间接自适应极点配置控制算法的跟踪能力研究

为了研究现有间接自适应极点配置控制算法的跟踪能力,提出了一般形式的间接自适应 极点配置控制算法.分析了这种算法用于已知和未知系统时的跟踪能力,并分别给出了渐近 跟踪参考输出的充要条件.利用上述结果,可以对现有间接算法的跟踪能力进行逐一研究.研 究表明,现有间接自适应极点配置控制算法的跟踪能力是有限的,它们至多能实现对一类参考 输出的渐近跟踪.     

Parallel adaptive wavefront algorithms solving Lyapunov equations for the Cholesky factor on message passing multiprocessors

The order of the matrices involved in several algebraic problems decreases during the solution process. In these cases, parallel algorithms which use adaptive solving block sizes offer better performance results than the ones obtained on parallel algorithms using traditional constant block sizes. Recently, new parallel wavefront algorithms solving the Lyapunov equations for the Cholesky factor using Hammarling's method on message passing multiprocessors systems have been designed. In this paper, new parallel adaptive versions of these parallel algorithms are described, and experimental results obtained on an SGI Power Challenge and a SUN UltraSparc cluster are presented. Copyright © 1999 John Wiley & Sons, Ltd.     

基于鲁棒控制的自适应分数阶梯度优化算法设计

当目标函数是强凸函数时, 一般的分数阶梯度下降法不能够使函数收敛到最小值点, 只能收敛到一个包含最小值点的区域内或者是发散的. 为了解决这个问题, 本文提出了自适应分数阶梯度下降法(AFOGD)和自适应分数阶加速梯度下降法(AFOAGD)两种新的优化算法. 受到鲁棒控制理论中二次约束和李雅普诺夫稳定性理论的启发, 建立了一个线性矩阵不等式去分析所提出的算法的收敛性. 当目标函数是L-光滑且m-强凸时, 算法可以达到R线性收敛. 最后几个数值仿真证明了算法的有效性和优越性.     

Adaptive state feedback control for switched stochastic high‐order nonlinear systems under arbitrary switchings

This paper studies the adaptive state feedback control for a class of switched time‐varying stochastic high‐order nonlinear systems under arbitrary switchings. Based on the common Lyapunov function and using the inductive method, virtual controllers are designed step by step and the form of the input signal of the system is constructed at the last. The unknown parameters are addressed by the tuning function method. In particular, both the designed state feedback controller and the adaptive law are independent of switching signals. Based on the designed controller, the boundness of the state variables can be guaranteed in probability. Furthermore, without considering the Wiener process or with the known parameter in the assumption, adaptive finite‐time stabilization and finite‐time stabilization in probability can be obtained, respectively. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed method.     

Effective wavenumber for thermal infrared bands-application to Landsat-TM

In this paper we suggest that brightness temperature from the band radiance measured by a thermal sensor using Planck's law can be obtained along with effective parameters adequate for a given band, and a given temperature range. We propose an accurate algorithm to obtain these parameters (effective wavenumber or effective wavelength), which is based on Taylor's expansion of the exponential function that appears in Planck's equation. The dependence of the effective wavenumber on temperature has been investigated and the advantages of working with large temperature ranges have been analysed. We have compared the effective wavenumbers obtained with this algorithm to the parameters provided by NOAA/NESDIS and the maximum difference obtained has been 0.5 cm-1. The method has been applied to Landsat-Thematic Mapper band-6 and the results indicate that the error in temperature determination of the method is less than 0.01 K. A comparison with other published algorithms is also included.     

An adaptive LSB matching steganography based on octonary complexity measure

Adaptive steganography methods tend to increase the security against attacks. Most of adaptive methods use LSB flipping (LSB-F) for embedding part of their algorithms. LSB-F is very much vulnerable against simple steganalysis methods but it allows the adaptive algorithms to be extractable at the receiver side. Use of LSB matching (LSB-M) could increase the security but extraction of data at the receiver is difficult or, in occasions, impossible. There are numerous attacks against LSB-M. In this paper we are proposing an adaptive algorithm which, unlike most adaptive methods, uses LSB-M as its embedding method. The proposed method uses a complexity measure based on a local neighborhood analysis for determination of secure locations of an image. Comparable adaptive methods that use LSB-M suffer from possible changes in the complexity of pixels when embedding is performed. The proposed algorithm is such that when a pixel is categorized as complex at the transmitter and is embedded the receiver will identify it as complex too, and data is correctly retrieved. Better performance of the algorithm is shown by obtaining higher PSNR values for the embedded images with respect to comparable adaptive algorithms. The security of the algorithm against numerous attacks is shown to be higher than LSB-M. Also, it is compared with a recent adaptive method and is proved to be advantageous for most embedding rates.     

Fuzzy-enhanced Adaptive Control for Flexible Drive System with Friction Using Genetic Algorithms

When a mechatronic system is in slow speed motion, serious effect of nonlinear friction plays a key role in its control design. In this paper, a stable adaptive control for drive systems including transmission flexibility and friction, based on the Lyapunov stability theory, is first proposed. For ease of design, the friction is fictitiously assumed as an unknown disturbance in the derivation of the adaptive control law. Genetic algorithms are then suggested for learning the structure and parameters of the fuzzy-enhancing strategy for the adaptive control to improve system's transient performance and robustness with respect to uncertainty. The integrated fuzzy-enhanced adaptive control is well tested via computer simulations using the new complete dynamic friction model recently suggested by Canudas de Wit et al. for modeling the real friction phenomena. Much lower critical velocity of a flexible drive system that determines system's low-speed performance bound can be obtained using the proposed hybrid control strategy.     

网络化控制系统两种时延预测算法及其比较

针对网络化控制系统（NCS）中的随机时变时延,提出了两种时延预测算法——自适应最小均方差（LMS）算法和在线最小二乘支持向量机（LS-SVM）算法,对其进行预测,并用实际测试得到的网络时延数据,对两种算法的时延预测效果进行了详细分析比较,指出了各自的特点和适用范围。     

Linear deterministic adaptive control: fundamental limitations?

This paper is concerned with the achievable performance of adaptive control algorithms. We show that when the only uncertainty is in the form of fixed parameter errors, then there exists an adaptive feedback law whose performance can be made arbitrarily close to that achievable when the system is a priori known. The result is not intended as a practical strategy. Instead, we use it to make the, perhaps obvious, point that meaningful results on performance of adaptive control algorithms must account for non-ideal factors including, at a minimum, noise, parameter time variations and unstructured uncertainty.     

Adaptive gains to super‐twisting technique for sliding mode design

This paper studies the super‐twisting algorithm (STA) for adaptive sliding mode design. The proposed method tunes the two gains of STA on line simultaneously such that a second order sliding mode can take place with small rectifying gains. The perturbation magnitude is obtained exactly by employing a third‐order sliding mode observer in opposition to the conventional approximations by using a first order low pass filter. While driving the sliding variable to the sliding mode surface, one gain of the STA automatically converges to an adjacent area of the perturbation magnitude in finite time. The other gain is adjusted by the above gain to guarantee the robustness of the STA. This method requires only one parameter to be adjusted. The adjustment is straightforward because it just keeps increasing until it fulfills the convergence constraints. For large values of the parameter, chattering in the update law of the two gains is avoided by employing a geometry based backward Euler integration method. The usefulness is illustrated by an example of designing an equivalent control based sliding mode control (ECBC‐SMC) with the proposed adaptive STA for a perturbed LTI system.     

Low complexity adaptive algorithms for Principal and Minor Component Analysis

This article introduces new low cost algorithms for the adaptive estimation and tracking of principal and minor components. The proposed algorithms are based on the well-known OPAST method which is adapted and extended in order to achieve the desired MCA or PCA (Minor or Principal Component Analysis). For the PCA case, we propose efficient solutions using Givens rotations to estimate the principal components out of the weight matrix given by OPAST method. These solutions are then extended to the MCA case by using a transformed data covariance matrix in such a way the desired minor components are obtained from the PCA of the new (transformed) matrix. Finally, as a byproduct of our PCA algorithm, we propose a fast adaptive algorithm for data whitening that is shown to overcome the recently proposed RLS-based whitening method.     

自适应差分进化算法及对动态环境经济调度问题应用

为了应对动态环境经济调度(DEED)问题的高维性和大规模约束性,提出了一种自适应多目标差分进化算法(ADEA)。设计自适应差分交叉模块,提出改进的current to best/1交叉策略提高种群的多样性,有效地提高传统进化算法的探索与开采能力,提出一种修补策略处理功率平衡约束和爬坡率约束。为了验证该方法的有效性,数值仿真将ADEA应用于10机系统进行测试,并与同类算法展开比较,仿真结果表明ADEA具有较好的收敛能力,获得的Pareto前沿具有较好的均匀性和延展性,通过模糊决策获得的最好折中解能为电力系统调度人员提供较为合理的调度方案。     

Stable genetic adaptive controllers for multivariable systems using a two-degree-of-freedom topology

This paper introduces an adaptive reference tracking controller based on the online genetic estimation of the parameters of the system. The main novelty of the paper relies on the fact that the stability of the genetic adaptive scheme is analytically proved and not simply validated by means of simulation as it is customary in the literature. The resulting set-up is flexible enough to be integrated within a great variety of genetic estimation algorithms, which can in many cases outperform traditional estimation procedures. The goal is achieved by using a certain two-degree-of-freedom (2-DOF) based implementation of the control law in which the reference tracking property is separated from the closed-loop stability. Within this framework, the here-presented procedure for the genetic controller synthesis just affects two time-varying pre-filter blocks that do not compromise the closed-loop stability under weak conditions. In this manner, the power and versatility of genetic algorithms can be safely used to achieve tracking performance disregarding stability, which is delegated to a static feedback controller designed on the basis of robust control theory.     

Adaptive terminal angle constraint interception against maneuvering targets with fast fixed‐time convergence

In this paper, a modified adaptive fast nonsingular terminal sliding mode guidance law is proposed based on the theory of fixed‐time convergence, which is applied for intercepting maneuvering targets considering terminal angle constraint. The proposed guidance law achieves system stabilization within bounded settling time independent on initial conditions and provides no singularity and globally rapid convergence property by accelerating the convergence rate when the system is close to the origin. The upper bound of settling time can be obtained in advance by the controller's parameters. Besides, in order to achieve chattering‐free property, a continuous adaptive switching control is introduced and the achieved acceleration‐magnitude constraints are rigorously enforced. Finally, the fixed‐time convergence of the sliding mode manifold and the system states is demonstrated by Lyapunov stability theory. Extensive numerical simulations are presented to validate the efficiency and superiority of the proposed guidance law.     

Adaptive neural network asymptotical tracking control for an uncertain nonlinear system with input quantisation

In this paper, the problem of adaptive neural network asymptotical tracking is investigated for a class of nonlinear system with unknown function, external disturbances and input quantisation. Based on neural network technique, an adaptive asymptotical tracking controller is provided for an uncertain nonlinear system via backstepping method. In order to reduce complexity of the control algorithm in the backstepping design process, a sliding mode differentiator is employed to estimate the virtual control law and only two parameters need to be estimated via adaptive control technique. The stability of the closed-loop system is analysed by using Lyapunov function method and zero-tracking error performance is obtained in the presence of unknown nonlinear function, external disturbances and input quantisation. Finally, an application example is employed to demonstrate the effectiveness of the proposed scheme.     

Optimal higher order learning adaptive control approach for a class of SISO nonlinear systems

In this paper,an optimal higher order learning adaptive control approach is developed for a class of SISO nonlinear systems.This design is model-free and depends directly on pseudo-partial-derivatives derived on-line from the input and output information of the system.A novel weighted one-step-ahead control criterion function is proposed for the control law.The convergence analysis shows that the proposed control law can guarantee the convergence under the assumption that the desired output is a set point.Simulation examples are provided for nonlinear systems to illustrate the better performance of the higher order learning adaptive control.     

Optimal higher order learning adaptive control approach for a class of SISO nonlinear systems

1IntroductionMany dynamic systems to be controlled have constant orslowly_varying uncertain parameters .Adaptive control is apopular approachtothe control ofsuchsystems [1] .Inthepast two decades ,significant progress has been made in theresearch and design of adaptive control systems [2,3] .Fairly complete and comprehensive guidelines are nowavailable for both design and implementation of adaptivecontrollers inthe cases where the systemunder control canbe adequately modeled as a linear dynami…     

求解矩形布局问题的自适应算法

矩形布局问题属于NP-Hard 问题,其求解算法多为启发式算法。该文侧重 于构造布局求解算法中定位函数（规则）的优化,将模拟退火算法的思想融入到遗传算法中, 提出了求解矩形布局问题的自适应算法,其利用自适应交叉、变异及接收劣质解的概率等方 法对定位函数中各参数进行优化。算法通过两种方式确定初始种群的数目,具有较强的适应 性。在算法搜索的后期,利用差异性较大的个体进行交叉操作,从而保持种群的多样性。最 后通过实例证明了该算法能够很好的应用于矩形布局问题的求解。     

Control allocation technology based on fault diagnosis for the unmanned aerial vehicle system subject to physical constraints and fault reconfiguration mismatch

The problem of the system robustness subject to physical constraints and mismatched fault reconstruction is discussed in this paper. In order to facilitate the design, a four-rotor unmanned aerial vehicle (UAV) system model was selected for research. First, the control allocation model of the nonlinear UAV system with disturbances is shown in the paper. Secondly, a weighted pseudo-inverse method based on adaptive weights is proposed, which reduces the impact of physical constraints on the system. After that, a dynamic weight control allocation method based on the fault efficiency matrix is designed. The weight matrix can dynamically adjust the control distribution law according to the fault estimation value provided by the observer. Then, a dynamic adaptive control allocation method for faults and physical constraints is carried out by combining adaptive weights and dynamic weights. Finally, a simulation example is presented to further illustrate the effectiveness of the algorithm proposed in this paper.     

基于贝叶斯最大后验估计的局部自适应小波去噪

利用图像小波子带内系数的相关性,提出了一种局部自适应小波去噪方法。首先在贝叶斯最大后验概率准则下推导出基于拉普拉斯先验分布的MAP估计表达式和子带MapShrink阈值。为得到局部自适应的MapShrink阈值和去噪算法,提出将子带内的每个小波系数建模为具有不同边缘标准差的拉普拉斯分布,而边缘标准差又假设为强局部相关的随机变量,可通过邻域局部窗口进行估计。实验结果表明,与经典的子带自适应去噪算法相比较,该方法获得了明显的峰值信噪比增益,主观视觉效果也得到了改善。