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In this paper, a novel image-based prescribed performance visual servoing control scheme with hysteresis quantised input is proposed for quadrotor unmanned aerial vehicles (QUAVs, for short). First, based on the perspective projection principle, effective image features are defined on a image plane called virtual image plane, and the decoupled image feature dynamics are achieved with respect to target points. Then, the image feature dynamics and QUAV dynamics are combined to derive a nonlinear dynamic system, and the nonlinear system is decoupled into two subsystems based on the commonly used inner–outer loop control framework. Furthermore, the prescribed performance control-based visual servoing control scheme with hysteresis quantizer is designed, and the necessary stability proof is presented. Finally, the numerical simulations and experiments are conducted to validate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the settling time problem for dynamical systems, and some novel results are proposed. First, by reviewing classic achievements about the settling time problem of the control system, a new framework is designed. Then, based on the proposed framework, the classic fixed time control scheme is improved to predefined time control and exact time control results, respectively. Similarly, finite time control scheme is transformed into predefined time control and exact time control results. Furthermore, considering the bounded control results are vital in actual applications, the novel predefined time-bounded control and exact time-bounded control schemes are given. In the end, numerical simulations are conducted for robotic manipulators to demonstrate the effectiveness of the developed control strategies. [ABSTRACT FROM AUTHOR]

The high positioning accuracy of robotic systems is fundamental and critical for dexterous and precise manipulation. In this paper, a novel accurate positioning method based on vision and tactile sensors for object pose estimation in robotic manipulation is proposed to improve the positioning accuracy of robotic systems. The proposed methodology mainly includes two stages of vision-based rough positioning stage and tactile-based precise positioning stage which makes full use of the multimodal information for positioning and object pose estimation in robotic systems. In the vision-based rough positioning stage, the position and orientation of the target object can be obtained by RGB and depth images, and the robot approaches the target object considering a safe distance. In the tactile-based precise positioning stage, the tactile images are captured by the designed tactile sensor through the touch movement of the robot, and the position and orientation of the target object can be precisely calculated further to complete the positioning process. Finally, the experiments have been conducted to verify the proposed method in a real application platform, and achieved positioning accuracies of 0.138 mm and 0.203° have shown great improvement of 96.1% and 93.9% from 3.552 mm and 3.343° which can meet the demands for robotic precise positioning and manipulation. [ABSTRACT FROM AUTHOR]

In this paper, we address the learning problem of classifying untouched tactile instance with the help of visual modality. The proposed method is based on dictionary learning and we impose different penalty terms on coding vectors between visual and tactile modalities. Using such structured coding vectors, the visual-tactile cross-modal transfer can be achieved. A set of optimization algorithms are developed to obtain the solutions of the proposed optimization problems. After then, we can use the obtained dictionary to predict the coding vectors of the new untouched tactile samples and further determine its label. Finally, we perform extensive experimental evaluations on publicly available datasets to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations. In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate.We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance on the standard COCO and Pascal VOC object detection benchmark. More importantly, FoveaBox avoids all computation and hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. We believe the simple and effective approach will serve as a solid baseline and help ease future research for object detection. The code has been made publicly available at https://github.com/taokong/FoveaBox. [ABSTRACT FROM AUTHOR]

Tactile recognition aims at identifying target objects according to tactile sensory readings. Tactile data have two salient properties: 1) sequentially real-time and 2) temporally correlated, which essentially calls for a real-time (i.e., online fixed-budget) and recurrent recognition procedure. Based on an efficient and robust spatio-temporal feature representation for tactile sequences, we handle the problem of real-time recurrent tactile recognition by proposing a bounded online-sequential learning framework, and incorporates the strength of batch-regularization bootstrapping, bounded recursive reservoir, and momentum-based estimation. Experimental evaluations show that it outperforms the state-of-the-art methods by a large margin on test accuracy; and its training performance is superior to most compared models from aspects of average online training error, computational complexity, and storage efficiency. [ABSTRACT FROM AUTHOR]

Robotic material perception is an extremely important but challenging problem for industrial intelligence. The main difficulties come from the fact that the material properties are difficult to be comprehensibly evaluated by single visual, auditory, or tactile modality. Conventional multimodal fusion methods require collecting all of the multimodal information for a testing sample before the recognition. This is expensive, redundant, and may incur large latency. To tackle this problem, a new active fusion framework for the multimodal material recognition is proposed in this paper. We first adopt the adversarial learning method to obtain the modal-invariant representations to effectively bridge the gap between different modalities and then develop a reinforcement learning method for active modality selection. The developed framework and algorithms are evaluated on a publicly available dataset and show promising material recognition results. The developed framework provides an effective method for industrial material inspection. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural network approach to classification which combines modified probabilistic neural network and D-S evidence theory (PNN-DS) is proposed. It attempts to deal with the drawbacks of information uncertainty and imprecision using single classification algorithm. This PNN-DS approach firstly adopts a modified probabilistic neural network (PNN) to obtain posteriori probabilities and make a primary classification decision in feature-level fusion. Then posteriori probabilities are transformed to masses noting the evidence of the D-S evidential theory. Finally advanced D-S evidential theory is utilized to gain more accurate classification results in the last decision-level fusion. In order to implement PNN-DS, covariance matrices are firstly employed in the modified PNN module to replace the singular smoothing factor in the PNN's kernel function, and linear function is utilized in the pattern of summation layer. Secondly, the whole scheme of the proposed approach is explained in depth. Thirdly, three classification experiments are carried out on the proposed approach and a large amount of comparable analyses are done to demonstrate the effectiveness and robustness of the proposed approach. Experiments reveal that the PNN-DS outperforms BPNN-DS, which provides encouraging results in terms of classification accuracy and the speed of learning convergence. [ABSTRACT FROM AUTHOR]

Gestures recognition provides an intelligent, natural, and convenient way for human–robot interaction (HRI). This paper presents a novel data glove for gestures capturing and recognition based on inertial and magnetic measurement units (IMMUs), which are made up of three-axis gyroscopes, three-axis accelerometers and three-axis magnetometers. The proposed data glove has eighteen low-cost IMMUs, which are compact and small enough to wear. The gestures included the three-dimensional motions of arm, palm and fingers are completely captured by the data glove. Meanwhile, we attempt to use extreme learning machine (ELM) for gesture recognition which has not found yet in the relevant application. The ELM-based recognition methods for both static gestures and dynamic gestures are respectively presented. The experimental results of gestures capturing and recognition verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Sound and acceleration measurements are two classes of sensing modalities which frequently occur in surface material categorization. Their fusion problem is extremely important in many practical scenarios, since they provide different properties about materials. In this paper, we investigate the multimodal measurements fusion categorization problem exhibiting nontrivial challenges that there does not exist sample-to-sample pairing relation between sound and acceleration measurements. To this end, we design a dictionary learning model that can simultaneously learn the projection subspace and the latent common dictionary for the different measurements. Furthermore, an optimization algorithm is developed to effectively solve the common dictionary learning problem. Based on the obtained solutions, the fusion categorization algorithm can be easily developed. Finally, we perform experimental validations on the publicly available data set to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the disturbance observer-based composite fuzzy control of a class of uncertain nonlinear systems with unknown dead zone. With fuzzy logic system approximating the unknown nonlinearities, composite learning is constructed on the basis of a serial–parallel identifier. By introducing the intermediate signal, the disturbance observer is developed to provide efficient learning of the compounded disturbance which includes the effect of time-varying disturbance, fuzzy approximation error, and unknown dead zone. Based on the disturbance estimation and fuzzy approximation, the adaptive fuzzy controller is synthesized with novel updating law. The stability analysis of the closed-loop system is rigorously established via Lyapunov approach. The performance of the proposed controller is verified via simulation that faster convergence and higher precision are obtained. [ABSTRACT FROM PUBLISHER]

Haptic sensing and feedback play extremely important roles for humans and robots to perceive, understand, and manipulate the world. Since many properties perceived by the haptic sensors can be characterized by adjectives, it is reasonable to develop a set of haptic adjectives for the haptic understanding. This formulates the haptic understanding as a multilabel classification problem. In this paper, we exploit the intrinsic relation between different adjective labels and develop a novel dictionary learning method which is improved by introducing the structured output association information. Such a method makes use of the label correlation information and is more suitable for the multilabel haptic understanding task. In addition, we develop two iterative algorithms to solve the dictionary learning and classifier design problems, respectively. Finally, we perform extensive experimental validations on the public available haptic sequence dataset Penn Haptic Adjective Corpus 2 and show the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

The consensus problem of fractional-order multi-agent systems is studied in this paper. To achieve consensus, a fractional-order observer-type consensus protocol based on relative output measurements is proposed and its stability is also certified theoretically. The notion of consensus region for fractional-order dynamic is introduced and analysed. A multistep consensus protocol design procedure is presented for given consensus region. Several numerical simulations are given to demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM PUBLISHER]

This paper presents a novel wearable device for gesture capturing based on inertial and magnetic measurement units that are made up of micromachined gyroscopes, accelerometers, and magnetometers. The low-cost inertial and magnetic measurement unit is compact and small enough to wear and there are altogether thirty-six units integrated in the device. The device is composed of two symmetric parts, and either the right part or the left one contains eighteen units covering all the segments of the arm, palm, and fingers. The offline calibration and online calibration are proposed to improve the accuracy of sensors. Multiple quaternion-based extended Kalman filters are designed to estimate the absolute orientations, and kinematic models of the arm-hand are considered to determine the relative orientations. Furthermore, position algorithm is deduced to compute the positions of corresponding joint. Finally, several experiments are implemented to verify the effectiveness of the proposed wearable device. [ABSTRACT FROM AUTHOR]

In this paper, we aim to achieve robust and cost-effective room-level localization for the indoor mobile robot. It is unrealistic to obtain precise localization information from the sonar sensors because of the sparseness and uncertainty. Our attempts show that the room-level localization can be achieved using sonar sensors by accumulating the sonar data to overcome the limitations of sensor performance. To this end, we formulate the room-level localization as a joint sparse coding problem, which encourages the coding vectors to share the common room sparsity, but different locations. We systematically evaluate the performance of the different coding strategies on the collected sonar measurement data set. [ABSTRACT FROM PUBLISHER]

The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs) for EEG signals feature extraction and classification. LDSs model has lots of advantages such as simultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore, a low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve the robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite Grassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from “BCI Competition III Dataset IVa” and “BCI Competition IV Database 2a.” The results show that our proposed three methods yield higher accuracies compared with prevailing approaches such as CSP and CSSP. [ABSTRACT FROM AUTHOR]

This note proposes a notion of scaled cluster consensus, wherein the final consensus states within different clusters converge to prescribed ratios. Unlike most results in existing literature on cluster consensus, no constraints are imposed on the system topologies under the designed protocol, i.e. the agents are not required to possess any cluster affiliation information of others. For the delay-free case, an explicit scaled cluster consensus function is provided by exploring the characteristics of stochastic matrices. Diverse input delays and asymmetric communication delays are both considered, and sufficient condition for scaled cluster consensus is derived based on frequency domain analysis. Finally, numerical examples are given to illustrate the effectiveness of the presented results. [ABSTRACT FROM PUBLISHER]

This paper investigates the cluster consensus problems of generic linear multi-agent systems with switching topologies. Sufficient criteria for cluster consensus, which generalise the results in existing literatures, are derived for both state feedback and observer-based control schemes. By using an averaging method, it is shown that cluster consensus can be achieved when the union of the acyclic topologies contains a directed spanning tree within each cluster frequently enough. We also provide a principle to construct digraphs with inter-cluster cyclic couplings that promote cluster consensus regardless of the magnitude of inter-agent coupling weights. Finally, numerical examples are given to demonstrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

This paper is concerned with the resilient control under denial-of-service attack launched by the intelligent attacker. The resilient control system is modelled as a multi-stage hierarchical game with a corresponding hierarchy of decisions made at cyber and physical layer, respectively. Specifically, the interaction in the cyber layer between different security agents is modelled as a static infinite Stackelberg game, while in the underlying physical layer the full-informationH∞minimax control with package drops is modelled as a different Stackelberg game. Both games are solved sequentially, which is consistent with the actual situations. Finally, the proposed method is applied to the load frequency control of the power system, which demonstrates its effectiveness. [ABSTRACT FROM AUTHOR]

The perception of the tactile and vision modalities plays a crucial role in object classification and robot precise operations. On one hand, the visual perception helps acquire the objects apparent characteristics (e.g., shape and color) that are paramount for grasp planning. On the other hand, when grasping the object, the tactile sensors can detect the softness or stiffness and the surface texture of the object, thus can be used to classify the objects. In this paper, two different tactile models are first developed to model the tactile sequences, namely bag-of-system (BoS) and deep dynamical system (DDS). To be specific, BoS applies the extreme learning machine method as the classifier, and DDS employs deep neural networks as the feature mapping functions in order to deal with the highly nonlinear data. As for visual sensing, we formulate a signature of histograms of orientations descriptor to model the shape of objects. Moreover, by using human experience, we identify the graspable components of objects on the category level instead of the object level. Finally, a fast planning method considering both contact points exaction and hand kinematics is proposed to accomplish the grasping manipulation. Comparative experiments demonstrate the effectiveness of our proposed methods on the tasks including the robot grasping and the object classification. [ABSTRACT FROM PUBLISHER]

The cluster consensus problem of high-order multi-agent systems is considered in this note with an observer-based control scheme. Sufficient condition for cluster consensus is presented in terms of easily checkable algebraic topology criterion. It is found that intra-cluster balanced topologies with antisymmetric (i.e., bidirectional and opposite signed) inter-cluster links promote cluster consensus, irrelevant of the magnitudes of intra-cluster coupling weights. The effectiveness of the theoretical results is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

This article investigates the H∞$$ {H}_{\infty } $$‐optimal estimation problem of a class of linear system with delays in states, disturbance input, and outputs. The estimator uses an extended Luenberger estimator format which estimates both the present and history states. The estimator is designed using an equivalent Partial Integral Equation (PIE) representation of the coupled nominal system. The advantage of the resulting PIE representation is compact and delay free—obviating the need for commonly used bounding technique such as integral inequalities which typically introduces conservatism into the resulting optimization problem. The H∞$$ {H}_{\infty } $$‐optimal estimator synthesis problem is then reformulated as a Linear Partial Inequality (LPI)—a form of convex optimization using operator variables and inequlities. Such LPI‐based optimization problems can be solved using semidefinite programming via the PIETOOLS toolbox in Matlab. Compared with previous work, the proposed method simplifies the analysis and computation process and resulting in observers which are non‐conservtism to 4 decimal places when compared with Pade'$$ \overset{\acute{\mkern6mu}}{\mathrm{e}} $$‐based ODE observer design methodologies. Numerical examples and simulation results are given to illustrate the effectiveness and scalability of the proposed approach. [ABSTRACT FROM AUTHOR]

Visual navigation involves a movable robotic agent striving to reach a point goal (target location) using vision sensory input. While navigation with ideal visibility has seen plenty of success, it becomes challenging in suboptimal visual conditions like poor illumination, where traditional approaches suffer from severe performance degradation. We propose E3VN (echo-enhanced embodied visual navigation) to effectively perceive the surroundings even under poor visibility to mitigate this problem. This is made possible by adopting an echoer that actively perceives the environment via auditory signals. E3VN models the robot agent as playing a cooperative Markov game with that echoer. The action policies of robot and echoer are jointly optimized to maximize the reward in a two-stream actor-critic architecture. During optimization, the reward is also adaptively decomposed into the robot and echoer parts. Our experiments and ablation studies show that E3VN is consistently effective and robust in point goal navigation tasks, especially under nonideal visibility. [ABSTRACT FROM AUTHOR]

Autonomous indoor service robots are affected by multiple factors when they are directly involved in manipulation tasks in daily life, such as scenes, objects, and actions. It is of self-evident importance to properly parse these factors and interpret intentions according to human cognition and semantics. In this study, the design of a semantic representation framework based on a knowledge graph is presented, including (1) a multi-layer knowledge-representation model, (2) a multi-module knowledge-representation system, and (3) a method to extract manipulation knowledge from multiple sources of information. Moreover, with the aim of generating semantic representations of entities and relations in the knowledge base, a knowledge-graph-embedding method based on graph convolutional neural networks is proposed in order to provide high-precision predictions of factors in manipulation tasks. Through the prediction of action sequences via this embedding method, robots in real-world environments can be effectively guided by the knowledge framework to complete task planning and object-oriented transfer. [ABSTRACT FROM AUTHOR]

Non-blind deconvolution, which is to restore a sharp version of a given blurred image when the blur kernel is known, is a fundamental step in image deblurring. While the problem has been extensively studied, existing methods have conveniently ignored an important fact that deformation can significantly affect the statistical characteristics of an image and introduce additional blurring effect. In this paper, we show how to enhance non-blind deconvolution by recovering and undoing the deformation while deconvolving a given blurred image. We show that this is the case for almost all popular regularizers that have been proposed for image deblurring such as total variation and its variants. We conduct extensive simulations and experiments on real images and verify that the incorporation of geometric deformation in deconvolution can significantly improve the final deblurring results. Combined with existing blur kernel estimation techniques, our method can also be used to enhance blind image deblurring. [ABSTRACT FROM AUTHOR]

This study is concerned with the low‐frequency (LF) integral quadratic constraints (IQC) of the linear singularly perturbed model (SPM). The conditions satisfying LF IQC are derived in terms of linear matrix inequalities by using the generalised Kalman–Yakubovich–Popov lemma. As special cases of LF IQC, LF bounded realness and LF positive realness are exploited in detail. In addition, upper bound of the small perturbation parameter, which relates to the robustness of SPM, can be estimated. Numerical simulations demonstrate that the proposed method can reduce the conservatism and characterise the SPM more specifically by ignoring the unrelated frequency range. [ABSTRACT FROM AUTHOR]

This study investigates the consensus problem of second‐order multi‐agent systems which are subject to Markov jumping input delays. A unified framework is established to address both the stationary and dynamic consensus issues in sampled‐data settings. The authors first derive the sufficient conditions for consensus in terms of matrix inequalities. Thereafter, a cone complementarity linearisation‐based algorithm and its simplified implementation alternative are proposed for the delay‐dependent switching controller design. Compared with the existing works, the proposed algorithms possess less conservativeness and larger solution domains. Finally, numerical examples are provided to illustrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Subspace segmentation is the problem of segmenting (or grouping) a set of n data points into a number of clusters, with each cluster being a (linear) subspace. The recently established algorithms such as Sparse Subspace Clustering (SSC), Low-Rank Representation (LRR) and Low-Rank Subspace Segmentation (LRSS) are effective in terms of segmentation accuracy, but computationally inefficient as they possess a complexity of O(n^{3}) , which is too high to afford for the case where n$ is very large. In this paper we devise a fast subspace segmentation algorithm with complexity of $O(n\log (n))$ . This is achieved by firstly using partial Singular Value Decomposition (SVD) to approximate the solution of LRSS, secondly utilizing Locality Sensitive Hashing (LSH) to build a sparse affinity graph that encodes the subspace memberships, and finally adopting a fast Normalized Cut (NCut) algorithm to produce the final segmentation results. Besides of high efficiency, our algorithm also has comparable effectiveness as the original LRSS method. [ABSTRACT FROM AUTHOR]

This paper addresses the state feedback stabilization problem for a class of network-based delta operator control systems. To compensate time delays, a gain scheduling controller based on one-way delay measurement is constructed, and the resulting closed-loop system is transformed into a switched delta operator system. The stability condition is derived for the closed-loop system, and the corresponding stabilizing and optimal controller design techniques are also developed. The simulation and experimental results are given to demonstrate the effectiveness and applicability of the proposed approach. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, the delay-dependent stability criterion for time-varying delay neural networks in the delta domain is investigated. The unified neural networks, which can be used in both continues-time space and discrete-time space, takes advantage with a high sampling frequency. In the framework of the newly proposed neural networks, the delay-dependent stability criteria is derived in terms of linear matrix inequality by constructing the Lyapunov-Krasovskii function in the delta domain. A numerical simulation is given to show the effectiveness and superiority of the proposed approach. [Copyright &y& Elsevier]

This paper is concerned with the stabilization problem of a class of networked control systems, where the plant measurements and the control signals are transmitted over a network and encounter both time delays and packet losses. To deal with the nonzero disturbance rejection issue, two kinds of networked optimal state feedback integral control (SFIC) methods are proposed in this paper: One is a networked guaranteed-cost SFIC method, and the other one is an SFIC method based on estimation of a distribution algorithm. It has been shown that the proposed methods have better nonzero disturbance rejection capability and can achieve zero steady-state error. Simulation and experimental results are given to demonstrate the effectiveness and applicability of the proposed approach. [ABSTRACT FROM PUBLISHER]

In this article, the adaptive neural controller in discrete time is investigated for the longitudinal dynamics of a generic hypersonic flight vehicle. The dynamics are decomposed into the altitude subsystem and the velocity subsystem. The altitude subsystem is transformed into the strict-feedback form from which the discrete-time model is derived by the first-order Taylor expansion. The virtual control is designed with nominal feedback and neural network (NN) approximation via back-stepping. Meanwhile, one adaptive NN controller is designed for the velocity subsystem. To avoid the circular construction problem in the practical control, the design of coefficients adopts the upper bound instead of the nominal value. Under the proposed controller, the semiglobal uniform ultimate boundedness stability is guaranteed. The square and step responses are presented in the simulation studies to show the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

In this paper, a novel robust training algorithm of multi-input multi-output recurrent neural network and its application in the fault tolerant control of a robotic system are investigated. The proposed scheme optimizes the gradient type training on basis of three new adaptive parameters, namely, dead-zone learning rate, hybrid learning rate, and normalization factor. The adaptive dead-zone learning rate is employed to improve the steady state response. The normalization factor is used to maximize the gradient depth in the training, so as to improve the transient response. The hybrid learning rate switches the training between the back-propagation and the real-time recurrent learning mode, such that the training is robust stable. The weight convergence and L stability of the algorithm are proved via Lyapunov function and the Cluett's law, respectively. Based upon the theoretical results, we carry out simulation studies of a two-link robot arm position tracking control system. A computed torque controller is designed to provide a specified closed-loop performance in a fault-free condition, and then the RNN compensator and the robust training algorithm are employed to recover the performance in case that fault occurs. Comparisons are given to demonstrate the advantages of the control method and the proposed training algorithm. [ABSTRACT FROM AUTHOR]

A discrete-time jump fuzzy system with two hidden Markov models (HMMs) is proposed to portray the asymmetric network characteristic of a class of nonlinear networked control systems (NCSs) with random but bounded communication delays and packets dropout. The less conservative state feedback controller and the dual-model-depend guaranteed cost controller are designed base on the model. A homotopy- based iterative algorithm solving for nonlinear matrix inequality (NMI) is developed to get the control gains. Simulation examples are carried out to show the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Abstract: This paper focuses on tracking control problem of flexible-link manipulators. In order to alleviate the effects of nonlinearities and uncertainties, a combined control strategy based on neural network (NN) and the concept of sliding mode control (SMC) is proposed systematically. The chattering phenomenon in conventional SMC is eliminated by incorporated a saturation function in the proposed controller, and the computation burden caused by model dynamics is reduced by applying a two-layer NN with an analytical approximated upper bound, which is used to implement a certain functional estimate. In addition, the Lyapunov analysis can guarantee the signals of closed-loop system bounded and the online NN adaptive laws can make the system states converge to the sliding surface. Furthermore, the boundary layer thickness as well as the gain of corrective control term is also discussed in detail. At last, the theoretic results are validated on the flexible-link manipulator experimental system in Tsinghua University. [Copyright &y& Elsevier]

Abstract: In this paper, both the state feedback and static output feedback controllers for fuzzy singularly perturbed systems are investigated. We give sufficient conditions for the existence of controller such that the closed-loop fuzzy control system is globally stable and achieves a prescribed level of disturbance attenuation for sufficiently small perturbation parameters. The linear matrix inequality (LMI) approach is proposed to obtain the state-feedback gains, and a homotopy-based iterative LMI algorithm is developed to get the static output feedback gains. Simulation results for two examples are included to demonstrate the effectiveness of the proposed approaches. [Copyright &y& Elsevier]

Abstract: This paper addresses the reduced-order filtering problem for continuous-time Makovian jump linear systems, where the jump parameters are modelled by a discrete-time Markov process. Sufficient conditions for the existence of the reduced-order filter are proposed in terms of linear matrix inequalities (LMIs) and a coupling non-convex matrix rank constraint. In particular, the sufficient conditions for the existence of the zero-order filter can be expressed in terms of a set of strict LMIs. The explicit parameterization of the desired filter is also given. Finally, a numerical example is given to illustrate the proposed approach. [Copyright &y& Elsevier]

In the above paper, Chang et al. (Fuzzy Sets and Systems 133 (2003) 37) proposed an interesting approach to design the fuzzy static output feedback controller with a common observability Gramian. However, since it involves a nonlinear output function, the results obtained are not correct for the general case. This note intends to circumvent these problems by modifying the approach proposed in Chang et al. (Fuzzy Sets and Systems 133 (2003) 37). [Copyright &y& Elsevier]

This paper presents a~stable neuro-fuzzy (NF) adaptive control approach for the trajectory tracking of the robotic manipulator with poorly known dynamics. Firstly, the fuzzy dynamic model of the manipulator is established using the Takagi–Sugeno (T–S) fuzzy framework with both structure and parameters identified through input/output data from the robot control process. Secondly, based on the derived fuzzy dynamics of the robotic manipulator, the dynamic NF adaptive controller is developed to improve the system performance by adaptively modifying the fuzzy model parameters on-line. The dynamic NF system aims to approximate the whole robot dynamics rather than its nonlinear components as is done by static neural networks. The dynamic inversion introduced for the controller design is constructed by the dynamic NF system and will help the NF controller design because it does not require the assumption that the robot states should be within a~compact set. It is generally known that the compact set cannot be specified before the control loop is closed. Thirdly, the system stability and the convergence of tracking errors are guaranteed by Lyapunov stability theory, and the learning algorithm for the dynamic NF system is obtained thereby. Finally, simulation studies are carried out to show the viability and effectiveness of the proposed control approach. [Copyright &y& Elsevier]

Features a study which proposed a neural network (NN)-based adaptive controller with an observer for the trajectory tracking of robotic manipulators with unknown dynamics nonlinearities. Background of the study; Preliminaries; Observer-based controller design using NN; Simulation results; Conclusion.

The volumetric positioning accuracy of multi-axis CNC machine tools indicates the deviation from desired to actual position of the tool; it directly influences the machining accuracy of the workpiece, and it is also one of the important indicators to measure the performance of the machine tool. The volumetric positioning accuracy of machine tools is supported to be fundamentally improved. The accuracy design methods for machine tools currently include a robust design method for machining accuracy and static geometric accuracy. The robust design method for machining accuracy is set up based on the effective evaluation of the volumetric positioning accuracy of machine tools, and the geometric error elements are used as the analysis variables without directly directing the tolerance design. The joint surface tolerance of key components is used as the analysis variable in the design method for static geometric accuracy, which is supported to be optimized according to the accuracy design requirements of different machine tools. The present paper summarizes and analyzes the aspects including the volumetric accuracy modeling, identification of geometric error elements for axis of motion, robust design method for machining accuracy, tolerance modeling, and design method for static geometric accuracy of machine tools, and analyzes the key problems to be solved in the method of improving the volumetric positioning accuracy of multi-axis CNC machine tools, and then a feasible research idea to improve the volumetric positioning accuracy of multi-axis CNC machine tools is proposed. [ABSTRACT FROM AUTHOR]

Presents information regarding the use of the stable neural-network-based adaptive control for sampled-data nonlinear systems, while referencing to the class of multiiput-multioutput (MIMO). How the sampled-data nonlinear system is assumed to be; Reference to the two purposes which variable structure control with a sector serves; What simulation studies of a two-link manipulator shows.

Existing precision design methods cannot directly guide the tolerance design. Therefore, in this study, an optimization design method of machine tool static geometric accuracy based on tolerance modeling is proposed. In this methodology, the mapping relationship between the geometric error of machine tools and tolerance design is established using the small displacement torsor to represent the tolerance information and the Monte Carlo simulation method is used to establish the response model of the torsor parameters and the tolerance variation bandwidths. An assembly accuracy model is then established by combining a machine tool topology analysis and the forming mechanism of the joint surface error. To calculate the tolerances of the component joint surface, a tolerance response model related to the component joint surface tolerance and torsor parameters is developed. Finally, according to the state function of assembly accuracy reliability, a function response model of the assembly accuracy, reliability, and tolerance is developed. Combining the assembly's processing cost model with the accuracy, reliability, and tolerance principles, a tolerance optimization model of the static geometric accuracy of a CNC machine tool, a linear axis motion guide, is constructed as a case study. Using a simulated annealing genetic algorithm to solve the tolerance optimization model, the tolerance optimization value is obtained, thereby verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]