Learning representation of the enclosing subgraph of node pairs is recognized as an efficient approach for link-oriented prediction tasks in network applications. The core challenge within this subgraph encoding approach is how to effectively distinguish and then properly aggregate the contribution of nodes in the subgraph into a single vector to indicate the relation between the target node pair. In this work, we propose a novel sphere-based subgraph encoding architecture, namely BS-SubGNN, to address the challenge. In detail, we design two key building blocks, including Bicentric Sphere Node Labeling (BSNL) and Bicentric Sphere Subgraph Pooling (BSSP) to assist message passing in BS-SubGNN. BSNL endows each node a label according to the sphere it belongs to in the subgraph to distinguish the contribution of nodes, while BSSP adopts an attention mechanism to aggregate the contribution of nodes in each sphere. Theoretically, we prove that BS-SubGNN can unify existing node distance labeling methods, and yield discriminative node features with less time complexity. We evaluate the performance of BS-SubGNN in link prediction tasks over a variety of network types, including undirected networks, attribute networks, directed networks, and signed directed networks. Our experimental results demonstrate that BS-SubGNN consistently achieves significant performance improvements over the above diverse types of networks. In particular, compared to those methods with a requisite of multi-hop neighborhood information, BS-SubGNN can obtain better performance even when only one-hop neighborhood information of the node pair is utilized.

In practical manufacturing scenarios, re-entrant manufacturing systems (RMSs) are vulnerable to uncertain workstation faults, which may cause sudden changes in the work-in-process (WIP) level and impair the overall production capacity. To ensure agile response to market demands and to maintain stable system behavior under fault scenarios, an active fault-tolerant control scheme via fault diagnosis for RMSs is developed in this paper. Firstly, a hybrid hyperbolic partial differential equation continuum model is developed to describe the evolution of WIP dynamics, where the workstation faults result in the discarding of defective products during processing. Subsequently, a fault diagnosis scheme is presented to detect and estimate the uncertain faults in real time, by integrating an observer-based fault detection method and an adaptive fault estimation algorithm. Utilizing the estimated fault information, a fault-tolerant control strategy is then proposed to compensate for fault-induced state jumps and achieve agile production control. Finally, a numerical simulation is conducted to demonstrate the effectiveness of the proposed fault-tolerant control approach. It is believed the proposed theory enriches the theory, analysis, design of control circuit systems and has potential of practical implementations to industrial manufacturing systems.

This article focuses on the design and analysis of resilient leaderless consensus for multiagent systems exposed to distributed denial-of-service (DoS) attacks. Unlike most existing works that focus on undirected communication topologies and synchronized attacks affecting all links simultaneously, this work considers a more general scenario with directed communication graphs and distributed DoS attacks, where different communication channels can be disrupted independently. To address this challenge, a dynamic event-triggered communication scheme is incorporated into the consensus protocol, under which asymptotic consensus stability can be preserved despite distributed DoS disruptions. Information exchange is performed only at triggering instants, which effectively suppresses redundant transmissions and enhances communication efficiency. To further reduce computational burden and remove the requirement of continuous monitoring, a self-triggered communication scheme is introduced, in which each agent determines its next triggering instant solely based on the most recently available data. Rigorous analysis verifies that both the event-triggered and self-triggered schemes preclude Zeno behavior. In addition, numerical simulations of a low Earth orbit satellite formation demonstrate the efficacy and advantages of the developed control strategies.

In the era of Big Data and generative artificial intelligence (AI), discovering the truth about various objects from different sources has become a pressing topic. Existing studies primarily focus on dependent sources with conflicting information, where sources may copy information from each other. However, real-world scenarios are often more complex, with dynamic dependence relationships among sources over time. This complexity makes it much more difficult to discover the truth. One of the key challenges centers on measuring the dynamic dependence among sources. To address this challenge, we have developed three models:$Depen\_{S}imple$,$Depen\_{C}omplex$, and$Depen\_{D}ynamic$. These models are based on the Hidden Markov Model (HMM) and are designed to handle different types of dependencies, namelysimple source dependence,complex source dependence, anddynamic source dependence. Based on the constructed models, we propose a generic framework for discovering the latent truth which are evaluated by three HMM-based methods. We conduct extensive experiments on three real-world datasets to evaluate the performance of the proposed methods, and the results demonstrate that all three methods achieve high accuracy over the state-of-the-art methods.

Vehicular Edge Metaverse (VEM) is a new paradise supported by the Internet of Things, AI, and wireless communication technologies which provide various Virtual Vehicle Services (VVSs), where users can immerse and enjoy their spiritual world. To provide various VVSs for users, there is a significant increase in computational demands. The limited computing resource available on the vehicles are insufficient to handle the massive volume of tasks and the diverse needs of users. Edge nodes could provide more services than vehicles but longer transmission time and the cloud node can provide more services than edge nodes with longer transmission time. To provide better experiences for users, we use a three-layer (cloud-edge-vehicles) resource framework in VEM. In this paper, we construct a VEM offloading framework with communication and computation capabilities for metaverse services. It considers tasks with different requirements and comprehensively evaluates offloading decisions and resource allocation to maximize user's satisfaction in the metaverse and minimize the energy consumption of vehicles. To achieve this, a two-stage offloading algorithm based on a hybrid heuristic approach is proposed, aiming to find the Pareto optimal solution for the bi-objective optimization problem. Finally, experiments demonstrate that the proposed algorithm over-performs other algorithms with real dataset, validating that the proposed algorithm can effectively enhance user service satisfaction and reduce energy consumption.

This article investigates the fault detection and isolation (FDI) problem for a class of re-entrant manufacturing systems (RMSs) subject to workstation faults, sensor faults, and measurement disturbances. The system dynamics are first characterized by a hybrid hyperbolic partial differential equation (HHPDE) continuum model. A two-stage observer-based FDI framework is then developed to enable timely and reliable fault diagnosis. In the first stage of this framework, a diagnostic observer equipped with residual evaluation logic is employed, which is capable of detecting the occurrence of faults yet incapable of differentiating between the sensor faults and the workstation faults. Once a fault is detected, the isolation procedure starts as the second stage to distinguish the fault types and localize the fault sources. To be specific, anH-/H∞observer-based isolation scheme is proposed to effectively decouple the sensor faults from the sensor disturbances, by exploiting the dual performance of disturbance attenuation and fault sensitivity; an adaptive observer-based isolation strategy is devised to identify the workstation faults by capturing the associated structural changes in the system dynamics. Finally, the effectiveness and robustness of the proposed methods are validated through comprehensive numerical simulations.

Local feature description is crucial for robotic tasks, yet existing methods struggle with motion blur, a prevalent challenge in high-dynamic and low-light environments. While effective on sharp images, they suffer significant degradation under blur. To address this issue, we propose Motion-Feat, an end-to-end motion blur-aware feature description method. Our approach introduces a Motion Deformable Block (MDB) that adaptively adjusts the receptive field based on pixel-wise motion information at different stages of the network, enhancing multi-scale feature descriptor robustness in blurred conditions. Additionally, we construct synthetic blurred datasets to systematically benchmark feature matching performance across varying blur intensities. Extensive experiments demonstrate that Motion-Feat outperforms state-of-the-art methods on blurred images while maintaining competitive performance on sharp images for relative camera pose estimation and homography estimation tasks. Both code and datasets are available at https://github.com/AndreGao08/Motion-Feat.

The increasing integration of heterogeneous processors on a chip presents significant challenges for efficient management in Multi-Processor System-on-Chip (MPSoC) platforms. Network-on-Chip (NoC) architectures offer a flexible and scalable interconnection paradigm through router-based communication. However, mapping dependent, real-time tasks in NoC environments critically affects data processing and transmission efficiency. An optimized task mapping scheme must address constraints such as real-time deadlines, energy consumption, and reliability, which are key metrics for modern NoCs. Existing approaches often overlook the complex interplay between communication paths and their associated energy costs, resulting in suboptimal resource utilization. This paper proposes a comprehensive task mapping framework that jointly optimizes energy efficiency and reliability by integrating Dynamic Voltage and Frequency Scaling (DVFS), multi-path data routing, task allocation, scheduling, and partial task duplication. We formulate the problem as a complex combinatorial optimization task and transform it into a solvable form with reduced computational complexity. Simulation results demonstrate that the proposed method achieves superior energy efficiency by reducing energy consumption by up to 39.7%, reducing computation time, and improving task schedulability compared to existing state-of-the-art approaches.

In this paper, the security control problem for a class of multi-tiered re-entrant manufacturing networks (RMNs) under cyber-attacks is investigated. First, a linear hyperbolic partial differential equation (PDE) continuum model is employed to model the dynamics of each single manufacturing line node and the overall RMN system is then characterized by a dual-layer coupling structure consisting of a production line network and a workshop network, both of which adhere to the global mass conservation law. Second, to mitigate the adverse effects of malicious cyber-attacks on RMNs, a node-dependent control approach and an edge-dependent control approach are developed, both of which guarantee the global exponential stability of the multi-tiered RMNs under cyber-attacks. Numerical simulations demonstrate the effectiveness of the proposed network architecture and control schemes.

In the era of data information explosion, there are different observations on an object (e.g., the height of the Himalayas) from different sources on the web, social sensing, crowd sensing, and data sensing applications. Observations from different sources on an object can conflict with each other due to errors, missing records, typos, outdated data, etc. How to discover truth facts for objects from various sources is essential and urgent. In this paper, we aim to deliver a comprehensive and exhaustive survey on truth discovery problems from the perspectives of concepts, methods, applications, and opportunities. We first systematically review and compare problems from objects, sources, and observations. Based on these problem properties, different methods are analyzed and compared in depth from observation with single or multiple values, independent or dependent sources, static or dynamic sources, and supervised or unsupervised learning, followed by the surveyed applications in various scenarios. For future studies in truth discovery fields, we summarize the code sources and datasets used in above methods. Finally, we point out the potential challenges and opportunities on truth discovery, with the goal of shedding light and promoting further investigation in this area.

The robust fuzzy control problem for a category of network-type re-entrant manufacturing systems (RMSs) is explored in this paper. A continuum model is first employed to represent the RMSs with external disturbances and time-varying communication delays in terms of a nonlinear hyperbolic partial differential equation (PDE) model. The nonlinear model is then reformulated in the framework of a T-S fuzzy model. A novel Lyapunov-Krasovskii-type stability theorem is proposed for the concerned PDEs with delays, extending the classical Lyapunov-Krasovskii framework to delayed PDE systems. Using the proposed stability theorem, LMI-based conditions are developed for stability analysis and control synthesis of the closed-loop RMSs under external disturbances and time-varying delays, which demonstrates effective alignment of production output with market demand despite environmental perturbations. At last the results of a simulation validate the developed control approach.

This paper investigates the fault-tolerant observer design problem for a class of re-entrant manufacturing systems (RMSs) in the presence of workstation faults during the production process. A hyperbolic hybrid partial differential equation (HHPDE) continuum model is constructed to describe the dynamics of RMSs suffering from unexpected workstation faults, by considering that machinery failures of workstations lead to discarding of defective products. In the case that the faults are known, a fault-tolerant impulsive observer is designed for state estimation of the RMSs. In the case that the fault information is uncertain, a diagnostic observer based residual evaluation logic is developed for fault detection first. Upon detecting the faults, an adaptive impulsive observer is then proposed to simultaneously estimate both the system states and the faults. In addition, by using a piecewise Lyapunov function candidate, sufficient stability conditions that guarantee the exponential input-to-state stability (EISS) of the estimation error are formulated in terms of linear matrix inequalities (LMIs). Finally, the feasibility and effectiveness of the proposed strategy are validated through numerical simulations.

This article proposes a quantum spatial graph convolutional neural network (QSGCN) model that is implementable on quantum circuits, providing a novel avenue to processing non-Euclidean type data based on the state-of-the-art parameterized quantum circuit (PQC) computing platforms. Four basic blocks are constructed to formulate the whole QSGCN model, including the quantum encoding, the quantum graph convolutional layer, the quantum graph pooling layer, and the network optimization. In particular, the trainability of the QSGCN model is analyzed through discussions on the barren plateau phenomenon. Simulation results from various types of graph data are presented to demonstrate the learning, generalization, and robustness capabilities of the proposed quantum neural network (QNN) model.

In this article, robustH∞synchronization problem for a class of networked re-entrant manufacturing systems (RMSs) is investigated by utilizing state feedback control and distributed adaptive state feedback control approaches. Different from the isolated single re-entrant manufacturing line, a networked RMS with three-tiered architecture is presented, which contains the production line, the production workshop and the workshop network. Based on the mass conservation law, the dynamics of the production line and the production workshop are established by a first-order linear hyperbolic PDE and a first-order semi-linear hyperbolic PDE, respectively. On one hand, in view of communication delays and external disturbances that might exist in the system, a delayed state feedback controller is constructed to address robustH∞synchronization of the networked RMSs. On the other hand, considering the uncertainty with coupling gain and the unavailability of global information, a distributed cooperative controller with the edge-dependent adaptive gain is further developed to ensure robustH∞synchronization of the networked RMSs. Numerical simulations validate the effectiveness of both proposed control schemes.

This study addresses the problem of guaranteed cost fault-tolerant fuzzy control for multiline re-entrant manufacturing systems (RMSs) against stochastic disturbances and workstation faults. Initially, a nonlinear hyperbolic impulsive partial differential equation model is employed to describe the complex and hybrid dynamics of RMSs suffering from unexpected faults within the working stations, and then the corresponding approximation T-S fuzzy model is constructed. In what follows, with the aid of the parallel distributed compensation fuzzy control scheme, the main results of stability analysis and controller synthesis for the closed-loop re-entrant manufacturing control system are derived using a timer-dependent Lyapunov functional with spatio-temporal auxiliary variables. It is found that by means of the proposed fault-tolerant control approach, the RMS can be effectively and robustly driven to a desired production mode with steady feeding and production rates while the upper bound of a quadratic cost function is minimized. Finally, the effectiveness of the proposed control approach is validated through numerical simulations.

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique.

This paper investigates the robust intelligent control problem of multi-line re-entrant manufacturing plants. The control system is designed with a hierarchical architecture, where a nonlinear stochastic hyperbolic partial differential equation (PDE) is used to describe the system dynamics and a robust controller is designed to exponentially drive the manufacturing plants to a desired operation mode with steady feeding and production rates. The developed robust control scheme is shown to be practically implementable through convex optimization techniques. Numerical experiments are presented to demonstrate the feasibility and advantages of the proposed approach.Note to Practitioners—The motivation of this work originates from the need to develop an intelligent robust control strategy for a class of practical complex re-entrant manufacturing plants, for instance, the semiconductor wafer factory and the chemical production lines with numerous process procedures. Discrete-model-based algorithms have been extensively employed in this field due to their excellent convenience and great accuracy. However, when dealing with coupled multi-line re-entrant manufacturing plants with nonlinearities, traditional discrete-model-based methods lack rigorous theoretical analysis and, more importantly, suffer from the curse of dimensionality in many cases. To equip the re-entrant manufacturing plant with a desired operation mode that enjoys significant robustness against stochastic noises, we propose a continuum-model-based intelligent robust control strategy. The proposed method is practically useful in the sense that it can be conveniently applied to various industrial scenarios with re-entrant characteristics and the control design problem can be well solved via available convex optimization algorithms.

This paper proposes a subgraph-based hierarchical Q-learning network (SgHQN) approach to solve the optimal resource scheduling problem for complex industrial networks. In the industrial network, each connection between two individual stations has limited communication bandwidth, while each station has limited computing and storage capability and is only accessible to its local information. The resource packages flowing within the industrial network are treated as agents that have different sizes and different levels of decision-making priority. This makes the industrial resource scheduling problem on the industrial network a multi-level decision-making problem with information asymmetry. Specifically, the resource packages with lower decision-making priority have knowledge of the decisions made by those with higher priority, but not vice versa. To solve this resource scheduling problem with information asymmetry, an SgHQN model is developed by exploiting partial observations. It is found that the proposed SgHQN can be used to solve resource scheduling problems for general industrial networks. Numerical experiments simulating industrial scheduling scenarios demonstrate the effectiveness and advantages of our method.

This article proposes an optimal controller for a team of underactuated quadrotors with multiple active leaders in containment control tasks. The quadrotor dynamics are underactuated, nonlinear, uncertain, and subject to external disturbances. The active team leaders have control inputs to enhance the maneuverability of the containment system. The proposed controller consists of a position control law to guarantee the achievement of position containment and an attitude control law to regulate the rotational motion, which are learned via off-policy reinforcement learning using historical data from quadrotor trajectories. The closed-loop system stability can be guaranteed by theoretical analysis. Simulation results of cooperative transportation missions with multiple active leaders demonstrate the effectiveness of the proposed controller.

Privacy preservation for distributed optimization in multiagent systems has been widely concerned in recent years. In this article, the accumulated noise privacy-preserving alternating direction method of multipliers (ANPPM) algorithm is proposed to preserve the private information of each agent. The masked states of each agent are sent to its neighbors with a designed noise-adding mechanism, and an accumulated term is introduced to confuse the gradients at each iteration. With ANPPM, all the agents can achieve privacy preservation for the information of real states and subgradients. Moreover, the states of all the agents can be guaranteed to converge to the optimal solution. The convergence rate of is consistent with standard ADMM, hence no adverse effect is induced by the privacy-preserving mechanism. Numerical results are provided to validate the effectiveness of the proposed ANPPM algorithm.

The stabilization problem of two categories of discrete-time linear time-varying (LTV) systems subject to unbounded distributed input delays is investigated in this article. A truncated predictor feedback law is first built for a category of systems under some common assumptions. Then, under some weakened assumptions, a predictor-type feedback law is developed for the other category of more general systems. The global exponential stability of the closed-loop systems is proved. Furthermore, the result on the truncated predictor feedback control law includes many existing results on LTV systems subject to bounded input delays and linear time-invariant (LTI) systems subject to unbounded input delays as special cases. Finally, the results of simulations validate the effectiveness of the developed control laws.

This article analyzes a security problem for industry cloud control systems (CCSs), where a malicious false data injection (FDI) attacker may deteriorate the system performance by tampering with the data being transmitted. To improve the robustness of CCSs against FDI attacks, a redundancy-based sensor configuration scheme is provided through analysis of the observability under attacks of the nodes in CCSs. Then, a defending resource allocation scheme is developed based on a two-stage Stackelberg game in order to optimize the overall defense capability of the CCSs with limited defending resource. In this two-stage Stackelberg game, the defender of the CCSs acts first and allocates the defending resource to secure the measurements of wireless sensors. With the knowledge of the defender’s strategy, the attacker then decides which target nodes to launch attacks on. The optimal resource allocation strategy is then designed in the sense of the Stackelberg equilibrium. Furthermore, the defense problem under different attacking resource constraints is investigated. Numerical examples illustrate the effectiveness of the proposed approach.

This paper analyzes the security problem of cloud control systems (CCSs), where a malicious false data injection (FDI) attacker may deteriorate the system performance by tampering with the measurements being transmitted. The CCS defender allocates defense budgets among nodes to ensure the safe operation of CCSs. The strategic interactions between the FDI attacker and the CCS defender are modeled as a Stackelberg game, and the optimal strategies for both sides are analyzed in the sense of Nash equilibrium. Numerical examples illustrate the main results of this paper.

This paper proposes a quantum self-attention neural network (QSAN) model that can be deployed on quantum circuits, providing a novel avenue to processing text classification tasks in natural language processing (NLP). The QSAN framework is established by integrating four basic blocks: the data preprocessing block, the quantum encoding block, the model design block, and the network optimization block. Simulation results demonstrate remarkable convergence and accuracy on various text classification datasets. In particular, the proposed QSAN surpasses the existing state-of-the-art quantum NLP (QNLP) model in terms of test accuracy.

This paper solves the problem of discrete-time fault-tolerant quantum filtering for a class of laser-atom open quantum systems subject to the stochastic faults. We show that by using the discrete-time quantum measurements, optimal estimates of both the atomic observables and the classical fault process can be simultaneously determined in terms of recursive quantum stochastic difference equations. A dispersive interaction quantum system example is used to demonstrate the proposed filtering approach.

This paper investigates the problems of universal fuzzy model and universal fuzzy controller for discrete-time nonaffine nonlinear systems (NNSs). It is shown that a kind of generalized T-S fuzzy model is the universal fuzzy model for discrete-time NNSs satisfying a sufficient condition. The results on universal fuzzy controllers are presented for two classes of discrete-time stabilizable NNSs. Constructive procedures are provided to construct the model reference fuzzy controllers. The simulation example of an inverted pendulum is presented to illustrate the effectiveness and advantages of the proposed method. These results significantly extend the approach for potential applications in solving complex engineering problems.

In this paper, a novel dynamic sliding mode control scheme is proposed for a class of uncertain stochastic nonlinear time-delay systems represented by Takagi-Sugeno fuzzy models. The key advantage of the proposed scheme is that two very restrictive assumptions in most existing sliding mode control approaches for stochastic fuzzy systems have been removed. It is shown that the closed-loop control system trajectories can be driven onto the sliding surface in finite time almost certainly. It is also shown that the stochastic stability of the resulting sliding motion can be guaranteed in terms of linear matrix inequalities; moreover, the sliding-mode controller can be obtained simultaneously. Simulation results illustrating the advantages and effectiveness of the proposed approaches are also provided.

In this paper, the universal integral sliding-mode controller problem for the general stochastic nonlinear systems modeled by Itô type stochastic differential equations is investigated. One of the main contributions is that a novel dynamic integral sliding mode control (DISMC) scheme is developed for stochastic nonlinear systems based on their stochastic T-S fuzzy approximation models. The key advantage of the proposed DISMC scheme is that two very restrictive assumptions in most existing ISMC approaches to stochastic fuzzy systems have been removed. Based on the stochastic Lyapunov theory, it is shown that the closed-loop control system trajectories are kept on the integral sliding surface almost surely since the initial time, and moreover, the stochastic stability of the sliding motion can be guaranteed in terms of linear matrix inequalities. Another main contribution is that the results of universal fuzzy integral sliding-mode controllers for two classes of stochastic nonlinear systems, along with constructive procedures to obtain the universal fuzzy integral sliding-mode controllers, are provided, respectively. Simulation results from an inverted pendulum example are presented to illustrate the advantages and effectiveness of the proposed approaches.

In this paper, a novel dynamic integral sliding-mode control (ISMC) scheme is proposed for a class of uncertain stochastic nonlinear time-delay systems represented by Takagi-Sugeno fuzzy models. The key advantage of the proposed scheme is that two very restrictive assumptions in most existing ISMC approaches for stochastic fuzzy systems have been removed. It is shown that the closed-loop control system trajectories are kept on the integral sliding surface almost surely since the initial time. It is also shown that the stochastic stability of the resulting sliding motion can be guaranteed in terms of linear matrix inequalities, and moreover, the sliding-mode controller can be obtained simultaneously. Simulation results from an inverted pendulum example illustrating the advantages and effectiveness of the proposed approaches are provided in the end.

This paper addresses the sliding-mode control (SMC) design problem for a class of uncertain nonlinear systems that can be represented by Takagi-Sugeno (T-S) fuzzy models. We propose a novel dynamic sliding-mode control scheme for T-S fuzzy models, aiming to eliminate the restrictive assumption that all subsystems share a common input matrix, which is required in most existing fuzzy SMC approaches. Sufficient conditions for the reachability of the sliding surface and asymptotic stability of the sliding motion are formulated in the form of linear matrix inequalities. Finally, simulation results that illustrate the advantages and effectiveness of the proposed approaches are provided.

This paper addresses the universal fuzzy integral sliding-mode controllers' problem for continuous-time multi-input multi-output nonlinear systems based on Takagi-Sugeno (T-S) fuzzy models. By using the approximation capability of T-S fuzzy models, the nonlinear systems are expressed by uncertain T-S fuzzy models with norm-bounded approximation errors. A novel fuzzy dynamic integral sliding-mode control (DISMC) scheme is then developed for the nonlinear systems based on their T-S fuzzy approximation models. One of the key features of the new DISMC scheme is that the restrictive assumption that all local linear systems share a common input matrix, which is required in most existing fuzzy integral sliding-mode control (ISMC) approaches, is removed. Furthermore, the results of universal fuzzy ISMCs for two classes of nonlinear systems, along with constructive procedures to obtain the universal fuzzy ISMCs, are provided, respectively. Finally, the advantages and effectiveness of the proposed approaches are illustrated via a numerical example.

In this paper, the sliding mode control (SMC) design problem for uncertain T-S fuzzy systems is investigated. It is noted that most existing fuzzy SMC approaches rely on a very restrictive assumption that all subsystems of the T-S fuzzy systems have the same input matrix. Aiming to remove this assumption, we propose a novel dynamic sliding mode control (DSMC) scheme for a class of uncertain T-S fuzzy systems. It is shown that the sliding surface can be reached in finite time and the asymptotic stability of the sliding motion can be guaranteed if a set of linear matrix inequalities are feasible. Simulation results from two numerical examples illustrating the effectiveness and advantages of the proposed approaches are also provided.

This paper investigates the universal fuzzy model and universal fuzzy controller problems for stochastic nonaffine nonlinear systems. The underlying mechanism of stochastic fuzzy logic is first discussed, and a stochastic generalized fuzzy model with new stochastic fuzzy rule base is then given. Based on their function approximation capability, these kinds of stochastic generalized fuzzy models are shown to be universal fuzzy models for stochastic nonaffine nonlinear systems under some sufficient conditions. An approach to stabilization controller design for stochastic nonaffine nonlinear systems is then developed through their stochastic generalized Takagi-Sugeno (T-S) fuzzy approximation models. Then, the results of universal fuzzy controllers for two classes of stochastic nonlinear systems, along with constructive procedures to obtain the universal fuzzy controllers, are also provided, respectively. Finally, a numerical example is presented to illustrate the effectiveness of the proposed approach.

This paper investigates the universal fuzzy models problem and universal fuzzy controllers problem for discrete-time general nonlinear systems based on a class of generalized T-S fuzzy models. The generalized T-S fuzzy models, which are shown to be universal function approximators, are also proved to be universal fuzzy models for non-affine nonlinear systems under some sufficient conditions. The results of static and dynamic universal fuzzy controllers for two classes of nonlinear systems are then given, respectively, and constructive procedures to obtain the universal fuzzy controllers are also provided.

This paper presents a novel approach to control general nonlinear systems based on Takagi-Sugeno (T-S) fuzzy dynamic models. It is first shown that a general nonlinear system can be approximated by a generalized T-S fuzzy model to any degree of accuracy on any compact set. It is then shown that the stabilization problem of the general nonlinear system can be solved as a robust stabilization problem of the developed T-S fuzzy system with the approximation errors as the uncertainty term. Based on a piecewise quadratic Lyapunov function, the robust semiglobal stabilization and H∞ control of the general nonlinear system are formulated in the form of linear matrix inequalities. Simulation results are provided to illustrate the effectiveness of the proposed approaches.

A novel approach to control of general nonlinear system based on T-S fuzzy model is presented in this paper. Firstly, it is shown that a general nonlinear system can be approximated by a generalized T-S fuzzy model to arbitrary degree of accuracy on any compact set. And the basic idea of the proposed approach is to stabilize the general nonlinear system by solving a robust stabilization problem of the developed T-S fuzzy system with the approximation errors as the uncertainty term. Then using a piecewise Lyapunov function, robust semi-global stabilization and generalized H2control of the general nonlinear system are formulated in the form of linear matrix inequalities. Finally, an example is provided to demonstrate the effectiveness of the proposed approaches.