Active noise control

Active noise control exploits the long wavelengths associated with low frequency sound. It works on the principle of destructive interference between the sound fields generated by the original primary sound source and that due to other secondary sources, acoustic outputs of which can be controlled. The acoustic objectives of different active noise control systems and the electrical control methodologies that are used to achieve these objectives are examined. The importance of having a clear understanding of the principles behind both the acoustics and the electrical control in order to appreciate the advantages and limitations of active noise control is emphasized. A brief discussion of the physical basis of active sound control that concentrates on three-dimensional sound fields is presented     

Active noise control: a tutorial review

Active noise control (ANC) is achieved by introducing a cancelling “antinoise” wave through an appropriate array of secondary sources. These secondary sources are interconnected through an electronic system using a specific signal processing algorithm for the particular cancellation scheme. ANC has application to a wide variety of problems in manufacturing, industrial operations, and consumer products. The emphasis of this paper is on the practical aspects of ANC systems in terms of adaptive signal processing and digital signal processing (DSP) implementation for real-world applications. In this paper, the basic adaptive algorithm for ANC is developed and analyzed based on single-channel broad-band feedforward control. This algorithm is then modified for narrow-band feedforward and adaptive feedback control. In turn, these single-channel ANC algorithms are expanded to multiple-channel cases. Various online secondary-path modeling techniques and special adaptive algorithms, such as lattice, frequency-domain, subband, and recursive-least-squares, are also introduced. Applications of these techniques to actual problems are highlighted by several examples     

Asynchronous digital linear?nonlinear control

At present, digital control solutions are becoming more attractive than analogue implementations in some power converter applications owing to easy design of complex control strategies and control reconfigurability. The digital implementation of an asynchronous linear-nonlinear (LnL) compensator to improve the dynamic response of interleaved buck converters is proposed and validated. It is important to highlight that the same digital LnL compensator is able to keep the dynamic response even with very important variations of the control and the power stage parameters.     

The control of input-constrained nonlinear processes usingnumerical generalized predictive control methods

A generalized predictive control algorithm based on numerical methods has been developed to cope with some of the practical control problems, such as input constraints, rate limits, and nonlinearities of industrial processes. Using numerical methods, different cost functions can be minimized to achieve the desired control performance without any significant change to the control algorithm. The results from a system used for an air-handling plant are given, in order to demonstrate the potential of this algorithm     

超短脉冲激光频率的线性及非线性扩展

通过采用若干激光增益介质和非线性频率变换技术,有效地扩展了皮秒及飞秒激光的波长.在可见光波段,利用自建的高能量400 nm倍频皮秒钛宝石激光作泵浦,通过LBO参量放大,得到了波长从450～750 nm可调、最高单脉冲能量达30 mJ的可调谐皮秒激光脉冲;在红外波段,通过上述方案不仅产生了波长850 nm～3.6μm的皮秒闲频光,而且相继利用Cr:Mg2SiO4和Cr:YAG晶体作为增益介质,获得了中心波长分别在1 280 nm和1 450 mn波段的飞秒激光脉冲.利用Yb:YAG、Yb:GYSO、Nd:GdVO4、Nd:LuVO4等晶体作增益介质,获得了中心波长1 053、1 093 nm的飞秒激光脉冲及912、916 nm的皮秒脉冲.这些具有不同波长特性的超短脉冲激光光源,可望在许多领域获得重要应用.     

基于非线性控制方法的AQM算法

首先利用非线性控制方法中的积分反步法设计了一种AQM算法,接着使用非线性控制方法中的模糊滑模变结构控制方法进行AQM算法的设计.仿真结果表明,与积分反步法相比,基于模糊滑模变结构控制的AQM算法性能上优于基于积分反步法的AQM算法.与基于线性控制方法的PID控制方法相比,基于积分反步法的AQM算法性能上优于基于PID控制的AQM算法.     

Guaranteed cost control of uncertain differential linear repetitive processes

This paper deals with the problem of designing a control law for differential linear repetitive processes based on minimizing a cost function in the presence of uncertainties in the process model. This control law results in a closed-loop stable process with an associated cost function which is bounded for all admissible uncertainties. Moreover, an optimization algorithm is developed to design this law such that it minimizes the upperbound of the closed-loop cost function.     

Active noise control using an adaptive bacterial foraging optimization algorithm

This paper presents an adaptive bacterial foraging optimization (ABFO) algorithm for an active noise control system. The conventional active noise control (ANC) systems often use the gradient-based filtered-X least mean square algorithms to adapt the coefficients of the adaptive controller. Hence, there is a possibility to converge to local minima. In addition, this class of algorithms needs prior identification of the secondary path. The ABFO algorithm helps the ANC system to prevent falling into local minima. The proposed ANC system is also simpler since it does not need any prior information of the secondary path. Moreover, the adaptive strategy of the algorithm results in improved search performance compared with the basic bacterial foraging optimization algorithm, as well as other conventional algorithms. Experimental studies are performed for nonlinear primary path along with linear and nonlinear secondary path. The results show the effectiveness of the proposed ABFO-based ANC system for different kinds of input noise.     

On the development of adaptive hybrid active noise control system for effective mitigation of nonlinear noise

The presence of nonlinearities as well as acoustic feedback deteriorates the cancellation performance of the conventional filtered-x LMS (FxLMS) algorithm based active noise control (ANC) systems. With an objective to improve the performance, a novel filtered-su LMS (FsuLMS) algorithm based ANC system which employs a convex combination of an adaptive IIR filter with a functional link artificial neural network (FLANN) is proposed in this paper. The corresponding learning algorithm of the ANC system is derived and used in the simulation study for performance evaluation. Simulation study reveals enhanced performance of the proposed system over that of its component filters.     

具有乘性噪声的线性系统的随机共振

研究了具有高斯白噪声的过阻尼二阶线性系统的随机共振现象,基于线性系统理论,得到了系统输出幅度增益的精确表达式。研究发现,输出幅度的增益是激励信号频率的非单调函数,系统固有频率与噪声强度对输出幅度增益的幅度影响是相反的。     

An efficient feedback active noise control algorithm based on reduced-order linear predictive modeling of FMRI acoustic noise

Functional magnetic resonance imaging (fMRI) acoustic noise exhibits an almost periodic nature (quasi-periodicity) due to the repetitive nature of currents in the gradient coils. Small changes occur in the waveform in consecutive periods due to the background noise and slow drifts in the electroacoustic transfer functions that map the gradient coil waveforms to the measured acoustic waveforms. The period depends on the number of slices per second, when echo planar imaging (EPI) sequencing is used. Linear predictability of fMRI acoustic noise has a direct effect on the performance of active noise control (ANC) systems targeted to cancel the acoustic noise. It is shown that by incorporating some samples from the previous period, very high linear prediction accuracy can be reached with a very low order predictor. This has direct implications on feedback ANC systems since their performance is governed by the predictability of the acoustic noise to be cancelled. The low complexity linear prediction of fMRI acoustic noise developed in this paper is used to derive an effective and low-cost feedback ANC system.     

Maximum principle for optimal control of two-directionally continuous linear repetitive processes

In the paper, maximum principle for a two-directionally continuous variant of a linear autonomous repetitive process with cost functional depending on a fixed “end-function” is obtained. Maximum condition has a pointwise form, a conjugate system has a Fornasini-Marchesini form. The result is derived from the extremum principle for smooth-convex problems, due to Ioffe and Tikhomirov.     

Active cancellation of acoustic noise using a self-tuned filter

In an increasingly noisy society, methods of reducing noise are becoming more important. This work proposes an analog electroacoustic circuit for active control of narrow-band low-frequency acoustic noise using adaptive filtering techniques. The circuit aims at producing antinoise, which is acoustically added to the disturbing noise to produce an error signal that is fed back to the circuit. The proposed circuit is a modified Kerwin-Huelsman-Newcomb biquad filter that tunes itself to the incoming noise frequency using the zero tuning techniques. The circuit was implemented on a printed circuit board and it was successful in reducing noise by 15-20 dB in open space. Active noise control specifically for narrow-band noise cancellation using adaptive analog filters seems to be a better solution than its digital signal processing counterpart in speed, cost, and robustness.     

Decoupling and iterative approaches to the control of discrete linear repetitive processes

This paper reports new results on the analysis and control of discrete linear repetitive processes which are a distinct class of 2D discrete linear systems of both systems theoretic and applications interest. In particular, we first propose an extension to the basic state-space model to include a coupling term previously neglected but which arises in some applications and then proceed to show how computationally efficient control laws can be designed for this new model.     

A tight upper bound on the gain of linear and nonlinear predictorsfor stationary stochastic processes

One of the striking questions in prediction theory is this: is there a chance to predict future values of a given signal? Usually, we design a predictor for a special signal or problem and then measure the resulting prediction quality. If there is no a priori knowledge on the optimal predictor, the achieved prediction gain will depend strongly of the prediction model used. To cope with this lack of knowledge, a theorem on the maximum achievable prediction gain of stationary signals is presented. This theorem provides the foundation for estimating a quality goal for the predictor design, independent of a special predictor implementation (linear or nonlinear). As usual, the prediction gain is based on the mean square error (MSE) of the predicted signal. The achievable maximum of the prediction gain is calculated using an information theoretic quantity known as the mutual information. In order to obtain the gain, we use a nonparametric approach to estimate the maximum prediction gain based on the observation of one specific signal. We illustrate this by means of well-known example signals and show an application to load forecasting. An estimation algorithm for the prediction gain has been implemented and used in the experimental part of the paper     

Determination of the noise parameters of a linear 2-port

A new algorithm is given for the calculation of the four noise parameters of a linear 2-port from experimental measurements of its noise figure as a function of source admittance. A computer program for performing this calculation is briefly described (more detailed documentation is available).     

Robust control with finite frequency specification for uncertain discrete linear repetitive processes

This paper is dedicated to the study of robust stability and controller synthesis for discrete linear repetitive processes with polytopic uncertainty. In the robust control domain, conditions based on parameter dependent Lyapunov functions are proposed in order to reduce the conservatism related to uncertainty problems. The solution is a class of Lyapunov functions that depends in a polytopic way on the uncertain parameters and that can be derived from linear matrix inequality conditions. Nevertheless, in many cases in practice, the frequency range of reference signals, noises and disturbances are known beforehand. Therefore, performing controller synthesis in the full frequency range is not practically suited and may introduce conservatism to some extent. Based on generalized Kalman–Yakubovich–Popov Lemma, a finite frequency controller is derived for uncertain discrete linear repetitive processes which are the most investigated class of 2D systems. Hence, the designer can specify a frequency range where the prescribed control performance is required, where, for example, this range could be determined by inspection of frequency spectrums of the available signals.     

Pulse-width-modulating control of a nonlinear electromagneticactuator

The performance of a fast, nonlinear, clapper-type electromagnetic actuator used in impact printing is controlled by real-time measurement and feedback. The objective is to regulate flight time, which is the time from start to actuation to impact. Toward this end, control is applied digitally via pulse-width modulation of a series of coil-driving pulses, which together propel the armature through its trajectory. Each pulse is modulated individually based on state-variable errors measured on its rising edge. The functional relationships between the measured state-variable errors and the required pulse-width modulations are derived systematically, using a computer-controlled method involving trial-and-error experimentation followed by statistical regression. The resulting control law accounts for both mechanical and electrical perturbations and is expressed in an analytic format that can be applied either by look-up tables or by direct computation. Using look-up tables, typical closed-loop operation is shown to achieve dramatic reductions in flight-time error when compared with open-loop operation     

On control laws for discrete linear repetitive processes with dynamic boundary conditions

Repetitive processes are characterized by a series of sweeps, termed passes, through a set of dynamics defined over a finite duration known as the pass length. On each pass an output, termed the pass profile, is produced which acts as a forcing function on, and hence contributes to, the dynamics of the next pass profile. This can lead to oscillations in the sequence of pass profiles produced which increase in amplitude in the pass-to-pass direction and cannot be controlled by application of standard control laws. Here we give new results on the design of physically based control laws for so-called discrete linear repetitive processes which arise in applications areas such as iterative learning control.