Multi-channel active noise control for periodic sources—indirect approach

This paper presents a multi-channel active noise control algorithm that is designed to reject periodic signals of unknown frequency. It is based on a so-called indirect approach, where the frequency of the disturbance is estimated in real time, and the estimate is used in a disturbance rejection scheme designed for a known frequency. Improvements over an earlier algorithm include an extension to multi-channel systems, a better frequency estimation algorithm, and a thorough experimental evaluation. For disturbance rejection, a so-called inverse G algorithm is proposed and its properties are compared through analysis and experiments to those of a gradient algorithm. A new frequency estimator is also considered that is simple and flexible in design, and is able to use multiple harmonics or multiple signals in order to estimate the fundamental frequency of the noise source. In this manner, the algorithm maintains tracking of the fundamental frequency despite significant changes in signal characteristics. The ability of the indirect approach to reject periodic noise with fixed or time-varying frequency and amplitudes is demonstrated in active noise control experiments. The algorithm may also be useful in other control applications where periodic disturbances of unknown frequency must be rejected.     

基于LMI优化的主动悬架多目标控制

首先将汽车主动悬架的控制问题归结为有时域硬约束的鲁棒干扰抑制问题.在多目标控制框架下,基于线性矩阵不等式(LMI)优化技术,提出了一种H2/广义H2混合控制策略:利用广义H2范数描述系统的时域硬约束,同时选择H2范数最小化系统的性能输出,最终将系统的控制律归结为求解具有LMI约束的半定规划问题.以半车模型为例设计了主动悬架控制器,并给出了较全面的分析和仿真结论.最后利用快速原型(RCP)和硬件在回路仿真(HILS)一体化技术进一步验证所提方法的有效性和可行性.     

Adaptive narrow band disturbance rejection applied to an active suspension—an internal model principle approach

This paper presents a methodology for feedback adaptive control of active vibration systems in the presence of time varying unknown narrow band disturbances. A direct adaptive control scheme based on the internal model principle and the use of the Youla-Kucera parametrization is proposed. This approach is comparatively evaluated with respect to an indirect adaptive control scheme based on the estimation of the disturbance model. The comparative evaluation is done in real time on an active suspension system.     

Adaptive regulation—Rejection of unknown multiple narrow band disturbances (a review on algorithms and applications)

The paper addresses the problem of attenuation (rejection) of unknown and time varying multiple narrow band disturbances without measuring them. In this context the disturbance model is unknown and time varying while the model of the plant is known (obtained by system identification) and almost invariant. This requires to use an adaptive feedback approach. The term “adaptive regulation” has been coined to characterize this control paradigm. Application domains include: mechanical and mechatronics systems, active vibration and noise suppression systems, some types of bio-chemical reactors.The paper reviews the various techniques proposed for solving this problem. It will focus on the presentation of the direct and indirect adaptive regulation strategies using the internal model principle and the Youla–Kucera parametrization which have been extensively used in applications.The paper also reviews a number of applications including: active suspension systems, active vibration control systems, active noise control, bio-chemical reactors, distributed flexible mechanical structures and Blu-ray disc drives. Real time results obtained on various applications will illustrate the methodology.     

An enhanced anti-disturbance attitude control law for flexible spacecrafts subject to multiple disturbances

As well-known disturbance rejection methods, active disturbance rejection control and disturbance observer-based control can effectively improve the control performances of complex systems in the presence of disturbances. However, the accurate rejection of multiple disturbances for control systems of practical engineering, for example, the attitude control system of flexible spacecraft, is still a bottleneck problem. In order to further improve the anti-disturbance capability and reduce the conservativeness, this paper proposes a novel enhanced anti-disturbance control law for the attitude control system of flexible spacecraft by combining active disturbance rejection control and disturbance observer-based control in a unified framework. More specifically, the disturbance from flexible vibration is described by an uncertain exogenous system based on the partially known information including elastic damping ratios and modal frequencies. The disturbance observer-based control is utilized to estimate and thereby reject this disturbance. On the other hand, the other disturbances such as external environmental disturbance and complex model nonlinearity are merged into a equivalent disturbance with bounded derivative, which is compensated by using the active disturbance rejection control law. Stability and robustness analysis are carried out for the disturbance observer and extended state observer. Finally, simulation results of low-earth-orbit flexible satellite are presented to verify the effectiveness of proposed methods.     

基于DSP的有源噪声控制系统设计

基于有源噪声控制算法,设计出一种有源噪声控制器.以TMS320C5509为核心,给出了系统的硬件解决方案,并利用混合编程在硬件系统上实现了基于LMS算法的有源噪声实时控制.实验结果表明,系统取得了良好的降噪效果.     

Binormalized data-reusing adaptive filtering algorithm for active control of impulsive sources

The main objective of active noise control (ANC) is to provide attenuation for the environmental acoustic noise. The adaptive algorithms for ANC systems work well to attenuate the Gaussian noise; however, their performance may degrade for non-Gaussian impulsive noise sources. Recently, we have proposed variants of the most famous ANC algorithm, the filtered-x least mean square (FxLMS) algorithm, where an improved performance has been realized by thresholding the input data or by efficiently normalizing the step-size. In this paper, we propose a modified binormalized data-reusing (BNDR)-based adaptive algorithm for impulsive ANC. The proposed algorithm is derived by minimizing a modified cost function, and is based on reusing the past and present samples of data. The main contribution of the paper is to develop a practical DR-type adaptive algorithm, which incorporates an efficiently normalized step-size, and is well suited for ANC of impulsive noise sources. The computer simulations are carried out to demonstrate the effectiveness of the proposed algorithm. It is shown that an improved performance has been realized with a reasonable increase in the computational complexity.     

Non-minimal state-space model-based continuous-time model predictive control with constraints

This article proposes a model predictive control scheme based on a non-minimal state-space (NMSS) structure. Such a combination yields a continuous-time state-space model predictive control system that permits hard constraints to be imposed on both plant input and output variables, whilst using NMSS output-feedback without the need for an observer. A comparison between the NMSS and observer-based approaches using Monte Carlo uncertainty analysis shows that the former design is considerably less sensitive to plant-model mismatch than the latter. Through simulation studies, the article also investigates the role of the implementation filter in noise attenuation, disturbance rejection and robustness of the closed-loop predictive control system. The results show that the filter poles become a subset of the closed-loop poles and this provides a straightforward method of tuning the closed-loop performance to achieve a reasonable balance between speed of response, disturbance rejection, measurement noise attenuation and robustness.     

Fundamental design limitations of the general control configuration

The theory of fundamental design limitations is well understood for the case that the performance variable is measured for feedback. In the present paper, we extend the theory to systems for which the performance variable is not measured. We consider only the special case for which the performance and measured outputs and the control and exogenous inputs are all scalar signals. The results of the paper depend on the control architecture, specifically, on the location of the sensor relative to the performance output, and the actuator relative to the exogenous input. We show that there may exist a tradeoff between disturbance attenuation and stability robustness that is in addition to the tradeoffs that exist when the performance output is measured. We also develop a set of interpolation constraints that must be satisfied by the disturbance response at certain closed right half plane poles and zeros, and translate these constraints into generalizations of the Bode and Poisson sensitivity integrals. In the absence of problematic interpolation constraints we show that there exists a stabilizing control law that achieves arbitrarily small disturbance response. Depending on the system architecture, this control law will either be high gain feedback or a finite gain controller that depends explicitly on the plant model. We illustrate the results of this paper with the problem of active noise control in an acoustic duct.     

Two-sensor control in active vibration isolation using hard mounts

To isolate precision machines from floor vibrations, active vibration isolators are often applied. In this paper, a two-sensor control strategy, based on acceleration feedback and force feedback, is proposed for an active vibration isolator using a single-axis active hard mount. The hard mount provides a stiff support while an active control system is used to get the desired isolation performance. In our previous work, we showed that a sensor fusion control strategy for active hard mounts can be used to realize three performance objectives simultaneously: providing isolation from floor vibrations, achieving a low sensitivity for direct disturbance forces, and adding damping to internal modes of the supported precision machine. In the present work, an enhanced control strategy is presented, referred to as two-sensor control. We will show that two-sensor control outperforms sensor fusion, because it has more possibilities for loop-shaping and has better stability properties. The two-sensor control strategy is successfully validated on an experimental setup.     

Adaptive rejection of disturbances having two sinusoidal components with close and unknown frequencies

The paper considers the problem of rejecting disturbances with two sinusoidal components in the case where the frequencies are unknown and closely spaced. A natural approach consists of cancelling the components using two separate adaptive algorithms combined in a single scheme. However, experiments in active noise control applications have shown that convergence using such an approach could be very slow. The alternative approach of this paper consists of representing the disturbance signal as a single sinusoid with time‐varying magnitude and phase. The theoretical basis and the limitations of this representation are first discussed. Then, an adaptive disturbance rejection algorithm is proposed and the resulting nonlinear system is analyzed using some approximations. Active noise control experiments demonstrate that the proposed algorithm has better convergence properties than an algorithm designed to cancel the two frequency components separately. In some cases, however, the cost is a small residual error on the output signal. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

The potential for aircraft noise reduction through active suppression from a ground-based system

The feasibility of devising ground-based active noise suppression systems for aircraft which are on the ground and in the air is considered. Detailed numerical calculations are given for various geometries involving an aircraft, sound cancelling sources, reference microphones as well as correction microphones. The calculations are for broadband noise based on a frequency spectrum typical of a jet aircraft. For an aircraft on the ground (during engine run-up and during takeoff from a runway), calculations indicate that noise suppression is both feasible and practical. For an aircraft in the air, while noise suppression is feasible, it is not necessarily practical due to the fact that the most advantageous locations for the suppressing control sources are midway between the ground and the flight path. However, this may not preclude a limited application for very confined areas and for aircraft at relatively low altitudes.     

纳卫星主动温控系统建模与仿真

纳卫星运行于复杂空间热环境中,各种热量干扰、轨道外热流波动和舱内热源的散热都会影响整个星体的温度系统,因此制定合理的温度控制策略尤为重要.在纳卫星动态特性平衡方程基础上建立了主动热控系统模型.分别采用常规PID控制方法与模糊控制方法,对纳卫星温度控制系统进行了仿真研究.仿真结果表明,PID控制能消除稳态误差,满足一定的控温要求,但超调大,过渡时间长;模糊控制则能实时跟踪纳卫星温度变化,较快地达到控温目标值,虽然无法消除静态误差,但其综合控制效果比PID要好.     

Adaptive feedforward compensation algorithms for active vibration control with mechanical coupling

Adaptive feedforward broadband vibration (or noise) compensation is currently used when a correlated measurement with the disturbance (an image of the disturbance) is available. However in most of the systems there is a ”positive” mechanical feedback coupling between the compensator system and the measurement of the image of the disturbance. This may lead to the instability of the system. The paper proposes new algorithms taking into account this coupling effect and provides the corresponding analysis. The algorithms have been applied to an active vibration control (AVC) system and real time results are presented. A theoretical and experimental comparison with some existing algorithms is also provided.     

Adjoint EKF learning in recurrent neural networks for nonlinear active noise control

In this paper, active noise control using recurrent neural networks is addressed. A new learning algorithm for recurrent neural networks based on Adjoint Extended Kalman Filter is developed for active noise control. The overall control structure for active noise control is constructed using two recurrent neural networks: the first neural network is used to model secondary path of active noise control while the second one is employed to generate control signal. Real-time experiment of the proposed algorithm using digital signal processor is carried-out to show the effectiveness of the method.     

Linear active disturbance rejection control for a raceway photobioreactor

In this paper, the problem of pH regulation in photobioreactors has been addressed by using a robust Proportional–Integral controller in combination with a linear active disturbance rejection approach. This formulation is based on the Generalized Proportional–Integral (GPI) observers design to estimate online external disturbances and non-modeled dynamics. The methodology uses a model reference optimization procedure with tracking and regulatory target closed-loop transfer functions for first-order plus dead-time models. The proposed controller is validated on a full-scale Raceway photobioreactor, whose results show a significant improvement in the accuracy of pH regulation and, consequently, a positive influence on biomass production. Moreover, a classical feedforward approach has been used for comparison purposes. The performance of the robust technique is evaluated with different indexes, whose results confirm the good performance of the proposed active disturbance rejection control scheme.     

Stabilization of stochastic nonlinear systems driven by noise ofunknown covariance

This paper poses and solves a new problem of stochastic (nonlinear) disturbance attenuation where the task is to make the system solution bounded by a monotone function of the supremum of the covariance of the noise. This is a natural stochastic counterpart of the problem of input-to-state stabilization in the sense of Sontag (1989). Our development starts with a set of new global stochastic Lyapunov theorems. For an exemplary class of stochastic strict-feedback systems with vanishing nonlinearities, where the equilibrium is preserved in the presence of noise, we develop an adaptive stabilization scheme (based on tuning functions) that requires no a priori knowledge of a bound on the covariance. Next, we introduce a control Lyapunov function formula for stochastic disturbance attenuation. Finally, we address optimality and solve a differential game problem with the control and the noise covariance as opposing players; for strict-feedback systems the resulting Isaacs equation has a closed-form solution     

Dynamic Modelling and Stability Analysis of Model-Scale Helicopters Under Wind Gust

The purpose of this paper is to present the stabilization (tracking) with motion planning of a reduced-order model having 3 Degrees Of Freedom (DOF). This last one represents a scale model helicopter mounted on an experimental platform. It deals with the problem of disturbance reconstruction acting on the autonomous helicopter, the disturbance consists in vertical wind gusts. The objective is to compensate these disturbances and to improve the performances of the control. Consequently, a nonlinear simple model with 3DOF of a helicopter with unknown disturbances is used. Three approaches of robust control are compared via simulations: a robust nonlinear feedback control, an active disturbance rejection control based on a nonlinear extended state observer and backstepping control.     

Application of the genetic algorithm to real-time active noise control

A modified model, in the form of an FIR filter, is proposed for the modelling of the acoustic dynamics of an active noise control system. This is a low order filter formulation but consists of two independent elements—a time delay and a d.c. gain. Empirical data has shown that this model constitutes a good representation of the equivalent high order FIR filter and has the additional feature of being a high frequency noise filtering device. Because of its specific structure, the time delay and gain must be identified independently. This restricts the use of the conventional least mean squares technique for parameter optimization, as the cost function intrinsically comprises multimodal error surfaces. The use of Genetic Algorithms could be the best solution to address this issue but their unpredictable response in real-time require some special attention. A fully developed active noise control system, based on the Genetic Algorithm, to achieve the objective of noise reduction is described. To further guarantee the reliability of this approach, a supervisory scheme is incorporated for governing the real-time learning operations. A parallel hardware architecture, using two independent TMS320C30 digital signal processors, is designed for such implementation. The experimental results indicate that this approach to noise control is sound, and that noise reduction of more than 15dB(A) is consistently obtained.     

基于滑动窗实时小波降噪的扩张状态观测器及自抗扰控制EI北大核心CSCD

自抗扰控制技术应用已日渐成熟,但当系统中存在高频非平稳噪声信号时,线性自抗扰控制(LADRC)存在难以选取合适的观测器带宽的问题:当带宽较小时,线性扩张状态观测器(LESO)难以实现对总扰动的实时观测,会造成时滞;当带宽较大时,LESO又会放大噪声对系统的影响,从而造成总扰动观测失真.为了解决这一问题,将小波降噪环节加入LADRC中,通过设计基于滑动窗实时小波降噪的LESO,对含噪输出信号进行实时降噪.使用Simulink搭建系统模型,分别在输出信号中加入高斯白噪声或谐波等不同类型的高频非平稳噪声进行仿真实验,并将所提方法与滑动平均法进行对比,结果验证了所提方法的有效性.