In this paper, a low-complexity appointed-accuracy tracking controller is developed for nonlinear systems with full-state constraints and input saturation. A new smooth function incorporating the Mean Value Theorem is used to approximate the saturated input. By introducing a prescribed performance function and a transformed term, a constrained tracking error is created to achieve an appointed accuracy within a given time while maintaining full-state constraints. The transformed system is stabilized through a low-complexity backstepping controller, which incorporates a compensation term for estimation errors associated with the input saturation. Through Lyapunov stability analysis, it is demonstrated that the proposed controller ensures the boundedness of the closed-loop system and satisfies both the tracking error bounds and state constraints. The effectiveness of our approach is validated via numerical simulations, including two case studies on a DC motor system.

This paper investigates the distributed set-membership filtering for a class of discrete systems over wireless sensor networks. A new type of coding-decoding method with mixed quantizer is proposed to balance the upper bound of decoding error and the communication burden. Then, a distributed filter is designed based on decoding measurements, and a recursive technique is utilized to ascertain sufficient conditions of the feasibility of the proposed filtering design. A fusion rule is further presented to achieve a more compact effective region by determining the intersection of the local estimation and prediction ellipsoids. A fusion estimate is then derived by addressing two optimization problems, which outperforms all local estimates. Finally, a simulation example is introduced to verify the effectiveness of the developed filtering algorithm.

This work delves into an optimal hybrid transmission strategy for remote state estimation (RSE), where some smart sensors observe various systems and transmit their local state estimates to a remote estimator. Inspired by the high bandwidth of a high-frequency (HF) link alongside the robust reliability and low energy consumption of a low-frequency (LF) link, a novel hybrid transmission strategy is proposed. This strategy allows smart sensors to utilize either the HF link or the LF link dynamically, each with distinct channel characteristics and energy consumption. To achieve highly reliable and energy-efficient transmission, it is imperative to devise an optimal transmission scheduling strategy that dictates the selection of the link and channel allocation. Formulating this challenge as a Markov decision process (MDP), a sufficient condition ensuring the existence of an optimal deterministic and stationary (ODS) policy is established. The structural characteristics of the optimal policy are derived. Furthermore, a deep reinforcement learning (DRL) approach, specifically the dueling double deepQ-network (D3QN) algorithm, is employed to approximate the optimal policy. Finally, the structural results and the effectiveness of the DRL algorithm are verified by simulation examples.

This article investigates distributed state estimation of complex networks (CNs) with limited communication capacity. A random transmission strategy is used to overcome the communication capacity constraint between two nodes. A distributed state estimator that makes use of the partially available measurements is designed. To handle the cross-term, Young’s inequality is employed, and an upper bound (UB) for the state prediction error covariance (PEC) is derived. An optimal estimation gain is then devised based on the derived state PEC. The stability of the UB is analyzed using a vectorization approach, and then a sufficient condition for stability is obtained. Finally, a numerical simulation is carried out to validate the effectiveness of the proposed distributed estimator and confirm the accuracy of the derived UB.

In this article, the problem of ultimately bounded state estimation is investigated for discrete-time multirate singularly perturbed complex networks under the bit rate constraints, where the sensor sampling period is allowed to differ from the updating period of the networks. The facilitation of communication between sensors and the remote estimator through wireless networks, which are subject to bit rate constraints, involves the use of a coding-decoding mechanism. For efficient estimation in the presence of periodic measurements, a specialized impulsive estimation method is developed, which aims to carry out impulsive corrections precisely at the instants when the measurement signal is received by the estimator. By employing the iteration analysis method under the impulsive mechanism, a sufficient condition is established that ensures the exponential boundedness of the estimation error dynamics. Furthermore, an optimization algorithm is introduced for addressing the challenges related to bit rate allocation and the design of desired estimator gains. Within the presented theoretical framework, the correlation between estimation performance and bit rate allocation is elucidated. Finally, a simulation example is provided to demonstrate the validity of the proposed estimation approach.

Spatio-temporal forecasting plays a crucial role in the dynamic perception of smart cities, such as traffic flow prediction, renewable energy forecasting, and load prediction. Its objective is to understand the patterns of spatio-temporal changes under the interaction of various factors. Accurate spatio-temporal forecasting relies on sufficient high-quality data and powerful models. However, in reality, data is often sparse. In such cases, while adaptive graphs and large language models (LLMs) can maintain performance, they face issues of spatial spurious associations and hallucinations, respectively. These issues hinder the ability of the model to learn and infer cross spatio-temporal and cross-scale features effectively. To address this, we propose a novel model termed spatio-temporal causal intervention large language model (STCInterLLM). This model employs a newly designed causal intervention encoder to update spatial spurious correlations in the spatio-temporal adaptive graph. Subsequently, the novel chain-of-action prompting text is utilized to enforce the decomposition of the prediction process, thereby enhancing the causal representation of features while mitigating hallucinations in LLMs. Finally, a lightweight marker alignment module ensures the consistency between the encoder, prompting text, and LLM, enabling accurate forecasting of distinct scale spatio-temporal evolution patterns. Extensive experiments conducted on power distribution systems integrated with renewable energy sources and transportation systems encompassing diverse types of data, demonstrate that the proposed STCInterLLM consistently achieves state-of-the-art performance across significantly varied scenarios. Codes are available at https://github.com/lishijie15/STCInterLLM.

This study focuses on anti-quasisynchronization for discrete-time asynchronous leader-follower Markovian neural networks (MNNs) with mismatched parameters. To overcome the energy constraint, the intermittent control transmission strategy is introduced. Meanwhile, to address the challenge of unknown Markovian models in the leader-follower MNNs, a hidden Markov model (HMM) is utilized to infer unknown modes from observable information. Then, an intermittent nonfragile controller based on HMM is designed for the follower MNNs. Furthermore, the exponential iteration method is employed to establish sufficient conditions for ensuring anti-quasisynchronization for leader-follower MNNs, and an optimal boundary of anti-quasisynchronization is obtained. Ultimately, the effectiveness of the proposed HMM-based intermittent controller is demonstrated via a numerical simulation.

The distributed set-membership filtering is investigated for linear discrete systems with incomplete measurements. A new ellipsoid-based event-triggered mechanism is proposed, which utilizes the estimation ellipsoid to design the triggered condition and reduces the transmission frequency of the estimator state and the shape matrix. Then, a distributed filter based on the event-triggered mechanism and incomplete measurements is constructed, and a sufficient condition of the feasibility of the filter is derived using mathematical induction and set theory. The desired filter gains are obtained by addressing a series of constrained optimization problems. Furthermore, a fusion algorithm is introduced to reconstruct more accurate estimation information. The constraint of the measurement noise is then updated online based on the measurement and the reconstructed estimation information. Finally, the validity of the proposed method is verified by a simulation example.

In this article, the state estimation problem is studied for Markovian jump neural networks (MJNNs) within a digital network framework. The wireless communication channel with limited bandwidth is characterized by a constrained bit rate, and the occurrence of bit flips during wireless transmission is mathematically modeled. A transmission mechanism, which includes coding-decoding under bit-rate constraints and considers probabilistic bit flips, is introduced, providing a thorough characterization of the digital transmission process. A mode-dependent remote estimator is designed, which is capable of effectively capturing the internal state of the neural network. Furthermore, a sufficient condition is proposed to ensure the estimation error to remain bounded under challenging network conditions. Within this theoretical framework, the relationship between the neural network's estimation performance and the bit rate is explored. Finally, a simulation example is provided to validate the theoretical findings.

In this article, we consider the impulsive estimation problem for a specific category of discrete-time complex networks (CNs) characterized by Markovian switching topologies. The measurement outputs of the underlying CNs, transmitted to the observer over wireless networks, are subject to bit rate constraints. To effectively reduce the estimation error and enhance estimation performance, a mode-dependent impulsive observer is proposed that employs the impulse mechanism. The application of stochastic analysis techniques leads to the derivation of a sufficient condition for ensuring the mean-square boundedness of the estimation error dynamics. The upper bound of the error is then analyzed by iteratively exploring the Lyapunov relation at both impulsive and non-impulsive instants. Moreover, an optimization algorithm is presented for handling the bit rate allocation, which is coupled with the design of desired observer gains using the linear matrix inequality (LMI) approach. Within this theoretical framework, the relationship between the mean-square estimation performance and the bit rate allocation protocol is further elucidated. Finally, a simulation example is provided to demonstrate the validity and effectiveness of the proposed impulsive estimation approach.

In this article, the ultimately bounded synchronization problem is investigated for a class of discrete-time stochastic complex networks under the pinning control strategy. Communication between system nodes and the remote controller is facilitated via wireless networks subject to bit rate constraints. The system model is distinguished by the inclusion of randomly occurring nonlinearities. A coding-decoding transmission mechanism under constrained bit rates is introduced to characterize the digital transmission process. To achieve synchronization of the network nodes with the unforced target node, a pinning controller is specifically devised based on the information from partially selected nodes. Through the application of the stochastic analysis method, a sufficient condition is derived for ensuring the mean-square boundedness of the synchronization error system. In addition, an optimization algorithm is introduced to address bit rate allocation and the design of desired controller gains. Within the presented theoretical framework, the correlation between the mean-square synchronization performance and bit rate allocation is further elucidated. To conclude, a simulation example is provided to substantiate the efficacy of the recommended pinning control approach.

This work addresses the state estimation problem for recurrent neural networks over capacity-constrained communication channels. The intermittent transmission protocol is used to reduce the communication load, where a stochastic variable with a given distribution is used to describe the transmission interval. A corresponding transmission interval-dependent estimator is designed, and an estimation error system based on it is also derived, whose mean-square stability is proved by constructing an interval-dependent function. By analyzing the performance in each transmission interval, sufficient conditions of the mean-square stability and the strict (Q,S,R) - γ -dissipativity are established for the estimation error system. Finally, the correctness and the superiority of the developed result are illustrated by a numerical example.

This article proposes an asynchronous and dynamic event-based sliding mode control strategy to efficiently address the synchronization problem of Markov jump neural networks. By designing an adaptive law, and a triggered threshold in the form of a diagonal matrix, a special dynamic event-triggered scheme is applied to send the control signals only at triggered moments. An asynchronous sliding mode controller with gain uncertainty is designed by constructing a specified sliding manifold. Then, linear matrix inequalities are used to represent sufficient conditions for guaranteeing system synchronization. The error system trajectories are pushed onto the sliding surface by the controller. Eventually, the availability of the presented control strategy is demonstrated by an illustrative example.

This work proposes a fully distributed moving horizon estimation method with an event-triggered communication strategy over wireless sensor networks. The proposed method calculates a local state estimation of each sensor by minimizing a quadratic objective function, which involves a fused arrival cost that is computed in a distributed manner. This approach adjusts data transmission rate by selectively transmitting local information and allows the incorporation of constraints on the noise and state variables. In addition, the estimation error is proved to be uniformly bounded in the mean square sense under the condition that the network topology is strongly connected and the system is collectively observable. Finally, a target tracking example is presented to demonstrate the validity of the proposed approach.

This article is concerned with the synchronization issue of discrete Markov jump neural networks (MJNNs). First, to save communication resources, a universal communication model, including event-triggered transmission, logarithmic quantization, and asynchronous phenomenon, is proposed, which is close to the actual situation. Here, to further reduce conservatism, a more general event-triggered protocol is constructed by developing the threshold parameter as a diagonal matrix. To cope with mode mismatch between the nodes and controllers due to potentially occurring time lag and packet dropouts, a hidden Markov model (HMM) method is adopted. Second, considering that state information of nodes may not be available, the asynchronous output feedback controllers are devised by a novel decoupling strategy. Then, sufficient conditions based on linear matrix inequalities (LMIs) for dissipative synchronization of MJNNs are proposed with the virtue of Lyapunov techniques. Third, by eliminating asynchronous terms, a corollary with less computational cost is devised. Finally, two numerical examples verify the effectiveness of the above results.

This paper concerns distributed state estimation for linear systems over a sensor network. Two novel algorithms utilizing the framework of moving horizon estimation (MHE) are proposed, which are fully distributed, scalable and allow for taking into account constraints on the noises and the system states. The proposed methods estimate the state by minimizing a local quadratic objective function, which can be efficiently solved by quadratic programming (QP). Consensus technique is employed to fuse information to construct the local quadratic objective function for each node. The first algorithm, which minimizes a quadratic function involving consensus on measurement costs (CM), approaches the centralized MHE with a sufficiently large number of consensus steps. The second one involving consensus both on arrival costs (CA) and measurement costs , enjoys the benefits of both the CA and CM. To avoid directly running consensus on some functions, a novel consensus strategy for the CM is developed. The estimation errors of the proposed methods are proven to be stochastically ultimately bounded under certain conditions. Finally, numerical results are presented to verify the effectiveness of the developed algorithms.

This paper focuses on distributed estimation and smoothing for linear dynamic systems monitored by sensors that are organized as a network. To achieve low power consumption of sensors, the system is decomposed into several subsystems based on the observable canonical decomposition method. Each sensor in the network performs state estimation for one subsystem at most, while estimations for the majority of subsystems are obtained from the neighbors of each sensor. The optimal gains of the estimator and the smoother are pre-calculated offline, and a numerical solution is obtained by taking advantage of optimization algorithms. Furthermore, the boundedness of the prediction error covariance is guaranteed resulting from the bounded solution of a Riccati equation, and the performance of the smoother is analyzed. A numerical example is presented to validate the feasibility of the algorithm even if the sensor network suffers from network interruptions. The proposed algorithm is more robust against partial loss of predictions and recovers more rapidly, compared to some consensus-based distributed estimation algorithms.

The problem of event-triggered resilient filtering for Markov jump systems is investigated in this article. The hidden Markov model is used to characterize asynchronous constraints between the filters and the systems. Gain uncertainties of the resilient filter are the interval type in this article, which is more accurate than the norm-bounded type to model the uncertain phenomenon. The number of linear matrix inequalities constraints can be decreased significantly by separating the vertices of the uncertain interval, so that the difficulty of calculation and calculation time can be reduced. Moreover, the event-triggered scheme is applied to depress the consumption of network resources. In order to find a balance between reducing bandwidth consumed and improving system performance, the threshold parameter is designed as a diagonal matrix in the event-triggered scheme. Utilizing the convex optimization method, the sufficient conditions are derived to guarantee that the filtering error systems are stochastically stable and satisfy the extended dissipation performance. Finally, a single-link robot arm system is delivered to certify the effectiveness and advantages of the proposed method.

Scheduling for multiple sensors to observe multiple systems is investigated. Only one sensor can transmit a measurement to the remote estimator over a Markovian fading channel at each time instant. A stochastic scheduling protocol is proposed, which first chooses the system to be observed via a probability distribution, and then chooses the sensor to transmit the measurement via another distribution. The stochastic sensor scheduling is modeled as a Markov decision process (MDP). A sufficient condition is derived to ensure the stability of remote estimation error covariance by a contraction mapping operator. In addition, the existence of an optimal deterministic and stationary policy is proved. To overcome the curse of dimensionality, the deep deterministic policy gradient, a recent deep reinforcement learning algorithm, is utilized to obtain an optimal policy for the MDP. Finally, a practical example is given to demonstrate that the developed scheduling algorithm significantly outperforms other policies.

In this article, the cluster synchronization control problem is studied for discrete-time complex dynamical networks when the data transmission is subject to constrained bit rate. A bit-rate model is presented to quantify the limited network bandwidth, and the effects from the constrained bit rate onto the control performance of the cluster synchronization are evaluated. A sufficient condition is first proposed to guarantee the ultimate boundedness of the error dynamics of the cluster synchronization, and then, a bit-rate condition is established to reveal the fundamental relationship between the bit rate and the certain performance index of the cluster synchronization. Subsequently, two optimization problems are formulated to design the desired synchronization controllers with aim to achieve two distinct synchronization performance indices. The codesign issue for the bit-rate allocation protocol and the controller gains is further discussed to reduce the conservatism by locally minimizing a certain asymptotic upper bound of the synchronization error dynamics. Finally, three illustrative simulation examples are utilized to validate the feasibility and effectiveness of the developed synchronization control scheme.

The issue of finite-time state estimation is studied for discrete-time Markovian bidirectional associative memory neural networks. The asymmetrical system mode-dependent (SMD) time-varying delays (TVDs) are considered, which means that the interval of TVDs is SMD. Because the sensors are inevitably influenced by the measurement environments and indirectly influenced by the system mode, a Markov chain, whose transition probability matrix is SMD, is used to describe the inconstant measurement. A nonfragile estimator is designed to improve the robustness of the estimator. The stochastically finite-time bounded stability is guaranteed under certain conditions. Finally, an example is used to clarify the effectiveness of the state estimation.

This article studies the distributed state estimation issue for complex networks with nonlinear uncertainty. The extended state approach is used to deal with the nonlinear uncertainty. The distributed state predictor is designed based on the extended state system model, and the distributed state estimator is designed by using the measurement of the corresponding node. The prediction error and the estimation error are derived. The prediction error covariance (PEC) is obtained in terms of the recursive Riccati equation, and the upper bound of the PEC is minimized by designing an optimal estimator gain. With the vectorization approach, a sufficient condition concerning stability of the upper bound is developed. Finally, a numerical example is presented to illustrate the effectiveness of the designed extended state estimator.

This study addresses estimator design for a class of nonuniformly sampled neural networks under the scenario of the sampling interval being inaccessible to the estimator. A new quantization model is described by a hidden Markov chain, where the emission probability depends on the network status and sampling interval. Two variables called hidden mode and observed mode are defined based on the assumption that the data receiver can recognize the quantization density instead of the sampling interval, and the associated observed mode-dependent estimator is designed. An augmented estimation error system is obtained, and the strict$(\mathcal {Q},\mathcal {S},\mathcal {R})-\gamma -$dissipativity for the nonuniformly sampled neural networks is investigated. Then the estimator gain is calculated by solving a set of linear matrix inequalities. Finally, the effectiveness of the proposed approach is demonstrated via an example.

The connectivity is an essential property of the connections between the nodes in networks. The efficient determination algorithm for the connectivity of complex directed networks is an important research direction in graph theory. Aiming at the determination problem of the strong connectivity of directed networks, we propose an improved algorithm over the Warshall algorithm, which extends the research object to complex directed networks and has only the half time complexity of that of the latter. In addition, this article also takes the lead in research on the determination algorithm for the unilateral connectivity of complex directed networks, and on this basis, we propose an algorithm to efficiently determine the unilateral connectivity. Finally, the above two algorithms are integrated into a unified and efficient algorithm with the time complexity of$\mathcal {O}({n}^{3}+4.5{n}^{2})$. This algorithm can determine not only the strong connectivity but also the unilateral connectivity of complex directed networks.

This work addresses quasisynchronization (QS) of the master-slave (MS) neural networks (NNs) with mismatched parameters. The logarithmic quantizer and the round-robin protocol (RRP) are used to deal with the limited communication channel (CC) capacity, then the intermittent control strategy is employed to improve the efficiency of CC and the controller. A transmission-dependent controller is designed, and the synchronization error system (SES) is established. The QS with a boundary is ensured for the MS NNs by a developed sufficient condition, and the controller design method is given. A numerical simulation is given to show the effectiveness of the obtained method.

This paper studies the distributed state estimator design method for periodic systems with time-varying delays and periodic communication scheduling strategy over sensor networks. To mitigate the stability attenuation and the performance loss caused by delays, a periodic delay-dependent Lyapunov function is constructed, which relaxes the conservatism and improves the ${H_\infty }$ performance of the estimators. Then, a periodic scheduling strategy, which updates the correlative innovations of each node during a period in an average way by solving an integer programming problem, is proposed to save energy. Periodic gains of distributed estimators are designed by using analog tool. Finally, a visual example is utilized to describe the periodic communication scheduling. The effectiveness of the strategy and the novel Lyapunov function is demonstrated by the example.

This paper investigates the problem of quasi-synchronization (QS) for the periodic neural networks (NNs). In order to address more general NNs, the parameter and the period mismatches are both considered, that is, excluding parameters, periods of the target dynamic and the followers are also different. In addition, the constrainted target information is studied, where the logarithmic quantizer is used to overcome the limited communication capacity and Bernoulli processes are employed to model cases of the information loss. A new period is established based on the lowest common multiple period of the target dynamic and the followers to obtain an augmented synchronization error system (ASES). Further, a suboptimal iterative algorithm is proposed to cut down the QS range of the ASES and the corresponding controllers are designed. At last, the controller design method is illustrated by a numerical example.

This article investigates the problem of state estimation for discrete-time systems with a Markov driven transmission strategy. A buffer with limited capacity is used to store the latest measurements, and they are transmitted simultaneously once the system accesses to the shared channel. A buffer-dependent smart estimator is then proposed to process the received measurements. A convex sufficient condition concerning the exponential mean-square stability and the$l_{2}-l_{\infty }$performance is established for the estimation error system to design the estimator gains. Finally, two examples are presented to illustrate the effectiveness of the derived result under different conditions.

The problem of quasi-synchronization (QS) for the Markovian jump master-slave neural networks with time-varying delay is studied in this article, where the mismatch parameters and unreliable communication channels are considered as well. A set of stochastic variables with different expectations are used to describe the fading phenomena of parallel communication channels. An impulsive-driven transmission strategy is designed to reduce the communication load, and a corresponding impulsive controller is then designed. A synchronization error system (SES) is obtained, and a convex QS condition is established for the SES. A linear matrix inequality-based iterative algorithm is proposed to reduce the bound of the SES, and the corresponding controller gains are calculated. A numerical example is provided to illustrate the effectiveness of the developed result.

This article addresses the problem of the average stochastic finite-time synchronization (ASFTS) for a set of coupled neural networks (NNs) with energy-bounded noises. Due to the channel capacity constraint, the impulsive approach is introduced so as to cut down the communication times among the leader NNs and the follower NNs. Then, a nonfragile controller is designed to improve the robustness of the controller with randomly occurred uncertainty. The sufficient conditions that guarantee the ASFTS of the coupled NNs and the leader NNs are achieved. The boundary of the synchronization error is also obtained by constructing the monotonic increasing functions. Finally, the controller gains are given based on the derived conditions, and their effectiveness is illustrated by a numerical example.

This article investigates synchronization for a group of discrete-time neural networks (NNs) with the uncertain exchanging information, which is caused by the uncertain connection weights among the NNs nodes, and they are transformed into a norm-bounded uncertain Laplacian matrix. Distributed impulsive observers, which possess the advantage of reducing the communication load among NNs nodes, are designed to observe the NNs state. The impulsive controller is proposed to improve the efficiency of the controller. An impulsive augmented error system (IAES) is obtained based on the matrix Kronecker product. A sufficient condition is established to ensure synchronization of the group of NNs by proving the stability of the IAES. An iterative algorithm is given to obtain a suboptimal allowed interval of the impulsive signal, and the corresponding gains of the observer and the controller are derived. The developed result is illustrated by a numerical example.

In this paper, the distributed H∞state estimators are designed for discrete-time stochastic periodic systems with randomly occurred uncertainties and topology-dependent quantizers over sensor networks. A norm-bounded uncertain model is introduced to describe the randomly occurred norm-bounded parameter uncertainties, where each component of the system occurs uncertainty independently. According to the changeable topologies of the sensor networks, a topology-dependent quantizer is proposed to enhance the channel utilization. Sufficient conditions are established to ensure the asymptotic stability and the H∞performance simultaneously for the augmented estimation error system. Then the gains of estimators are derived in every period. Finally, the average of one thousand simulations are depicted in the figures. Moreover, simulation comparisons are given to explain the quantizer design method.

This paper focuses on the linear minimum mean square estimator for a networked discrete time-varying linear system subject to data quantification and communication constraints. The communication limitation is that only one transmission node can get access to the shared communication channel at each time step, and that different transmission nodes in the networked systems are scheduled to transmit information according to a Markov protocol. Then the remote estimator completes the estimation with only partially available observations, which are quantified. Suppose that the Markov chain is unknown to the remote estimator. By using orthogonal projection principle and innovation analysis method, a Kalman type filter is designed in a recurrence form. It is shown that estimation performance depends on the transition probability matrix of the Markov chain, quantization error, and the shared channel weighting parameter. Finally, an illustrative example is given to show the effectiveness of the proposed method.

This paper investigates an${H}_\infty $estimator design for time-varying coupled neural networks (NNs) over a finite-horizon. In order to reduce the information exchanged among the NNs, a periodic inner-coupling strategy is proposed. In addition, a Markov driven transmission scheme is introduced to overcome the communication capacity constraint between the NNs and the estimators, where an inner-coupling-dependent Markov chain is used to improve the efficiency of the communication channel. Subsequently, the time-varying Markov estimators are designed to enhance the performance of the estimators. A recursive matrix inequality (RMI)-based sufficient condition is established to ensure that the time-varying estimation error system meets the finite-horizon${H}_\infty $performance. Afterward, the estimator gains are designed by transforming the RMIs into linear RMIs. Finally, a numeral example is used to illustrate the developed results.

This paper studies the synchronization issue of time-varying Markovian jump neural networks (NNs). The denial-of-service (DoS) attack is considered in the communication channel connecting master NNs and slave NNs. An observer is designed based on the measurements of master NNs transmitted over this unreliable channel to estimate their states. The deception attack is used to destroy the controller by changing the sign of the control signal. Then, the mixed-type attacks are expressed uniformly, and a synchronization error system is established using this function. A finite-horizon l2- l∞performance is proposed, and sufficient conditions are derived to ensure that the synchronization error system satisfies this performance. The controllers are then obtained by a recursive linear matrix inequality algorithm. At last, a simulation result to show the feasibility of the developed results is given.

This paper considers finite-time distributed state estimation for discrete-time nonlinear systems over sensor networks. The Round-Robin protocol is introduced to overcome the channel capacity constraint among sensor nodes, and the multiplicative noise is employed to model the channel fading. In order to improve the performance of the estimator under the situation, where the transmission resources are limited, fading channels with different stochastic properties are used in each round by allocating the resources. Sufficient conditions of the average stochastic finite-time boundedness and the average stochastic finite-time stability for the estimation error system are derived on the basis of the periodic system analysis method and Lyapunov approach, respectively. According to the linear matrix inequality approach, the estimator gains are designed. Finally, the effectiveness of the developed results are illustrated by a numerical example.

This paper considers the problem of I2- I∞filtering for discrete-time Takagi-Sugeno (T-S) fuzzy systems with multiplicative sensor noises over the channels with limited capacity. A more general multidensity logarithmic quantizer is designed to increase the utilization of the communication resources, and a sojourn-time-dependent Markov chain is used to model the variation of the quantizer density. Then, the fuzzy basis-, quantizer density-, and sojourn-time-dependent filter is designed for T-S fuzzy systems on the basis of the quantized measurements to improve the performance of the filter. Sufficient conditions are proposed to guarantee that the filtering error system is exponentially mean-square stable and achieves a prescribed I2- I∞performance. Finally, three examples are given to illustrate the developed new design techniques.

The problem of dissipativity-based resilient filtering for discrete-time periodic Markov jump neural networks in the presence of quantized measurements is investigated in this paper. Due to the limited capacities of network medium, a logarithmic quantizer is applied to the underlying systems. Considering the fact that the filter is realized through a network, randomly occurring parameter uncertainties of the filter are modeled by two mode-dependent Bernoulli processes. By establishing the mode-dependent periodic Lyapunov function, sufficient conditions are given to ensure the stability and dissipativity of the filtering error system. The filter parameters are derived via solving a set of linear matrix inequalities. The merits and validity of the proposed design techniques are verified by a simulation example.

This paper investigates the estimator design for the neural networks, where distributed delays and imperfect measurements are included. A randomly occurred neuron-dependent nonlinearity is used to describe the uncertain measurements disturbed by neurons. The measurements are transmitted over multiple transmission channels, and Markov chains are introduced to model packet dropouts of these channels. A one-to-one map is constructed to transform $m$ independent Markov chains to an augmented one to facilitate system analysis. A new variable called channel state is defined based on the cases of packet dropouts, and the channel-state-dependent estimator is designed to trade off between the number and the performance of the estimator. Sufficient conditions are established to guarantee that the augmented system is stochastically stable and satisfies the strict $(Q, S, R)-\gamma -$ dissipativity. The estimator gains are derived using linear matrix methods. Finally, an example is applied to illustrate the effectiveness of the developed methods.

This paper studies the issue of robust state estimation for coupled neural networks with parameter uncertainty and randomly occurring distributed delays, where the polytopic model is employed to describe the parameter uncertainty. A set of Bernoulli processes with different stochastic properties are introduced to model the randomly occurrences of the distributed delays. Novel state estimators based on the local coupling structure are proposed to make full use of the coupling information. The augmented estimation error system is obtained based on the Kronecker product. A new Lyapunov function, which depends both on the polytopic uncertainty and the coupling information, is introduced to reduce the conservatism. Sufficient conditions, which guarantee the stochastic stability and the performance of the augmented estimation error system, are established. Then, the estimator gains are further obtained on the basis of these conditions. Finally, a numerical example is used to prove the effectiveness of the results.

This paper studies the state estimator design for periodic neural networks, where stochastic weight matrices B(k) and packet dropouts are considered. The stochastic variables, which may influence each other, are introduced to describe uncertainties of weight matrices. In order to model the time-varying conditions of the communication channel, a Markov chain is employed to study the jumping cases of the stochastic properties of the packet dropouts (i.e., Bernoulli process with jumping means and variances being used to handle the packet dropouts). A state estimator is constructed such that the augmented system is stochastically stable and satisfies the H∞performance. The estimator parameters are derived by means of the linear matrix inequalities method. Finally, a numerical example is provided to illustrate the effectiveness of the proposed results.

This paper investigates the issue of finite-time state estimation for coupled Markovian neural networks subject to sensor nonlinearities, where the Markov chain with partially unknown transition probabilities is considered. A Luenberger-type state estimator is proposed based on incomplete measurements, and the estimation error system is derived by using the Kronecker product. By using the Lyapunov method, sufficient conditions are established, which guarantee that the estimation error system is stochastically finite-time bounded and stochastically finite-time stable, respectively. Then, the estimator gains are obtained via solving a set of coupled linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the proposed new design method.

This paper addresses the problem of state estimation for a class of discrete-time stochastic complex networks with a constrained and randomly varying coupling and uncertain measurements. The randomly varying coupling is governed by a Markov chain, and the capacity constraint is handled by introducing a logarithmic quantizer. The uncertainty of measurements is modeled by a multiplicative noise. An asynchronous estimator is designed to overcome the difficulty that each node cannot access to the coupling information, and an augmented estimation error system is obtained using the Kronecker product. Sufficient conditions are established, which guarantee that the estimation error system is stochastically stable and achieves the strict (Q, S, R)-γ-dissipativity. Then, the estimator gains are derived using the linear matrix inequality method. Finally, a numerical example is provided to illustrate the effectiveness of the proposed new design techniques.

Mobile robots tracking a reference trajectory are constrained by the motion limits of their actuators, which impose the requirement for high autonomy driving capabilities in robots. This paper presents a model predictive control (MPC) scheme incorporating neural-dynamic optimization to achieve trajectory tracking of nonholonomic mobile robots (NMRs). By using the derived tracking-error kinematics of nonholonomic robots, the proposed MPC approach is iteratively transformed as a constrained quadratic programming (QP) problem, and then a primal-dual neural network is used to solve this QP problem over a finite receding horizon. The applied neural-dynamic optimization can make the cost function of MPC converge to the exact optimal values of the formulated constrained QP. Compared with the existing fast MPC, which requires repeatedly calculating the Hessian matrix of the Langragian and then solves a quadratic program. The computation complexity reaches O(n3), while the proposed neural-dynamic optimization contains O(n2) operations. Finally, extensive experiments are provided to illustrate that the MPC scheme has an effective performance on a real mobile robot system.