Piezoelectric sensors or actuators are bonded to the surfaces of a flexible cantilever beam. The finite element method is used to build the model of the beam. The dynamic response data of the piezoelectric smart structures is obtained through the FEM analysis. According to the nonlinear approximation capability of neural networks, a dynamic recursive BP neural network is used to identify the piezoelectric vibration system, and the predictive model is built. On the basis of the neural network predictive model, the generalized predictive control algorithm is proposed to control the piezoelectric vibration system and to suppress the undesired vibration of the structures. Simulation results demonstrate the excellent performance of the developed control system. It has some significance for guiding the application of intelligent control algorithms in smart structures.

Engine room’s noise is the main noise source. The noise signal of the engine room of a military ship is measured and its spectrum is analyzed. The main radiation frequency of the noise is found. The contribution of reverberation sound to engine room noise is studied. The scheme for reverberation sound control is worked out, in which a new damping soundabsorption material, the melamine foam, is utilized. Based on the statistical energy analysis (SEA), the geometrical model, the physical model and the mathematical model of the engine room are established. The software for simulating sound absorption of the engine room is made. According to the scheme of design, the melamineformaldehyde?foam coating is equipped in the engine room and then the noise testing is carried out. Result shows that the noise of the engine room is reduced 5.6dB(A).