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To solve the vibration suppression problem for a piezoelectric flexible cantilever beam with varying load, this study proposes a new type of multiple model hybrid adaptive vibration control method with a new multiple model switching cost index function. It employs a pre-filter consisting of secondary path and positive feedback path model to eliminate the positive feedback between the secondary actuator and reference sensor. By constructing a piezoelectric flexible cantilever beam as the controlled plant, we constructed a real-time control verification experiment platform based on a real-time MATLAB xPC target. We did a real-time control experiment comparison for vibration suppression of single-frequency, narrowband, and broadband disturbances. Experimental results show that the proposed multiple model hybrid adaptive vibration control algorithm is feasible and has good vibration suppression performance with rapid convergence speed.

This study concerns adaptive feedforward and hybrid (combined feedback and feedforward) control systems for active vibration suppression of smart flexible beam with a tip mass. By analysing traditional feedforward filtered-X least mean square (FXLMS) algorithm and filtered-X variable step size least mean square (FX-VSSLMS) algorithm, a new hybrid FX-VSSLMS algorithm is developed to improve the control performance of feedforward controllers. A stainless steel beam with a tip mass is employed to simulate flexible robot arm with varying loads. Piezoelectric actuators and sensors are attached to the surface of the beam. Comparison experiments show the effectiveness and benefits of the proposed hybrid FX-VSSLMS method.

Control algorithm is one of the key elements for active vibration suppression of the space flexible structure based on smart materials. As least mean square (LMS) algorithm and recursive least square (RLS) algorithm are two fundamental algorithms for adaptive feed-forward filter algorithm drawing wide attention, this paper focuses on the algorithm process analysis and performance comparison of the two adaptive algorithms based on finite impulse response (FIR) structure. The design and related characteristic analysis of the controller is given as well as the realization process based on the theoretical analysis. Simulation is done using Matlab 7.0 to verify the correctness of the theoretical analysis. The simulation comparison between the two type algorithms shows that RLS algorithm has faster convergence and better control performance than LMS algorithm. On the basis of the simulation analysis, the actual vibration suppression experiment is done on the constructed experimental platform for piezoelectric flexible beam, and experiment result confirms the simulation effects.

As the accuracy of the control channel model directly affects the stability and the convergence of the active vibration controller for smart flexible structures, offline identification of the control channel model is inappropriate for a vibration system whose characteristic and parameter is time-varying. To solve the problem of the control channel model identification, an active vibration control algorithm based on online control channel identification is proposed in this paper. Adopting a noise signal as the input identification signal, the FIR filter structure as the system model structure, and FxLMS algorithm as the control algorithm, the detailed derivation of the algorithm is presented. To test and verify the feasibility and priority of the proposed algorithm, simulation analysis is done by Matlab, while the actual experiment is done on the experimental active vibration control platform. The experiment results show that the proposed algorithm is feasible with good control performance and rapid convergence.

Taking the piezoelectric flexible plate as research object, this paper presents an efficient method to simulate and analyze intelligent structures with MATLAB and ANSYS applied. Firstly, this paper focuses on the development of modeling method for intelligent structures with distributed sensors and actuators. Modal analysis and transient analysis were done by using ANSYS 11.0 software, with the finite model of the intelligent plate constructed. Then based on an APDL (the design language of ANSYS) program, the vibration control experiments were done. By analyzing different control performance of different actuator assign position, the optimization criterion suggested by NASA for piezoelectric sensors and actuators arrangement was verified. While the optimal configuration was chosen, the control channel and primary path model of the vibration control system were obtained with MATLAB system identification toolbox employed. Finally according to the identified model an iterative learning control system was developed. The analysis and experimental results show that the analysis method for intelligent structures proposed in this paper is economical and effective and the designed controller has a good vibration suppression performance.

Taking the piezoelectric flexible beam as the research object, this paper proposed a FXLMS algorithm based MIMO adaptive vibration control method to attenuate the oscillations of flexible structures at low frequency. By introducing the piezoelectric constitutive equations and analyzing the multi-channel adaptive filtered feedforward control algorithm and its FXLMS algorithm process, the model parameter identification strategy for the controlled structure as well as the controller design structure diagram are given in this paper. Structure modal characteristic analysis was done to determine the optimal configuration of piezoelectric sensors and actuators. Active vibration control experimental platform was established to verify the effectiveness of the FXLMS control scheme. The vibration control experiment was done after the control channel model was obtained. The experimental results show that the MIMO adaptive vibration controller proposed in this paper has a good control performance, with low order model and rapid convergence.

Taking the piezoelectric beam as research object, filtered-U least mean square (FULMS) control algorithm for feed-forward adaptive control is analyzed and compared with filtered-x least mean square (FXLMS) feed-forward control algorithm, which can solve the problem that the vibration feedback may affect the measuring of the reference signal in FXLMS. By introducing FXLMS and FULMS control algorithm, this paper gives the controller design structure, while structure modal analysis to determine the optimal placement of piezoelectric sensors and actuators and model parameter identification for the controlled structure are done. Then the active vibration control experimental platform is established to verify the effectiveness of FULMS control scheme as well as FXLMS control scheme. Meanwhile the experimental results of FXLMS and FULMS are compared. The performance comparison shows that the FULMS adaptive vibration controller has a better control performance, with more rapid convergence than FXLMS controller.

Vibration control of flexible structures is observed to be a good way to maintain structural integrity as well as to optimize performance for space structures currently. Taking the piezoelectric flexible structure as research object, this paper focuses on the methodology and implementation of a vibration suppression system. An efficient method to simulate and analyze the piezoelectric plate is presented with ANSYS applied, while an iterative learning control (ILC) algorithm is proposed as the control strategy. A finite element model of the flexible plate is constructed by an APDL (ANSYS Parameter Design Language) program. By transient analysis of piezoelectric flexible plate, the models of the control channel and the primary path are obtained. Finally, an ILC based controller, having good cancellation of the vibration of the flexible plate, is developed. The ANSYS experiments suggest that analyzing the piezoelectric flexible plate by ANSYS is a cost-effective method. The SIMULINK results show that the open-loop PID-type learning algorithm is effective for the active vibration control.