MIMOMH feed-forward adaptive vibration control of helicopter fuselage by using piezoelectric stack actuators

Abstract

The vibration of a helicopter fuselage is mainly produced by the rotor exciting loads which are three forces and three moments containing harmonics in three directions at rotor hub center, and has the characteristic of large, steady and harmonic vibration. The active control of helicopter fuselage vibration by using inertia actuator needs to pay a heavy weight cost for achieving a good control effect. In this paper, a method for multi-input multi-output multi-harmonic (MIMOMH) feed-forward adaptive control of helicopter fuselage vibration by using piezoelectric stack actuators is presented. The method is composed of the harmonic coefficients identification and the MIMOMH feed-forward adaptive filtering algorithm, and has excellent characteristics such as light weight, fast response, wide working frequency range, adaptive ability and high effect. The numerical simulations for a frame structure scaled by the Z11 helicopter fuselage floor with four actuating inputs, four vibration outputs, three rotor exciting loads and the first three harmonic frequencies contained in each exciting load were performed, indicating that the vibration levels at objective points are reduced by 99% by using the method and are much lower than the results by only controlling the first harmonic frequency. When the phases, amplitudes and frequencies of exciting loads are changed, the method demonstrates an excellent adaptability to control vibration.

An Improved Feedforward-Robust Algorithm for Active Vibration Control of Helicopter Maneuver Flight

“…From 10 s on, external disturbance changes every 10 s and frequency decreases 5%. In each change, excitation phase raises 30 °and amplitude is 13 International Journal of Aerospace Engineering increased by 50% within 2 s, which remains unchanged until it decreases to initial the last 2 s. According to reference [14], fuselage pitch angle of UH-60A helicopter changes about 30 °during pull-up maneuver in high-speed forward flight. Therefore, the turntable is driven to rotate back and forth between 154 °and 186 °within 5 s. Figure 19 displays the changing process of the elastic beam position.…”

“…Based on linear and quasistatic assumptions, frequency-domain HHC can effectively reduce steady-state harmonic vibration [2,4]. However, due to the block processing characteristics of the discrete Fourier transform, the control output and sampling of uncontrolled response are unable to be synchronized, which contributes to slow updating speed and poor adaptability in face of rapid changes of disturbance amplitude, frequency, and system parameters [14,15]. The time-domain Fx-LMS shows good narrowband control effects and signal tracking ability without accurate system model, but the algorithm performance depends heavily on the correlation between reference signal and excitation [16].…”

Active Vibration Control of Helicopter Maneuvering Flight Using Feedforward‐Robust Hybrid Control Based on Reference Signal Reconstruction

“…In order to confirm the benefits of the proposed LMS-MSRC to the robustness of ACSR system in maneuvering flight, four helicopter ACSR methods are compared in Figure 9. e employed ACSR methods for comparison including enhanced HHC, frequency-domain LMS, and multichannel LMS are wildly studied in the literature [19,21,23]. e parameters in all the methods are optimized after several attempts.…”

“…In view of the shortcomings of traditional HHC, Zhao and Song used least mean square and recursive least square algorithm, respectively, to update harmonic parameters [19,20]. However, when there are frequency errors between the basis function and the excitations, the control system converges slowly [21]. Furthermore, the adverse impact of the secondary path mutation during maneuvering flight on vibration control is not considered.…”

Error Signal Differential Term Feedback Enhanced Variable Step Size FxLMS Algorithm for Piezoelectric Active Vibration Control

“…e LMS algorithm proposed by Widrow and Hoff [24] in 1960 is widely used in the fields of system identification [25], signal processing [26], and adaptive control [27] because of its advantages such as small calculation, easy implementation, and great stability. e LMS algorithm based on the steepest descent method can be summarized as the following iterative process:…”