This article proposes a novel discrete event-triggered scheme (DETS) for the synchronization of delayed neural networks (NNs) using the dynamic output-feedback controller (DOFC). The proposed DETS uses both the current and past samples to determine the next trigger, unlike the traditional event-triggered scheme (ETS) that uses only the current sample. The proposed DETS is employed in a dual setup for two network channels to significantly reduce redundant data transmission. A DOFC is designed to achieve the synchronization of the NNs. Stability criteria of the synchronisation error system are derived based on the Lyapunov-Krasovskii functional method, and the co-design of the DOFC and DETS parameters are accomplished using the Cone-complementarity linearization (CCL) approach. The effectiveness and advantages of the proposed method are illustrated considering an example of the chaotic system.

This article focuses on the bumpless transfer$H_{\infty }$anti-disturbance control problem for switching Markovian LPV systems under a hybrid switching law. A parameter-dependent multiple piecewise disturbance observer-based bumpless transfer control strategy is put forward to reject multiple disturbances and reduce switching bumps. First, a hybrid switching law making full use of determinacy and randomness is proposed to improve the bumpless transfer anti-disturbance level by introducing a fixed dwell time in random switching. Second, a generalized bumpless transfer anti-disturbance specification is given to describe the switching quality at the switching points of switching Markovian LPV systems. Third, a solvability condition is established for the bumpless transfer$H_{\infty }$anti-disturbance control problem, and a parameter-dependent multiple piecewise disturbance observer-based bumpless transfer controller is designed. Finally, an application example has been supplied to demonstrate the availability of the developed method.

In this article, a novel reinforcement learning (RL) method is developed to solve the optimal tracking control problem of unknown nonlinear multiagent systems (MASs). Different from the representative RL-based optimal control algorithms, an internal reinforce Q-learning (IrQ-L) method is proposed, in which an internal reinforce reward (IRR) function is introduced for each agent to improve its capability of receiving more long-term information from the local environment. In the IrQL designs, a Q-function is defined on the basis of IRR function and an iterative IrQL algorithm is developed to learn optimally distributed control scheme, followed by the rigorous convergence and stability analysis. Furthermore, a distributed online learning framework, namely, reinforce-critic-actor neural networks, is established in the implementation of the proposed approach, which is aimed at estimating the IRR function, the Q-function, and the optimal control scheme, respectively. The implemented procedure is designed in a data-driven way without needing knowledge of the system dynamics. Finally, simulations and comparison results with the classical method are given to demonstrate the effectiveness of the proposed tracking control method.

This article investigates the problem of memory-event-triggered H∞ output feedback control for neural networks with mixed delays (discrete and distributed delays). The probability density of the communication delay among neurons is modeled as the kernel of the distributed delay. To reduce network communication burden, a novel memory-event-triggered scheme (METS) using the historical system output is introduced to choose which data should be sent to the controller. Based on a constructed Lyapunov-Krasovskii functional (LKF) with the distributed delay kernel and a generalized integral inequality, new sufficient conditions are formed by linear matrix inequalities (LMIs) for designing an event-triggered H∞ controller. Finally, experiments based on a computer and a real wireless network are executed to confirm the validity of the developed method.

An observer-based dissipativity control for Takagi-Sugeno (T-S) fuzzy neural networks with distributed time-varying delays is studied in this article. First, the network channel delays are modeled as a distributed delay with its kernel. To make full use of kernels of the distributed delay, a Lyapunov-Krasovskii functional (LKF) is established with the kernel of the distributed delay. It is noted that the novel LKF and delay-dependent reciprocally convex inequality plays an important role in dealing with global asymptotical stability and strict (Q, S,R) - α -dissipativity of the T-S fuzzy delayed model. Through the constructed LKF, a new set of less conservative linear matrix inequality (LMI) conditions is presented to obtain an observer-based controller for the T-S fuzzy delayed model. This proposed observer-based controller ensures that the state of the closed-loop system is globally asymptotically stable and strictly (Q, S,R) - α -dissipative. Finally, the effectiveness of the proposed results is shown in numerical simulations.

This article investigates an output antisynchronization problem of multiagent systems by using an input-output data-based reinforcement learning approach. Till now, most of the existing results on antisynchronization problems required full-state information and exact system dynamics in the controller design, which is always invalid in practical scenarios. To address this issue, a new system representation is constructed by using just the available input/output data from the multiagent system. Then, a novel value iteration algorithm is proposed to compute the optimal control laws for the agents; moreover, a convergence analysis is presented for the proposed algorithm. In the implementation of the data-based controllers, an actor-critic network structure is established to learn the optimal control laws without the requirement of information of the agent dynamics. An incremental weight updating rule is proposed to improve the learning performance. Finally, simulation results are presented to demonstrate the effectiveness of the proposed antisynchronization control strategy.

This article considers an event-triggered H∞synchronization of neural networks (NNs) with mixed (discrete and distributed) delays. To release the communication burden, a novel weighted integral event-triggered scheme (IETS) is proposed based on the past information of the system dynamics. In this scheme, for the first time, a weighting function is proposed to weight the system state over a given period, which can be viewed as a forgetting factor. Moreover, a waiting time interval is added in the proposed IETS to exclude Zeno phenomenon. By constructing a novel Lyapunov-Krasovskii functional with the distributed delay kernel and the weighting function, sufficient linear matrix inequality conditions for the existence of an event-triggered controller that guarantees an exponential synchronization of the delayed NNs with an H∞performance are derived. Finally, an illustrative example and an application to image encryption are used to demonstrate the advantage of the proposed approach.

The article considers the impulsive synchronization for inertial neural networks with unbounded delay and actuator saturation via sampled-data control. Based on an impulsive differential inequality, the difficulties caused by unbounded delay and impulsive effect may be effectively avoid. By applying polytopic representation technique, the actuator saturation term is first considered into the design of impulsive controller, and less conservative linear matrix inequality (LMI) criteria that guarantee asymptotical synchronization for the considered model via hybrid control are given. As special cases, the asymptotical synchronization of the considered model via sampled-data control and saturating impulsive control are also studied, respectively. Numerical simulations are presented to claim the effectiveness of theoretical analysis. A new image encryption algorithm is proposed to utilize the synchronization theory of hybrid control. The validity of image encryption algorithm can be obtained by experiments.

This article investigates the problem of the decentralized adaptive output feedback saturated control problem for interconnected nonlinear systems with strong interconnections. A decentralized linear observer is first established to estimate the unknown states. Then, an auxiliary system is constructed to offset the effect of input saturation. With the aid of graph theory and neural network technique, a decentralized adaptive neuro-output feedback saturated controller is designed in a nonrecursive manner. A sufficient criterion is established to achieve the uniform ultimate boundedness (UUB) of the closed-loop system. An application example of autonomous underwater vehicle (AUV) is provided to verify the effectiveness of the developed algorithm.

This article examines multiagent relay tracking systems with the varying number of tracking agents. We first establish an accurate mathematical model to describe a relay tracking process with communication delays, jumps in the tracking errors and time-varying number of tracking agents. Then, a novel impulse-time-dependent average Lyapunov function is proposed for designing a multiagent relay tracking system. Sufficient conditions for the existence of a relay tracking controller are derived in terms of linear matrix inequalities. Finally, the effectiveness of our approaches is verified by numerical simulations.

The impulsive stabilization of nonlinear time-delay system with input saturation via delay-dependent polytopic approach is studied in this article. Different from polytopic representation technique, delay-dependent polytopic technique is able to estimate a larger domain of attraction. Based on this approach, the actuator saturation term is first introduced into the design of impulsive controller, which is expressed as a convex combination of the product of delay-dependent state vectors and auxiliary matrices. By applying delay-dependent polytopic technique and delay-dependent Lyapunov–Krasovskii functional (LKF) approach, a new series of less conservative linear matrix inequality (LMI) criteria are obtained to ensure the stability of the established model. Finally, two examples are presented to claim the effectiveness of theoretical analysis results.

This article is concerned with the anti-disturbance control for Markov jump systems with matched and mismatched disturbances. First, the matched part is estimated by an output-based disturbance observer. Meanwhile, the mismatched part is attenuated by the$H_{\infty }$controller. Therefore, a composite static output control scheme is built to make the closed-loop system stable with the required$H_{\infty }$performance. Second, an integral sliding-mode output (ISMO) control approach is proposed to restrain the mentioned disturbances by using the upper bounds of disturbances. Third, an ISMO anti-disturbance strategy is established to integrate the advantages of the above two methods. It is noteworthy to point out that the proposed output observer structure could be reduced to the state case. Moreover, the diagonal constraint on Lyapunov variables is removed in this article. Finally, a numerical example is simulated to validate the effectiveness of the proposed methods.

Manufacturing of composites using automated fiber placement (AFP) is a complex process that involves large number of processing conditions and variables. Improper selection of these parameters adversely affects the quality and integrity of the manufactured laminates. Thus, it is important to develop a predictive model that can assess how changes in critical process conditions alter the outputs of the manufacturing process. The goal of this investigation is to learn the complex behavior of composites by developing an intelligent model, which can subsequently be used for the prediction of various characteristics of the composites. However, manufacturing of AFP composites is both expensive and time-consuming and therefore the available data samples are less, from the prospective of machine learning, which leads to the small data learning problem. This article first solves this problem through virtual sample generation, and then a neural network based predictive model is developed to accurately learn the complex relationships between various processing parameters in AFP.

This article investigates the event-triggered synchronization of delayed neural networks (NNs). A novel integral-based event-triggered scheme (IETS) is proposed where the integral of the system states, and past triggered data over a period of time are used. With the proposed IETS, the integral event-triggered synchronization problem becomes a distributed delay problem. Using the Bessel-Legendre inequalities, sufficient conditions for the existence of a controller that ensures asymptotic synchronization are provided in the form of linear matrix inequalities (LMIs). Illustrative examples are used to demonstrate the advantages of the proposed IETS method over other event-triggered scheme (ETS) methods. Moreover, this IETS method is applied to the image encryption and decryption. A novel encryption algorithm is proposed to enhance the quality of the encryption process.

This paper explores the application of a capactive power transfer (CPT) technology to power multiple receivers on a movable conveyor. An autonomous push-pull converter is used to power the system. Compared to the existing applications, this application has the challenges against variations of coupling capacitance and loads due to multiple receivers. The unique working principle of autonomous push-pull converter guarantees the zero-voltage switching (ZVS) condition regardless of these variations. Firstly, the circuit models with a single pick-up and multiple pick-ups are analyzed. Then a MAXWELL simulation is conducted to simulate the electric field distribution and to compute coupling capacitances variations. Circuit simulation results show a promising sign to apply CPT technology into the conveyor belt applications. Finally, an inverter prototype has been implemented and the ZVS condition is met.

Many applications in control, signal processing and manufacturing require the inverse model of a system. Identification of the inverse of a system (inverse modelling) is often an ill-posed problem and therefore a challenging task. The learning capability of the artificial neural network (ANN) has been exploited in the past to identify the inverse of a system. However, in certain applications, such as manufacturing, where the available data samples are less, the complexity of fitting an inverse model increases significantly. This results in small data learning problem. The present study solves this small data learning problem from the perspective of the inverse system identification using neural networks. Initially, the effectiveness of different combinations of various virtual sample generation (VSG) methods and machine learning tools are investigated to determine the optimum combination which gives the highest learning accuracy. Simulation results are included to demonstrate the effectiveness of ANN in identifying the inverse of various systems from small data.

This paper presents the event-triggered control for Takagi-Sugeno (T-S) fuzzy networked systems with transmission delay. An integral-based model is proposed for designing a new event-triggered scheme, which relies on the mean of the system state and the last triggered state. To handle the asynchronous premises of the fuzzy system and fuzzy controller, a novel triggering condition is added into the event-triggered mechanism. Then, the closed-loop T-S fuzzy event-triggered control system is established as a distributed delay system. With the help of the Legendre polynomials and their properties, the co-design conditions of triggering parameters and controller gains are given in linear matrix inequalities to ensure the asymptotic stability of the resulting closed-loop system. Finally, an experiment via a practical wireless network is implemented to illustrate the effectiveness of the proposed approach.

This brief investigates the analysis issue for global asymptotic stability of a class of generalized neural networks with multiple discrete and distributed delays. To tackle delays arising in different neuron activation functions, we employ a generalized model with multiple discrete and distributed delays which covers various existing neural networks. We then generalize the Bessel-Legendre inequalities to deal with integral terms with any linearly independent functions and nonlinear function of states. Based on these inequalities, we design the Lyapunov-Krasovskii functional and derive hierarchical linear matrix inequality stability conditions. Finally, three numerical examples are provided to demonstrate that the proposed method is less conservative with a reasonable numerical burden than the existing results.

Incorporating the disturbance frequency into system analysis and synthesis, this paper is dedicated to the quantized${H_\infty }$static output control of linear Markov jump systems. The output quantization is transformed into a sector bound form, and the finite frequency performance is handled by Parseval’s theorem. With the aid of Finsler’s lemma, sufficient conditions for the resulting closed-loop system are first established to satisfy the required finite frequency performance. To treat the static output feedback control problem in the framework of linear matrix inequalities, a new strategy is developed to decompose the coupling among Lyapunov variables, controller gain, and system matrices. In contrast to the existing results in the literature, no additional assumptions are imposed on the system matrices. Numerical examples are presented to demonstrate the validity of the established results.

The present study investigates the small data learning problem and proposes the optimum combination of Virtual Sample Generation (VSG) methods and various machine learning tools which would give highest learning accuracy. Although this small data problem has been studied by various researchers in the recent past, the selection of appropriate VSG methods and machine learning tools, which would yield better accuracy, have comparatively received less attention. This study bridges this gap by investigating the learning performance of three popular VSG methods and five well known machine learning tools. The results of the investigation shows that the learning accuracy is dependent jointly on the choice of the VSG method and the machine learning tool. It has been shown that among all the VSG methods, the Trend Similarity Assessment (TSA) method of VSG when combined with Back Propagation Neural Network (BPNN), gives highest learning accuracy.

This study proposes a novel robust video tracking algorithm consists of target detection, multi‐feature fusion, and extended Camshift. Firstly, a novel target detection method that integrates Canny edge operator, three‐frame difference, and improved Gaussian mixture model (IGMM)‐based background modelling is provided to detect targets. The IGMM‐based background modelling divides video frames into meshes to avoid pixel‐wise processing. In addition, the output of the target detection is utilised to initialise the IGMM and to accelerate the convergence of iterations. Secondly, low‐dimensional regional covariance matrices are introduced to describe video targets by fusing multiple features like pixel location, colour index, rotation and scale invariant features as well as uniform local binary patterns, and directional derivatives. Thirdly, an extended Camshift based on adaptive kernel bandwidth and robust H ∞ state estimation is proposed to predict the states of fast moving targets and to reduce the mean shift iterations. Finally, the effectiveness of the proposed tracking algorithm is demonstrated via experiments.

This paper proposes an H∞robust state-feedback controller design for uncertain Takagi-Sugeno fuzzy systems using a nonmonotonic Lyapunov function. In the nonmonotonic approach, the monotonicity requirement of the Lyapunov function is relaxed by allowing it to increase locally. Based on the nonmonotonic Lyapunov function approach, sufficient conditions for the existence of a robust state-feedback H∞controller that guarantees stability and a prescribed H∞performance are given in terms of linear matrix inequalities. The proposed design technique is shown to be less conservative than the existing k-samples variations of the Lyapunov function. The effectiveness of the proposed approach is further illustrated via numerical examples.

The distributed filtering problem is addressed in this paper for the discrete-time Takagi–Sugeno (T–S) fuzzy systems with incomplete measurements. The system under consideration includes various network-induced uncertainties, e.g., sensor saturation, quantization error, communication delay, and packet dropouts. Specifically, all these uncertainties are assumed to occur in a stochastic way. In addition, the measurement scheduling issue is also addressed such that only a portion of measurements are broadcasting due to the communication constraints. The main focus is on the design of distributed filters based on the information received locally and from the neighborhood such that the desired estimation performance is guaranteed in terms of the decay rate and disturbance attenuation level. Based on the Lyapunov stability theory, the existence condition for such filters is first proposed and the filter gain parameters are then determined by solving an optimization problem. A simulation example is finally presented to illustrate the effectiveness of the new filtering techniques.

In this paper, we predeploy a large number of smart agents to monitor an area of interest. This area could be divided into many Voronoi cells by using the knowledge of Voronoi diagram and every Voronoi site agent is responsible for monitoring and tracking the target in its cell. Then, a cooperative relay tracking strategy is proposed such that during the tracking process, when a target enters a new Voronoi cell, this event triggers the switching of both tracking agents and communication topology. This is significantly different from the traditional switching topologies. In addition, during the tracking process, the topology and tracking agents switch, which may lead the tracking system to be stable or unstable. The system switches either among consecutive stable subsystems and consecutive unstable subsystems or between stable and unstable subsystems. The objective of this paper is to design a tracking strategy guaranteeing overall successful tracking despite the existence of unstable subsystems. We also address extended discussions on the case where the dynamics of agents are subject to disturbances and the disturbance attenuation level is achieved. Finally, the proposed tracking strategy is verified by a set of simulations.

This paper investigates the problems of robust H∞control for a class of discrete-time nonhomogenous Markovian jump linear systems (NMJLSs) by a multistep Lyapunov function (LF) approach. The proposed multistep LF is allowed to increase during the period of several sampling time step ahead of the current time within the Jump mode. First, a less conservative stability criterion is derived based on this multistep LF approach. Second, an H∞performance is analyzed under the multistep case by properly dealing with the knowledge of future states and exogenous noises. These two results are then employed to facilitate a robust H∞control design for NMJLSs, which yields a better disturbance attenuation performance as compared with the existing results. Numerical examples are included to elucidate the effectiveness of the developed results.

This paper investigates the problem of sensor-network-based distributed control for large-scale networked control systems, in which the communication constraint and topology switching problems are addressed. In the considered system, a discrete-time interconnected process is controlled by a network of sensors, controllers, and actuators that collect information about the plant, and apply control actions to manage the plant dynamics. The motivation for this paper is that the communication between the plant network and the controller network can be exploited for the system-wide purpose. To deal with the communication constraint problem, strategies such as the event-based communication and logarithmic quantization are applied. Furthermore, in a networked environment, the real-time information of the topology switching is not always available at the controller network side, hence a bunch of synchronous/asynchronous controllers are designed such that the closed-loop system is exponentially stable and achieves a prescribed H∞disturbance attenuation level. Finally, an illustrative example on a benchmark continuous stirred-tank reactor system is presented to demonstrate the effectiveness of the proposed new control technique.

This paper addresses the problem of observer-based output feedback controller designs for discrete-time T–S fuzzy systems based on a relaxed approach in which the fuzzy Lyapunov functions are used. Different from the existing two-step method, a single-step linear matrix inequality method is provided for the observer-based controller design. It is shown that the controller and observer parameters can be obtained by solving a set of strict linear matrix inequalities that are numerically feasible with commercially available software. The new design method not only overcomes the drawback induced by the two-step approach but also provides less conservative results over some existing results. Finally, the effectiveness of the proposed approach is demonstrated by an example.

In this paper, a methodology for designing a fuzzy dynamic output feedback controller for discrete-time nonlinear networked control systems is presented where the nonlinear plant is modelled by a Takagi-Sugeno fuzzy model and the network-induced delays by a finite state Markov process. The transition probability matrix for the Markov process is allowed to be partially known, providing a more practical consideration of the real world. Furthermore, the fuzzy controller's membership functions and premise variables are not assumed to be the same as the plant's membership functions and premise variables, that is, the proposed approach can handle the case, when the premise of the plant are not measurable or delayed. The membership functions of the plant and the controller are approximated as polynomial functions, then incorporated into the controller design. Sufficient conditions for the existence of the controller are derived in terms of sum of square inequalities, which are then solved by YALMIP. Finally, a numerical example is used to demonstrate the validity of the proposed methodology.

This paper is concerned with the problem of induced l2 filter design for a class of discrete-time Takagi-Sugeno fuzzy Itô stochastic systems with time-varying delays. Attention is focused on the design of the desired filter to guarantee an induced l2 performance for the filtering error system. A new comparison model is proposed by employing a new approximation for the time-varying delay state, and then, sufficient conditions for the obtained filtering error system are derived by this comparison model. A desired filter is constructed by solving a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

This paper examines the problem of robust state feedback control of networked control systems with a simple congestion control scheme. This simple congestion control scheme is based on comparing current measurements with last transmitted measurements. If their difference is less than a prescribed percentage of the current measurements then no measurement is transmitted to the controller. The controller always uses the last transmitted measurements to control the system. With this simple congestion control scheme, a robust H∞state feedback controller design methodology is developed based on the Lyapunov-krasovskii functional approach. Sufficient conditions for the existence of delay mode dependent controllers are given in terms of bilinear matrix inequalities (BMIs). These BMIs are converted into quasi convex linear matrix inequalities (LMIs) and solved by using the cone complementarity linearization algorithm. The effectiveness of the simple congestion control in terms of reducing the network's bandwidth is elaborated using simulation examples.

This paper examines the problem of designing a nonlinear robust state feedback controller for uncertain polynomial discrete-time systems. In general, this is a challenging controller design problem due to the fact that the relation between Lyapunov function and the control input is not jointly convex, hence, this problem cannot be solved by a semidefinite programming (SDP). In this paper, a novel approach is proposed, where an integral action is incorporated into the controller design so that a convex solution to the problem can be rendered. Based on the sum of squares (SOS) approach, sufficient conditions for the existence of a nonlinear state feedback controller for polynomial discrete-time systems are given in terms of solvability of polynomial matrix inequalities (PMIs), which can be solved by the recently developed SOS solver. Numerical examples are provided to demonstrate the validity of this integral action approach.

This paper investigates the problem of disturbance attenuation for a class of uncertain polynomial discrete-time systems with an H∞performance. In general, this is a difficult problem because it cannot be formulated as a convex problem. This is because the Lyapunov function and the control input is not jointly convex. Hence it cannot be solved by a semidefinite programming (SDP). In this paper, a novel approach is proposed, where an integrator is introduced into the controller structures so that a convex solution can be obtained. Furthermore, based on the sum of squares approach, sufficient conditions for the existence of a proposed nonlinear feedback controller with the integrator for a polynomial discrete-time system are given in terms of solvability of polynomial matrix inequalities (PMIs). These inequalities are then be solved by the recently developed SOS solvers. The validity of the proposed method is confirmed through a tunnel diode circuit.

Accurate expressions for sequence acquisition in a chaos-based spread-spectrum system are derived using the statistical properties of the chaos-based spreading sequences. The expressions are validated by comparing the analytical predictions of the acquisition performance with the simulation results for three channel scenarios. Additive white Gaussian noise and Rayleigh fading channels are considered in the first two scenarios. As the third scenario a blind chip interleaving serial search algorithm is proposed and system performance is shown to improve. The simulation results show excellent agreement with the theoretical results for all three scenarios considered. This is significant as previous examinations of the same have only yielded upper-bounds.

In this paper, exact analytical expressions for a chaos-based non-coherent tracking loop are derived in presence of noise. The control law governing the timing estimation of the tracking loop are then derived based on the correlation properties of Chebychev polynomials. The loop error curves are simulated for various timing differences between early and late gates and the results are compared to the experimental results obtained from a digital signal processor with very close agreement. The tracking loop performance in terms of instantaneous and cumulative timing estimation is examined with and without noise by simulation and hardware implementation. It is found that robust non-coherent tracking of the chaotic chips is possible using signal processing algorithms if discrete mapped chaotic signals are used. It is also found that the theoretical concept is fully implementable in hardware showing close agreement with the theoretical analysis.

The above paper gives a sufficient condition for the existence of a Takagi-Sugeno (T-S) fuzzy H (infinity) tracking controller for a class of nonlinear networked control systems. The aim of this paper is to show that if there exists a T-S fuzzy H (infinity) tracking controller, then there exists a linear H (infinity) tracking controller that guarantees the same prescribed H (infinity) tracking performance.

This paper studies the problem of designing a robust fault-detection system for uncertain Takagi-Sugeno fuzzy models. The worst case fault sensitivity measure is formulated in terms of linear matrix inequalities. The existence of a robust fault detection system that guarantees i) the L2-gain from a fault signal to a residual signal greater than a prescribed value and ii) the L2-gain from an exogenous input to a residual signal less than a prescribed value is given in terms of the solvability of linear matrix inequalities. Numerical examples are used to illustrate the effectiveness of the proposed design techniques.

A new method for the parallel hardware implementation of artificial neural networks (ANNs) using digital techniques is presented. Signals are represented using uniformly weighted single-bit streams. Techniques for generating bit streams from analog or multibit inputs are also presented. This single-bit representation offers significant advantages over multibit representations since they mitigate the fan-in and fan-out issues which are typical to distributed systems. To process these bit streams using ANNs concepts, functional elements which perform summing, scaling, and squashing have been implemented. These elements are modular and have been designed such that they can be easily interconnected. Two new architectures which act as monotonically increasing differentiable nonlinear squashing functions have also been presented. Using these functional elements, a multilayer perceptron (MLP) can be easily constructed. Two examples successfully demonstrate the use of bit streams in the implementation of ANNs. Since every functional element is individually instantiated, the implementation is genuinely parallel. The results clearly show that this bit-stream technique is viable for the hardware implementation of a variety of distributed systems and for ANNs in particular.

This paper examines the problem of designing an H/sub /spl infin// output feedback controller with pole placement constraints for singular perturbed Takagi-Sugeno (TS) fuzzy models. We propose a fuzzy H/sub /spl infin// output feedback controller that not only guarantees the /spl Lscr//sub 2/-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value, but also ensures closed-loop poles of each subsystem are in a prespecified linear matrix inequality (LMI) region. In order to alleviate the numerical stiffness caused by the singular perturbation /spl epsiv/, the design technique is formulated in terms of a family of /spl epsiv/-independent linear matrix inequalities. The proposed approach can be applied both standard and nonstandard singularly perturbed nonlinear systems. A numerical example is provided to illustrate the design developed in this paper.

This paper examines the problem of robust H infinity static output feedback control of a Takagi-Sugeno fuzzy system. The proposed robust H infinity static output feedback controller guarantees the pounds 2 gain of the mapping from the exogenous disturbances to the regulated output to be less than or equal to a prescribed level. The existence of a robust H infinity static output feedback control is given in terms of the solvability of bilinear matrix inequalities. An iterative algorithm based on the linear matrix inequality is developed to compute robust H infinity static output feedback gains. To reduce the conservatism of the design, the structural information of membership function characteristics is incorporated. A numerical example is used to illustrate the validity of the design methodologies.

This note responds to the comments published by Ni Zhao and Fu-Chun Sun in IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART B, vol. 34, no. 6, p. 2422, Dec. 2004. Note that Theorems 3.1 and 3.2 are correct. The errors in the proofs have been fixed.

This paper considers the problem of designing an H infinity fuzzy controller with pole placement constraints for a class of nonlinear singularly perturbed systems. Based on a linear matrix inequality (LMI) approach, we develop an H infinity fuzzy controller that guarantees 1) the L2-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value, and 2) the closed-loop poles of each local system to be within a pre-specified LMI stability region. In order to alleviate the ill-conditioned LMIs resulting from the interaction of slow and fast dynamic modes, solutions to the problem are given in terms of linear matrix inequalities which are independent of the singular perturbation, epsilon. The proposed approach does not involve the separation of states into slow and fast ones and it can be applied not only to standard, but also to nonstandard singularly perturbed non-linear systems. A numerical example is provided to illustrate the design developed in this paper.

Addresses the problem of stabilizing a class of nonlinear systems by using an H/sub /spl infin// fuzzy output feedback controller. First, a class of nonlinear systems is approximated by a Takagi-Sugeno (TS) fuzzy model. Then, based on a well-known Lyapunov functional approach, we develop a technique for designing an H/sub /spl infin// fuzzy output feedback control law which guarantees the L/sub 2/ gain from an exogenous input to a regulated output is less or equal to a prescribed value. A design algorithm for constructing an H/sub /spl infin// fuzzy output feedback controller is given. In contrast to the existing results, the premise variables of the H/sub /spl infin// fuzzy output feedback controller are not necessarily to be the same as the premise variables of the TS fuzzy model of the plant. A numerical simulation example is presented to illustrate the theory development.