Adaptive Algorithm For Active Control Research Articles

Hybrid vibration isolator based on piezoelectric actuator and metal rubber and its adaptive control method

To achieve vibration isolation across the entire working frequency range, this paper proposes a novel hybrid vibration isolator combining metal rubber and piezoelectric actuators, along with an adaptive active control algorithm. The passive isolation system allows for manual adjustment of stiffness and damping based on the preload, overcoming the issue of fixed stiffness and damping in traditional passive isolation units. For the active isolation system, an improved Filtered-x Normalized Least Mean Squares with adaptive step size active control algorithm is proposed, overcoming the problem of narrow frequency range applicability in traditional active control algorithms. A hybrid model of electromechanical coupling and hysteresis for the piezoelectric actuator is developed for the secondary path in the algorithm. Experimental results confirm that the hybrid isolator possesses vibration isolation capabilities within the 5–500 Hz working frequency range, with vibration suppression at the first and second resonance frequencies improved by 98.9% and 92.2%, respectively, compared to the passive system. Additionally, the hybrid isolator effectively suppresses complex multifrequency signals and demonstrates robustness and adaptability. This research provides advanced technological means to address complex vibration problems, and establishes both theoretical and practical foundations for multi-degree-of-freedom vibration isolation.

An adaptive algorithm, based on modified tanh non-linearity and fractional processing, for impulsive active noise control systems

This paper presents an adaptive algorithm for active control of noise sources that are of impulsive nature. The impulsive type sources can be better modeled as a stable distribution than the Gaussian. However, for stable distributions, the variance (second order moment) is infinite. The standard adaptive filtering algorithms, which are based on minimizing variance and assuming Gaussian distribution, converge slowly or become even unstable for stable (impulsive) processes. In order to improve the performance of the standard filtered-x least mean square (FxLMS)-based impulsive active noise control (IANC) systems, we propose two enhancements in this paper. First, we propose employing modified tanh function-based nonlinear process in the reference and error paths of the standard FxLMS algorithm. The main idea is to automatically give an appropriate weight to the various samples in the process, i.e. appropriately threshold the very large values so that system remains stable, and give more weight to samples below threshold limit so that the convergence speed can be improved. A second proposal is to incorporate the fractional-gradient computation in the update vector of IANC adaptive filter. Computer simulations have been carried out using experimental data for the acoustic paths. The simulation results demonstrate that the proposed algorithm is very effective for IANC systems.

Robust adaptive algorithm for active control of impulsive noise

Active noise control (ANC) systems employing adaptive filters suffer from stability issues in the presence of impulsive noise. To overcome this limitation, new methods must be investigated. In this paper, we propose the filtered-x state-space recursive least square (FxSSRLS), an SSRLS-based practical and adaptive algorithm for ANC. Computer simulations are executed to verify the enhanced performance of the FxSSRLS algorithm. Symmetric α-stable (SαS) distributions are used to model impulsive noise. The results show that the proposed FxSSRLS algorithm is more robust in eliminating high-peaked impulses than the recently reported algorithms for ANC applications. Moreover, the suggested solution exhibits better stability and faster convergence, without jeopardizing the performance of the proposed solution in terms of residual noise suppression in the presence of impulses.

Data-reusing-based filtered-reference adaptive algorithms for active control of impulsive noise sources

This paper deals with the adaptive algorithms for active noise control (ANC) systems being employed for the impulsive noise sources. The standard filtered-x least mean square (FxLMS) algorithm; based on the minimization of the variance of the error signal; is well suited for attenuation of Gaussian noise sources. For the impulsive noise; modeled as a stable non-Gaussian process; however, the second order moments do not exist and hence the FxLMS algorithm becomes unstable. The filtered-x least mean p-power (FxLMP) algorithm – based on minimizing the fractional lower order moment (FLOM) – gives robust performance for impulsive ANC; however, its convergence speed is very slow. This paper proposes two data-reusing (DR)-based adaptive algorithms for impulsive ANC. The Proposed-I DR algorithm is based on the normalized step-size FxLMS (NSS-FxLMS) algorithm, and the Proposed-II DR algorithm is based on the Author’s recently proposed NSS generalized FxLMP (NSS-GFxLMP) algorithm. Extensive simulations are carried out, which demonstrate the effectiveness of the proposed algorithms in comparison with the existing algorithms.

Fast Adaptive Algorithms for Active Control of Nonlinear Noise Processes

This correspondence attempts to derive the exact implementation of two nonlinear active noise control (ANC) algorithms, viz. FSLMS and VFXLMS. The concept of reutilizing a part of the computations performed for the first sample while computing the next sample, for a block length of two samples, is exploited here to implement the fast and exact versions of the FSLMS and VFXLMS algorithms which are computationally efficient. Detailed computational complexity analysis for both addition and multiplication requirements is presented to show the advantage of the proposed algorithms. Appropriate simulation experiments are carried out to compare the performance equivalence of the proposed fast algorithms with their original versions.

Adaptive algorithm for active control of impulsive noise

When active noise control (ANC) systems are applied for reducing impulsive noise, conventional adaptive algorithms for ANC may not be effective. In this paper, a new adaptive algorithm for active control of impulsive noise is presented. Compared to the filtered-x least-mean-square algorithm, the proposed algorithm has a much better convergence and stability. Illustrative simulations are conducted to verify the proposed algorithm.