This paper investigates the asynchronous filtering problem for single-machine infinite bus (SMIB) power systems subject to stochastic transmission line faults. The system is modeled as a discrete-time Markov jump system (MJS) to capture the random switching behavior induced by transmission line faults. To address the asynchrony between the system modes and the filter operation, a hidden Markov model (HMM) is adopted. The filtering problem is reformulated as a regulation problem by introducing a quadratic performance index based on output estimation errors, offering a filtering-based alternative to control strategies. To solve the associated coupled algebraic Riccati equations (CAREs), an inverse reinforcement learning (IRL)–based algorithm is developed, which enables model-free filtering without requiring prior knowledge of the system dynamics or transition probabilities. The convergence of the proposed algorithm is rigorously analyzed, and a numerical example based on an SMIB power system with stochastic faults is provided to validate its effectiveness.

This article is concerned with the asynchronous $H_{\infty }$ control design based on model-free policy iteration (PI) algorithm for a class of discrete-time hidden Markov jump system, where a hidden Markov model is developed to characterize the asynchronous phenomenon between the controller modes and the system modes. A pair of zero-sum asynchronous control and disturbance strategies are constructed to achieve a tradeoff between value function and control performance. The presented approach shows two pivotal aspects: 1) the asynchronous PI algorithm is not dependent on strict temporal alignment between the controller and the system's dynamics, enhancing flexibility of the control scheme and 2) it relies on the collected data to solve the algebraic Reccati equation iteratively, which avoids the need for system-internal and transfer probability information, and circumvents the interference of coupled terms. Subsequently, it is verified that the designed PI algorithm monotonically converges to an optimal solution and the system based on this optimal solution is stochastically stable in the mean-square sense. Finally, the effectiveness of this approach is validated by conducting a simulation experiment on a DC motor device system.

This article presents a sliding mode control (SMC) strategy to address the finite-time consensus problem of multiagent systems (MASs) under denial-of-service (DoS) attacks. Agents exchange information over network channels that are vulnerable to stochastic DoS attacks, which may disrupt communication and change the network topology. To capture these stochastic variations, a Markov jump model is employed to describe the switching of communication topologies. By introducing a disagreement vector, the consensus problem of the MAS within a finite-time interval is transformed into the stochastic finite-time boundedness (SFTB) problem of the disagreement error dynamic system. A feasible SMC law is developed to drive the disagreement error dynamic system onto a specified sliding surface within a finite time. Furthermore, a partitioning policy is used to ensure the SFTB of the system during both the reaching phase and the sliding phase. A reduced-order approach is used to resolve potential uncontrollability in the system, and sufficient conditions are established to ensure the SFTB of the disagreement error dynamic system under the proposed SMC strategy. Finally, a multiaircraft system example is provided to demonstrate the correctness and effectiveness of the proposed approach.

This study introduces a novel integrated control and communication framework specifically designed for two-dimensional systems. First, a super-twisting-based second-order sliding mode control strategy is proposed to against uncertainties and effectively eliminate chattering. Second, a self-triggered communication protocol is developed, markedly reducing communication overhead by proactively scheduling transmissions without continuous error monitoring. Third, a dynamic binary encoding strategy is introduced, dynamically adjusting quantization intervals according to real-time historical data, thus improving signal accuracy and reducing quantization errors. Explicit sufficient conditions are derived, guaranteeing both practical reachability of the sliding manifold and the ultimate boundedness of the closed-loop system. Additionally, an optimization framework employing the Grey Wolf Optimizer algorithm is formulated to optimize sliding mode parameters, further reducing the convergence bounds. Comprehensive simulation results illustrate the superior performance and effectiveness of the proposed framework compared to existing methods.

This article investigates the distributed aggregation optimization problem in multiagent systems (MASs), with a particular focus on addressing the aggregation effect commonly encountered in modern engineering and technological applications. In such scenarios, the local objective function of an agent depends not only on its own decision variables but also interacts with the decision variables of other agents, resulting in complex coupling relationships. To solve these challenges while ensuring that the optimization variables satisfy the coupled inequality constraints, this article introduces a novel framework called distributed aggregative parameter projection (DAPP). Specifically, the proposed distributed protocol is based on an improved parameter projection, including two direction updates, which minimizes the cost function and keeps the search direction obeying the inequality at each iteration. In addition, the linear convergence performance of the proposed scheme over the undirected and connected graph is ensured by rigorous theoretical proof with mild assumptions. Finally, simulation results demonstrate the superior performance of DAPP with smaller global function and faster convergence speed in comparison to the existing method.

This article explores the optimal containment control of discrete-time multiagent systems (MASs) with the digraph and unknown dynamics under process disturbances. We first demonstrate, through a model transformation, that the mean square bounded containment of MASs can be achieved by guaranteeing the mean square boundedness of the containment error systems. Hence, we can transform the optimal stochastic containment control problem of MASs into a stochastic optimal control problem for containment error systems. Subsequently, utilizing the Bellman optimality principle and the stochastic Lyapunov equation (SLE), we design a model-based policy iteration (PI) algorithm for the optimal stochastic containment control of MASs. This model-based algorithm, by minimizing the cost function in linear quadratic form, enables MASs to achieve mean square bounded containment with the least possible energy input. To circumvent the dependency on the model information, we introduce an online model-free algorithm for the stochastic optimal control problem. The model-free algorithm is developed based on the Q-learning algorithm. Specifically, it uses a historical MAS trajectory to estimate the kernel matrixHof theQfunction, enabling the resolution of the optimal stochastic containment control problem without model information. To realize the model-free algorithm, the LSTD estimator with bounded bias is employed in the policy evaluation step. We prove the equivalence between the model-free algorithm and the model-based algorithm. Finally, a numerical case is presented to demonstrate the efficacy of the proposed algorithms in achieving the optimal stochastic containment control of MASs.

Accurate detection of leaks in underground water pipelines poses significant challenges due to the complexity of acoustic signals and environmental noise. The deep burial and widespread distribution of water supply pipelines in urban areas make current manual detection methods inefficient and time-consuming. This paper introduces the deep semi-supervised anomaly detection multiscale (DSMS) algorithm, which combines a multi-scale convolutional network with the deep semi-supervised anomaly detection (deep SAD) framework to address these challenges. The multi-scale convolutional network is specifically designed to extract both fine-grained and global acoustic features from Mel frequency cepstral coefficients (MFCCs), enabling effective differentiation between leakage sounds and background noise. Additionally, a pre-training module based on an autoencoder initializes the network weights, improving convergence and increasing the evaluation metric by 27%. The DSMS algorithm achieves state-of-the-art performance, with a test accuracy of 99.14% and a false positive rate of 0.89%. This approach offers a promising solution for efficient and precise leak detection in urban water distribution systems.

This paper investigates the fixed-time sliding mode control (FTSMC) problem for a quadcopter unmanned aerial vehicle (QUAV), which is driven by a lip-reading recognition module. The lip-reading recognition module is consisted of a trained deep neural network with the structure of 2D-Conv+GhostNet+TCN. In order to reduce the communication burden between the remote controller and the QUAV as well as reduce the computation burden in running the lip-reading recognition module, the event-triggered mechanism is introduced to the position controller design. The low-bound of the triggering interval is derived explicitly so that the Zeno phenomenon can be excluded. Furthermore, in order to overcome the main obstacle in high-accuracy control of QUAV, this paper launches a novel wind disturbance rejection approach by using wind field model, which is motivated by the physical dynamic characteristics of the practical wind. Specifically, the wind disturbance is estimated in the designed FTSMC by applying a specific wind field equation with preassigned physical parameters. To further reduce the chattering in the controller, a fitting technique is introduced via a local multivariate linear regression. Finally, both simulation and human-in-the-loop experiment results verify the applicability of the proposed control approach for the lip-reading-driven QUAV system. Note to Practitioners—This research is motivated by the need to design lip-reading-driven QUAV. In noisy environments or when silence is required, the efficiency of traditional human-computer interaction methods such as speech recognition is greatly reduced. Especially for people with damaged vocal cords, speech recognition is not achievable. In addition, it is difficulty to realize high-precision anti-interference control of QUAV with lower computational and communication burdens. In order to solve these problems, this research designs a lip-reading recognition module for QUAV control to cope with various complex application scenarios and realizes high-performance control by FTSMC algorithm. The key of this work to save system resources is to introduce the event-triggered mechanism into the position controller of the QUAV. In addition, this paper introduces the wind field model into the QUAV model to realize the wind disturbance suppression. The lip-reading-driven QUAV proposed in this paper have a wide range of applications, such as controlling QUAV in hazardous environments and improving the efficiency of interaction between human and QUAV.

In this paper, a novel sliding mode control (SMC) method is designed to investigate the finite-time consensus tracking (FTCT) problem of the second-order leader-following multi-agent systems (MASs) with imprecise communication topology of each agent. First, a T-S fuzzy model is introduced to characterize the inexact communication topology of the leader-following MASs. Moreover, a fuzzy SMC law is designed to ensure the reachability of the constructed sliding mode surface (SMS). Meanwhile, in light of the partitioning strategy, sufficient conditions for FTCT of the leader-following MASs are established. It is worth mentioning that the distributed SMC method proposed in this paper is based on the unknown sliding gain, which can greatly reduce the conservatism. Finally, the simulation study on a group of single-link robots and a numerical simulation demonstrate the feasibility of proposed control method. Note to Practitioners—This paper aims to ensure the transient performance and good robustness of second-order multi-agent systems. Generally, the classic nonlinear control method, sliding mode control (SMC), is accompanied by a certain level of conservativeness due to its fixed sliding gain matrix. Moreover, excessive conservatism may prevent the proposed control strategy from being implemented in practical engineering. To address this issue, we formulate an improved finite-time SMC framework to reduce the conservatiness. We also designed an improved algorithm based on genetic algorithm (GA) and linear matrix inequalities (LMIs) to solve the difficulties brought by the new control algorithm, thereby facilitating its practical implementation and enhancing the overall performance of the second-order multi-agent systems.

This paper studies the stabilization problem of two-dimensional (2D) Markov jump systems (MJSs) with directional communication delays, where delays exist in both states and modes. Based on whether the delay mode can be directly observed, the mode-delayed and asynchronous controllers are designed, respectively. For the mode-delayed case, the closed-loop system with current modes and delayed modes is re-planned as a closed-loop 2D MJS. For the asynchronous case, an extended hidden Markov model is developed to describe the asynchronous modes in controllers. Based on the Lyapunov theory, sufficient conditions are derived to ensure the asymptotic mean square stability of the closed-loop 2D MJSs under these two cases. Finally, two different examples from a representative model of some thermal processes are verified in simulations to demonstrate the effectiveness of the designed approaches. Note to Practitioners—2D systems have found extensive applications in thermal processes, gas absorption, and water stream heating, etc. In these applications, sudden changes in parameters and structures are difficult to avoid, which will result in the system being unable to be described. Fortunately, this problem can be handled by the Markov model, which consists of modes and states. In the control problem of 2D MJSs, delayed states are usually considered in the plant. Consider a more practical case that delays exist in directional communication channels between the plant and the controller, resulting in directional delayed modes and states in the controller. In this case, how to handle these complex modes and state information and stabilize the system is of practical significance. Based on whether the delay mode can be directly observed, the mode-delayed and asynchronous controllers are designed, respectively. Finally, two different examples from a representative model of some thermal processes are verified in simulations to demonstrate the effectiveness of the designed approaches.

This paper studies the distributed optimization problem of high-order multi-agent systems with unknown nonlinear terms and input saturation. Unlike existing results, nonlinear functions in the considered system are not required to satisfy the Lipschitz linear growth condition. Moreover, a more general convexity condition is provided for certain local functions, relaxing the traditional strong convexity condition. In addition, the contradiction issue between input saturation and the requirement of a large initial input in existing fixed-time control schemes is handled by constructing an appropriate auxiliary system. In the paper, to begin with, the original optimization problem is transformed into an unconstrained optimization one by constructing a quadratic penalty function. Furthermore, by resorting to fuzzy logic systems with adaptive technique, nonlinear functions in systems are dealt with. And, by the back-stepping method, a distributed fixed-time optimization control strategy based on a penalty function is developed. The proposed controllers can ensure the achievement of the output consensus and the expected optimization objective within a fixed time. Finally, stability analysis and simulation examples are provided to illustrate the effectiveness of the proposed control scheme.

This article investigates a new$H\infty$control method for networked control systems (NCSs) under deception attacks, and applies it to autonomous aerial vehicles (AAVs). First, a novel adaptively event-triggered strategy (AETS) is proposed for reducing the transmitted data packets. Compared with existing ETS, the proposed AETS can adaptively adjust the event-triggered threshold and save limited networked resources in light of weighted average error. Second, a networked closed-loop system model is constructed by considering AETS, deception attacks, and network-induced delays. Then, the expected$H\infty$control performance can be ensured in parallel with saving limited communication resources through the derived stability criterion. Finally, the validity and advantage of the presented approach are proved by a AAV system.

This study addresses the optimal control problem for continuous-time nonlinear systems with unknown dynamics. A policy iterative-based optimization algorithm is proposed to solve this problem by leveraging a novel neural network representation termed multivariable neural network linear differential inclusion (MVNNLDI). MVNNLDI approximates the initial nonlinear model with a linear differential equation formulation that includes bounded disturbances. Based on this linearized representation, the relevant adaptive optimal control and disturbance compensation approach are derived to tackle the nonlinear optimization problem. Capitalizing on model-free control principles, the optimal solutions can be obtained using only measured state and input data, thus simplifying algorithmic complexity and accelerating convergence speed substantially. Finally, we use two simulation experiments to demonstrate the feasibility and effectiveness of the proposed method.

This article investigates the optimal control problem for a class of continuous-time nonlinear Markov jump systems (CTNMJSs), in which an adaptive optimal control policy is developed based on the Takagi–Sugeno (T–S) fuzzy approximation and reinforcement learning (RL) technique. Especially, the original nonlinear system model is first represented in terms of fuzzy rules, and the optimal control problem is transformed into a fuzzy controller design problem for a linear Markov jump fuzzy system without knowledge of the system matrices and input matrices. The policy iteration (PI) algorithm is a powerful RL tool to design adaptive optimal control policy. However, the PI algorithm acquires a stabilizing control policy as its initial policy, which depends extremely on the system dynamics, when system knowledge is unknown, finding an initial stabilizing policy is rather difficult or even impossible. To overcome this shortcoming, in this article, a new off-policy PI-based RL algorithm, i.e., the data-driven homotopic RL (DDHRL), is developed in this article. This DDHRL algorithm improves the traditional PI algorithm, and it is used to obtain the adaptive optimal controller based on the sample data without knowing the system dynamics, and the most significant advantage of the proposed DDHRL is that, by adding a constant sequence, the condition of seeking an initial stabilizing control policy can be avoided. This is in sharp contrast with the traditional PI-based algorithms. The convergence of the DDHRL algorithm is proved, and its feasibility and good performance are validated by simulation examples.

In this work, we address the issue of asynchronous deconvolution filter design for 2-D Markov jump systems with random packet losses. First, the considered plant is established by a well-known Fornasini-Marchesini model. Then, an asynchronous 2-D deconvolution filter is proposed to reconstruct the 2-D signal with measurement noise to satisfy a prescribed performance specification. The asynchronization phenomenon between the system modes and filter modes is characterized by a hidden Markov model. Besides, in practical applications, the congestion of the transmission channel between the system and the filter may lead to data losses, which may make the system performance degraded or even unstable. For this, an improved 2-D single exponential smoothing scheme is proposed to generate some predictions of the lost information to compensate for lost packets. By means of the 2-D Lyapunov stability theory, some sufficient conditions are acquired, which can make the resultant system asymptotic mean-square stable and satisfies an$\mathcal{H}_{\infty}$disturbance attenuation performance. At last, an example concerning image processing is adopted to verify the correctness of the presented asynchronous 2-D deconvolution filtering scheme.Note to Practitioners—In practical applications, many dynamics may suffer from undergoing sudden structural or parameter changes, resulting in a system that is difficult to describe clearly. The Markov jump systems, consisting of states and modes, can handle this problem satisfactorily. Considering the effects of some unfavorable factors, i.e., delay, quantization, and environmental noise, a hidden Markov model is employed to handle the asynchronous problem between the system and the filter. On the other hand, the emergence of 2-D systems effectively solves the problem of the system’s state evolving in two directions. In addition, the congestion of the transmission channel between the system and the filter may lead to data loss. To compensate for the impact of data packet loss, an improved 2-D single exponential smoothing scheme is proposed.

This paper addresses the problem of finite-time decentralized sliding mode control (SMC) for interconnected systems under Round-Robin communication protocol. In this protocol, only one sensor node accesses to the communication network at each transmission moment, and each node accesses to the communication network cyclically. A decentralized token-dependent SMC law is constructed to drive the state trajectories into the sliding domain around the specified sliding surface before the given finite time. Sufficient conditions are derived via the Lyapunov method to ensure finite-time stability of the closed-loop system over the finite-time interval. To tackle the challenges of nonlinear constraint conditions in the SMC design problem, a solving algorithm that combines genetic algorithm (GA) with linear matrix inequality approaches is proposed. Furthermore, a method for selecting the initial values for GA is also presented. Finally, a numerical example and a four areas power system example are given to demonstrate the effectiveness of the developed method.

In this article, we investigate the optimal control problem for semicar active suspension systems (SCASSs). First, we model the SCASSs by Newtonian dynamics as well as considering the uncertainties and nonlinear dynamics of the actuator. Second, in order to solve the complexity brought by uncertainties, we apply the Takagi–Sugeno (T-S) fuzzy approach to transform the SCASSs as multilinear systems, as well as solving the optimal control problem as a zero-sum problem to find the solution of Nash-equilibrium. Third, we construct a novel self-learning method based on the reinforcement learning framework, and propose two algorithms to solve the fuzzy game algebraic Riccati equation. Especially, in the second algorithm, without using any model information of the SCASSs, we only use the state and input information in control design by a self-learning manner removing the traditional dependence problem, which is more preferable for practical applications. Finally, we give a simulation result of the SCASSs to demonstrate the effectiveness and practicability for the designed self-learning algorithms.

In this article, we study the fault detection problem of unmanned surface vehicles through the implementation of fuzzy multiprocessors. By employing the Takagi–Sugeno fuzzy technique, the linear approximation of unmanned surface vehicles is obtained, and a fuzzy multiprocessor architecture is proposed to estimate the state of unmanned surface vehicles. With the residual signal generated by multiprocessors, a detection logic is designed to realize the fault detection. Based on the Lyapunov method, sufficient conditions are given to ensure that the error dynamic system is asymptotically stable and meets the given$H_{\infty }$and$H\_$performance. Assisted by genetic algorithms, a two-step optimization algorithm is proposed to optimize the mixed$H_{\infty }$and$H\_$performance. Finally, case studies are provided to verify the effectiveness and superiority of the proposed method.

This article presents a sliding mode controller to address the challenge of achieving mean-square bounded consensus tracking for leader–follower multiagent systems (MASs) under stochastic denial-of-service (DoS) attacks. Such cyber attacks can reduce the effective transmission of measurement signals by interrupting the communication between the MASs and the control station, thereby corrupting the feasibility of control. Existing descriptions of DoS attacks typically rely on two energy assumptions regarding attack frequency and duration, which introduce conservatism into the stability analysis of the system. Conversely, this article models DoS attacks using a two-mode Markov process, thereby preventing the necessity for explicit energy constraints. To ensure control feasibility under DoS attacks, a control scheme that uses the latest uncontaminated control input signal and uses it as the new primary control input signal until the DoS attack ceases is adopted to mitigate the effects of DoS attacks effectively. Based on the Lyapunov function method, it is shown that the designed sliding mode controller guarantees the reachability and mean-square bounded consensus tracking of the resulting global tracking error dynamic system under Markov-type DoS attacks. At last, the correctness and the effectiveness are verified by a web-based multiple dc motors angle coordinated control experiment.

This article focuses on solving a distributed convex optimization problem of multiagent systems with multiple inequality constraints. Considering communications between agents are prone to failures and not synchronized among themselves in some cases, a novel asynchronous adaptive step sizes-DIGing algorithm is proposed through integrating the projection operators and logic-andframework. In specific, a bilaterally adjustable adaptive step size mechanism is introduced to automatically abandon the irrational evolutionary route which relieves the limitations of traditional DIGing algorithm. By adopting the operator theory, the almost sure convergence of the proposed algorithm under asynchronous communication is proved. Finally, the theoretical and simulation results for the plantwide optimization problem in the ethylene production process illustrate the effectiveness of the proposed algorithm.

This article presents a distributed optimization framework in order to solve the plant-wide energy-saving problem of an ethylene plant. First, the ethylene production process is abstracted into a distributed network, and then, a new distributed consensus algorithm is proposed, which is called adaptive step-size-based distributed proximal consensus algorithm (ASS-DPCA). This algorithm can dynamically adjust the step size and automatically abandon the irrational evolutionary route while eliminating the dependence of optimization algorithms on model gradient information. Moreover, the designed algorithm is able to converge to an optimal solution for any convex cost functions and approach to a convex constraint set of agents over an undirected connected graph. Finally, the results of numerical simulation and industrial experiments show that the algorithm can reduce the total energy consumption of an ethylene plant with less computing time and assured consensus.

This work solves the issue of event-triggered fault detection for networked systems under the denial-of-service (DoS) attacks. To improve the utilization rate of network resources, an event-triggered mechanism is employed to reduce the transmission frequency. A fault detection observer is designed to generate the residual signal for the concerned system with external disturbances and faults. Note that the input signal of the fault detection observer (FDO) transmitted over a communication network is assumed to be vulnerable to cyber attacks, in which the adversaries may interrupt the transmission process. The${\mathcal {H}}_\infty$attenuation index and${\mathcal {H}}_{\_}$increscent index are introduced into the fault detection observer design, which reflects the robustness to external disturbances and sensitivity to faults, respectively. By applying the Lyapunov functional technology, some nonlinear matrix inequalities are acquired to guarantee the existence of the fault detection observer with the appearance of DoS attacks. Then, a genetic algorithm is adopted to cope with the derived nonlinear matrix inequalities without introducing any conservatism. The simulation results related to an unmanned aerial vehicle model are presented to illustrate the correctness and effectiveness of the presented fault detection strategy.

This technique note investigates the problem of path and motion planning for autonomous mobile robots in complex environments. A hybrid algorithm composed of a smoother A-star$(\mathrm{s}-\mathrm{A}^{*})$algorithm for global path planning and a faster & more accurate Dynamic Window Approach (fma-DWA) is proposed. Firstly, three technical tricks are designed based on the standard$\mathrm{A}^{*}$algorithm to ensure the smoothness and safety of the generated paths, which are the improvement of evaluation function, the redundant node deletion strategy and the B-splinebased path smoothing respectively. Then regarding the motion planning phase after path generation, we further advance the traditional DWA to choose a faster and more accurate motion command of the mobile robot. To this end, a composite evaluation function of the DWA is proposed by further considering the curvature of the generated path and the distance between the mobile robot and the target. Besides, the weight of the robot's velocity in the evaluation function is adjusted according to the closest distance to obstacles instead of keeping fixed in the traditional DWA method. Finally, extensive simulation results show the efficiency and accuracy of the proposed methods.

This article concerns the co-design scheme of the adaptive event-triggered mechanism (AETM) and asynchronous$H_{\infty }$control for two-dimensional (2-D) Markov jump systems. First, we introduce a hidden Markov model with the observation that the asynchronous phenomenon is inevitable between the plant mode and the controller mode. Besides, for economizing the communication times, an innovative 2-D AETM is constructed, which can dynamically regulate the event-triggered thresholds to strive for better system performance. Then, by utilizing the 2-D Lyapunov stability theory, nonlinear matrix inequalities are built to ensure the asymptotic mean-square stability with an$H_{\infty }$performance for the closed-loop 2-D system. To avoid introducing any conservatism when handling the above nonlinear matrix inequalities, a binary-based genetic algorithm (BGA) is exploited to treat some variables as known, such that derive some directly solvable linear matrix inequalities. Finally, a simulation example is provided to verify the effectiveness of the proposed 2-D AETM-based asynchronous controller strategy with a BGA.

A novel completely mode-free integral reinforcement learning (CMFIRL)-based iteration algorithm is proposed in this article to compute the two-player zero-sum games and the Nash equilibrium problems, that is, the optimal control policy pairs, for tidal turbine system based on continuous-time Markov jump linear model with exact transition probability and completely unknown dynamics. First, the tidal turbine system is modeled into Markov jump linear systems, followed by a designed subsystem transformation technique to decouple the jumping modes. Then, a completely mode-free reinforcement learning algorithm is employed to address the game-coupled algebraic Riccati equations without using the information of the system dynamics, in order to reach the Nash equilibrium. The learning algorithm includes one iteration loop by updating the control policy and the disturbance policy simultaneously. Also, the exploration signal is added for motivating the system, and the convergence of the CMFIRL iteration algorithm is rigorously proved. Finally, a simulation example is given to illustrate the effectiveness and applicability of the control design approach.

This article investigates the co-design problem of adaptive event-triggered schemes (AETSs) and asynchronous fault detection filter (AFDF) for nonhomogeneous higher-level Markov jump systems, involving the hidden Markov model (HMM), higher-level Markov chain (MC), and conic-type nonlinearities. The transformation of the system transition probability can be reflected by the designed higher-level MC. An HMM with another conditional transition probability is applied to detect higher-level Markov processes and make the system be more practical. In order to balance the utilization of network resources and system performance, a novel AETS is proposed and used in the construction of the AFDF. By the Lyapunov theory, sufficient conditions are given to ensure the existences of the AETS and AFDF. It is not only an appropriate tradeoff between the utilization of network resources and system performance, but also reduces the conservatism. Finally, a numerical example is given to detect the faults effectively by the co-designed AFDF.

In this work, we consider the problem of finite-region asynchronous dissipative control and pay more attention to the transient behavior of a class of two-dimensional fuzzy Markov jump systems (MJSs). First, the considered plant is modeled based on a well-known Fornasini–Marchesini equation. The asynchronization phenomenon between the system modes and controller modes is characterized by a hidden Markov model. Then, by a fuzzy-basis-dependent and mode-dependent Lyapunov function, sufficient conditions are established, which can make the overall closed-loop fuzzy dynamic MJSs be finite-region bounded with a strictly$(T, S, R)$-$\theta $-dissipative performance. Finally, a numerical example concerning the Darboux equation is employed to validate the effectiveness and performance of the presented control scheme.

This article addresses the design issue of fuzzy asynchronous fault detection filter (FAFDF) for a class of nonlinear Markov jump systems by an event-triggered (ET) scheme. The ET scheme can be applied to cut down the transmission times from the system to FAFDF. It is assumed that the system modes cannot be obtained synchronously by the filter, and instead, there is a detector that can measure the estimated modes of the system. The asynchronous phenomenon between the system and the filter is characterized via a hidden Markov model with partly accessible mode detection probabilities. Applying the Lyapunov function methods, sufficient conditions for the presence of FAFDF are obtained. Finally, an application of a wheeled mobile manipulator with hybrid joints is employed to clarify that the devised FAFDF can detect the faults without any incorrect alarm.

In this article, the problem of the asynchronous fault detection (FD) observer design is discussed for 2-D Markov jump systems (MJSs) expressed by a Roesser model. In general, the FD observer cannot work synchronously with the system, that is, the mode of the observer varies with the mode of the system in line with some conditional transitional probabilities. For dealing with this difficult point, a hidden Markov model (HMM) is employed. Then, combining the$H_{\infty }$attenuation index and$H_{\_{}}$increscent index, a multiobjective solution to the FD problem is formed. In terms of linear matrix inequality technology, sufficient conditions are gained to guarantee the existence of the asynchronous FD. Simultaneously, an asynchronous FD algorithm is generated to acquire the optimal performance indices. Finally, a numerical example concerned with the Darboux equation is demonstrated to exhibit the soundness of the developed approach.

This article explores a novel adaptive optimal control strategy for a class of sophisticated discrete-time nonlinear Markov jump systems (DTNMJSs) via Takagi–Sugeno fuzzy models and reinforcement learning (RL) techniques. First, the original nonlinear system model is represented by fuzzy approximation, while the relevant optimal control problem is equivalent to designing fuzzy controllers for linear fuzzy systems with Markov jumping parameters. Subsequently, we derive the fuzzy coupled algebraic Riccati equations for the fuzzy-based discrete-time linear Markov jump systems by using Hamiltonian–Bellman methods. Following this, an online fuzzy optimization algorithm for DTNMJSs as well as the associated equivalence proof is given. Then, a fully model-free off-policy fuzzy RL algorithm is derived with proved convergence for the DTNMJSs without using the information of system dynamics and transition probability. Finally, two simulation examples, respectively, related to the single-link robotic arm and the half-car active suspension are given to verify the effectiveness and good performance of the proposed approach.

Based on the interval type-2 fuzzy (IT2F) approach, this article investigates the fault detection filter design problem for a class of nonhomogeneous higher level Markov jump systems with uncertain transition probabilities. Considering that the mode information of the system cannot be obtained synchronously by the filter, the hidden Markov model can be seen as a detector to handle this asynchronous problem, and the parameter uncertainty can be processed by the IT2F approach with the lower and upper membership functions. Then, the asynchronous IT2F filter is designed to deal with the fault detection problem. Furthermore, the Gaussian transition probability density function is introduced to describe the uncertainty transition probabilities of the system and the filter. Based on the Lyapunov theory, the existence of the designed asynchronous IT2F filter and the dissipativity of the filter error system can be well ensured. In this article, the simulation study on a quarter-car suspension system verifies that the designed asynchronous IT2F filter can detect faults without error alarms.

In this paper, the state estimation problem for singularly perturbed switched complex dynamic networks (CDNs) is addressed, in which the persistent dwell-time (DT) switching mechanism is employed to depict the switchings among parameters of each node. In the aforementioned switching mechanism, the concept of stage consisting of the persistent portion and the DT portion is introduced. Based on the singular perturbation theory, a two-time-scaling variables containing fast and slow states are taken into account on the CDNs simultaneously, which make the constructed networks more realistic. Furthermore, the main aim is to design a mode-dependent estimator to track the state information that cannot be directly obtained and further ensures the global uniform exponential stability of the investigated systems with a generalized dissipativity property. Some sufficient criteria on the existence of the desired mode-dependent state estimator are established by utilizing a modified matrix decoupling method. Finally, the availability of the estimator design procedures is verified by a simulation example.

This article concerns the admissibility analysis of singular time-delay systems. The aim is to get superior criteria in both low conservativeness and less decision variables. To this end, the systems are decomposed and treated with different methods. The Lyapunov–Krasovskii functionals (LKFs) are constructed based on the state decomposition. By using tighter integral inequalities and the free weighting matrix method, some new admissibility criteria are derived to compare with the previous results, and are applied to the partial element equivalent circuits (PEECs) in numerical examples, by which the advantages of the proposed method are verified.

Abstract The authors investigates the problem of fuzzy fault detection filter (FFDF) design for a class of discrete‐time conic‐type nonlinear systems. By applying Takagi–Sugeno fuzzy models, the conic‐type dynamic FFDF system is established. Then, utilizing the Lyapunov function method to find a sufficient condition which ensures that the conic‐type dynamic FFDF system is asymptotically stable. After that, using linear matrix inequalities techniques, the FFDF design problem is transformed into an optimization algorithm. Finally, the simulation results demonstrate that the designed FFDF is effective for detecting the faults.

This article investigates the finite-time asynchronous control problem for continuous-time positive hidden Markov jump systems (HMJSs) by using the Takagi-Sugeno fuzzy model method. Different from the existing methods, the Markov jump systems under consideration are considered with the hidden Markov model in the continuous-time case, that is, the Markov model consists of the hidden state and the observed state. We aim to derive a suitable controller that depends on the observation mode which makes the closed-loop fuzzy HMJSs be stochastically finite-time bounded and positive, and fulfill the given L2performance index. Applying the stochastic Lyapunov-Krasovskii functional (SLKF) methods, we establish sufficient conditions to obtain the finite-time state-feedback controller. Finally, a Lotka- Volterra population model is used to show the feasibility and validity of the main results.

This article focuses on the finite-time asynchronous output feedback control scheme for a class of Markov jump systems subject to external disturbances and nonlinearities. The conic-type nonlinearities hold a constraint condition which locates in a known hyper-sphere with an indefinite center. In addition, the asynchronization phenomenon occurs between the system and the controller, which can be represented by means of a hidden Markov model. A sufficient condition is derived not only to guarantee the finite-time boundedness of the acquired closed-loop systems but also to possess a desired$H_{\infty }$performance on the basis of Lyapunov functional technique. Finally, the validity and feasibility of the proposed method are demonstrated with a dc-motor experiment.

This paper studies the H∞sliding mode control (SMC) problem for a class of discrete-time conictype nonlinear systems with time-delays and uncertainties. The nonlinear terms satisfy the conic-type constraint condition that lies in a know hyper-sphere with an uncertain center. By choosing a proper Lyapunov candidate, sufficient conditions are derived to ensure the asymptotic stability of the sliding mode dynamics while achieving a prescribed H∞disturbance attenuation level and finally converted into a minimization problem. The controller is constructed to guarantee the discrete-time reach condition and maintain the states on the prespecified sliding surface. A simulation result and a practical example related to the Chua's circuit are given at last to show the validity of our SMC strategy.

This paper is concerned with the sliding mode control (SMC) via finite-time stabilization (FTS) for a class of conic-type nonlinear semi-Markovian jumping systems (SMJSs). Comparing with the classical Markovian jumping systems, the transition rates of SMJSs are related to the random sojourn-time g. Based on this, a suitable SMC law for driving the state trajectories to the designed sliding surface within a finite-time interval is given. Then, the FTS over reaching phase and sliding motion phase is further proved to guarantee the FTS of the whole SMJSs. Finally, the effectiveness of the proposed method is demonstrated by a time-delayed Chua's circuit simulation.

This article study the asynchronous control problem for a class of discrete-time Markovian jumping Lur’e systems (MJLSs) over the finite-time interval. The partial accessibility of system modes with respect to the designed controller is described by a hidden Markov model (HMM). The asynchronous control law consists of two parts, i.e., the states and the nonlinearities involved in the dynamics of the controlled system. By selecting the appropriate Lyapunov functional and applying the modified sector condition, the finite-time stabilization conditions under the control constraints are derived. Finally, the effectiveness of the designed method is verified by an illustrative simulation.

To solve the problem of high‐order moment Gaussian distribution (HGD) noise in state estimation, a fusion filter for Markov jump linear systems (MJLSs) with high‐order moment information obtained from sensor data is designed. To obtain high‐order moment information, the multi‐sensor MJLS is converted to a single‐mode system composed of high‐order moment components by using a cumulant generating function. Next, a filter design based on Bayesian theory is established to achieve state estimation with a high‐order moment information form according to the transformed single‐mode deterministic system. Subsequently, a high‐order moment fusion technique based on entropy theory is proposed to obtain a more accurate estimation result of the state by using the high‐order moment information obtained from various sensors. Comparing the first‐ and second‐order moment information obtained by traditional Gaussian distribution, the HGD introduces higher‐order moment information and makes the fusion process more reasonable. In this way, a more precise and reasonable performance of the state estimation is achieved, depending on the sensor fusion technique. To confirm the effectiveness and advantages of the proposed method, a numerical simulation example is provided with various fusion methods. Thus, the performance of the proposed fusion filter design is verified.

This paper studies the online adaptive optimal controller design for a class of nonlinear systems through a novel policy iteration (PI) algorithm. By using the technique of neural network linear differential inclusion (LDI) to linearize the nonlinear terms in each iteration, the optimal law for controller design can be solved through the relevant algebraic Riccati equation (ARE) without using the system internal parameters. Based on PI approach, the adaptive optimal control algorithm is developed with the online linearization and the two-step iteration, i.e., policy evaluation and policy improvement. The convergence of the proposed PI algorithm is also proved. Finally, two numerical examples are given to illustrate the effectiveness and applicability of the proposed method.

This paper investigates the asynchronous resilient controller design problem for a class of nonlinear switched systems with time-delays and uncertainties in a given finite-time interval. By constructing proper multiple Lyapunov-Krasovskii functions and applying average dwell time methods, a switching law and the relevant asynchronous resilient controller are designed to guarantee the finite-time boundedness of the closedloop system with a specified H∞performance index. The H∞resilient controller design problems can be derived by solving a set of linear matrix inequalities. A practical example is employed to demonstrate the availability of the proposed methods.

The finite-time sliding mode controller design problem of a class of conic-type nonlinear systems with time-delays and mismatched external disturbance is studied. The time-delay conic nonlinearities are considered to lie in a known hypersphere with an uncertain center. A scalar selection criterion dependent sliding mode control (SMC) law is constructed to drive the state trajectories onto the specified sliding surface during any assigned short time interval. By using slack matrix approach, a delay-dependent sufficient condition is derived to ensure the finite-time boundedness of the closed-loop systems over the finite-time interval. Then, the algorithm for designing the finite-time SMC law is established. Finally, two examples related to the time-delayed Chua's circuit is given to demonstrate the effectiveness of the developed methods.

Similarity learning ranges over an extensive field in machine learning and pattern recognition. This paper deals with similarity learning based on multiple support vector data description (SVDD). It is well known that SVDD was proposed for one-class or two-class unbalanced learning problems. Thus, we propose a multiple SVDD (MSVDD) algorithm and apply it to multi-class learning problems. A SVDD model is trained by similar pairwise samples in the same class instead of all similar ones. In addition, the dissimilar pairwise samples are not considered in MSVDD. Experimental results validate that MSVDD is promising in similarity learning.

The idea that with the help of proper dimensionality reduction, trying to make the samples with the same label be compact and the ones with the different labels be separate after projection, is introduced into classification problems with high-dimensional data. Based on the analysis of the drawbacks of Discriminant Neighborhood Embedding (DNE) and Locality-Based Discriminant Neighborhood Embedding (LDNE), being the two relatively successful Locally Discriminant Analysis methods proposed in recent years, this paper proposes a method called Similarity-balanced Discriminant Neighborhood Embedding (SBDNE). When constructing the adjacent graph, SBDNE fully takes into account the geometric construction of manifold and the problem of imbalance between the intra-class points and the inter-class points. By endowing these two kinds of samples with different similarities and selecting the near neighbors according to the similarity matrix, not only the structure in the original space can be preserved more efficiently, but also the choice of discriminative information increases. The method proposed here has a better recognition with comparisons to some classical methods, which fully shows that SBDNE method has the capacity to efficiently solve the classification problem.

This paper studies the finite-timeH∞control problem for time-delay nonlinear jump systems via dynamic observer-based state feedback by the fuzzy Lyapunov-Krasovskii functional approach. The Takagi-Sugeno (T-S) fuzzy model is first employed to represent the presented nonlinear Markov jump systems (MJSs) with time delays. Based on the selected Lyapunov-Krasovskii functional, the observer-based state feedback controller is constructed to derive a sufficient condition such that the closed-loop fuzzy MJSs is finite-time bounded and satisfies a prescribed level ofH∞disturbance attenuation in a finite time interval. Then, in terms of linear matrix inequality (LMIs) techniques, the sufficient condition on the existence of the finite-timeH∞fuzzy observer-based controller is presented and proved. The controller and observer can be obtained directly by using the existing LMIs optimization techniques. Finally, a numerical example is given to illustrate the effectiveness of the proposed design approach.