【摘要】 基于压电智能结构开展振动主动控制，控制作动器的控制作用会对参考传感器采集到的振动参考信号产生结构内部正反馈，导致控制器的性能下降甚至失效。为保证内部反馈回路的稳定性，增强振动主动控制器的自适应和鲁棒性能，本文结合自适应滤波控制与鲁棒控制，基于Youla-Ku?era参数化方法提出了一种前馈鲁棒自适应振动控制器。通过引入历史时刻数据信息来改进参数自适应算法；引入基于误差阈值的平稳更新策略来提高参数估计精度。在系统辨识应用中，为更加精确且快速地估计不同阶次下的模型参数，进一步提出了针对不同模型部分参数分开估计的改进分段参数自适应算法。以粘贴压电纤维复合材料（macro-fiber composite,MFC）的航空发动机压电智能叶片为验证试验研究对象，构建了xPC Target实时控制系统（xPC target real-time control system），验证了本文提出的基于改进参数自适应算法的Youla-Ku?era参数化前馈鲁棒自适应控制器在单频、多频及噪声干扰下振动控制的有效性。更多还原

【Abstract】 While active vibration control is carried out with piezoelectric smart structures,the control action of the actuators can generate structural internal positive feedback on the vibration reference signal collected by the reference sensor,leading to performance degradation or even failure of the controller. To ensure the stability of the internal feedback loop and enhance the adaptive and robust performance of the active vibration controller,this paper proposes a novel feedforward robust adaptive vibration controller based on the Youla-Ku?era parameterization method by combining adaptive filtering control and robust control. By introducing historical data information to improve the parameter adaptation algorithm and incorporating a steady update strategy based on an error threshold,the accuracy of parameter estimation is enhanced.Furthermore,for system identification applications,an improved segmented parameter adaptation algorithm is proposed to separately estimate the parameters of different model segments, enabling more precise and rapid parameter estimation for models of varying orders. The effectiveness of the proposed Youla-Ku?era parameterization-based feedforward robust adaptive controller with the improved parameter adaptation algorithm is verified under single-frequency,multi-frequency,and noise interference conditions using a piezoelectric smart blade of an aero-engine with bonded macro-fiber composite(MFC) with xPC target real-time control system.更多还原