Experiments on resonant vibration suppression of a piezoelectric flexible clamped–clamped plate using filtered-U least mean square algorithm

Abstract

This article investigates the adaptive filtered-U least mean square feed-forward algorithm for active resonant vibration control of a clamped-clamped flexible piezoelectric plate structure under persistent harmonic excitation. Different from the widely used filtered-X least mean square algorithm based on the finite impulse response filter, the filtered-U least mean square algorithm uses the infinite impulse response filter. An infinite impulse response filter can be constituted simply by using two adaptive transversal filters. The filtered-U least mean square algorithm can model the system accurately with much fewer coefficients. Moreover, the filtered-U least mean square algorithm has better control performance and stability in the presence of vibration control feedback, owing to the inherent zero-pole structure of the infinite impulse response filter. In this investigation, the filtered-U least mean square algorithm is implemented only in experiments. Two experimental cases are carried out, including the reference signal extracted from the function signal generator and the lead zirconate titanate sensor. A proportional-derivative feedback control algorithm is also applied as a comparison. The experimental results demonstrate the feasibility and performance of the designed proportional-derivative controller and filtered-U least mean square controller.

Analysis and experiments of adaptive feedforward and combined vibration control system with variable step size and reference filter

“…As mentioned before, the FUVSSLMS control algorithm can be defined as the combination of the conventional filtered-U least mean square (FULMS) with variable step size (VSS). Then the block diagram of the adaptive feedforward FUVSSLMS control algorithm with variable step size and reference filter is shown in Figure 1 [10,20]. Figure 1.…”

“…The iterative process of the adaptive feedforward FUVSSLMS control algorithm can be summarized as follows [6,10,20] …”

“…Since the theory of the FULMS algorithm has been explained in references [6] and [20] in detail, one can ignore the explanation and focus on the main points to make the paper concise. The meaning of the given symbols is shown in Table 1.…”

“…From Figure 1 and 2, it can be seen that the control path from the control part to the error sensor 2 T is needed to be identified for using the adaptive feedforward FUVSSLMS control algorithm and the adaptive combined FUVSSLMS control algorithm. Generally speaking, two kinds of system identification methods includes online modeling [21] and offline modeling [10,20] methods. The online modeling method can identify the system in real time at the expense of increasing computation.…”

Active Vibration Control of a Large Flexible Appendage Using a Novel Joint Mechanism

“…As an intelligent material, piezoelectric is typically used for additional sti ening and damping activities to inhibit unwanted vibration. To improve control performance, numerous active control algorithms have been proposed (e.g., neural network control [15], sliding mode control [16], fuzzy control [17][18][19], robust control [20,21], adaptive control [22,23], nonlinear control algorithm [17,24], and data-driven control [25]). Despite excellent micro-amplitude drive, intelligent materials exhibit a few shortcomings related to large stroke drive and control [26][27][28][29][30][31].…”