<Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> digital redesign for LTI systems

The so-called digital redesign (DR) is a sampled-data (SD) controller design method where an analogue controller is designed firstly, and then transformed to an approximately equivalent digital controller in the sense of state-matching. In this approach, the SD controller is designed by reducing the discrepancy between the discrete-time (DT) counterpart of the closed-loop SD control system and the continuous-time (CT) closed-loop system. In this paper, we develop a DR strategy for CT linear time-invariant systems. More specifically, H _∞ norm of the error dynamic system between the CT and DT plants is minimized for the optimal state-matching performance at every sampling point. The design problem is formulated as linear matrix inequalities which can be efficiently solved by using convex optimization techniques. Finally, an example is given to illustrate the effectiveness of the proposed method.     

Effective digital implementation of fuzzy control systems based on approximate discrete-time models

This paper addresses an effective digital implementation of fuzzy control systems via an intelligent digital redesign (IDR) approach. The purpose of IDR is to effectively convert an existing continuous-time fuzzy controller to an equivalent sampled-data fuzzy controller in the sense of the state-matching. The authors show that, under reasonable assumptions, the IDR based on the exact discrete-time models can be reduced to the IDR based on the approximate discrete-time models. The state-matching error between the closed-loop trajectories is carefully analyzed using the integral quadratic functional approach. The estimation of the state-matching error is presented using the linear matrix inequality (LMI) techniques. The problem of designing the sampled-data fuzzy controller to minimize the estimation as well as to guarantee the stability is formulated and solved as the convex optimization problem with LMI constraints. It is also shown that the resulting sampled-data fuzzy controller recovers the performance of the continuous-time fuzzy controller as the sampling period approaches zero. A numerical example is used to demonstrate the effectiveness of the proposed design technique.     

Intelligent digital redesign for nonlinear systems using a guaranteed cost control method

In this paper, a novel intelligent digital redesign (IDR) technique using the guaranteed cost control method is proposed for nonlinear systems which can be represented by a Takagi-Sugeno (T-S) fuzzy model. The IDR technique, which is one of the sampled-data fuzzy controller design methods, guarantees not only the stability condition of the sampled-data closed-loop system with the sampleddata fuzzy controller and the state-matching error is presented. By using the concept of the guaranteed cost control method, sufficient conditions are obtained for both minimization of the state-matching error and stabilization of the sampled-data closed-loop system and derived in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to verify the effectiveness of the proposed technique.     

Digitalizing a Fuzzy Observer-Based Output-Feedback Control: Intelligent Digital Redesign Approach

This paper concerns an intelligent digital redesign (IDR) technique for a Takagi-Sugeno fuzzy observer-based output-feedback control (FOBOFC) system. The term IDR involves converting an existing analog control into an equivalent digital counterpart in the sense of state-matching. The IDR problem is herein viewed as a minimization problem of the norm distances between nonlinearly interpolated linear operators to be matched. Its constructive condition with global rather than local state-matching is formulated in terms of bilinear matrix inequalities. The main features of the proposed method are that the state estimation error in the plant dynamics is considered in the IDR condition that plays a crucial role in the performance improvement; the stability property is preserved by the proposed IDR algorithm; the separation principle is shown when the premise variables are measurable; finally, the IDR condition for a more general FOBOFC-an estimated premise variable case is conducted. A numerical example is demonstrated to visualize the feasibility of the developed methodology     

Observer-based sampled-data control for nonlinear systems: Robust intelligent digital redesign approach

This paper suggests a solution for a robust digital implementation of the output-feedback control system by utilizing intelligent digital redesign (IDR). The general IDR method involves designing a digital fuzzy controller which is comparable to a pre-designed analog one via state-matching. However, under containing the parametric uncertainties, the new problem about the structural property of the uncertainties is inevitably occurred in the procedure of discretization. For solving that, we use the bilinear and inverse-bilinear approximation method and the concerned IDR is viewed as a convex minimization problem of the norm distances between linear operators to be matched. Also, we consider the state estimation error in the plant dynamics so that we attempt to improve the control performance. The robust stability property is preserved by the proposed IDR method and its condition is represented in terms of linear matrix inequalities (LMIs). The simulation results for permanent magnet synchronous motor (PMSM) system are demonstrated to visualize the feasibility of the proposed method.     

Multi-rate digital redesign of cascaded and dynamic output feedback systems

In this paper, a new indirect digital redesign method is presented for multi-rate sampled-data control systems with cascaded and dynamic output feedback controllers. These analogue controllers are often pre-designed based on desirable frequency specifications, such as bandwidth, natural angular frequency, etc. To take advantage of the digital controller over the analogue controller, digital implementation of these analogue controllers are often desirable. As only measured input-output signals are available, an ideal state reconstructing algorithm is utilised to obtain the multi-rate discrete-time states of the original continuous-time system. Based on the Chebyshev quadrature method, the gains of the multi-rate cascaded and the output feedback digital controllers are determined from their continuous-time counterparts according to the different sampling rates employed in the different parts of the closed-loop system. As a result, the respective analogue controllers with the high-frequency and low-frequency characteristics can be implemented using the respective fast-rate sampling and slow-rate sampling digital controllers. Unlike the classical direct bilinear transform method, which is an open-loop direct digital redesign method, the proposed digital controllers take into account the state-matching of the original continuous-time closed-loop system and the digitally redesigned sampled-data closed-loop system. To further improve the state-matching performance, an improved digital redesign approach is also developed to construct the multi-rate cascaded and dynamic output feedback digital controllers. Illustrative examples are given to demonstrate the effectiveness of the developed methods.     

Asymptotic stability of digitally redesigned control systems

This paper analyses an asymptotic stability of a digitally redesigned control system when the states of the analogue and the digital control systems are approximately matched at every sampling point. The digital redesign is a simple method of converting a given analogue controller to an equivalent digital controller in the sense of state-matching. The concerned state-matching technique is to minimise the norm distance between the discretised closed-loop system matrix of linear analogue control system and that of linear digital control system. It is shown that (i) there exists an upper bound of the norm distance to guarantee the asymptotic stability of the digitally redesigned control system and (ii) the trajectories of the linear analogue and the linear digital control systems coincide at every sampling point if the norm distance is zero. Also, a robustness result is provided in the case that nonlinear perturbations occur in the analogue and the digital control systems. Moreover, design conditions for the developed stability analysis are proposed in terms of linear matrix inequalities.     

基于UKF的环境自适应UWB/DR室内定位方法

针对复杂室内环境下超宽带（Ultra WideBand,UWB）信号传播的非视距（Non Line Of Sight,NLOS）误差问题,本文提出了一种基于无迹卡尔曼滤波（Unscented Kalman Filter,UKF）的环境自适应UWB/DR室内定位方法.该方法通过建立自适应UKF滤波模型,将UWB定位信息和航迹推算（Dead Reckoning,DR）定位信息进行融合.依据新息和高斯分布的3σ原则来对UWB定位结果进行非视距检测,再通过新息的实时估计协方差和理论协方差来构建环境适应系数,进而用此系数动态修正UWB定位的观测噪声,使得观测噪声自适应真实环境,降低NLOS误差对融合定位结果的影响.实验结果表明,该方法能有效减小UWB定位的NLOS误差,并且由于环境适应系数的创新引入,比UKF定位和粒子滤波定位（Particle Filtering,PF）有更高的定位精度和更强的抗NLOS误差性能.     

A new intelligent digital redesign for T-S fuzzy systems: global approach

This paper proposes a novel and efficient global intelligent digital redesign technique for a Takagi-Sugeno (T-S) fuzzy system. The term of intelligent digital redesign involves converting an existing analog fuzzy-model-based controller into an equivalent digital counterpart in the sense of state-matching. The proposed method should be notably discriminated from the previous works in that it allows us to globally match the states of the overall closed-loop T-S fuzzy system with the predesigned analog fuzzy-model-based controller and those with the digitally redesigned fuzzy-model-based controller, and further to examine the stabilizability by the redesigned controller in the sense of Lyapunov. The key idea is that the global intelligent digital redesign problem is viewed as a convex minimization problem of the norm distance between nonlinearly interpolated linear operators to be matched. Sufficient conditions for the global state-matching and the stability of the digitally controlled system are formulated in terms of linear matrix inequalities (LMIs). A complex nonlinear system, Duffing-like chaotic oscillator is simulated and demonstrated to validate the feasibility and effectiveness of the proposed digital redesign technique, which implies the safe applicability to the digital control system.     

Fuzzy filter for nonlinear sampled-data systems: Intelligent digital redesign approach

This paper presents a fuzzy filter design method for nonlinear sampled-data systems using an intelligent digital redesign (IDR) technique. Based on a Takagi–Sugeno (T–S) fuzzy model, discretized closed-loop systems with pre-designed analog fuzzy and digital fuzzy filters are presented. An IDR problem is given to guarantee both state-matching condition and asymptotic stability. Sufficient conditions for solving the IDR problem are proposed and are derived in terms of linear matrix inequalities (LMIs). Finally, a simulation example is given to show the effectiveness of the proposed method.     

量子PSO粒子滤波在DR/GPS组合导航系统中的应用

将量子粒子群优化(QPSO)算法与粒子滤波(PF)相结合,提出了量子PSO粒子滤波(QPSO-PF)算法,对航位推算(DR)与GPS组合导航系统中的里程系数误差和航向误差进行辨识估计,并对里程系数和航向进行修正。该算法采用量子位对粒子进行编码,引入量子旋转门与变异操作保持了粒子集的多样性,通过QPSO搜索寻优重新分配粒子,使粒子集有效地逼近真实的后验概率分布,从而有效地减轻了退化现象,提高了PF的精度。DR/GPS组合导航系统跑车实验结果表明:该算法有效地抑制了DR导航系统误差的增长,提高了组合导航系统的定位精度。     

The localization of a mobile robot using a pseudolite ultrasonic system and a dead reckoning integrated system

In this paper, an integration system is proposed to improve the positioning performance of a mobile robot by fusing a Pseudolite Ultrasonic System (PUS), an absolute position measurement system using direct ultrasonic waves, with a Dead Reckoning (DR) odometer. As an integration algorithm of the absolute position measurement system and DR, two methods are proposed. In the loosely coupled method, the PUS and the DR calculate the position independently and a Kalman filter estimates the position using position information from the PUS and the DR. In the tightly coupled method, the PUS provides the distance between the ultrasonic transmitters and receivers without calculating the position directly and the DR provides the translational and rotational displacement of the mobile robot. The Kalman filter then estimates the position using information from the PUS and the DR. In addition, to improve the positioning performance in case the line-of-sight (LOS) between the ultrasonic transmitter and receiver is blocked due to obstacles, a positioning failure detection algorithm and reckoning methods are proposed. The positioning performances of the proposed PUS/DR integrated systems and the validity of the positioning failure detection algorithm are verified and evaluated by experiments.     

Discriminative functional analysis of human movements

This paper investigates the use of statistical dimensionality reduction (DR) techniques for discriminative low dimensional embedding to enable affective movement recognition. Human movements are defined by a collection of sequential observations (time-series features) representing body joint angle or joint Cartesian trajectories. In this work, these sequential observations are modelled as temporal functions using B-spline basis function expansion, and dimensionality reduction techniques are adapted to enable application to the functional observations. The DR techniques adapted here are: Fischer discriminant analysis (FDA), supervised principal component analysis (PCA), and Isomap. These functional DR techniques along with functional PCA are applied on affective human movement datasets and their performance is evaluated using leave-one-out cross validation with a one-nearest neighbour classifier in the corresponding low-dimensional subspaces. The results show that functional supervised PCA outperforms the other DR techniques examined in terms of classification accuracy and time resource requirements.     

Controlling linear systems with nonlinear perturbations under sampled-data output feedback: digital redesign approach

This article develops a digital redesign (DR) technique for sampled-data observer-based output-feedback control of a continuous-time linear system with nonlinear perturbation. It is assumed that the nonlinear perturbation is a locally Lipschitz function. To deal with the discrete-time modelling error in nonlinear systems, as opposed to the previous approach, the DR problem is configured as a stabilisation one for error dynamics between the closed-loop system of nominal linear model under an analogue state-feedback controller and that of the linear system with the nonlinear perturbation under a sampled-data output-feedback controller. A constructive DR condition is formulated in the format of linear matrix inequalities. The stability of the actual sampled-data control system is guaranteed within the DR procedure. The effectiveness of the proposed DR methodology is demonstrated through a numerical simulation.     

Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model

In recent times, Internet of Things (IoT) and Deep Learning (DL) models have revolutionized the diagnostic procedures of Diabetic Retinopathy (DR) in its early stages that can save the patient from vision loss. At the same time, the recent advancements made in Machine Learning (ML) and DL models help in developing Computer Aided Diagnosis (CAD) models for DR recognition and grading. In this background, the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network (ODBN) model i.e., NS-ODBN model for diagnosis of DR. The presented model involves Interval Neutrosophic Set (INS) technique to distinguish the diseased areas in fundus image. In addition, three feature extraction techniques such as histogram features, texture features, and wavelet features are used in this study. Besides, Optimal Deep Belief Network (ODBN) model is utilized as a classification model for DR. ODBN model involves Shuffled Shepherd Optimization (SSO) algorithm to regulate the hyperparameters of DBN technique in an optimal manner. The utilization of SSO algorithm in DBN model helps in increasing the detection performance of the model significantly. The presented technique was experimentally evaluated using benchmark DR dataset and the results were validated under different evaluation metrics. The resultant values infer that the proposed INS-ODBN technique is a promising candidate than other existing techniques.     

一种基于路况的渐消记忆陀螺误差补偿方法

针对GPS/DR组合导航系统在汽车直行和转弯时速率陀螺误差特性不同的情况,提出了一种基于路况的渐消记忆陀螺误差补偿方法。这种方法在GPS信号正常时根据路况不同实时计算陀螺刻度因子误差;当GPS丢星时,用GPS正常时计算的刻度因子误差补偿速率陀螺。在实际工程应用中,用此方法补偿低成本GPS/DR组合导航系统的陀螺误差后再进行EKF容错滤波,在GPS丢星后一段时间内,仍能保持较好的导航精度。     

空地协同分布交互仿真中DR算法研究

推算定位（ｄｅａｄ ｒｅｃｋｏｎｉｎｇ,ＤＲ）技术是大型分布交互仿真（ＤＩＳ）系统的关键技术,在研究传统的一阶、二阶和高阶ＤＲ算法基础上,针对民航飞行、领航和地面管制综合训练的空、地协同分布交互仿真系统（ＤＡＧＩＳ）,提出了基于模型的ＳＲ算法（ＭＢＤＲ）,探讨一种自适应模型转换方法和动态门限选取问题,比常规ＤＲ算法更好地夺缩了网络通信量,讨论了与ＤＲ算法相关的平滑和延时补偿问题,在构造的ＤＡＧＩＳ     

A new efficient error control algorithm for wireless sensor networks in smart grid

Error detection and correction is an important issue in the design and maintenance of a smart grid communication network to provide reliable communication between sender and receiver. Various error-control coding techniques are employed to reduce bit error rates (BER) in wireless sensor networks (WSNs). The performance of these techniques is also compared and evaluated to find the most suitable technique for WSNs. This is the first study to compare the most efficient coding techniques in the smart grid environment, and it suggests a new error correction algorithm based on this comparison result. Therefore, this article first examines and compares two forward error control (FEC) coding techniques such as Bose-Chaudhuri-Hochquenghem code (BCH) and Reed Solomon code (RS) with various modulation methods including frequency shift keying (FSK), offset quadrature phase-shift keying (OQPSK), and differential phase shift keying (DPSK) in a 500 kV line-of-sight (LoS) substation smart grid environment. Second, as a result of this comparison, a new adaptive error control (AEC) algorithm is proposed. Adaptive error control adaptively changes error correction code (ECC) based on the channel behavior that is observed through the packet error rate (PER) in the recent previous transmissions. The link-quality-aware capacitated minimum hop spanning tree (LQ-CMST) algorithm and the multi-channel scheduling algorithm are used for data transmission over the log-normal channel. Therefore, the performance of compared coding techniques and AEC are also evaluated when multiple channels are used during transmission. Further, AEC is compared with static RS and without-FEC methods based on performance metrics such as the throughput, BER, and delay in different smart grid environments, e.g., 500 kV Substation (LoS), underground network transformer vaults (UTV) (LoS), and main power control room (MPR) (LoS). Our simulation results indicate that the proposed AEC algorithm achieves better performances than all those techniques.     

SSPS: A Semi-Supervised Pattern Shift for Classification

Recently, a great amount of efforts have been spent in the research of unsupervised and (semi-)supervised dimensionality reduction (DR) techniques, and DR as a preprocessor is widely applied into classification learning in practice. However, on the one hand, many DR approaches cannot necessarily lead to a better classification performance. On the other hand, DR often suffers from the problem of estimation of retained dimensionality for real-world data. Alternatively, in this paper, we propose a new semi-supervised data preprocessing technique, named semi-supervised pattern shift (SSPS). The advantages of SSPS lie in the fact that not only the estimation of retained dimensionality can be avoided naturally, but a new shifted pattern representation that may be more favorable to classification is obtained as well. As a further extension of SSPS, we develop its fast and out-of-sample versions respectively, both of which are based on a shape-preserved subset selection trick. The final experimental results demonstrate that the proposed SSPS is promising and effective in classification application.