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Considering the variety of uncertainties and disturbances in real active vibration control systems, a novel piezoelectric multimode control strategy for an all-clamped stiffened plate (ACSP) structure is proposed in this paper. First, an active disturbance rejection control (ADRC) method, i.e., the extended state observer (ESO)-based vibration control scheme, is employed to ensure the performance of the vibration suppression and rejection of the lumped disturbances of the closed-loop system. Second, a proportional differential controller and an acceleration feedforward controller produce the control law for each vibration mode. Moreover, a chaos optimization method based on a logistic map is introduced to automatically tune the parameters of the feedback channel. The stability and superiority of the proposed controller are theoretically analyzed. Compared with the classical ADRC method, the multimode vibration experimental results demonstrate that the control performance of the first mode is improved from 12.04 to 14.06 dB; meanwhile, the performance of the second mode is also improved, having changed from 11.1 to 13.68 dB.

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In this paper, a novel modeling and parameter learning method for the Hammerstein–Wiener model with disturbance is proposed, and the Hammerstein–Wiener model is implemented to approximate complex nonlinear industrial processes. The proposed Hammerstein–Wiener model has two static nonlinear blocks represented by two independent neuro-fuzzy models that surround a dynamic linear block described by the finite impulse response model. The parameter learning method of the Hammerstein–Wiener model with disturbance can be summarized in the following three steps: First, the designed input signals are implemented to completely separate the parameter learning problem of output nonlinear block, linear block, and input nonlinear block. Meanwhile, the static output nonlinear block parameters can be learned based on input and output data of two sets of separable signals with different sizes. Second is to determine the dynamic linear block parameter using correlation analysis algorithm using one set of separable signal; thus, the process disturbance can be compensated by the calculation of correlation function. The final one is to achieve unbiased estimation of the static input nonlinear block parameters using least squares method according to the input–output data of random signal. Furthermore, with the parameter learning method, the proposed model can achieve less computation complexity and good robustness. The simulation results of two cases are provided to demonstrate the advantage of the proposed modeling and parameter learning method.