考虑量化输入和输出约束的互联系统自适应分散跟踪控制

本文考虑具有量化输入和输出约束的一类非线性互联系统的自适应分散跟踪控制设计. 分别针对量化参数已知和未知两种情况, 基于反推(Backstepping)设计法, 利用神经网络逼近特性, 设计自适应分散跟踪控制策略. 通过定义新的未知常量和非线性光滑函数, 设计自适应参数估计项来消除未知互联项对系统的影响. 进一步考虑量化参数未知的情形, 引入一个新的不等式来转化输入信号, 并构建新的自适应补偿项来处理量化影响. 同时, 障碍李雅普诺夫函数的引入, 确保了系统输出不违反约束条件. 与现有量化输入设计相比, 本文所提方法不要求未知非线性项满足李普希兹条件, 并且允许量化参数未知. 该设计方法保证了闭环系统所有信号最终一致有界, 而且跟踪误差能够收敛到原点的小邻域内, 同时保证输出不违反约束条件. 最后, 仿真算例验证了所提方法具备良好的跟踪控制性能.     

网络诱导延迟的量子化和增广对象向量方法

对于网络控制系统中一般的动态输出反馈控制问题,应用延迟量子化和增广对象向量方法建立离散时间Markov跳变系统模型,并给出稳定化控制器的设计算法和倒立摆上的仿真计算.由于应用延迟量子化方法,所建立的模型和给出的设计方法也能用于求解具有常分布律的随机延迟的动态输出反馈控制问题.     

DOA estimation based on the database retrieval technique with nonuniform quantization and clustering

A direction-of-arrival (DOA) estimation method using an array antenna has been developed based on a database retrieval technique. This method uses a database consisting of a set of correlation matrices of the array output vectors for various combinations of the quantized angles and signal powers. When a correlation matrix is estimated from an observed output vector, several correlation matrices close to the estimated one are searched out from the database, and the DOA is estimated based on the retrieved data. This method gives an accurate estimation, but the use of uniform quantization step size requires a large amount of storage space. In this paper, the relation between the quantization step size and the estimation accuracy is analyzed, and a nonuniform quantization scheme is developed to reduce the database size without sacrificing the estimation accuracy. A clustering technique is also introduced to alleviate the performance degradation caused by the retrieval of data which have similar correlation matrices but have much different angles. We show by simulations that the nonuniform quantization reduces the database size and the clustering improves the estimation accuracy, and that the proposed method is applicable to the array of three elements at the present.     

Model Free Adaptive Control for a Class of Nonlinear Systems Using Quantized Information

This paper considers the stability of model free adaptive control systems with quantized information. Two quantized model free adaptive control (QMFAC) algorithms are proposed by using different signal quantization schemes, and here the logarithmic quantizer is introduced to decode these signals with a number of quantization levels. By using the sector bound method, the stability conditions of proposed QMFAC algorithms can be given and the effect of quantization error for such systems can also be discussed. It is shown that the tracking error under the QMFAC algorithm with system output quantized signal is proved to converge to a bound, and the bound depending on quantization density and desired trajectory. Thus, the tracking error under the QMFAC algorithm with tracking error quantized signal converges to zero. Two illustrative examples are given to validate the theoretical results.     

考虑数据量化的改进无模型自适应迭代学习控制算法

针对一类存在数据量化的离散时间单输入单输出非线性系统,提出一种带有编码解码量化机制的无模型自适应迭代学习控制(MFAILC)算法.首先使用伪偏导数将受控非线性系统动态线性化,进而考虑系统输出数据经由均匀量化器进行量化处理的过程,并设计了一种编码解码量化机制,最后基于这种编码解码量化机制提出了一种改进的MFAILC算法.理论上给出了算法的收敛性分析,结果表明,当系统存在数据量化时,所提出的算法仍可保证系统收敛.与已有算法相比,所提算法仅利用较少的输入输出数据,就可以实现跟踪误差的零收敛.仿真进一步验证了算法的有效性.     

一种新型的数据采集系统性能量化方法的研究

考虑到数据采集系统的精度对应于系统对信号的不失真传输及系统本身的时间稳定性问题,提出了一种新型的数据采集系统性能量化方法。该方法将数据采集系统看作为一个开环的线性时不变系统,把采集系统本身看作滤波器,其输入为需要采集的信号,输出为采集到的信号,然后利用LMS算法的基本原理反推通道模型参数,最后利用求得的模型参数计算数据采集系统对正弦信号的传输特性,实现了对数据采集系统的性能考核。对系统模型和量化方法的数学仿真结果表明,所采取的量化方法和系统模型精确地反映了数据采集系统的正弦信号的响应,可以作为实际数据采集系统的精度考核手段。     

Output-feedback control design for NCSs subject to quantization and dropout

In this paper, the output-feedback control problem is considered for networked systems involving in signal quantization and data packet dropout. The states of the controlled system are unavailable and the output signals are quantized before being communicated. An estimation method is introduced to cope with the effect of random packet loss that is modelled as a Bernoulli process. The quantized measurement signals are dealt with by utilizing the sector bound method, in which the quantization error is treated as sector-bounded uncertainty. The output-feedback controller is designed which guarantees the closed-loop system is exponentially mean-square stable. The simulation example is given to illustrate the proposed method.     

Bilateral Teleoperation of Multiple Agents with Formation Control

This paper studies the formation problem for multislave teleoperation system over general communication networks, where multiple mobile slave agents are coupled with a single master robot. The forward and backward network transmission time delays are assumed to be asymmetric and time-varying. Due to the quantization in the network, a dynamic quantization strategy is provided to quantize the output signals of the master robot and slave agents before transmitting. Then, a novel masterslave protocol is designed to achieve the formation task under variable time delays and quantization. Additionally, the sufficient conditions for stability are presented to show that the formation protocol can stabilize the master-slave system under variable time delays and quantization. Finally, simulation are performed to show effectiveness of the main results.      

实时低功耗飞行器神经网络

为了满足飞行器实时飞行过程中对大量异构输入数据的信息处理需求,文中提出了一种神经网络,其包括卷积定点滑动核、池化压缩量化核以及全连接压缩融合核,将飞行器异构传感器多路并行数据作为系统的输入,将辨识结果作为系统的输出。卷积滑动窗口核通过排除冗余数据的滑动窗快速实现数据特征的提取;池化压缩量化核使用压缩量化技术来提高系统的执行效率;全连接压缩融合核经删减量化后压缩融合并输出。该设计满足了飞行器对高可靠性、低功耗的在线智能集成需求。使用所提压缩量化方法,准确率最高可达98.54%,压缩率为77.8%,运行速度提升了40倍。     

A Hybrid Color Image Quantization Algorithm Based on k-Means and Harmony Search Algorithms

Color quantization is one of the most important preprocessing stages in many applications in computer graphics and image processing. In this article, a new algorithm for color image quantization based on the harmony search (HS) algorithm is proposed. The proposed algorithm utilizes the clustering method, which is one of the most extensively applied methods to the color quantization problem. Two variants of the algorithm are examined. The first is based on a standalone HS algorithm, and the second is a hybrid algorithm of k-means (KM) and HS. The objective of the hybrid algorithm is to strengthen the local search process and balance the quantization quality and computational complexity. In the first stage, the high-resolution color space is initially condensed to a lower-dimensional color space by multilevel thresholding. In the second stage, the compressed colors are clustered to a palette using the hybrid KMHS to obtain final quantization results. The algorithm aims to design a postclustering quantization scheme at the color-space level instead of the pixel level. This significantly reduces the computational complexity while maintaining the quantization quality. Experimental results on some of the most commonly used test images in the quantization literature demonstrate that the proposed method is a powerful method, suggesting a higher degree of precision and robustness compared to existing algorithms.     

直接数字频率合成中幅度量化误差分析

直接数字频率合成中，由于ＲＯＭ的数据位字长有限，因此存放在其中的正弦波幅值有量化误差，这个量化误差将在Ｄ／Ａ输出端产生杂散分量。本文把量化误差看作周期序列，对其在Ｄ／Ａ输出端产生的杂散分量平均功率进行了讨论，并分析了它的频谱特性。     

Parametric Stabilization of Quantized Interconnected Systems with Application to Coupled Inverted Pendulums

This paper studies the analysis of parametric stability and decentralized state feedback control of a kind of quantized interconnected systems. The output of each controller is quantized logarithmically before it is input to the subsystem, and the quantized density would affect the stability of the systems. First, a decentralized state feedback controller is designed for interconnected systems without quantization and the corresponding stable region is obtained. Second, for a given controller, the lower bound of the quantization density is evaluated from parameters of local controllers. Finally, the proposed method is applied to coupled inverted pendulums systems which can be viewed as quantized interconnected systems. The simulation results show that by using the proposed quantized controllers, the interconnected inverted pendulum systems are parametrically stabilized.     

基于点对相似度的深度非松弛哈希算法

哈希学习能够在保持数据之间语义相似性的同时, 将高维数据投影到低维的二值空间中以降低数据维度实现快速检索. 传统的监督型哈希学习算法主要是将手工设计特征作为模型输入, 通过分类和量化生成哈希码. 手工设计特征缺乏自适应性且独立于量化过程使得检索的准确率不高. 本文提出了一种基于点对相似度的深度非松弛哈希算法, 在卷积神经网络的输出端使用可导的软阈值函数代替常用的符号函数使准哈希码非线性接近-1或1, 将网络输出的结果直接用于计算训练误差, 在损失函数中使用$\ell_1$范数约束准哈希码的各个哈希位接近二值编码. 模型训练完成之后, 在网络模型外部使用符号函数, 通过符号函数量化生成低维的二值哈希码, 在低维的二值空间中进行数据的存储与检索. 在公开数据集上的实验表明, 本文的算法能够有效地提取图像特征并准确地生成二值哈希码, 且在准确率上优于其他算法.     

Optimal Quantization of Signals for System Identification

In this technical note, we examine the optimal quantization of signals for system identification. We deal with memoryless quantization for the output signals and derive the optimal quantization schemes. The objective functions are the errors of least squares parameter estimation subject to a constraint on the number of subsections of the quantized signals or the expectation of the optimal code length for either high or low resolution. The optimal quantizer has the property that it is coarse near the origin of its output and becomes dense away from the origin in the usual situation. Finally the required quantity of data to decrease the total parameter estimation error, caused by quantization and noise, is discussed.      

Stabilization of switched nonlinear systems with dynamic output quantization and disturbances

This paper studies the problem of quantized output feedback stabilization for a disturbed discrete‐time switched system. With the quantized output measurement, an extended state current estimator is constructed to estimate the state and disturbance. In the presence of switches and disturbances, the design of quantization scheme becomes a big challenge to avoid the quantizer saturation and guarantee the control precision. In this setup, the well‐applied time‐triggered method to design the update policy of dynamic quantization parameter is hard to implement. We solve the above problem by proposing a novel event‐triggered update policy of quantization parameter, by which the quantizer update is adaptive to the switching signal and the bound of disturbance difference. Consequently, the quantizer saturation is avoided and, combined with the designed dwell‐time switching law, the system state can converge to the origin. The proposed method is illustrated by a numerical example.     

关联量化系统参数稳定性与控制器设计

量化问题广泛存在于计算机控制系统和数字通信的传输通道中. 本文研究一类非线性关联量化系统的参数稳定性及分散状态反馈控制器的设计问题. 每个控制器的输出经过对数量化器量化后输入到子系统中,其量化密度的大小会影响系统的稳定性. 首先,设计分散状态反馈控制器,使得无量化器存在时的关联闭环系统参数稳定,并确定参数稳定的区域;然后,对每个子系统的控制输入采用对数量化器进行量化,通过局部信息确定子系统中对数量化器量化密度的下界,使得整个闭环关联量化系统在参数稳定域内仍然保持稳定;最后,对给定量化密度,优化控制器使系统能容许最大的非线性. 仿真结果表明,本文所设计的分散量化控制器在参数稳定域内能够镇定关联大系统.     

Reference properties of uniform quantizers—comparison of Widrow's and direct approaches

We present an analysis of the quantization error or generalized (nonuniform and/or nonmonotonic) quantizers. The mean value and mean-squared value of the quantization error and the quantized output are derived in terms of the observed signals. The results can be used to identify basic parameters of ADC converters. The results can be utilized in the reference models used in the ADC test methods recommended by the IEEE.     

An efficient color quantization based on generic roughness measure

Color quantization is a process to compress image color space while minimizing visual distortion. The quantization based on preclustering has low computational complexity but cannot guarantee quantization precision. The quantization based on postclustering can produce high quality quantization results. However, it has to traverse image pixels iteratively and suffers heavy computational burden. Its computational complexity was not reduced although the revised versions have improved the precision. In the work of color quantization, balancing quantization quality and quantization complexity is always a challenging point. In this paper, a two-stage quantization framework is proposed to achieve this balance. In the first stage, high-resolution color space is initially compressed to a condensed color space by thresholding roughness indices. Instead of linear compression, we propose generic roughness measure to generate the delicate segmentation of image color. In this way, it causes less distortion to the image. In the second stage, the initially compressed colors are further clustered to a palette using Weighted Rough K-means to obtain final quantization results. Our objective is to design a postclustering quantization strategy at the color space level rather than the pixel level. Applying the quantization in the precisely compressed color space, the computational cost is greatly reduced; meanwhile, the quantization quality is maintained. The substantial experimental results validate the high efficiency of the proposed quantization method, which produces high quality color quantization while possessing low computational complexity.     

微惯性传感器零点漂移融合滤波算法研究

为提高微惯性传感器的测量精度,提出了基于指数平滑的GM（1,1）改进模型,通过该模型对漂移中的确定性漂移信号进行提取,运用AR（3）模型对提取趋势项后的随机漂移进行了建模分析,并在AR（3）模型基础上进行了Kalman滤波处理,经Allan方差分析表明：经模型补偿后的漂移噪声降低了1-3个数量级,有效减小了零漂信号中的角度随机游走、角速度随机游走、速率斜坡、零偏不稳定性和量化噪声,提高MEMS陀螺零漂输出的稳定性。     

有效降低方块效应的DCT系数量化方法

方块效应是基于块的混合视频编码算法产生的重要特征失真,严重影响了压缩后视频的主观质量。分析产生方块效应的根本原因,在此基础上提出一种有效降低方块效应的DCT系数量化方法。该方法通过调整量化过程,减小量化误差在块边界处的变化,增强相邻块间量化误差的相关性,从而降低了压缩后视频的方块效应。实验结果表明该方法能够有效降低方块效应,改善视频帧的主观质量。另外,该算法计算复杂度较低,并且与视频压缩标准兼容,具有实用性。