Pedestrian trajectory prediction is fundamental in various applications, such as autonomous driving, intelligent surveillance, and traffic management. Existing methods generally fall into two categories: model-driven approaches and data-driven approaches. However, both approaches have inherent limitations when applied to real-world scenarios, particularly in capturing the complex interactions between pedestrians and modeling the stochastic nature of human motion. Notably, there is a lack of research on integrating the strengths of model-driven and data-driven paradigms, which can better address these challenges. This paper aims to fill these limitations by proposing a novel model-data dual-driven approach, called Social Entropy Informer (SEI), for pedestrian trajectory prediction. SEI simultaneously models local and global pedestrian interactions while incorporating information entropy to capture human motion’s inherent randomness and uncertainty quantitatively, which provides a robust framework for predicting pedestrian trajectories. Furthermore, we propose a new loss function derived from information theory, which accounts for the stochasticity of pedestrian movement and enhances the model’s ability to generalize across diverse scenarios. The SEI framework integrates feature extraction, entropy-based stochastic modeling, and the new loss function, improving prediction accuracy and model interpretability. Experimental results demonstrate that SEI outperforms other benchmark methods in prediction accuracy.

This article is concerned with the$H_{\infty }$state estimation problem for a class of bidirectional associative memory (BAM) neural networks with binary mode switching, where the distributed delays are included in the leakage terms. A couple of stochastic variables taking values of 1 or 0 are introduced to characterize the switching behavior between the redundant models of the BAM neural network, and a general type of neuron activation function (i.e., the sector-bounded nonlinearity) is considered. In order to prevent the data transmissions from collisions, a periodic scheduling protocol (i.e., round-robin protocol) is adopted to orchestrate the transmission order of sensors. The purpose of this work is to develop a full-order estimator such that the error dynamics of the state estimation is exponentially mean-square stable and the$H_{\infty }$performance requirement of the output estimation error is also achieved. Sufficient conditions are established to ensure the existence of the required estimator by constructing a mode-dependent Lyapunov-Krasovskii functional. Then, the desired estimator parameters are obtained by solving a set of matrix inequalities. Finally, a numerical example is provided to show the effectiveness of the proposed estimator design method.

In this paper, the sampled-based consensus problem is investigated for a class of nonlinear multiagent systems subjected to deception attacks. Due to network fluctuations and limited resources, deception attacks might destroy the sampled-data in communication networks. Additionally, the success of deception attacks greatly depends on some randomly fluctuated factors. This paper takes into account the deception attacks that are randomly launched at each sampling instant. The eigenvector of Laplacian matrix is utilized to construct a novel Lyapunov functional. Then, the decoupled criterion connected with eigenvalues of Laplacian matrix is obtained such that the addressed multiagent systems achieve the mean square consensus. Furthermore, the solutions of a set of matrix inequalities represent the controller gain matrix. Finally, a numerical example is provided to validate the effectiveness of the derived results.

In this article, the$H_{\infty }$control problem is investigated for a class of discrete-time singularly perturbed systems (DTSPSs) with time-delays and sensor saturations. In order to save the network bandwidth with satisfactory communication reliability during the signal transmissions, a dynamic event-triggered mechanism (DETM) is put forward to regulate the data-packet transmissions from the saturated sensors to the proposed controller. By constructing a novel Lyapunov–Krasovskii functional dependent on the singular perturbation parameter (SPP), the dynamics of the DTSPSs under the DETM are rigorously analyzed, and an output-feedback controller design scheme is then developed to ensure that, for any SPP not exceeding a prescribed upper bound, the closed-loop system is asymptotically stable with a guaranteed$H_{\infty }$performance index. By resorting to the convex programming techniques, the desired controller gain is parameterized in light of the feasible solutions to a set of matrix inequalities. Finally, the effectiveness and merits of the proposed algorithm are demonstrated by a simulation example.

There are few results concerning consensus of multiagent systems (MASs) based on partial differential equations (PDEs), and the problem of how to act boundary control based on distributed measurement on spatial boundary points of MASs has not been solved. This paper addresses boundary control based on distributed measurement for output consensus of leader-following directed MASs modeled by parabolic PDEs. First, a boundary controller acting on spatial boundary points is designed by considering the delivered information produced by agents communicating with neighborhoods. Without considering input constraint, the Lyapunov's direct method is used to obtain a sufficient condition on the existence of the boundary controller to achieve output consensus. The condition is expressed as a form of the feasibility of LMIs. After that, the whole input constraint for MASs is given. And then, one more condition on control gains is obtained to ensure the existence of the boundary controller with input constraint. Finally, one numerical example with two cases illustrates the theoretical analysis results.

The control design problem plays a fundamental role in the study of logical control networks (LCNs). This paper presents a detailed survey on new developments in control design techniques of LCNs. First, some preliminary results on the semi-tensor product method and LCNs are reviewed. Then, we move on to some new developments for control design techniques of LCNs, including the reachable set approach, the pinning control technique, the control Lyapunov function approach, the event-triggered control technique, and the sampled-data control technique. Finally, an illustrative example is given to demonstrate the effectiveness of these techniques.

This paper addresses the nonfragile sensor fault estimation problem for a class of two-dimensional (2-D) nonlinear systems. The underlying system is described by the well-known Fornasini-Marchesini model. The system parameters are subject to abrupt changes regulated by the Markovian process for which the entries of the mode transition probability matrix are partially accessible. A novel 2-D nonfragile state estimator is constructed to achieve the sensor fault estimation where the estimator gains are allowed to be randomly perturbed. Then, together with the Lyapunov stability theory, the stochastic analysis techniques are employed to derive the sufficient conditions that guarantee the following three performance requirements: 1) the exponential stability of the estimation error dynamics; 2) the prespecified constraint on the energy-to-peak gain; and 3) the prespecified restriction on the prescribed power bound. Moreover, the estimator gains are parameterized by using the convex optimization method. Finally, a numerical example is provided to illustrate the effectiveness of the addressed estimation algorithm.

This paper is concerned with the state estimation problem for a class of discrete-time singularly perturbed systems with distributed time-delays. During the data transmission through a network channel of limited bandwidth, for the sake of collision avoidance and energy saving, a dynamic event-triggered scheme is employed to schedule the data communication from the sensors to the designed estimator. First, for a given singular perturbation parameter (SPP), by constructing a novel Lyapunov-Krasovskii SPP-dependent functional, sufficient conditions are obtained to guarantee the exponentially mean-square ultimate boundedness of the error dynamics of the state estimation. Furthermore, in the case that the SPP does not exceed a predefined upper bound, a design algorithm is developed for the desired state estimator ensuring that the error dynamics is exponentially mean-square ultimately bounded. In this case, by solving certain matrix inequalities, the estimator gain is characterized without needing to know the exact SPP (as long as it stays below the given upper bound). Moreover, the ultimate bound of the error dynamics is estimated. Finally, simulation results are given to confirm the validity and advantages of the proposed design scheme of the state estimator.

This paper studies the set stabilization of Boolean control networks (BCNs) under sampled-data state feedback control (SDSFC). The main research content is divided into two parts. First, the topological structure of BCNs under given SDSFC is investigated. The fixed point and sampled cycle are defined, respectively. It is found that sampled cycles allow elements to be repeated and not every element can be regarded as an initial state, and this is quite different from conventional cycles of BCNs. A theorem is presented to calculate the number of fixed points and an algorithm is given to find all fixed points and sampled cycles. Second, the set stabilization problem of BCNs by SDSFC is investigated based on the sampled point set and the sampled point control invariant set (SPCIS). A necessary and sufficient condition is derived for the global set stabilization of BCNs by SDSFC, and further sampled-data state feedback controllers are also designed. The interesting thing is that if a state enters the SPCIS as an unsampled point, then it may run out of the given set again, which is in sharp contrast to conventional BCNs. Finally, an example is given to illustrate the efficiency of the obtained results.

In this paper, the Hamy mean (HM) operator, weighted HM (WHM), dual HM (DHM) operator, and dual WHM (WDHM) operator under the q-rung orthopair fuzzy sets (q-ROFSs) is studied to propose the q-rung orthopair fuzzy HM (q-ROFHM) operator, q-rung orthopair fuzzy WHM (q-ROFWHM) operator, q-rung orthopair fuzzy DHM (q-ROFDHM) operator, and q-rung orthopair fuzzy weighted DHM (q-ROFWDHM) operator and some of their desirable properties are investigated in detail. Then, we apply these operators to multiple attribute decision-making problems. Finally, a practical example for enterprise resource planning system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness.

In this paper, according to the Maclaurin symmetric mean (MSM) operator, the dual MSM (DMSM) operator and the q-rung interval-valued orthopair fuzzy set (q-RIVOFS), we develop some novel MSM operators under the q-rung interval-valued orthopair fuzzy environment, such as, the q-rung interval-valued orthopair fuzzy MSM operator, the q-rung interval-valued orthopair fuzzy weighted MSM (q-RIVOFWMSM) operator, the q-rung interval-valued orthopair fuzzy DMSM operator, and the q-rung interval-valued orthopair fuzzy weighted DMSM operator. In addition, some precious properties and numerical examples of these new operators are given in detail. These new operators have the advantages of considering the interrelationship of arguments and can deal with multiple attribute group decision-making problems with q-rung interval-valued orthopair fuzzy information. Finally, a reality example for green suppliers selection in green supply chain management is provided to demonstrate the proposed approach and to verify its rationality and scientific.

In this paper, the consensus control problem is investigated for a class of discrete-time networked multiagent systems (MASs) with the coding-decoding communication protocol (CDCP). Under a directed communication topology, an observer-based control scheme is proposed for each agent by utilizing the relative measurement outputs between the agent itself and its neighboring ones. The signal delivery is in a digital manner, which means that only the sequence of finite coded signals is sent from the observer to the controller. To be specific, the observed data is encoded to certain codewords by a designed coder via the CDCP, and the received codewords are then decoded by the corresponding decoder at the controller side. The purpose of the addressed problem is to design an observer-based controller such that the close-loop MAS achieves the expected consensus performance. First, with the help of the input-to-state stability theory, a theoretical framework for the detectability is established for analyzing and designing the CDCP. Then, under such a communication protocol, some sufficient conditions for the existence of the proposed observer-based controller are derived to guarantee the asymptotic consensus of the MASs. In addition, the controller parameter is explicitly determined in terms of the solution to certain matrix inequalities associated with the information of the communication topology. Finally, a simulation example is given to demonstrate the effectiveness of the developed control strategy.

In this paper, the problem of adaptive neural state-feedback tracking control is considered for a class of stochastic nonstrict-feedback nonlinear switched systems with completely unknown nonlinearities. In the design procedure, the universal approximation capability of radial basis function neural networks is used for identifying the unknown compounded nonlinear functions, and a variable separation technique is employed to overcome the design difficulty caused by the nonstrict-feedback structure. The most outstanding novelty of this paper is that individual Lyapunov function of each subsystem is constructed by flexibly adopting the upper and lower bounds of the control gain functions of each subsystem. Furthermore, by combining the average dwell-time scheme and the adaptive backstepping design, a valid adaptive neural state-feedback controller design algorithm is presented such that all the signals of the switched closed-loop system are in probability semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in probability. Finally, the availability of the developed control scheme is verified by two simulation examples.

In this paper, an adaptive neural-network-based dynamic surface control (DSC) method is proposed for a class of stochastic interconnected nonlinear nonstrict-feedback systems with unmeasurable states and dead zone input. First, an appropriate state observer is constructed to estimate the unmeasured state variables of the stochastic interconnected system. Then radial basis function neural networks combined with adaptive backstepping technique are applied to model the unknown nonlinear system functions of the stochastic interconnected system; and the DSC method is adopted to ensure the computation burden is greatly reduced. Furthermore, the proposed controllers guarantee that the closed-loop stochastic interconnected system is semi-globally bounded stable in probability. In the end, two simulation examples are provided to show the effectiveness and practicability of the proposed control scheme.

In this paper, a novel neural-network (NN)-based adaptive tracking controller design method is presented for the single-input/single-output nonlinear stochastic switched systems in lower triangular structures with an output constraint. First, a well-designed nonlinear mapping is introduced to transform the switched stochastic system to a new system without constraints, which implies the controller design of the transformed system is equivalent to that of the stochastic switched system. Then radial basis function NNs are applied to model the unknown nonlinearities and the adaptive backstepping technique is employed to construct two classes of adaptive neural controllers under different adaptive laws. It is proved that both controllers can assure all the signals in the closed-loop remain bounded in probability, and the tracking error finally converges to a neighborhood of the origin without violating the constraint. Furthermore, the use of the nonlinear mapping to deal with the asymmetric output constraint is also studied as a generalization result. Two illustrative examples with numerical data and simulation results are given to show the validity and performance of the proposed control schemes.

This technical correspondence considers finite-time synchronization of dynamical networks by designing aperiodically intermittent pinning controllers with logarithmic quantization. The control scheme can greatly reduce control cost and save both communication channels and bandwidth. By using multiple Lyapunov functions and convex combination techniques, sufficient conditions formulated by a set of linear matrix inequalities are derived to guarantee that all the node systems are synchronized with an isolated trajectory in a finite settling time. Compared with existing results, the main characteristics of this paper are twofold: 1) quantized controller is used for finite-time synchronization and 2) the designed multiple Lyapunov functions are strictly decreasing. An optimal algorithm is proposed for the estimation of settling time. Numerical simulations are provided to demonstrate the effectiveness of the theoretical analysis.

This paper deals with the nonfragile H∞ control problem for a class of discrete-time Takagi-Sugeno fuzzy systems with both randomly occurring gain variations (ROGVs) and channel fadings. The system measurement is subject to fading channels described by Rice fading model where the channel coefficients are random variables taking values within given intervals. The gain matrices of the output feedback controllers are subject to random fluctuations referred to as the ROGVs. The purpose of the addressed problem is to design a parameter-dependent nonfragile output-feedback controller such that, in the presence of both ROGVs and channel fadings, the closed-loop system is exponentially mean-square stable while achieving the guaranteed H∞ disturbance attenuation level. A gain-scheduling approach is developed to tackle the addressed problem where the designed controller gains are dependent on certain parameters of practical significance (e.g., packet dropout rate). Through stochastic analysis and Lyapunov functional approach, sufficient conditions are derived for the existence of the desired output feedback controller ensuring both the exponential mean-square stability and the prescribed H∞performance. The explicit expression of the feedback controller is also characterized by using a semidefinite programming method. Finally, an illustrative example is given to show the usefulness and effectiveness of the proposed design method.

In this brief, the new problem of partial-nodes-based (PNB) state estimation problem is investigated for a class of complex network with unbounded distributed delays and energy-bounded measurement noises. The main novelty lies in that the states of the complex network are estimated through measurement outputs of a fraction of the network nodes. Such fraction of the nodes is determined by either the practical availability or the computational necessity. The PNB state estimator is designed such that the error dynamics of the network state estimation is exponentially ultimately bounded in the presence of measurement errors. Sufficient conditions are established to ensure the existence of the PNB state estimators and then the explicit expression of the gain matrices of such estimators is characterized. When the network measurements are free of noises, the main results specialize to the case of exponential stability for error dynamics. Numerical examples are presented to verify the theoretical results.

This paper deals with the event-based finite-time state estimation problem for a class of discrete-time stochastic neural networks with mixed discrete and distributed time delays. In order to mitigate the burden of data communication, a general component-based event-triggered transmission mechanism is proposed to determine whether the measurement output should be released to the estimator at certain time-point according to a specific triggering condition. A new concept of finite-time boundedness in the mean square is put forward to quantify the estimation performance by introducing a settling-like time function. The objective of the addressed problem is to construct an event-based state estimator to estimate the neuron states such that, in the presence of both mixed time delays and external noise disturbances, the dynamics of the estimation error is finite-time bounded in the mean square with a prescribed error upper bound. Sufficient conditions are established, via stochastic analysis techniques, to guarantee the desired estimation performance. By solving an optimization problem with some inequality constraints, the explicit expression of the estimator gain matrix is characterized to minimize the settling-like time. Finally, a numerical simulation example is exploited to demonstrate the effectiveness of the proposed estimator design scheme.

In this correspondence paper, we provide a new look at the boundedness problems of error covariance of Kalman filtering. First, by utilizing the mathematical induction technique, a new bound function which is dependent on system parameters is proposed. In this manner, the boundedness problems of the error covariance can be converted to the study of the corresponding uniform bounds of the bound function. Second, based on such a bound function, the dynamic behaviors, monotonicities, and boundedness problems of error covariance are deeply explored. Consequently, a few quantitative results under minimal conditions about the uniform bounds on error covariance are obtained. Finally, examples are given to verify the correctness and effectiveness of our theoretical analyses.

This paper is concerned with the distributed filtering problem over wireless sensor networks for a class of state-saturated systems subject to fading measurements and quantization effects. Each sensor node in the network communicates with its neighbors according to the network topology described by a directed graph. The fading phenomena of measurements are assumed to occur in a random way and the attenuation coefficients of the fading measurements are described by a set of random variables with known stochastic properties. By solving two sets of matrix difference equations, an upper bound for the filtering error covariance is presented. Subsequently, with the topology information of the sensor network, such an upper bound is minimized by properly designing the filter parameters. Moreover, the performance of the proposed filter is investigated through establishing sufficient conditions ensuring that the trace of the upper bound is bounded. The relationship between the filter performance and the mean of attenuation coefficient is also discussed. A numerical simulation is exploited to demonstrate the effectiveness of the proposed filtering method.

This letter focuses on lag synchronization control analysis for memristor-based coupled neural networks with parameter mismatches. Due to the parameter mismatches, lag complete synchronization in general cannot be achieved. First, based on the [Formula: see text]-measure method, generalized Halanay inequality, together with control algorithms, some sufficient conditions are obtained to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error. Moreover, the error level is estimated. Second, we show that memristor-based coupled neural networks with parameter mismatches can reach lag complete synchronization under a discontinuous controller. Finally, two examples are given to illustrate the effectiveness of the proposed criteria and well support theoretical results.

In this paper, we consider the H∞control problem for a class of 2-D Takagi-Sugeno fuzzy described by the second Fornasini-Machesini local state-space model with time delays and missing measurements. The state delays are allowed to be time-varying within a known interval. The measurement output is subject to randomly intermittent packet dropouts governed by a random sequence satisfying the Bernoulli distribution. The purpose of the addressed problem is to design an output feedback controller such that the closed-loop system is globally asymptotically stable in the mean square and the prescribed H∞performance index is satisfied. By employing a combination of the intensive stochastic analysis and the free weighting matrix method, several delay-range-dependent sufficient conditions are presented that guarantee the existence of the desired controllers for all possible time-delays and missing measurements. The explicit expressions of such controllers are derived by means of the solution to a class of convex optimization problems that can be solved via standard software packages. Finally, a numerical simulation example is given to demonstrate the applicability of the proposed control scheme.

This paper is concerned with the distributed ℋ∞ consensus control problem for a discrete time-varying multiagent system with the stochastic communication protocol (SCP). A directed graph is used to characterize the communication topology of the multiagent network. The data transmission between each agent and the neighboring ones is implemented via a constrained communication channel where only one neighboring agent is allowed to transmit data at each time instant. The SCP is applied to schedule the signal transmission of the multiagent system. A sequence of random variables is utilized to capture the scheduling behavior of the SCP. By using the mapping technology combined with the Hadamard product, the closed-loop multiagent system is modeled as a time-varying system with a stochastic parameter matrix. The purpose of the addressed problem is to design a cooperative controller for each agent such that, for all probabilistic scheduling behaviors, the ℋ∞ consensus performance is achieved over a given finite horizon for the closed-loop multiagent system. A necessary and sufficient condition is derived to ensure the ℋ∞ consensus performance based on the completing squares approach and the stochastic analysis technique. Then, the controller parameters are obtained by solving two coupled backward recursive Riccati difference equations. Finally, a numerical example is given to illustrate the effectiveness of the proposed controller design scheme.

This paper is concerned with the robust H∞filtering problem for a class of 2-D uncertain fuzzy systems with randomly occurring mixed delays (ROMDs). The underlying 2-D systems are described by the Fornasini-Marchesini model, and the uncertainty is expressed in a linear fraction form. An improved Takagi-Sugeno (T-S) fuzzy model corresponding to the spatial promise variables is adopted to represent the complicated 2-D nonlinear system. The mixed delays consisting of both discrete and distributed delays are allowed to appear in a random manner governed by two sets of Bernoulli distributed white sequences with known probability. A full-order fuzzy filter is constructed to estimate the output signal such that, in the presence of parameter uncertainties and ROMDs, the dynamics of the estimation errors is asymptotically stable with a prescribed H∞disturbance attenuation level. Based on the stochastic analysis technique and the Lyapunov-like functional, sufficient conditions are established to ensure the existence of the desired filters, and the explicit expressions of such filters are derived by means of the solution to a class of convex optimization problems that can be solved via standard software packages. A numerical example is provided to demonstrate the effectiveness of the developed filter design algorithms, and the filter performances with and without fuzzy rules are also compared.

Complex-valued (CV) B-spline neural network approach offers a highly effective means for identifying and inverting practical Hammerstein systems. Compared with its conventional CV polynomial-based counterpart, a CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. This paper reviews the optimality of the CV B-spline neural network approach. Advantages of B-spline neural network approach as compared with the polynomial based modeling approach are extensively discussed, and the effectiveness of the CV neural network-based approach is demonstrated in a real-world application. More specifically, we evaluate the comparative performance of the CV B-spline and polynomial-based approaches for the nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE) of single-carrier Hammerstein channels. Our results confirm the superior performance of the CV B-spline-based NIFDDFE over its CV polynomial-based counterpart.

In this paper, the event-based finite-horizon H∞state estimation problem is investigated for a class of discrete time-varying stochastic dynamical networks with stateand disturbance-dependent noises [also called (x, v)-dependent noises]. An event-triggered scheme is proposed to decrease the frequency of the data transmission between the sensors and the estimator, where the signal is transmitted only when certain conditions are satisfied. The purpose of the problem addressed is to design a time-varying state estimator in order to estimate the network states through available output measurements. By employing the completing-the-square technique and the stochastic analysis approach, sufficient conditions are established to ensure that the error dynamics of the state estimation satisfies a prescribed H∞performance constraint over a finite horizon. The desired estimator parameters can be designed via solving coupled backward recursive Riccati difference equations. Finally, a numerical example is exploited to demonstrate the effectiveness of the developed state estimation scheme.

By employing the finite-time control method, the consensus control algorithm for higher-order multiagent systems is designed in this paper. Under a neighbor-based rule, a higher-order finite-time consensus algorithm is explicitly constructed, which only uses local information. The finite-time consensus control algorithm can guarantee that the state consensus is achieved in a finite time. In addition, for multiagent systems having a leader-following structure, the consensus algorithm is also designed. Finally, two examples are presented to show the effectiveness.

This paper is concerned with the robust H∞fault detection problem for a class of uncertain discrete-time nonlinear 2-D systems subject to Round-Robin scheduling protocol. The Takagi-Sugeno fuzzy model is used to approximate the nonlinearities, where the linear fractional uncertainties enter the system in a random way. A kind of widely used communication mechanism, namely, Round-Robin communication protocol, is adopted to periodically schedule the sensors and the fault detectors to realize the information exchange in order to reduce the bandwidth usage in a networked environment with limit resource. An improved 2-D fuzzy residual generator is constructed to detect the possible fault, where the stability analysis of the resulting augmented 2-D system is discussed. It is accomplished by using a combination of the basis-dependent Lyapunov-like function and the stochastic analysis techniques. Sufficient conditions are first established to guarantee the globally asymptotic stability of the error dynamics of the state estimation with prescribed H∞performance constraints. Then, a residual generator is proposed to detect the possible faults. The effectiveness of the developed algorithm is demonstrated via application to the fault detection problem for a thermal process.

In this paper, the dynamic output-feedback control problem is studied for a class of discrete-time nonlinear stochastic systems with successive packet dropouts and uniform quantization effects. The stochastic system under investigation involves state-, control-, and disturbance-dependent noises (also called (x, u, v)-dependent noises) that bring in substantial difficulties in the stability analysis. The phenomenon of successive packet dropouts is governed by a binary switching random sequence. The measurement output is subject to the uniform quantization which results in the norm-bounded disturbances, and the concept of input-to-state stability in probability is introduced to deal with this kind of disturbances. In virtue of intensive stochastic analysis, several sufficient conditions are established to guarantee that the closed-loop system is input-to-state stable in probability under the effects of probabilistic packet dropouts as well as uniform quantizations. As an easy consequence, the design problem with linear output-feedback controllers is discussed for the benefits of practical applications and some simplified conditions are derived. Finally, a numerical example is presented to illustrate the effectiveness of the proposed method.

Complex-valued (CV) B-spline neural network approach offers a highly effective means for identification and inversion of Hammerstein systems. Compared to its conventional CV polynomial based counterpart, CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. In this paper, we review the optimality of CV B-spline neural network approach and demonstrate its excellent approximation capability for a real-world application. More specifically, we develop a CV B-spline neural network based approach for the nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE) of single-carrier Hammerstein channels. Advantages of B-spline neural network approach as compared to polynomial based modeling approach are extensively discussed, and the effectiveness of CV neural network based NIFDDFE is demonstrated in a simulation study.

This paper is concerned with the nonfragile H∞filtering problem for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems with both randomly occurring gain variations (ROGVs) and channel fadings. the phenomenon of the ROGVs is introduced into the system model so as to account for the parameter fluctuations occurring during the filter implementation. Two sequences of random variables obeying the Bernoulli distribution are employed to describe the phenomenon of the ROGVs bounded by prescribed norms. In addition, the Rice fading model is utilized to describe the phenomena of channel fadings, where the occurrence probabilities of the random channel coefficients are allowed to time varying. Through stochastic analysis and Lyapunov functional approach, sufficient conditions are established under which the filtering error dynamics is exponentially mean-square stable with a prespecified H∞performance. The set of the desired nonfragile H∞filters is characterized by solving a convex optimization problem via the semidefinite programming method. An illustrative example is given to show the usefulness and effectiveness of the proposed design method in this paper.

Energy harvesting (EH) based communication has raised great research interests due to its wide applications and the feasibility of commercialization. In this paper, we consider wireless communications with EH constraints at the transmitter. First, for delay-tolerant traffic, we investigate the long-term average throughput maximization problem and analytically compare the throughput performance against that of a system supported by conventional power supplies. Second, for delay-sensitive traffic, we analyze the outage probability by studying its asymptotic behavior in the high energy arrival rate regime, where the new concept of energy diversity is formally introduced. Moreover, we show that the speed of outage probability approaching zero, termed energy diversity gain, varies under different power supply models.

This paper investigates the resource allocation problem for the Gaussian multiple access channel (MAC) with conferencing links, where the two transmitters can talk to each other via wired rate-limited channels. Moreover, the two transmitters are powered by a shared energy harvester which captures energy from the environment. We consider both the non-causal (the energy arrival levels at future time slots are known before transmissions) and the causal (only the energy arrival levels of past and present slots are known) energy-harvesting (EH) models. For the non-causal case, we formulate a resource allocation problem over a finite horizon ofNtime slots to characterize the boundary of the maximum departure region. We then develop the optimal offline power and rate allocation scheme by exploiting the hidden convexity of this problem. Interestingly, it is shown that there exists a maximum transmission rate (named the capping rate) for one of the transmitters. For the causal case, we examine the performance of the greedy scheme, in which the energy is depleted within each slot. In particular, we measure the utility of this scheme against the optimal offline one by competitive analysis, where the competitive ratio of the online greedy scheme, i.e., the maximum ratio between the profits obtained by the offline and online schemes over arbitrary energy arrival profiles, is derived.

This paper aims to establish a unified framework to handle both the exponential synchronization and state estimation problems for a class of nonlinear singularly perturbed complex networks (SPCNs). Each node in the SPCN comprises both "slow" and "fast" dynamics that reflects the singular perturbation behavior. General sector-like nonlinear function is employed to describe the nonlinearities existing in the network. All nodes in the SPCN have the same structures and properties. By utilizing a novel Lyapunov functional and the Kronecker product, it is shown that the addressed SPCN is synchronized if certain matrix inequalities are feasible. The state estimation problem is then studied for the same complex network, where the purpose is to design a state estimator to estimate the network states through available output measurements such that dynamics (both slow and fast) of the estimation error is guaranteed to be globally asymptotically stable. Again, a matrix inequality approach is developed for the state estimation problem. Two numerical examples are presented to verify the effectiveness and merits of the proposed synchronization scheme and state estimation formulation. It is worth mentioning that our main results are still valid even if the slow subsystems within the network are unstable.

The spot-diffusing geometry is one of the attractive configurations considered in the literature. It provides a better signal-to-noise ratio (SNR) than the conventional diffuse system (CDS), but its SNR can be degraded due to mobility. Three methods: imaging reception, beam angle and beam power adaptation are introduced to the design of spot-diffusing OW systems to effectively mitigate the degradation due to mobility in the presence of ambient light noise and multipath propagation. In an imaging spot-diffusing system, the SNR is independent of the transmitter position and can be maximized at all receiver locations when our new methods (beam angle and beam power adaptation) are implemented. Regardless of the transmitter position, beam angle adaptation can target the spots at the optimum location that yields the best SNR at the receiver. A significant SNR improvement of 36 dB in the imaging spot-diffusing performance can be achieved when angle adaptation is introduced. Further SNR gain of 4 dB can be obtained if the power is adaptively distributed among the spots. Furthermore, an increase in the channel bandwidth from 43 MHz (non-imaging CDS) to 8.19 GHz can be achieved through the combination of these methods. The increase in channel bandwidth and SNR can enable the OW system to achieve higher data rates and 2.5 Gbit/s and 5 Gbit/s mobile OW systems are shown to be feasible.

Mobility can induce significant signal-to-noise ratio (SNR) performance degradation in optical wireless (OW) systems based on diffuse as well as spot diffusing configurations. Two methods (beam angle and beam power adaptation) are introduced to the design of OW multibeam systems to effectively mitigate the mobility based performance degradation in the presence of ambient light noise and multipath propagation. Simulation results indicate that in an angle diversity spot-diffusing system, the SNR is independent of the transmitter position and can be maximized at all receiver locations when our new methods are implemented. A multibeam power and angle adaptive system (MBPAAS) offers a significant SNR improvement of 29 dB over the traditional line strip multibeam system (LSMS) at a transmitter-receiver separation of 6 m, when both systems employ an angle diversity receiver and operate at 50 Mbit/s. This SNR improvement can be useful in transmit power reduction. Furthermore, an increase in the channel bandwidth from 647 MHz (LSMS) to 5.57 GHz can be achieved when the new MBPAAS is employed. The increase in channel bandwidth and SNR can enable the OW system to achieve higher data rates and a 2.5 Gbit/s mobile OW system was shown to be feasible.

Mobility can induce significant signal-to-noise ratio (SNR) performance degradation in optical wireless (OW) systems based on diffuse as well as spot-diffusing configurations. Two methods (beam angle and beam power adaptation) are introduced to the design of OW multibeam systems to effectively mitigate the mobility-based performance degradation in the presence of ambient light noise, multipath propagation, and shadowing. Simulation results indicate that in an angle diversity multibeam system, the SNR is independent of the transmitter position and can be maximized at all receiver locations when our new methods are implemented. A multibeam power and angle adaptive system (MBPAAS) offers a significant SNR improvement of 29 dB over the traditional line strip multibeam system (LSMS) at a transmitter-receiver separation of 6 m, when both systems employ an angle diversity receiver and operate at 50 Mbit/s. This improvement comes at the cost of complexity. The complexity can be reduced by increasing the beam angle adaptation step size from 2.3° to 26.6° resulting in a typical search time reduction from 12.5 ms to 80 μs when our modified MBPAAS replaces the main MBPAAS. However, a power penalty of 11.7 dB at receiver locations near the room edges and 1.3 dB elsewhere can be induced. An increase in the channel bandwidth from 647 MHz (LSMS) to 5.57 GHz can also be achieved when the two new methods (beam angle and beam power adaptation) are implemented. The increase in channel bandwidth and SNR can enable the OW system to achieve higher data rates and 2.5 Gbit/s and 5 Gbit/s mobile OW systems are shown to be feasible. Furthermore, simulation results prove that our modified MBPAAS can sufficiently combat shadowing and signal blockage.

In this paper, a novel beam power adaptation method is proposed, studied and shown to be a desirable means for improving the performance of an optical wireless (OW) system that operates under the constraints of background noise, multipath dispersion, and mobility. We propose and evaluate a new OW configuration that employs an adaptive beam clustering method (ABCM) in conjunction with diversity detection. Our goal is to reduce the effect of transmitter/receiver mobility and the associated impacts in terms of a weak received optical power and reduction in bandwidth. Previous work has shown that multiple spot diffusing techniques suffer from these two fundamental limitations associated with mobility. Our new ABCM can help overcome the impairments introduced by mobility, introduce gain in the received optical power, and increase bandwidth even at large transmitter and receiver separations. Our results indicate that, at the least successful locations, the ABCM system can reduce the signal delay spread by nearly a factor of twenty and enhance the SNR by almost 15 dB over a line strip multibeam system (LSMS). We also incorporate the concept of relaying into the Adaptive line strip multibeam system and prove that this technique can lead to considerable performance improvements.

In this paper, we propose and evaluate a novel optical wireless (OW) configuration that employs a two dimensional adaptive beam clustering method (2DABCM) in conjunction with imaging diversity receivers. Our goal is to reduce the effect of intersymbol interference and to improve the signal-to-noise ratio (SNR) when the system operates under the constraints of background noise (BN) and multipath dispersion. In the conventional diffuse system, an SNR improvement of 22 dB and an increase in the bandwidth from 38 MHz to 200 MHz approximately, is achieved when an imaging receiver is implemented. Furthermore, the new methods introduced (transmit power adaptation, beam clustering, and diversity imaging) increase the bandwidth from 38 MHz to 5.56 GHz. Previous optical wireless systems operated typically at 30 to 100 Mbit/s and here we report a system that operates at 2.5 Gbit/s. Our results indicate that the new 2.5 Gbit/s 2DABCM transmitter with a new imaging diversity receiver (with select best (SB)) demonstrates an SNR improvement of 16 dB over the non-imaging CDS system operating at 30 Mbit/s. The results also show that the proposed system (imaging 2DABCM at a bit rate of 2.5 Gbit/s) produces an SNR improvement of 10 dB when maximum ratio combining (MRC) replaces SB.

Previous indoor mobile optical wireless systems operated typically at 30 Mbit/s to 100 Mbit/s and here we report on systems that operate at 2.5 Gbit/s and 5 Gbit/s. We are able to achieve these improvements through the introduction of three new approaches: transmit beam power adaptation, a two dimensional beam clustering method (2DBCM), and diversity imaging. Through channel and noise modeling we evaluated the performance of our systems. The performance of a novel optical wireless (OW) configuration that employs a two dimensional adaptive beam clustering method (2DABCM) in conjunction with imaging diversity receivers is evaluated under multipath dispersion and background noise (BN) impairments. The new proposed system (2DABCM transmitter with imaging diversity receiver) can help reduce the effect of intersymbol interference and improve the signal-to-noise ratio (SNR) even at high bit rate. At a bit rate of 30 Mbit/s, previous work has shown that imaging conventional diffuse systems (CDS) with maximal ratio combining (MRC) offer 22 dB better SNR than the non-imaging CDS. Our results indicate that the 2DABCM system with an imaging diversity receiver provides an SNR improvement of 45 dB over the imaging CDS with MRC when both operate at 30 Mbit/s. In the CDS system, an increase in bandwidth from 38 MHz (non-imaging CDS) to 200 MHz approximately, is achieved when an imaging receiver is implemented. Furthermore, the three new methods introduced increase the bandwidth from 38 MHz to 5.56 GHz. At the least successful receiver locations, our 2.5 Gbit/s and 5 Gbit/s imaging 2DABCM systems with MRC offer significant SNR improvements, almost 26 dB and 19 dB respectively over the non-imaging CDS that operates at 30 Mbit/s.

Three methods (transmit power adaptation, imaging reception, and Multicarrier Code Division Multiple Access (MCCDMA)) are introduced to the optical wireless (OW) system and a significant improvement is achieved in the presence of very directive noise, multipath propagation, mobility, and shadowing typical in a real indoor environment. In the absence of shadowing, replacing a single non-imaging receiver by an imaging receiver with maximal ratio combining (MRC) improves the signal-to-noise ratio (SNR) by 20 dB in a conventional diffuse system (CDS) operating at 30 Mbit/s at a transmitter-receiver separation of 6 m in agreement with previous results in the field. Further SNR improvement of 24 dB is achieved when a line strip multi-beam system (LSMS) replaces the CDS when both systems employ an imaging MRC receiver. Furthermore, our new adaptive LSMS (ALSMS) system coupled with the imaging MRC receiver offers an SNR improvement of 23 dB over the imaging MRC LSMS illustrating the gain achieved through adaptation. The results also show that combining transmit power adaptation with spot diffusing (i.e. ALSMS) coupled with an imaging receiver based on select best (SB) increases the bandwidth from 46.5 MHz (non-imaging CDS) to 7.53 GHz thus enabling the OW system to achieve higher data rates and provide multi-user capabilities in our case by employing a MC-CDMA scheme. In a 10 user MC-CDMA OW system, a bit error rate (BER) improvement from 4.9 times 10-1to 2.1 times 10-5is achieved when the imaging MRC ALSMS system replaces the imaging CDS in a shadowed environment.

In this paper, we introduce a mobile optical wireless (OW) multicarrier - code division multiple access (MC-CDMA) system that employs a new adaptive line strip multibeam system (ALSMS) with diversity detection. Our results indicate that a significant improvement in the bit error rate (BER) can be obtained in the presence of very directive noise, multipath propagation and shadowing typical in a real indoor environment. With transmitter and/or receiver mobility, ALSMS can improve the BER performance by almost 10-5with 2 active users, and increase the signal-to-noise ratio (SNR) by more than 13 dB compared to the unadaptive LSMS.

Multicarrier code division multiple access (MC-CDMA) combines some of the desirable feature of orthogonal frequency division multiplexing (OFDM) and CDMA in that it offers multiple access facilities at a reduced channel rate. In this paper, the channel characteristics of mobile infrared links have been modelled in a highly impaired environment in the presence of windows, office cubicles, bookshelves, and shadowing. We introduce an adaptive line strip multibeam system (ALSMS) in conjunction with diversity detection and show that it dramatically improves the SNR performance of infrared links in the presence of very directive noise and shadowing. Our results indicate that, the mobile MC-CDMA ALSMS system with an angle diversity receiver offers a significant performance improvement including a reduction in the background noise (BN) effect, a strong received power, reduction in delay spread, and improvement in the SNR over the mobile MC-CDMA LSMS system in the poor communication environment considered. Furthermore, the OW MC-CDMA system operates at a channel rate lower than that associated with OW CDMA systems and we demonstrate the performance improvement obtained in a real OW environment in the presence of transmitter and receiver mobility.

Multicarrier code division multiple access (MC-CDMA) is a transmission scheme that combines the robustness of orthogonal modulation with the flexibility of CDMA schemes. It can lower the symbol rate and is therefore suited to the bandwidth limited optical wireless indoor channel. In this paper, new work is presented for an optical wireless (OW) system that utilizes MC-CDMA to improve the bit error rate (BER) performance. Our results indicate that at a BER of 10-4a 7 dB SNR improvement is obtained by a 4 user MC-CDMA OW system compared to a single user OW OOK system. This improvement is obtained while enabling multi-user communication. The performance degrades gradually as the number of users increases due to the increase in multiple access interference (MAI). A consistent BER performance is achieved for all receiver positions in a line strip spot-diffusing transmitter system (LSMS) with an angle diversity receiver, whereas a fluctuating BER performance is obtained in a conventional diffuse optical wireless system (CDS).