Abstract
Traditional adaptive filtering algorithms are non-recursive systems that cannot use a time-variant reference input in real time and are not appropriate for control signals with uncertainties and unanticipated conditions. The main purpose of this research is to design novel adaptive digital filtering algorithms based on internal model control (IMC). The new methods consist of a process model for the target plant so as to estimate its dynamic behavior for active vibration and noise attenuation schemes in order to improve the stability, robustness, and tracking performance. On the basis of on the existing least mean squares, the methods are combined with an internal model controller, or the whole adaptive filtering system could become a feedback control system structure based on IMC. The performances were validated in numerical simulations with various conditions that could have happened in realistic applications, and the results were compared with the original algorithms. This study shows that the active noise and vibration systems that are applied to vehicles, mechanical systems, and other targets are enhanced through improving the performance of conventional adaptive filtering algorithms and by using internal model control effectively.
Highlights
Various components, devices, and systems that have been created with ferroelectric substances have been broadly employed for energy transformation and precise positioning devices
The main purpose of this study is to suggest a least mean square (LMS) algorithm based on state-of-the-art internal model control (IMC) for smart structures
Two enhanced LMS methods were introduced for an adaptive digital filtering system combined
Summary
Devices, and systems that have been created with ferroelectric substances have been broadly employed for energy transformation and precise positioning devices. A number of research efforts have been dedicated to active vibration and noise mitigation by smart materials and structures. Active control applications apply force actively with various devices, such as piezoelectric, electromagnetic, and magnetorheological actuators, in order to mitigate the vibration and unexpected noise of given systems. For active control systems on noise, vibration, and harshness (NVH), adaptive digital filtering systems with the least mean square (LMS) algorithm have been used. Previous research on adaptive algorithms could deal with signals that contain relatively simple frequency spectra without unanticipated situations, but the realistic conditions would be affected by various factors, such as unexpected disturbance, uncertainties, and noise. Even after the amplitude of a governing frequency component is considerably attenuated, the left over spectral elements still induce severe vibration and noise. More investigations on enhanced adaptive filtering algorithms are required for better performance of the active control systems (such as tracking, robustness, stability, and adaptability)
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