications: • Active control for booming noise by Nissan in 1991, • Active vibration mount for idle noise and vibration by Toyota in 1997, • Active vibration mount for diesel engines by Nissan in 1998, • Active control for low-frequency road noise by Honda in 2000. Other manufacturers have, of course, developed similar systems, but these have not progressed to production applications. Additionally, active control engine mufflers using cancelling loudspeakers have been successfully demonstrated. A new type of active muffler, using a controlled oscillating butterfly valve in the exhaust line has been developed recently. The future of active control in vehicles is dependent on customers who are willing to pay the higher price for additional noise and vibration control, so that cost reductions are the essential factor in further commercial products. Trends towards fuel efficiency and lighter vehicles run counter to reducing noise and vibration. This has given new interest in active noise control and active control engine mounts as a solution to these conflicting issues.

In environment with impulsive noise, most learning algorithms are encountered difficulty in distinguishing the nature of large error signal, whether caused by the impulse noise or large model error. Consequently, they suffer from slow convergence or large misadjustment. A new gradient based variable forgetting factor nonlinear RLS algorithm uses correlation function of error signal with nonzero lags (GCVFF) is introduced. The correlation of nonzero lags maintains the sensitivity of the algorithm responding to the model error and becomes sluggish to the impulse noise. Simulation results show that it achieves fast convergence speed and small misadjustment and outperforms other variable forgetting factor (VFF) RLS algorithms.

This paper deals with the active vibration control of piezoelectric sandwich plate. The structure consists of a substrate plate layer sandwiched between two layers of piezoelectric sensor and actuator. Based on laminate theory and constitutive equation of piezoelectric material, the vibration active control dynamic equation of the sandwich structure is established by using hypothetical mode method and Hamilton principle. The Rayleigh-Ritz method is used to solve it. The form of hypothetical solution is used for approximate solution, which is simple and accurate. The method of this paper is verified by several examples. The parametric studies of the sandwich plate structures are carried out. The results show that applying different boundary conditions and piezoelectric patch positions to the structures have a great influence on the natural frequency. When the driving voltage increases, the deflection of the plate structures increase approximately linearly. The active vibration control studies are investigated as well. The results show that within a certain range, the larger the value of the speed feedback coefficient, the better the active control effect. The positions of the piezoelectric patches affect the effectiveness and cost of active control. When the piezoelectric plate is located at the fixed end, the effect and cost of active control are better than that at the midpoint and free end of the plate.

This paper presents the methodology to design and optimize a feasibility demonstrator for low- frequency active vibration control of a composite plate. An original approach has been achieved and several problems due to the behavior of the piezoceramic explained and solved.

The dynamic response and active vibration control of flexible composite material barrel under moving projectile are studied in this paper. After analyzing its vibration characteristics, the active vibration control technology is introduced to suppress its vibration. Combining theoretical modeling and experimental developing, some key problems of applying the active vibration control technology to vibration control of flexible composite barrel are discussed, which provides the technology foundation for later developing of flexible smart composite barrel. The coupling vibration model between the moving projectile and flexible composite barrel is established and solved by applying numerical iterative method and simulation experimental study, and many valuable conclusions are made. Besides, the dynamic response model and state space model for active vibration control of barrel under a moving projectile is established. And the adaptive fuzzy control strategy is introduced to suppress the flexible composite material barrel vibration. The testing system of dynamic response and active vibration control for barrel under a moving projectile is designed and developed. The experimental results showed the effectiveness of this kind of control strategy for flexible composite material barrel bonded with piezoelectric self-sensing actuator under a moving projectile, which demonstrated that using the smart material could suppress the time-varying system flexible structure vibration active control.

Vibration control strategies strive to reduce the effect of harmful vibrations such as machining chatter. In general, these strategies are classified as passive or active. While passive vibration control techniques are generally less complex, there is a limit to their effectiveness. Active vibration control strategies, which work by providing an additional energy supply to vibration systems, on the other hand, require more complex algorithms but can be very effective. In this work, a novel artificial neural network-based active vibration control system has been developed. The developed system can detect the sinusoidal vibration component with the highest power and suppress it in one control cycle, and in subsequent cycles, sinusoidal signals with the next highest power will be suppressed. With artificial neural networks trained to cover enough frequency and amplitude ranges, most of the original vibration can be suppressed. The efficiency of the proposed methodology has been verified experimentally in the vibration control of a cantilever beam. Artificial neural networks can be trained automatically for updated time delays in the system when necessary. Experimental results show that the developed active vibration control system is real time, adaptable, robust, effective and easy to be implemented. Finally, an experimental setup of chatter suppression for a lathe has been successfully implemented, and the successful techniques used in the previous artificial neural network-based active vibration control system have been utilized for active chatter suppression in turning.

A methodology for estimating model error statistics is proposed. Its application to the global operational model ARPEGE of Météo‐France provides valuable insights into the spatio‐temporal dynamics of model error variances. In particular larger model errors are found in the midlatitude storm tracks (high cyclonic activity) for dynamical variables such as 500 hPa geopotential height and the 850 hPa wind speed.The average model errors over both hemispheres show a linear growth until they reach saturation. Model errors are also shown to grow more rapidly than predictability errors; this leads to a crossover time beyond which model error contribution to forecast error starts to play the dominant role. Moreover, model errors saturate more rapidly than predictability errors. On the other hand, spectral analysis shows an upscale energy transfer and a faster growth at synoptic scales for both model errors and predictability errors. This indicates that, for dynamical variables, the growth of both errors is most likely driven by baroclinic instability.The results found in this study could provide valuable information for a future implementation of a stochastic physics approach to account for model errors in the operational ensemble prediction system.

Data-driven learning approaches have gained a lot of interest in evaluating and validating complex dynamic systems. One of the main challenges for developing a reliable learning model is the lack of training data covering a large range of various operational conditions. Extensive training data can be generated using a physics-based simulation model. On the other hand, the learning algorithm should be still tested with experimental data obtained from the actual system. Modeling errors may lead to a statistical divergence between the simulation training data and the experimental testing data, causing poor performance, especially for domain-agnostic black-box learning methods. To close the gap between the simulation and experimental domains, this paper proposes a physics-guided learning approach that combines the power of the neural network with domain-specific physics knowledge. Specifically, the proposed architecture integrates physical parameters extracted from the physics-based simulation model into the intermediate layers of the neural network to constrain the learning process. To demonstrate the effectiveness of the proposed approach, the architecture is adopted to a damage classification problem for a three-story structure. Our results show that the accuracy for localizing the damage correctly based on experimental data improves significantly over black-box models, especially under large modeling errors. In this paper, we also use the physics-based intermediate layers to analyze the interpretability of the classification results.

Active control of friction-induced self-excited vibration using adaptive fuzzy systems

In this paper we provide a review of the state of the art for active vibration control of axially moving materials (string and beam, etc.) in the past ten years, with particular regard to the subjects of some important active control methods of axially moving strings. First, an introduction is given to explain the aims and scope of this paper. This is followed by a comprehensive discussion of the active wave control methods presented by many investigators in the past ten years, including feedback control methods (such as the modal control method, the direct velocity feedback control method and the control method via transfer function formulation), active wave control methods combining transfer function, boundary control methods using the Lyapunov function, the variable structure control method, the adaptive control method, the vibration control of an axially moving beam and the active control of a moving material with arbitrarily varying length, etc. Finally, we conclude with a discussion of several issues for future research in this area.