Traffic signal control (TSC) is still one of the most challenging and promising research issues in the field of transportation. Since traditional methods have difficulty in handling dynamically changing traffic flows, reinforcement learning (RL) methods have been introduced into TSC. However, the cost of practical application is critically high due to multiple sampling trials and long learning process. The Transformer architecture has recently attained remarkable results in natural language processing (NLP), but when applied to the field of RL, the standard Transformer architecture is difficult to optimize and faces the problem of hyperparameter sensitivity. In the paper, we transform TSC into a sequence modeling issue and propose a new evolution Transformer architecture to adjust the autoregressive model through reward, past states and actions in the traffic environment to directly generate the best predicted action. In addition, we use the feature evolution module (FEM) instead of residual connections to make the learning process more stable and efficient. Through experiments on public datasets, we demonstrate that our ETLight model achieves a state-of-the-art (SOTA): 1) It achieves the overall best performance on average travel time (ATT) metric, with improvements of up to 6.85%, 3.73% and 3.10% over the best conventional, RL and Transformer methods, respectively; 2) It has a more stable learning process, faster learning speed and better convergence compared to published TSC methods so far; and; 3) it has good robustness and is less sensitive to hyperparameter selection.

Multi-agent adversarial tasks such as swarm robotics and autonomous vehicle coordination, demand efficient decentralized collaboration under partial observability. While model-free multi-agent RL (MF-MARL) methods suffer from necessitating extensive environment interactions, most existing multi-agent model-based RL (MA-MBRL) methods fail to align with the Centralized Training with Decentralized Execution (CTDE) paradigm, which limits system flexibility. This paper proposes a novel modular architecture with refined observations (MARO) to achieve the CTDE paradigm by decoupling agents from the world model. Key innovations include: 1) an enhanced world model with weighted loss and history-augmented rollout for high-quality data generation; 2) a dual-stream semantic decomposition network (DSDN) that performs fine-grained decomposition of observations to refine action mapping and mitigate performance degradation from information loss. Extensive experiments on the StarCraft Multi-Agent Challenge (SMAC) demonstrate superior performance over opponents, validating the effectiveness and advancement of MARO.

In recent years, visible-infrared object detection has achieved significant progress. However, most existing methods primarily emphasize the shared features between the two modalities while overlooking their feature differences. To address this limitation, we propose the Difference-Guided Modality Fusion Network, which can effectively improve the fusion and detection performance of modalities. Specifically, we propose a cross-modal data augmentation strategy to overcome the limitations of single-modality reliance by exchanging the partial modal information. To further capture and analyze feature differences between modalities, we introduce a differential attention fusion approach that models a difference matrix across modal channels, thereby quantifying and strengthening the salient features of the two modalities. Additionally, we develop a modality-aware dynamic learning mechanism that employs a loss function that can simultaneously focus on the differences and common parts of the modalities, guiding the model to adaptively learn features between the modalities. Experimental results on FLIR, LLVIP and M3FD datasets demonstrate the effectiveness of the proposed method, with mAP reaching 42.3%, 67.5% and 59.0% respectively.

This letter is concerned with a three-dimensional target motion analysis issue using azimuth and elevation measurements. The nonlinear relationship between these measurements and target dynamics often poses challenges for conventional methods, especially in high-noise environments. To address this challenge, a novel multi-agent deep reinforcement learning (MADRL)-based estimator is proposed for target motion parameter estimation. Specifically, by modeling each component of the target motion parameter as an individual agent, the target motion parameter estimation process is framed as a cooperative Markov game. An MADRL framework is then introduced to solve this problem. Simulation results demonstrate that the proposed algorithm achieves higher estimation accuracy than existing estimators.

This paper investigates the cooperative fuzzy tracking control of nonlinear unmanned surface vehicles with input quantization and event-triggered mechanism. The proposed cooperative control scheme consists of two parts: (i) the distributed observer and (ii) the dynamic event-based fuzzy tracking controller. The distributed observer is designed to obtain the nonlinear leader’s trajectory information on a directed communication topology. Under this framework, uncertain nonlinearity within the vehicle model is approximated through fuzzy logic systems, and, according to the state of the distributed observer, the dynamic event-based adaptive fuzzy tracking control law is developed with an input switching quantizer. Furthermore, a prescribed performance method is introduced to ensure the transient performance of tracking errors and obtain zero-tracking errors ultimately, which is proved through Lyapunov stability theory. Finally, the effectiveness of the proposed control strategy is verified by simulation experiments.

This article addresses the cooperative tracking control problem for heterogeneous multiple marine vehicles with a nonautonomous leader. A fully distributed smooth observer is proposed to estimate the trajectory of the leader, mitigating the influence of its control input. Based on the observer, three decentralized adaptive fuzzy event-based controllers are designed with distinct triggering strategies, i.e., fixed, relative, and switching threshold triggering strategies, which utilize fuzzy-logic systems and event-triggering mechanisms to address the challenge of model uncertainties and communication constraints of marine vehicles. The proposed methods ensure the zero-error tracking without Zeno behavior, as demonstrated through Lyapunov analysis. Numerical simulations validate the effectiveness of the proposed approaches.

This paper investigates the problem of distributed target tracking via underwater acoustic sensor networks (UASNs) with fading channels. The degradation of signal quality due to wireless channel fading can significantly impact network reliability and subsequently reduce the tracking accuracy. To address this issue, we propose a modified distributed unscented Kalman filter (DUKF) named DUKF-Fc, which takes into account the effects of measurement fluctuation and transmission failure induced by channel fading. The channel estimation error is also considered when designing the estimator and a sufficient condition is established to ensure the stochastic boundedness of the estimation error. The proposed filtering scheme is versatile and possesses wide applicability to numerous scenarios, e.g., tracking a maneuvering underwater target with underwater sensor nodes (USNs) equipped with acoustic sensors. Considering the constraints of network energy resources, the issue of investigating the energy cost of DUKF-Fc is discussed in the simulation and accordingly, the results demonstrate the robustness and energy-efficiency of the proposed filtering procedure.

The localization of underwater acoustic sensor networks (UASNs) has emerged as a critical research area in the marine information fusion field. Generally, the convex optimization method is adopted to solve the localization problem. However, this method has limitations in complex underwater environments, since it is difficult to transform the nonconvex optimization problem into a convex optimization problem under such conditions. Recently, deep reinforcement learning (DRL) has shown great potential and promise in solving intricate optimization tasks. Motivated by this, we propose to adopt DRL for UASNs localization to improve accuracy and robustness. The key challenge is that existing DRL-based methods require discretization of the environment, which leads to a compromise between search time and localization precision. To address this challenge, we first model the localization problem as a Markov decision process (MDP) with continuous state and action spaces and subsequently introduce a continuous control DRL framework to solve the localization problem. Within this framework, we develop three continuous control DRL-based localization estimators to address the localization problem in unsupervised, supervised, and semisupervised scenarios. Comprehensive simulations demonstrate the effectiveness of our approach, as the proposed solutions exhibit several advantageous features compared to traditional methods, such as: 1) compared with the convex optimization-based method, the convex relaxation is not required; 2) compared with the least squares method, the proposed estimators are capable of converging to a global optimal state; and 3) compared with the discrete control DRL method, the proposed estimators reduce localization time and enhance localization accuracy significantly.

Currently, decarbonization has become an emerging trend in the power system arena. However, the increasing number of photovoltaic units distributed into a distribution network may result in voltage issues, providing challenges for voltage regulation across a large-scale power grid network. Reinforcement learning based intelligent control of smart inverters and other smart building energy management (EM) systems can be leveraged to alleviate these issues. To achieve the best EM strategy for building microgrids in a power system, this paper presents two large-scale multi-agent strategy evaluation methods to preserve building occupants’ comfort while pursuing system-level objectives. The EM problem is formulated as a general-sum game to optimize the benefits at both the system and building levels. The α -rank algorithm can solve the general-sum game and guarantee the ranking theoretically, but it is limited by the interaction complexity and hardly applies to the practical power system. A new evaluation algorithm (TcEval) is proposed by practically scaling the α -rank algorithm through a tensor complement to reduce the interaction complexity. Then, considering the noise prevalent in practice, a noise processing model with domain knowledge is built to calculate the strategy payoffs, and thus the TcEval-AS algorithm is proposed when noise exists. Both evaluation algorithms developed in this paper greatly reduce the interaction complexity compared with existing approaches, including ResponseGraphUCB (RG-UCB) and α InformationGain ( α -IG). Finally, the effectiveness of the proposed algorithms is verified in the EM case with realistic data.

This article addresses the cooperative time-varying formation fuzzy tracking control problem for a cluster of heterogeneous multiple marine surface vehicles subject to unknown nonlinearity and actuator failures. The proposed cooperative control scheme consists of two parts: 1) a distributed time-varying formation observer and 2) a decentralized adaptive fuzzy tracking controller. The distributed observer is designed to obtain a predefined time-varying formation pattern under a directed communication topology. Subsequently, based on the states of the distributed observer, a decentralized fuzzy tracking control law is developed using fuzzy-logic systems and the adaptive approach. Lyapunov functions are constructed to guarantee that the controlled marine vehicles attain the desired time-varying formation with asymptotical stability of tracking errors. Finally, simulation results are presented to validate the efficacy of the proposed control methodology.

This article intends to study the asynchronous control problem for 2-D Markov jump systems (MJSs) with nonideal transition probabilities (TPs) under the Roesser model. Two practical considerations motivate the current work. First, considering that the system mode cannot always be observed accurately, a hidden Markov model (HMM) is adopted to describe the relationship between the mismatched modes. Second, considering that the TPs information related to the Markov process and the observation process is difficult to obtain, the nonideal TPs (unknown or uncertain) are simultaneously considered on the two processes. Under the considerations, several new sufficient conditions are developed for concerned closed-loop 2-D MJSs with nonideal TPs, by which the asymptotic mean square stability is ensured with an${\mathcal {H}}_{\infty }$performance index. A nonconservative separation strategy is utilized to decouple the system mode TPs and the observation TPs to facilitate the analysis of nonideal TPs. An unified LMI-based condition is finally developed for the concerned closed-loop 2-D MJSs with/without nonideal TPs, showing more satisfactory conservatism than that in the literature. In the end, we present two examples to validate the superiority of the proposed design method.

The article investigates the decentralized periodic dynamic event-based load frequency control problem for a class of multiarea nonlinear power systems with uncertain parameters. For overcoming the limitations on the knowledge of studied power systems, the interval type-2 (IT2) fuzzy model is synthesized by using local linear models relevant to some operation points. Under the IT2 fuzzy framework, the decentralized periodic dynamic event-based fuzzy control law is proposed to reduce the bandwidth burden of communication networks. Based on the Lyapunov stability theory, a sufficient condition is presented such that closed-loop systems are exponentially stable with a given$H_{\infty }$performance. The existence condition of the controller gains and the triggering scheme's parameters is expressed in terms of matrix inequalities. The obtained results are extended to two situations, i.e., the decentralized periodic static event-based fuzzy control and the decentralized periodic sampling fuzzy control. Compared with the latter two control approaches, the developed decentralized periodic dynamic triggering strategy can provide the lowest communication frequency. Finally, the validity and superiority of the developed method are demonstrated by simulation results.

Marine target searching is a complex task due to large search areas, unique signal propagation characteristics, and limited visibility, posing significant challenges for single-agent or homogeneous multi-agent systems. In response, we propose a novel hierarchical heterogeneous multi-agent (HHMA) framework designed for underwater search scenarios. This framework integrates three types of vehicles moving in different domains—unmanned aerial, surface, and underwater vehicles, effectively overcoming the limitations of single or double-agent configurations. We begin by elucidating the advantages of the HHMA system in target searching, providing the kinematic modeling, while also transforming sonar detecting data and defining the search problem. The mission is decomposed to three human-comprehensible subtasks that are adaptive to both environmental conditions and equipment capabilities: moving, target estimating and trajectory planning. The target estimating subtask is effectively modeled as a Markov Decision Process, retaining its memory capability. Additionally, we extend multi-agent reinforcement learning to multi-policy reinforcement learning, facilitating the training of interdependent policies. The efficacy of our approach is demonstrated through simulations, comparing it with rule-based methods. Simulation results underscore the significance of the HHMA system and validate the proposed training methodology.

Under the purpose of achieving the optimal voltage control strategy in power grid system, multi-agent evaluation algorithms like a-rank are widely used. However, in large-scale systems with massive agents and strategies, these methods are not time feasible. Therefore, a two-stage population-based multi-agent evaluation algorithm is proposed to solve voltage control problem in large-scale power grid systems. For stage one, a population is first established for each agent. And then, individuals in the populations randomly combined to form joint strategies. Base on the max and mean reward from the interaction between joint strategies and the environment, populations evolve to a near-optimal joint strategy. Stage two takes the above near-optimal joint strategy as the starting point, and uses a strategy search algorithm with maximum transfer possibility to find the Markov-Conley chain in the system. Finally, the above two-stage method is simulated in 10 and 32-agent power grid systems to verify the effectiveness.

Information and communication technology tremendously facilitates the operation efficiency and economy of modern power systems in recent years. However, risks such as bandwidth constraints and malicious attacks threaten the secure load frequency control (LFC) of power systems. To mitigate such risks, this paper proposes distributed observer-based event-triggered LFC schemes for multi-area power systems under cyber attacks. Considering the practical situation that only local system output information may be available, distributed observer-based LFC schemes are designed. Meanwhile, to reduce the communication burden, an event-triggered mechanism is adopted to design control laws, where both static and dynamic event-triggered approaches are taken and the dynamic one is proved to be more economical in terms of control cost. Verifiable sufficient conditions are established to guarantee the stability of the closed-loop system in the presence of cyber attacks and the controller gains are explicitly derived. Finally, validation studies on a three-area interconnected power system are carried out to demonstrate the proposed control schemes. Note to Practitioners—Load frequency is a crucial index for evaluating the quality of electric energy and thus LFC has brought considerable attention in the area of power systems control. Although many achievements have been made on the LFC of multi-area power systems, the risks such as bandwidth constraints and malicious attacks affect the normal operation of LFC due to the interconnection between different power systems. To deal with these risks, this paper focuses on designing event-triggered LFC to guarantee the stability of the frequency deviation while reducing the communication burden and mitigating cyber attacks. More importantly, the proposed event-triggered LFC is designed based on an observer and can be implemented in a distributed manner, which is relatively practical in real applications. The results presented in this paper aim to provide a helpful reference for stable and secure LFC design of multi-area power systems, such that the corresponding application research can be promoted.

Load frequency control (LFC) is crucial for the economic operation and safety of power systems. Therefore this paper addresses the LFC problem for uncertain multi-area power systems with actuator failures. Specifically, actuator failures, uncertainties and communication bandwidth constraints appearing in multi-area power systems are taken into account simultaneously, and novel reliable event-triggered LFC schemes are proposed to cope with these troubles. The proposed schemes can ensure the asymptotical stability of the closed-loop system when only matched uncertainty exists. For the case of coexisting matched and mismatched uncertainties, the state trajectories of the closed-loop system can be controlled within a bounded set, where the size of the bounded set is only related to the mismatched uncertainty. To illustrate the theoretical results, a numerical example of three-area interconnected power system is presented. Note to Practitioners—Load frequency directly affects the quality of electric energy and is one of the main observation states of power systems, hence LFC has been widely investigated in the literature. For multi-area power systems, the system model to be controlled may be subjected to multiple unfavorable factors in practical situations, such as limited bandwidths, model uncertainties and actuator failures. To cope with these unfavorable factors, this paper is devoted to developing a unified control framework to guarantee the stability of the frequency deviation based on the event-triggered mechanism. Considering both matched and unmatched system uncertainties may exist as well as the bound of system uncertainties can be unknown, event-triggered control schemes including static event-triggered LFC and adaptive event-triggered LFC are accordingly designed to deal with aforementioned situations such that the closed-loop system is asymptotically or boundedly stable. The research outcome of this paper provides simple but effective LFC approaches that can be used to maintain the reliable and stable operation of multi-area power systems.

This article investigates the adaptive fuzzy asynchronous control problem for discrete-time nonhomogeneous Markov jump power systems under hybrid attacks. A nonhomogeneous Markov process is used to describe the phenomenon of transient failures occurring in power lines and subsequent switching of associated circuit breakers. The corresponding nonhomogeneous hidden Markov model is utilized to detect the jump modes of power systems. Both deception attack and denial-of-service attack are analyzed simultaneously owing to the vulnerability of power systems. With detected modes and fuzzy logic systems, an adaptive fuzzy asynchronous control strategy is proposed. Using the mode-dependent Lyapunov function, the existence conditions of the desired controller law are obtained such that the closed-loop power systems are bounded stable in the mean-square sense. Finally, the usefulness of the developed control strategy is demonstrated by a numerical example.

This article investigates the cooperative output regulation problem for heterogeneous nonlinear multiagent systems subject to disturbances and quantization. The agent dynamics are modeled by the well-known Takagi-Sugeno fuzzy systems. Distributed reference generators are first devised to estimate the state of the exosystem under directed fixed and switching communication graphs, respectively. Then, distributed fuzzy cooperative controllers are designed for individual agents. Via the Lyapunov technique, sufficient conditions are obtained to guarantee the output synchronization of the resulting closed-loop multiagent system. Finally, the viability of proposed design approaches is demonstrated by an example of multiple single-link robot arms.

This article investigates the cooperative output regulation problem for discrete-time heterogeneous multiagent Markov jump systems. Two cases are studied: 1) output regulation quadratic control in the case where the exosystem is accessible to all agents and 2) cooperative output regulation quadratic control in the case where only a part of agents can directly communicate with the exosystem. The hidden Markov models are employed to describe the asynchronous modes of the agents and their corresponding controllers. Via the jumping regulator equation, asynchronous control laws are constructed and the algorithms to obtain control parameters are presented in terms of linear matrix inequalities. For the first case, the optimal synchronous/mode-dependent control law, which is a special case of the asynchronous control protocol, is also given via the stochastic dynamic programming approach. Finally, an example is given to illustrate the effectiveness of the proposed approaches.

This article analyzes the problem of the sliding-mode control (SMC) design for discrete-time piecewise nonhomogeneous Markov jump nonlinear systems (MJNSs) subject to an external disturbance with time-varying transition probabilities (TPs). A discrete-time asynchronous integral sliding surface is constructed, which yields matched-nonlinearity-free sliding-mode dynamics (SMDs). Then, by using the mode-dependent Lyapunov function technique, a sufficient condition is established for ensuring the stochastic stability of SMD with extended dissipation. The solution to designing controller gains is obtained. Moreover, an SMC law and an adaptive law are, respectively, derived for driving the system trajectories to move into a predetermined sliding-mode region with specified precision. Finally, the feasibility and effectiveness of the new design are verified and demonstrated by a simulation example.

This article is concerned with the cluster consensus control problem for multiagent linear systems with a directed communication topology, where only relative output measurements of neighboring agents are available to each agent. Motivated by the pinning control technique, both static and dynamic intermittent output control strategies are proposed. Using Lyapunov functions, sufficient conditions are developed to ensure cluster consensus with existence-guaranteed control parameters. Both periodic and nonperiodic operations of intermittent controllers are investigated. Finally, the effectiveness of the theoretical results is demonstrated by a simulation example.

This paper addresses the mean-square leader-follower consensus problem for the multiagent Markov jump linear systems. The leader has the general linear dynamics while the followers are subject to parameter changes modeled by Markov jump. By using the output measurement of the leader, two types of observers, namely, the common observer and the distributed adaptive observer, are first constructed together to estimate the leader state. Then based on the estimated state and the follower self information, two kinds of controllers, namely, the synchronous controller and the asynchronous controller, are designed to achieve the mean-square leader-follower consensus. Finally, the simulation results are given to illustrate the feasibility and effectiveness of the proposed approaches.

In this paper, the problem of sliding mode control (SMC) is considered for a class of nonlinear continuous-time Markov jump systems (MJSs) with uncertainties and time delay. A novel integral-type switching sliding surface function is designed, where the controller gain may jump asynchronously with original MJSs. Then, an SMC law is constructed to force system trajectories onto the specified switching sliding surface in a finite time. The stochastic stability and dissipative performance of sliding mode dynamics are analyzed and the delay-dependent sufficient condition for the existence of the desired switching surface is developed. Moreover, we also extend SMC to investigate the finite-time stability problem during both reaching phase and sliding motion phase in the stochastic setting. Finally, simulation results are given to illustrate the effectiveness of the proposed design techniques.

This paper investigates the problem of dissipativity-based asynchronous fuzzy integral sliding mode control (AFISMC) for nonlinear Markov jump systems represented by Takagi-Sugeno (T-S) models, which are subject to external noise and matched uncertainties. Since modes of original systems cannot be directly obtained, the hidden Markov model is employed to detect mode information. With the detected mode and the parallel distributed compensation approach, a suitable fuzzy integral sliding surface is devised. Then using Lyapunov function, a sufficient condition for the existence of sliding mode controller gains is developed, which can also ensure the stochastic stability of the sliding mode dynamics with a satisfactory dissipative performance. An AFISMC law is proposed to drive system trajectories into the predetermined sliding mode boundary layer in finite time. For the case with unknown bound of uncertainties, an adaptive AFISMC law is developed as well. The studied T-S fuzzy Markov jump systems involve both continuous-time and discrete-time domains. Finally, some simulation results are presented to demonstrate the applicability and effectiveness of the proposed approaches.

In this paper, the problem of asynchronous output feedback control is investigated for a class of Takagi-Sugeno fuzzy switched systems subject to intermittent measurements. The Bernoulli process is employed to model the phenomenon of stochastic intermittent measurements. Based on the hidden Markov model and output measurements, an asynchronous controller is designed. Then, sufficient conditions for the existence of an asynchronous controller are proposed, which ensure the stochastic stability of the closed-loop system with desired extended dissipative performance. Finally, an example is presented to illustrate the effectiveness and advantages of the proposed new design techniques.

This paper investigates the H∞ output consensus problem for multiagent systems with Markov jump and external disturbance in both continuous-time and discrete-time domains. The communication network is directed and fixed with uncertainties. Based on the hidden Markov model, an output feedback controller is constructed. Then, the original system is transformed into a reduced-order system, which features the error dynamics. By using a Lyapunov function, sufficient conditions are developed to ensure that all agents can reach the consensus with the desired H∞ performance in the mean-square sense. Finally, simulation results are presented to illustrate the efficiency of the proposed approaches.

This paper is concerned with the problem of asynchronous and reliable filter design with performance constraint for nonlinear Markovian jump systems which are modeled as a kind of Takagi-Sugeno fuzzy switched systems. The nonstationary Markov chain is adopted to represent the asynchronous situation between the designed filter and the considered system. By using the mode-dependent Lyapunov function approach and the relaxation matrix technique, a sufficient condition is proposed to ensure the filtering error system, which is a dual randomly switched system, is stochastically stable and satisfies a given l2-l∞performance index simultaneously. Two different approaches are developed to construct the asynchronous and reliable filter. Owing to the Finsler's lemma, the second approach has fewer decision variables and less conservatism than the first one. Finally, two examples are provided to show the correctness and effectiveness of the proposed methods.

This paper addresses the dissipative asynchronous filtering problem for a class of Takagi-Sugeno fuzzy Markov jump systems in the continuous-time domain. The hidden Markov model is applied to describe the asynchronous situation between the designed filter and the original system. Based on the stochastic Lyapunov function, a sufficient condition is developed to guarantee the stochastic stability of the filtering error systems with a given dissipative performance. Two different methods for the existence of desired filter are established. Due to the Finsler's lemma, the second approach has fewer variables to decide and brings less conservatism than the first one. Finally, an example is provided to demonstrate the correctness and advantage of the proposed approaches.

This paper considers the problem of asynchronous guaranteed cost control (GCC) for nonlinear Markov jump systems with stochastic quantization. Hidden Markov model is used to describe the nonsynchronous controller and the random quantization phenomenon. Based on Takagi-Sugeno fuzzy technique and Lyapunov function approach, a sufficient condition is obtained, which can not only ensure the asymptotic stability of the closed-loop system and existence of the desired controller, but also can yield the minimal upper bound of GCC performance. Finally, two examples are provided to demonstrate the correctness and reliability of our developed approaches.

This paper is concerned with the reliable ℒ2- ℒ∞filter design problem for the nonlinear continuous-time Markov jump systems based on Takagi-Sugeno fuzzy model. A stochastic variable is introduced to describe the encountered sensor failures, the value of which is dependent on the considered plant mode based on a hidden Markov process. In practice, generally the information on plant modes is not fully accessible to the reliable filter, which results in the nonsynchronous phenomena between the modes of involved plant and filter, and has a negative effect on the system performance. A hidden Markov model is also adopted to depict such kinds of nonsynchronous phenomena. The filtering error systems are called fuzzy dual hidden Markov jump systems. A sufficient condition, associated to the modes of the plant, sensor failures, and the filter are proposed for the filtering error systems to ensure the stochastic stability and guaranteed ℒ2- ℒ∞performance, based on which the existence condition and explicit design method of a nonsynchronous filter are both given. Finally, two simulation examples illustrate the effectiveness of the proposed approach.

The paper investigates the problem of l2-l∞ asynchronous static output feedback control for discrete-time Markov jump linear systems. The hidden Markov model is applied to describe the asynchronization between the original system and the designed controller. Via the system augmentation technique, the closed-loop system is represented as a descriptor system. Based on stochastic Lyapunov function technique, a sufficient condition is developed to ensure that the closed-loop system is stochastically admissible with a given l2-l∞ performance. By Finsler's lemma and the convexification approach, controller parameters can be obtained by solving a set of linear matrix inequalities. Finally, the applicability and effectiveness of the proposed approach is demonstrated by an example.

The problem of asynchronous dissipative control is investigated for Takagi-Sugeno fuzzy systems with Markov jump in this paper. Hidden Markov model is introduced to represent the nonsynchronization between the designed controller and the original system. By the fuzzy-basis-dependent and mode-dependent Lyapunov function, a sufficient condition is achieved such that the resulting closed-loop system is stochastically stable with a strictly ( , , )- -dissipative performance. The controller parameter is derived by applying MATLAB to solve a set of linear matrix inequalities. Finally, we present two examples to confirm the validity and correctness of our developed approach.

This paper deals with the problem of dissipativity-based asynchronous fault detection (FD) for Takagi-Sugeno fuzzy Markov jump systems with network data dropouts. It is assumed that data dropouts happen intermittently from the plant to the FD filter, which is described by Bernoulli process. The hidden Markov model is employed to describe the asynchronous phenomenon between the plant and filter. Based on Lyapunov theory, a sufficient condition is developed to guarantee that the FD system is stochastically stable with strictly dissipative performance. By choosing an appropriate Lyapunov function with the slack matrix technique and Finsler's Lemma, two approaches are proposed to compute filter gains by solving linear matrix inequalities. Finally, an example is provided to illustrate the usefulness and effectiveness of the proposed design methods.

The paper is concerned with the H∞and I2-I∞filtering design problems for discrete-time nonlinear switched systems with quantized measurements using the Takagi-Sugeno (T-S) fuzzy model. The systems under consideration inherently combine features of the switched hybrid systems and the T-S fuzzy systems. The sector bound approach is employed to deal with quantization effects. Based on the fuzzy-basis-dependent Lyapunov function, sufficient conditions are established such that the filtering error system is stochastically stable and a prescribed noise attenuation level in an H∞or I2-I∞sense is achieved. Both numerical and practical examples are provided to show the feasibility and efficiency of the design schemes.

This paper is concerned with the problem of reliable switched controller design for a class of discrete-time Takagi-Sugeno fuzzy systems with randomly occurring infinitedistributed delays and quantization as well as actuator failures. A random Bernoulli process is used to describe the stochastic infinite-distributed delays. Due to limited communication capacity, the control signal is quantized before being transmitted to the actuator by the logarithmic quantizer. We apply the switching mechanisms to categorize the stochastic behavior of actuator faults. Based on the parallel distributed compensation, the switched feedback controller is designed. By the fuzzy-basisdependent Lyapunov functional approach, sufficient conditions are obtained to ensure that the resulting closed-loop system is exponentially stable in the mean-square sense with a given l2-l∞performance index. Then, a numerical example is presented to demonstrate the effectiveness of the proposed new design method.

This paper investigates the problem of nonfragile guaranteed cost control for discrete-time Takagi-Sugeno fuzzy Markov jump systems with time-varying delays. With the help of the parallel distributed compensation, a nonfragile fuzzy controller is designed. Then via Lyapunov-Krasovskii functional approach, sufficient conditions are obtained ensuring that the resulting closed-loop system is asymptotically stable with an upper bound of the guaranteed cost index. The optimal upper bound of the guaranteed cost index and the controller gain can be achieved via the optimization technique. Finally, an example is presented to show the effectiveness of the proposed new design techniques.