针对目前主动控制方法主要集中于减振降噪方面的研究,无法满足工程中频率特性改变的需求等问题,结合神经网络的多目标并行处理能力,提出基于神经网络的多目标振动优化控制方法。首先,基于神经网络算法,构造频域主动控制架构,相较于时域方法,该架构一个控制循环只需一次傅里叶变换(Fast Fourier transform, FFT),无傅里叶逆变换(Inverse fast Fourier transform, IFFT),因此,控制时效性得到有效保证。其次,基于全局频域误差与特征频点误差,构造混合型误差评判准则,提升算法的自适应性,可靠性与抗干扰能力。再次,基于多自由度系统方程,研究了多目标控制中的可控性问题,保证控制的可行性。最后,通过大型薄壳结构的八点多目标振动优化控制,有效验证了方法的有效性与可行性。
Active control method mainly focuses on vibration and noise suspension at present thus can’t satisfy the requirement of frequency characteristics control. Therefore, based on the multi-objective parallel processing ability of neural network, the multi-objective vibration optimization method is proposed to deal with this problem. First, the frequency-domain control frame is constructed based on neural network algorithm. Compared with traditional time-domain methods, the proposed control frame just require once FFT in each iteration and no IFFT needed, so the control efficiency can be guaranteed. Second, hybrid error criterion is constructed by combining global frequency error and frequency node error together to improve the adaptability, reliability and anti-interference ability. Third, the controllability problem of the multi-objective method in implementation is studied through mathematical analysis. At last, the effectiveness of the proposed multi-objective method is verified through vibration optimization on eight points of shell structure.
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