Nguyen Thi Trung Tin, Zhang Faxiang, Na Jing, Nguyen Le Thai, Li Gengen, Ahmed Altyib Abdallah Mahmoud
Yunnan Key Laboratory of Intelligent Control and Application, Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China.
Faculty of Engineering and Technology, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam.
Sensors (Basel). 2025 Apr 18;25(8):2569. doi: 10.3390/s25082569.
Active noise control (ANC) represents an efficient technology for enhancing the noise suppression performance and ensuring the stable operation of multi-sensor systems through generative model-enhanced data representation and dynamic information fusion across heterogeneous sensors due to the complexity of the real-world environment. To address problems caused by a nonlinear noise source, a novel adaptive neuro-fuzzy network controller is proposed for feedforward nonlinear ANC systems based on a variable step-size filtered-x least-mean-square (VSS-LMS) algorithm. Specifically, the LMS algorithm is first introduced to update the weight parameters of the controller based on the adaptive neuro-fuzzy network. Then, a variable step-size adjustment strategy is proposed to calculate the learning gain used in the LMS algorithm, which aims to improve the nonlinear noise suppression performance. Additionally, the stability of the proposed method is proven by the discrete Lyapunov theorem. Extensive simulation experiments show that the proposed method surpasses the mainstream ANC methods with regard to nonlinear noise.
由于现实世界环境的复杂性，有源噪声控制（ANC）是一种通过生成模型增强数据表示以及跨异构传感器的动态信息融合来提高噪声抑制性能并确保多传感器系统稳定运行的有效技术。为了解决由非线性噪声源引起的问题，基于可变步长滤波x最小均方（VSS-LMS）算法，为前馈非线性ANC系统提出了一种新型自适应神经模糊网络控制器。具体而言，首先引入LMS算法，基于自适应神经模糊网络更新控制器的权重参数。然后，提出了一种可变步长调整策略来计算LMS算法中使用的学习增益，旨在提高非线性噪声抑制性能。此外，通过离散Lyapunov定理证明了所提方法的稳定性。大量仿真实验表明，所提方法在非线性噪声方面优于主流ANC方法。
