A recursive deconvolution approach to disturbance reduction

Active noise control (ANC) uses an estimate of the noise affecting a system in order to remove its effect from the output. In some applications, it is possible to directly measure the noise and a feedforward compensator can be used. Other applications require instead that the noise be estimated from its effect on the system. This results in an adaptive feedback ANC, see a previous paper by Gan and Kuo. The identification of the disturbance from the output of the linear system is a deconvolution problem. In this paper, we study a deconvolution technique for the active reduction of the influence of the disturbance on the output of a linear control system. The regulator that we present does not affect the behavior of the system in the absence of disturbances.     

基于自然梯度的后非线性盲解卷算法

给出了一个基于自然梯度的后非线性多信道盲解卷算法。混合系统由线性卷积混合后接一个可逆非线性失真组成。分离系统由多层感知器(非线性部分)后接一个线性盲解卷过程(线性部分)组成。分离系统的线性部分和非线性部分参数学习都采用自然梯度算法。仿真结果显示,自然梯度算法比传统梯度算法收敛速度更快,分离效果更好。     

Self-tuning weighted measurement fusion white noise deconvolution estimator and its convergence analysis

White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. For the multisensor linear discrete time-invariant stochastic systems with correlated measurement noises, and with unknown ARMA model parameters and noise statistics, the on-line AR model parameter estimator based on the Recursive Instrumental Variable (RIV) algorithm, the on-line MA model parameter estimator based on Gevers–Wouters algorithm and the on-line noise statistic estimator by using the correlation method are presented. Using the Kalman filtering method, a self-tuning weighted measurement fusion white noise deconvolution estimator is presented based on the self-tuning Riccati equation. It is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steady-state white noise deconvolution estimator in a realization by using the dynamic error system analysis (DESA) method, so that it has the asymptotic global optimality. The simulation example for a 3-sensor system with the Bernoulli–Gaussian input white noise shows its effectiveness.     

Recursive subspace identification subject to relatively slow time-varying load disturbance

In this paper, a recursive subspace identification method is proposed to identify linear time-invariant systems subject to load disturbance with relatively slow dynamics. Using the linear superposition principle, the load disturbance response is decomposed from the deterministic-stochastic system response in the form of a time-varying parameter. To ensure unbiased estimation of the deterministic system matrices, a recursive least-squares (RLS) identification algorithm is established with a fixed forgetting factor, while another RLS algorithm with an adaptive forgetting factor is constructed based on the output prediction error to quickly track the time-varying parameter of load disturbance response. By introducing a deadbeat observer to represent the deterministic system response, two extended observer Markov parameter matrices are constructed for recursive estimation. Consequently, the deterministic matrices are retrieved from the identified system Markov parameter matrices. The convergence of the proposed method is analysed with a proof. Two illustrative examples are shown to demonstrate the effectiveness and merit of the proposed identification method.     

On Active Noise Control Systems With Online Acoustic Feedback Path Modeling

The presence of strong acoustic feedback degrades the convergence speed of the active noise control (ANC) filter, and in the worst case the ANC system may become unstable. A fixed feedback neutralization filter, obtained offline, can be used to neutralize the acoustic feedback. The feedback path, however, may be time varying, and we may need continual adjustments during online operation of the ANC system. This paper proposes a new method for online modeling of the acoustic feedback path in ANC systems. The proposed method uses three adaptive filters; a noise control filter, a feedback path modeling (FBPM) filter, and an adaptive noise cancelation (ADNC) filter. The objective of ADNC filter is to remove the disturbance from the desired response of FBPM filter. In comparison with the existing method, which works only for predictable noise sources, the proposed method can work, as well, with the broadband noise sources. The computer simulations are carried out for narrowband (predictable) (case I) and broadband (random) noise sources (case II). It is demonstrated that the proposed method performs better than the existing method in both cases     

线性自抗扰控制参数b0 辨识及参数整定规律

针对自抗扰控制中参数b0 整定困难的问题, 提出一种新的参数辨识方法, 对线性自抗扰控制器(LADRC) 中的参数b0 进行辨识, 并提出了LADRC参数整定的基本规律. 通过频域分析研究了控制器参数b₀ 和控制器带宽b₀ 的变化对闭环系统扰动抑制能力的影响. 通过分析闭环控制系统的稳定区域研究了控制器的鲁棒性. 仿真结果表明, 根据辨识得到的b0 可以快速整定LADRC的参数, 使LADRC具有较强的鲁棒性.

     

基于模型信息的电静液作动器降阶线性自抗扰控制

针对电静液作动器(electro-hydrostatic actuators, EHA)系统存在内外部扰动、参数不确定性和变控制增益等问题,提出一种基于模型信息的降阶线性自抗扰位置控制方法.首先,基于系统模型信息选取控制增益.其次,通过降阶线性扩张观测器对系统总扰动进行估计,并在控制器中加入扰动项进行补偿.利用奇异摄动理论证明所提控制器可使闭环系统是半全局最终一致有界的,并且当观测器带宽足够大时,所提出的控制器理论上可以使系统输出以所需精度跟踪期望轨迹.仿真结果表明,所提控制方法响应速度较快,控制精度较高,对外部扰动和参数不确定性具有较强的鲁棒性.     

基于内模的机器人迭代学习控制

本文根据内模控制的概念，设计一个扰动控制器，使机器人系统表现为固定参数的解耦线性化系统．基于此线性系统，提出了一种迭代学习控制律，给出了算法收敛的充分条件．算法的参数选择非常简单，从而易于满足收敛条件．仿真结果表明了算法的有效性．     

一种基于ICA的同态盲反卷积算法

盲反卷积是图像处理、语音信号处理、通信、系统辨识和声学等许多研究和应用的基本问题,具有重要的理论与应用价值。根据无损检测中盲反卷积问题的特点,提出了一种新的基于ICA的同态盲反卷积算法。该算法首先将检测信号变换到复倒谱域,将卷积混合模型变为线性混合模型,即ICA问题;然后通过ICA将系统冲击响应和输入信号分离;最后,根据分离的复倒谱信号,重构其时域信号。论文提出的盲反卷积算法具有运算量小,计算速度快,分离精度高等特点,且不受信道是否为最小相位信道的影响。计算机模拟和实验数据都证明了算法的有效性。     

周期时变不确定性线性系统的MINIMAX控制方法

应用约束最优化方法和微分对策理论,讨论周期时变不确定性线性系统在范数有界外 部干扰情况下的MINIMAX控制和参数摄动情况下的MINIMAX控制.问题可解的充分条件 是一类Riccati微分方程具有稳定化解,且关于最坏扰动的某个附加条件满足相应的MINIMAX 控制恰为一个线性状态反馈.此外,还给出了闭环系统的性能指标的保证值.     

多输入不确定离散时滞系统的灰色滑模控制

针对存在时变参数不确定性和随机干扰的多输入不确定离散时滞系统,设计了基于LMI的滑模控制器以消除时滞的影响,并使系统状态在有限时间内收敛到零,然后对系统的不确定部分建立灰色估计模型,并进一步设计了灰色补偿器。仿真结果表明,采用所设计的灰色滑模控制器,不仅有效地消除了时滞的影响,抑制了时变参数不确定因素和随机干扰,而且保证多输入不确定离散时滞系统具有良好的鲁棒稳定性。     

Convergence properties of constrained linear system under MPC control law using affine disturbance feedback

This paper shows new convergence properties of constrained linear discrete time system with bounded disturbances under Model Predictive Control (MPC) law. The MPC control law is obtained using an affine disturbance feedback parametrization with an additional linear state feedback term. This parametrization has the same representative ability as some recent disturbance feedback parametrization, but its choice together with an appropriate cost function results in a different closed-loop convergence property. More exactly, the state of the closed-loop system converges to a minimal invariant set with probability one. Deterministic convergence to the same minimal invariant set is also possible if a less intuitive cost function is used. Numerical experiments are provided that validate the results.     

Active Disturbance Rejection in Affine Nonlinear Systems Based on Equivalent‐Input‐Disturbance Approach

This paper presents a disturbance rejection method for an affine nonlinear system. The control system is constructed based on the equivalent‐input‐disturbance (EID) approach. An affine nonlinear state observer is used to reconstruct the state of the affine nonlinear system and to estimate an EID. The well‐known differential mean value theorem enables us to describe the closed‐loop system in the state space as a linear‐parameter‐varying system. This makes it easy to derive sufficient conditions of global uniform ultimate boundedness in term of linear matrix inequalities (LMIs) by using a Lyapunov function and convexity theory. Controllers are designed based on the LMIs. A numerical example is used to illustrate the design of the control system. And a comparison between the EID‐based control and the sliding‐mode control demonstrates the effectiveness and advantages of the EID‐based control method.     

一类轻载电液位置伺服系统线性自抗扰控制

为解决一类轻载电液位置伺服系统线性自抗扰控制器设计过程中面临的阶次选择问题,本文从系统特性、频域等角度,分析自抗扰框架中“积分器串联结构”与轻载电液位置伺服系统之间的内在联系,得到轻载电液位置伺服系统在自抗扰控制框架下是本质“一阶”系统的结论,从而合理设计了1阶线性自抗扰控制器.在此基础上,提出了有效的控制器参数整定方法,并分析了闭环系统的稳定性.仿真和试验结果表明,与高阶相比1阶线性自抗扰控制器可以更好地控制动态过程较快、负载较轻的惯性负载电液位置伺服系统,为自抗扰控制在液压伺服领域的工程应用提供了参考.     

无人机着陆抗风鲁棒H_∞控制器设计研究

无人机在进场着陆过程中存在低空风切变和外界各种不确定性干扰等因素,根据无人机精确进场自动着陆性能要求,利用线性矩阵不等式凸优化方法设计鲁棒H∞控制器,在满足自动着陆系统轨迹跟踪及速度控制性能的同时,能够有效抑制低空风切变及外界不确定性干扰,将所设计的自动着陆控制律在低空风切变大气干扰下进行验证,仿真结果表明,无人机着陆下滑及拉平轨迹响应具有良好的动态跟踪性能和鲁棒性,验证了该设计方法的正确性和有效性。     

线性/非线性自抗扰切换控制方法研究

非线性自抗扰控制 (Nonlinear active disturbance rejection control, NLADRC) 较线性自抗扰控制 (Linear active disturbance rejection control, LADRC) 具有跟踪精度高、抗干扰能力强等优点, 但在参数整定、稳定性分析以及控制性能分析等方面有一定的困难, 阻碍了非线性自抗扰控制在实际中的应用, 而线性自抗扰控制成为工程应用的首选.本文提出一种线性/非线性自抗扰控制切换控制方法, 该方法既综合了线性/非线性自抗扰控制的优点, 又解决了非线性自抗扰控制在参数整定、稳定性分析等方面的困难:首先, 分析线性/非线性自抗扰控制各自优缺点, 并给出了一种切换控制策略; 其次, 提出一种基于优化进行查表或利用拟合公式的参数整定方法; 再次, 提出基于劳斯判据和鲁棒波波夫判据的稳定性分析方法.通过仿真验证了该切换方法在跟踪精度、抗干扰能力等方面具有一定优势.该切换控制方法将有助于更好地发挥非线性机制在要求实现高精度、高抗扰能力场合的独特优势, 有望在工程实际中获得应用.     

一种抑制扰动的最优化设计方法

对于多输入系统,配置一组闭环极点所需的状态反馈阵并非唯一。本文研究了怎样利用这种非唯—性提供的自由度来提高系统的动态抗扰能力,提出了一种动态抗扰-最优极点配置法。该方法使极点配置和参数优化能在两个独立的步骤中完成,计算工作量较小。     

Feedback control for a class of second order hyperbolic distributed parameter systems一类二阶双曲型分布参数系统的反馈控制

This paper deals with the problem of state feedback control for a class of the distributed parameter systems with the disturbance term. And the considered distributed parameter systems are composed of the second order hyperbolic partial differential equations. Two different classes of restrictions on the disturbance term are given, one is that the disturbance term satisfies the linear growth constraint condition to the state variables of the system, and the other is that the disturbance term obeys the bound constraint under the significance of L₂. Based on a variable structure method, the state feedback controllers are obtained by means of constructing appropriate Lyapunov functional. The closed-loop systems are globally asymptotically stable on W^1,2(0, 1) × L₂(0, 1) space under the effect of the state feedback control laws. Simulation results illustrate the effectiveness of the proposed method.     

A new splitting and preconditioner for iteratively solving non-Hermitian positive definite systems

A new iteration method for solving a linear system with coefficient matrix being non-Hermitian positive definite is presented in this note. We study the spectral radius and contraction properties of the iteration matrix and then analyze the best possible choice of the parameter. With the results obtained, we show that the new method is convergent for a non-Hermitian positive definite linear system and propose a preconditioner to improve the condition number of the system. The numerical examples show that the new method is much more efficient than the HSS (or PSS) iteration method.