This article investigates the smooth control problem of switched fuzzy systems, where the modes asynchronously switch under a partly stochastic sojourn time (PSST) switching signal, i.e., a duration of the sojourn time is governed by a random distribution. The formulated PSST switching signal is composed of a mode-dependent activated time and a duration subject to certain stochastic processes, which covers the conventional (average) dwell time (DT) switching signals or stochastic switching signals as special cases. Considering the measuring and computing delay in mode and membership degree identifying of the fuzzy switched systems, the asynchronous phenomena caused by unmatched case between control and system modes are included in the PSST switching signal, and a detected-mode-based Lyapunov candidate is formulated for the mean-square stability (MSS) and robustness analysis, which has not been considered before. To overcome the undesired control bump between adjacent modes, a multistage membership degree interpolation approach is proposed to obtain a smooth control transition after the asynchronous duration to carry out an anti-asynchronously stochastically smoothly switched fuzzy controller (A2S3-FC), unlike the existing literature that only considers part of the property of the practical systems. The effectiveness and the advantages of the proposed A2S3-FC are verified via a numerical example and a simulation of aerial manipulator attitude control.

This paper studies the issue of switched data driven model predictive control (MPC) for a class of hybrid nonlinear systems with modal dwell time (MDT) restriction, where each subsystem is approximated by a T-S fuzzy system with bounded uncertainties. The unknown system matrices are characterized by a quadratic-matrix-inequality representation using the input-state-membership data. On this basis, a numerically tractable semi-definite programming (SDP) problem is formulated to design fuzzy-dependent feedback control law in a receding horizon manner for each switched mode, resulting in the optimization of worst-case infinite-horizon performance cost. Utilizing a set of feasible solutions of the constructed SDP problem, a feasible region and the corresponding approximated reachable set are deduced for each subsystem, based on which an algorithm is proposed to determine an admissible MDT ensuring the persistent feasibility of the switched data-driven MPC and the robust stability of the closed-loop system. The validity and potential of the theoretical results are illustrated through numerical applications to a single-link robot arm and a class of tail-sitter vertical take-off and landing unmanned air vehicles.

This article is concerned with stability analysis and observer-based control synthesis for a class of discrete-time switched linear systems with limited statistical information. Instead of commonly studied switching signals such as dwell-time (DT) or Markov chain, a more general class of switching signals, random mode-dependent persistent sojourn-time (RMPST) switching, is investigated. It is composed of fixed parts with no mode switching, random parts without distribution restrictions, and intervals where arbitrary switching is allowed. To tackle the challenges posed by inaccessible modes, unknown transition probabilities, and partially unknown sojourn-time distribution, stability analysis is performed via constructing a Lyapunov function tailored to accommodate the characteristics of RMPST switching. The proposed Lyapunov function is not only mode-dependent but also elapsed-time and quasi-time dependent during fixed-random parts and arbitrary switching intervals, respectively. By means of the new Lyapunov function, an observer-based control approach is introduced for underlying switched systems, and criteria for the existence of observers and controllers are established with the set of admissible switching signals. A novel algorithm is also developed to solve the existence condition by extending the traditional cone complementary linearization (CCL) technique. The effectiveness and applicability of the theoretical results are revealed by a practical space robot manipulator system.

Artificial intelligence techniques have been extensively employed in anomaly detection tasks for massive systems and equipment across numerous industries, achieving notable success. Nevertheless, classical machine learning methods typically possess a simplistic design, which occasionally fails to satisfy the detection demands of minor anomalies in certain complex tasks. Conversely, the interpretability of deep learning methods is often insufficient to convince domain experts and operators. Consequently, achieving a balance between detection accuracy and interpretability remains a critical and often conflicting challenge. This article proposes an expert experience-based algorithm unfolding (EAU) network and a conditional canonical correlation analysis (CCCA) theory for anomaly detection of spacecraft under multiple operating conditions, aiming to ensure detection accuracy while enhancing interpretability. First, the EAU network integrates the experiential knowledge with the Lasso regression model and employs sparse coding to iteratively expand it layer by layer (LbL), facilitating deep feature extraction from the original telemetry data. Second, the CCCA method formulates the residual vector on the premise that the correlation between the regularization components of the input and output sets will change markedly before and after the anomaly appears. It subsequently compares the HotellingT2statistic of each sample with the detection threshold, which was constructed based on the kernel density estimation (KDE) method, to discover the evolution of the anomaly. Finally, multigroup comparisons on two simulations and two real satellite-telemetry datasets verify the superior overall performance of the proposed method and provide guidance for selecting anomaly detection approaches.

This paper addresses the problem of the model predictive control (MPC) for a class of T–S fuzzy Markovian jump systems with bounded inputs and states. Two scenarios are considered, which are often overlooked in existing literature but common in practice: (i) asynchronous switching between the observed mode and the actual mode, and (ii) the excessive bump of system states. By virtue of the emission probability, the underlying systems are modeled as hidden Markovian jump systems to describe the asynchrony phenomenon. The time-varying emission probabilities are transformed into the ones in a more tractable form. Moreover, the bumpless transfer MPC (BT-MPC) is introduced to tackle the state bump phenomenon. An asynchronous fuzzy controller is obtained by the receding-horizon optimization, while ensuring recursive feasibility and stochastic stability. Besides, by a saturated state feedback scheme, the control law exploits the full control range with less conservatism compared with the case of directly dealing with control constraints. Two simulation examples are provided to demonstrate the effectiveness and advantages of the proposed control approach.Note to Practitioners—As a special hybrid system, Markovian jump systems can effectively describe the randomness of multi-modal systems. However, both the asynchrony phenomenon and hard constraints in the actual process reduce the range of existing control strategies. In addition, bumpless transfer performance is significant in many practical engineering systems. In this paper, a novel methodology is proposed to design bumpless transfer control for the fuzzy hidden Markovian jump system with time-varying emission probabilities. The proposed approach can handle both multiple control objectives and system constraints under the framework of BT-MPC. The proposed theoretical result is of a convex optimization characterization, which can be implemented algorithmically by means of the advanced and proven convex optimization toolbox. Finally, both a numerical example and a practical example of a robot arm are presented to demonstrate the effectiveness and great application prospect of the proposed approach.

This paper investigates the problem of control synthesis for a class of continuous-time singular switched systems, based on which a modeling and control framework is developed for Hybrid Terrestrial and Aerial Quadrotors (HyTAQs). To accurately describe the frequent switching phenomenon for HyTAQs during the landing process, persistent dwell time (PDT) switching signals are taken into consideration. Besides, mode-dependent state constraints are introduced to singular switched systems, which are more practical and general than the ones considered in earlier studies. Numerically testable stability criteria are obtained, and thereby the conditions on the existence of controllers capable of satisfying the mode-dependent state constraints are derived. Finally, two illustrative examples including HyTAQs are presented to demonstrate the necessity and validity of the designed controllers. Note to Practitioners—This paper is motivated by the modeling and stabilization of Hybrid Terrestrial and Aerial Quadrotors (HyTAQs), a class of vehicles with both terrestrial and aerial mobility, which have received extensive attention due to the low energy requirement and high maneuverability at the same takeoff weight. Note that the dynamic differences between aerial and terrestrial modes, exacerbate the challenges for performance analysis and controller design, let alone the differences in state dimensions. Therefore, the paper proposes a unified framework for modeling HyTAQs based on singular switched systems, and the mode-dependent state constraints are considered to reduce conservatism. In addition, the frequent switching phenomenon during the landing process has never been considered in the field. To this end, the PDT switching signals are introduced to describe the bounce and shake behaviors during the mode switching process. Numerically testable stability criteria are obtained, based on which the stabilization conditions for such systems are provided. Finally, a numerical example and an application for HyTAQs are presented to demonstrate the validity and applicability of the theoretical results.

Existing modeling and control methods for real-world systems typically deal with uncertainty and nonlinearity on a case-by-case basis. We present a universal and robust control framework for the general class of uncertain nonlinear systems. Our data-driven deep stochastic Koopman operator (DeSKO) model and robust learning control framework guarantee robust stability. DeSKO learns the uncertainty of dynamical systems by inferring a distribution of observables. The inferred distribution is used in our robust and stabilizing closed-loop controller for dynamical systems. We also develop a model predictive control framework with integral action to compensate for run-time parametric uncertainty, such as manipulating unknown objects. Modeling and control experiments in simulation show that our presented framework is more robust and scalable for robotic systems than state-of-the-art controllers using deep Koopman operators and reinforcement learning (RL) methods. We demonstrate that our method resists previously unseen uncertainties, such as external disturbances, at a magnitude of up to five times the maximum control input. Furthermore, we test our DeSKO-based control framework on a real-world soft robotic arm. It shows that our framework outperforms model-based controllers that have full knowledge of the model parameters, and the controller can conduct object pick-and-place tasks without further training. Our approach opens up new possibilities in robustly managing internal or external uncertainty while controlling high-dimensional nonlinear systems in a learning framework. This approach serves as a foundation to greatly simplify high-level control and decision-making for robots.

Model-free reinforcement learning (RL) is inherently a reactive method, operating under the assumption that it starts with no prior knowledge of the system and entirely depends on trial-and-error for learning. This approach faces several challenges, such as poor sample efficiency, generalization, and the need for well-designed reward functions to guide learning effectively. On the other hand, controllers based on complete system dynamics do not require data. This paper addresses the intermediate situation where there is not enough model information for complete controller design, but there is enough to suggest that a model-free approach is not the best approach either. By carefully decoupling known and unknown information about the system dynamics, we obtain an embedded controller guided by our partial model and thus improve the learning efficiency of an RL-enhanced approach. A modular design allows us to deploy mainstream RL algorithms to refine the policy. Simulation results show that our method significantly improves sample efficiency compared with standard RL methods on continuous control tasks, and also offers enhanced performance over traditional control approaches. Experiments on a real ground vehicle also validate the performance of our method, including generalization and robustness.

This article is concerned with the switched control of hybrid terrestrial and aerial quadrotors (HyTAQs) via stochastic hybrid fuzzy system methodology, in which the terrestrial and aerial mode switching is subject to a Markov process with lower-bounded sojourn time. For the first time, the bimodal nonlinear attitude dynamics of HyTAQs is analyzed and modeled based on the Takagi-Sugeno (T-S) fuzzy model, and switched fuzzy controllers are developed to stabilize the hybrid fuzzy system. The characteristic of state dimension switching caused by ground contact is modeled via the singular system presentation with mode-dependent singularity matrices, based on which numerically testable criteria of stability and stabilization in the stochastic sense are derived. Compared with the previous control approaches based on Markov jump systems, the proposed one is able to describe the deterministic dwelling duration in practice and integrate multiple subsystems with algebraic equations of different dimensions, while achieving lower conservatism. Illustrative examples are provided to demonstrate the effectiveness and potential of the designed variable-dimension fuzzy controllers.

This article is concerned with a class of discrete-time hybrid fuzzy systems subject to semi-Markov switching, in which the sojourn time of each mode is with upper and lower bounds. A practical scenario oftransitional asynchronyis taken into account for the first time, where the switchings of controllers to be designed lag behind the ones of the controlled plant, and the lags depend on the transition between adjacent modes. By means of the semi-Markov kernel approach, numerically testable stability criteria are obtained, based on which existence conditions of the anticipated stabilizing controller capable of overcoming the transitional asynchrony are derived. Compared with the previous studies assuming the mode-independent or mode-dependent lags, the derived results are less conservative. Two illustrative examples including a class of bicopters are given to demonstrate the effectiveness and potential of the designed anti-transitional-asynchrony controllers.

In this study, a topology-guided unified nonlinear model predictive control (NMPC) approach is proposed for autonomous navigation of a class of Hybrid Terrestrial and Aerial Quadrotors (HyTAQs) in unknown environments. The approach can fully exploit the hybrid terrestrial-aerial locomotion of the vehicle and as such ensure a high navigation efficiency. A unified terrestrial-aerial NMPC is first formulated with a type of complementarity constraints involving the hybrid dynamics, together with the collision avoidance constraints for safety. Further, a topological roadmap with both terrestrial and aerial paths is leveraged to guide the kinodynamic path searching and thus the unified NMPC. Then, a complete and distinctive navigation framework is established and validated on our self-developed HyTAQ. Compared with the existing unified terrestrial-aerial planning methods, ours takes the vehicle dynamics into account for the first attempt and achieves a more reasonable decision of modes switching. Experimental results are presented to demonstrate the effectiveness and superiority of the proposed approach.

This article investigates the state estimation issue of discrete-time Takagi–Sugeno fuzzy Markov jump affine systems that cover both traditional fuzzy Markov jump systems and fuzzy affine systems as two special cases. The original system is transformed into a Markov jump piecewise-affine system that varies with different operating regions. The fuzzy rules are dependent on the system mode, and accordingly, the partition of the state space is mode dependent. To analyze the stochastic stability of the estimation error system, a novel mode-dependent piecewise Lyapunov function is constructed, in which the region indices of both the plant state and the estimator state are mode dependent. Then, the existence for a simultaneously mode-dependent and region-dependent fuzzy affine estimator is investigated by virtue of the$\mathcal {S}$-procedure and ellipsoidal outer approximation such that the estimation error system is stochastically stable with a prescribed$\mathcal { H}_{\infty }$performance. In the end, an illustrative example of a tunnel diode circuit is adopted to showcase the effectiveness and practicability of the developed state estimation strategy.

This article investigates the stochastic stability analysis and stabilization problems for discrete-time Takagi–Sugeno fuzzy semi-Markov jump systems with upper-bounded sojourn time. The fuzzy rules can be different for different system modes. Consequently, the membership functions for fuzzy rules are dependent on the system modes. Allowing for the fact that semi-Markov kernel (SMK) are difficult to fully obtain in practice, the elements in the SMK of the underlying systems are deemed to be partly known, which is more general than both semi-Markov jump systems with completely available SMK and Markov jump systems with unknown transition probabilities. Afterward, the stability and stabilization conditions are established by part of the known SMK information and then by all the known SMK information. In the end, the validity and the superiority of our proposed theoretical results are exemplified via a single-link robot arm and a truck-trailer model.

Reinforcement learning with safety constraints is promising for autonomous vehicles, of which various failures may result in disastrous losses. In general, a safe policy is trained by constrained optimization algorithms, in which the average constraint return as a function of states and actions should be lower than a predefined bound. However, most existing safe learning-based algorithms capture states via multiple high-precision sensors, which complicates the hardware systems and is power-consuming. This article is focused on safe motion planning with the stability guarantee for autonomous vehicles with limited size and power. To this end, the risk-identification method and the Lyapunov function are integrated with the well-known soft actor-critic (SAC) algorithm. By borrowing the concept of Lyapunov functions in the control theory, the learned policy can theoretically guarantee that the state trajectory always stays in a safe area. A novel risk-sensitive learning-based algorithm with the stability guarantee is proposed to train policies for the motion planning of autonomous vehicles. The learned policy is implemented on a differential drive vehicle in a simulation environment. The experimental results show that the proposed algorithm achieves a higher success rate than the SAC.

This article proposes a novelH∞state estimation scheme for a family of Takagi-Sugeno fuzzy semi-Markov jump systems with indirectly accessible mode information and nonideal data transmission. To address estimation of the indirectly accessible modes, the observed-mode sequence emitted by emission probabilities is utilized in this article. By extending the classic Lyapunov stability theory, a set of novel convex stability criteria is proposed by eliminating the nonconvex terms in stabilization conditions with the aid of certain techniques. The proposed stability criteria are utilized to ensure theH∞performance of the studied fuzzy systems. In addition, numerically checkable conditions on the existence of a fuzzy observed-mode-dependent estimator are formulated to guarantee the σ-error mean square stability of the underlying error system with a guaranteedH∞disturbance attenuation level. The developed theoretical results are illustrated by an application of a single-link robotic arm.

This paper investigates the stability analysis and fuzzy control problems for a class of discrete-time fuzzy systems with hidden semi-Markov stochastic uncertainties. The nonlinear plant is described via the Takagi-Sugeno (T-S) fuzzy model, and the parameter uncertainties are represented by a hidden semi-Markov chain. Owing to the semi-Markov kernel (SMK), the probability density functions (PDFs) of sojourn time for different modes in describing the stochastic uncertainties can address different types of distributions according to different target modes. A novel Lyapunov function that depends on the hidden mode and the observed mode with the elapsed time is proposed to analyze the stability and the H∞performance of the fuzzy system. Then, the sufficient criteria for the elapsed time-dependent and observed-mode-dependent fuzzy controller are achieved by exploiting the observations of hidden modes, ensuring that the closed-loop system is σ-error mean square stable with guaranteed H∞performance. A cart-pendulum system is used to demonstrate the effectiveness and applicability of the proposed theoretical results.

The Spring-Loaded Inverted Pendulum (SLIP) has been widely exploited in both biomechanical and robotics research due to its simple form in mathematics and high accuracy in fitting experimental biology data. However the intrinsic nonlinearity of the SLIP dynamics makes accurate analytical representation unavailable. Traditional methods take advantage of numerical integration to handle this issue while several existing analytical approximations focusing on 2D-SLIP model. The 3D-SLIP suitable to physical reality is rarely investigated. This paper presents a novel perturbation-based approach to obtain the closed-form analytical approximations of the 3D-SLIP model in stance phase. In contrast to existing work ignoring the gravitational forces, the proposed approach just relies on assumptions of small leg compression and small leg swept angle. The performance of the derived approximations has been evaluated via comprehensive numerical analysis. The quality of accurate apex prediction promises the approximation as an advantageous and reliable tool for locomotion control of legged robots.

Vision-based detection and estimation technologies play crucial roles in target tracking for autonomous vehicles. However, for vision system it is difficult to continuously collect the image information due to occlusion, feature matching failure, etc. This paper is concerned with the target tracking problem for a Mecanum wheeled mobile robot (MWMR) with camera in the presence of intermittent image losses. A switching scheme with two control strategies has been developed to deal with the problem. During the period when the target image is acquired, a stabilizing controller is designed to ensure the convergence of tracking error. When the target image is lost, a predictor is used to provide the controller with estimated tracking error, by which a regular graphic region containing the real position of the target is determined. The stability condition for the tracking system with minimum dwell-time during which the image is acquired has been obtained utilizing the region. Simulation results verify the effectiveness of the proposed methods.

This paper addresses robust adaptive stabilization of uncertain switched time-delay systems with unknown disturbances in a high-order form. In addition to parametric uncertainty, an extra source of uncertainty arises from having time-varying delays. In particular, the upper bound of the changing rate of the delay is assumed to be unknown. To this purpose, a new reparametrization method is proposed, which incorporates both sources of uncertainty in a single scalar parameter. Therefore, a single scalar adaptive law is designed for parametric uncertainties and time-varying delays, which is combined with a new dynamic gain embedded in the control action. The new dynamic gain is designed to dominate the nonlinear effects caused by both the parametric uncertainty and the unknown variation of the time delay. The proposed design guarantees global asymptotic stability for arbitrary switching. A numerical example illustrates the effectiveness of the method.

This paper investigates the problem of event-triggered control for a class of fuzzy Markov jump systems with general switching policies. A novel event-triggered scheme is proposed to improve the transmission efficiency at each sampling instance. Each transition rate allows to be unknown, known, or only its uncertain domains value is known. With the help of a tailored technique to bind the uncertain terms and an asynchronous operation approach to tackle the fuzzy system and fuzzy controller, sufficient conditions for the resulting fuzzy Markovian jump systems are established in terms of coupled linear matrix inequalities. Finally, an example is given to illustrate the validity of the developed technique.

This paper addresses a distributed estimator design problem for linear systems deployed over sensor networks within a multiple communication channels (MCCs) framework. A practical scenario is taken into account such that the channel used for communication can be switched and the switching is governed by a Markov chain. With the existence of communicational imperfections and external disturbances, an estimation algorithm is proposed such that the developed distributed estimators are able to give accurate state estimates against the channel switching phenomenon. The distributed estimation framework is applied to a chemical process to illustrate the effectiveness of the proposed methodology and the superiority of the MCCs framework featured by channel switching.

This paper is concerned with the problem of stabilization and tracking for a class of underactuated systems subjected to external disturbances. Based on the mathematical model of a 4 degrees of freedom (4DOF) ball and plate system, a robust backstepping controller with disturbance rejection is developed. The proposed controller is capable of handling bounded uncertainties with unknown periodicity affecting the control. A comprehensive comparison between linear quadratic regulator (LQR) and the robust backstepping controller is provided, which affirms the superior performance of the proposed control design.

Wireless bilateral control based haptic teleoperation is a potential and challenging technique that extends human beings' sensing to a remote environment. However, communication delay in a bilateral control system directly influences the performance and even produces instability. This paper presents a stable and high performance bilateral control with a switching algorithm. This proposal uses the reaction force from the slave robot as a beacon to determine whether the robot touches an environment object. Accordingly, the bilateral control algorithm is switched between a force-type three-channel control and a position-type three-channel control to improve the transparency. The stability is guaranteed by designing a passive system. In the proposal, a new idea of trade-off is proposed that guarantees the system stability and sacrifices the operational experience (transparency) only in the moment of contact. During the other operation time, the transparency in the states of free motion and contact is effectively improved. The proposed method is verified by experiments.

The paper is concerned with the robust finite-time stabilization problem of quadrotors subject to inertia uncertainty and disturbances. The underlying stabilization problem consists of controller designs for position loop and attitude loop, both of which are carried out based on the terminal sliding mode control approach. First, a robust finite-time position controller is designed by considering the bound of disturbances such that the position control loop can be stabilized in finite time. As a consequence, the thrust for altitude stabilization, as well as the desired attitude angles for tracking of the attitude loop are determined. Then, attitude tracking control is realized by further combining robust adaptive control strategy to achieve finite-time attitude tracking with nonlinearities of uncertain inertia and disturbances tackled by an introduced adaption term. A numerical example is presented to verify the proposed control scheme.

This paper is concerned with the problem of asynchronous H∞filtering for a class of discrete-time Takagi-Sugeno fuzzy affine systems against time-varying signal transmission delays and measurement quantization. The asynchrony refers to the situation that the plant state and the filter state belong to different local state space regions, and the quantization density can be adjusted to satisfy different performance requirements at different time instants. By transforming the filtering error system into an input-output form consisting of two interconnected subsystems, sufficient conditions on the existence of the desired asynchronous filter are established via the scaled small gain theorem to ensure that the closed-loop system is asymptotically stable with a prescribed H∞performance index with the aid of a novel piecewise Lyapunov-Krasovskii functional and the S-procedure approach. Finally, a practical example of cart-pendulum with a modified model is provided to illustrate the effectiveness of the obtained theoretical results.

This paper is concerned with the problem of robust filter design for a class of discrete-time networked nonlinear systems. The Takagi-Sugeno fuzzy model is employed to represent the underlying nonlinear dynamics. A multi-channel communication scheme that involves a channel switching phenomenon described by a Markov chain is proposed for data transmission. Two typical communication imperfections, network-induced time-varying delays and packet dropouts are considered in each channel. The objective of this paper is to design an admissible filter such that the filter error system is stochastically stable and ensures a prescribed disturbance attenuation level bound. Based on the Lyapunov-Krasovskii functional method and matrix inequality techniques, sufficient conditions on the existence of the desired filter are obtained. A numerical example is provided to illustrate the effectiveness of the proposed design approach.

In this paper, the state estimation problem for a class of discrete-time switched neural networks with modal persistent dwell time (MPDT) switching and mixed time delays is investigated. The considered switching law, not only generalizes the commonly studied dwell-time (DT) and average DT (ADT) switchings, but also further attaches mode-dependency to the persistent DT (PDT) switching that is shown to be more general. Multiple communication channels, which include one primary channel and multiredundant channels, are considered to coexist for the state estimation of underlying switched neural networks. The desired mode-dependent filters are designed such that the resulting filtering error system is exponentially mean-square stable with a guaranteed nonweighted generalized 112 performance index. It is verified that better filtering performance index can be achieved as the number of channels to be used increases. The potential and effectiveness of the developed theoretical results are demonstrated via a numerical example.

This paper is concerned with the problem of extended dissipativity-based state estimation for discrete-time Markov jump neural networks (NNs), where the variation of the piecewise time-varying transition probabilities of Markov chain is subject to a set of switching signals satisfying an average dwell-time property. The communication links between the NNs and the estimator are assumed to be imperfect, where the phenomena of signal quantization and data packet dropouts occur simultaneously. The aim of this paper is to contribute with a Markov switching estimator design method, which ensures that the resulting error system is extended stochastically dissipative, in the simultaneous presences of packet dropouts and signal quantization stemmed from unreliable communication links. Sufficient conditions for the solvability of such a problem are established. Based on the derived conditions, an explicit expression of the desired Markov switching estimator is presented. Finally, two illustrated examples are given to show the effectiveness of the proposed design method.

The global asymptotic stabilization problem is investigated for a class of stochastic nonlinear time-varying delay systems under the weaker condition on nonlinear functions. The new parameter-dependent state and output feedback controllers are, respectively, proposed. Based on the stochastic time-delay system stability criterion, by tactfully introducing a suitable Lyapunov-Krasovskii functional, the globally asymptotically stable in probability of the closed-loop system is guaranteed by rigorous proof. As a practical application, the stochastic model of a two-stage chemical reactor system is established by reasonably introducing the Gaussian white noise. The developed approach is applied to the control design for this practical system. The simulation results demonstrate the efficiency of the proposed design approach.

In this work, we investigate a state estimation problem for a full-car semi-active suspension system. To account for the complex calculation and optimization problems, a vehicle-to-cloud-to-vehicle (V2C2V) scheme is utilized. Moving horizon estimation is introduced for the state estimation system design. All the optimization problems are solved in a remotely-embedded agent with high computational ability. Measurements and state estimates are transmitted between the vehicle and the remote agent via networked communication channels. The effectiveness of the proposed method is illustrated via a set of simulations.

It is expected that the use of cognitive radio for smart grid communication will be indispensable in near future. Recently, RPL for cognitive radio enabled Advanced Metering Infrastructure (AMI) networks is attractive. Our objective in this paper is to propose an enhance RPL to improve efficiency and reliability of cognitive radio enabled AMI networks. Our protocol is receiver-based in nature, which can achieve better reliability of the network along with protecting the primary users as well as meeting the utility requirements of secondary network. System level performance evaluation shows the effectiveness of proposed protocol as a viable solution for practical cognitive AMI networks.

This paper is concerned with the control problem for a class of switched nonlinear systems possibly composed of all unstable modes by using time-controlled switching signals. To tackle the problem, a new mode-dependent average dwell time (MDADT) switching property is proposed, which is different from the existing one in the literature. Then, the stabilization condition under such MDADT switching signals is established for the switched nonlinear systems with possibly all unstable subsystems. By proposing a class of time-scheduled multiple quadratic Lyapunov function and applying T–S fuzzy models to represent the underlying nonlinear subsystems, numerically easily verified stabilization conditions are further derived in the form of linear matrix inequalities. A numerical example is finally provided to illustrate the effectiveness of the obtained theoretical results.

This paper addresses the problems of synchronization and state estimation for a class of discrete-time hierarchical hybrid neural networks (NNs) with time-varying delays. The hierarchical hybrid feature consists of a higher level nondeterministic switching and a lower level stochastic switching. The latter is used to describe the NNs subject to Markovian modes transitions, whereas the former is of the average dwell-time switching regularity to model the supervisory orchestrating mechanism among these Markov jump NNs. The considered time delays are not only time-varying but also dependent on the mode of NNs on the lower layer in the hierarchical structure. Despite quantization and random data missing, the synchronized controllers and state estimators are designed such that the resulting error system is exponentially stable with an expected decay rate and has a prescribed H∞ disturbance attenuation level. Two numerical examples are provided to show the validity and potential of the developed results.

This paper is concerned with the distributed H∞filtering problem for a class of discrete-time Takagi-Sugeno fuzzy systems with time-varying delays. The data communications among sensor nodes are equipped with redundant channels subject to random packet dropouts that are modeled by mutually independent Bernoulli stochastic processes. The practical phenomenon of the uncertain packet dropout rate is considered, and the norm-bounded uncertainty of the packet dropout rate is asymmetric to the nominal rate. Sufficient conditions on the existence of the desired distributed filters are established by employing the scaled small gain theorem to ensure that the closed-loop system is stochastically stable and achieves a prescribed average H∞performance index. Finally, an illustrative example is provided to verify the theoretical findings.

This paper addresses the problems of stability and stabilization for a class of discrete-time Takagi-Sugeno fuzzy systems with semi-Markov stochastic uncertainties. By means of the semi-Markov kernel, the probability density function of the sojourn time for different modes in describing the underlying semi-Markov stochastic uncertainties can be mode-dependent in contrast with the previous studies. Both the sojourn-time-independent and sojourn-time-dependent Lyapunov functions are proposed, by which the stability criteria are obtained in terms of a new σ-error mean square stability concept, and the latter is demonstrated to be less conservative and more practical than some existing results. Then, control synthesis problem is investigated and the sufficient conditions on the existence of admissible mode-dependent state-feedback stabilizing controller are developed. A numerical example and a cart-pendulum system are given to show the effectiveness and potential of the new design techniques.

In this paper, the problems of stabilization for interval type-2 fuzzy systems with time-varying delay and parameter uncertainties are investigated. The objective is to design an interval type-2 fuzzy controller such that the closed-loop control system is asymptotically stable. The conditions for the existence of such a controller are delay dependent and membership function dependent in terms of linear matrix inequalities (LMIs). Based on a basic lemma, we formulate and solve the problem with more flexibility due to imperfect premise matching that the number of rules and premise membership functions are not necessary the same between the interval type-2 fuzzy model and interval type-2 fuzzy controller. A systematic approach making use of the information embedded in the lower and upper membership functions is employed to facilitate the stability analysis. A numerical example indicates the effectiveness of the derived results.

In this paper, the resilient H∞filtering problem for a class of discrete-time Markov jump systems with unideal measurements is investigated. The unideal measurements contain both quantization and missing measurements simultaneously, which occur randomly satisfying two mutually independent Bernoulli distribute white sequences. A unified model is used to describe the unideal measurements phenomena, and a norm-bounded additive gain perturbation is introduced to model the resilient filter. A mode-dependent full-order filter is designed such that the filtering error system is stochastically stable with an ensured H∞performance index. An application on a single-link robot arm is provided to verify the theoretical results.

This paper is concerned with H∞ control problem for a class of discrete-time Takagi-Sugeno fuzzy Markov jump systems with time-varying delays under unreliable communication links. It is assumed that the data transmission between the plant and the controller are subject to randomly occurred packet dropouts satisfying Bernoulli distribution and the dropout rate is uncertain. Based on a fuzzy-basis-dependent and mode-dependent Lyapunov function, the existence conditions of the desired H∞ state-feedback controllers are derived by employing the scaled small gain theorem such that the closed-loop system is stochastically stable and achieves a guaranteed H∞ performance. The gains of the controllers are constructed by solving a set of linear matrix inequalities. Finally, a practical example of robot arm is provided to illustrate the performance of the proposed approach.

This paper is concerned with the resilient H∞ filtering problem for a class of discrete-time Markov jump neural networks (NNs) with time-varying delays, unideal measurements, and multiplicative noises. The transitions of NNs modes and desired mode-dependent filters are considered to be asynchronous, and a nonhomogeneous mode transition matrix of filters is used to model the asynchronous jumps to different degrees that are also mode-dependent. The unknown time-varying delays are also supposed to be mode-dependent with lower and upper bounds known a priori. The unideal measurements model includes the phenomena of randomly occurring quantization and missing measurements in a unified form. The desired resilient filters are designed such that the filtering error system is stochastically stable with a guaranteed H∞ performance index. A monotonicity is disclosed in filtering performance index as the degree of asynchronous jumps changes. A numerical example is provided to demonstrate the potential and validity of the theoretical results.

In this paper, the problem of energy-to-peak state estimation for a class of discrete-time Markov jump recurrent neural networks (RNNs) with randomly occurring nonlinearities (RONs) and time-varying delays is investigated. A practical phenomenon of nonsynchronous jumps between RNNs modes and desired mode-dependent filters is considered, and a nonstationary mode transition among the filters is used to model the nonsynchronous jumps to different degrees that are also mode dependent. The RONs are used to model a class of sector-like nonlinearities that occur in a probabilistic way according to a Bernoulli sequence. The time-varying delays are supposed to be mode dependent and unknown, but with known lower and upper bounds a priori. Sufficient conditions on the existence of the nonsynchronous filters are obtained such that the filtering error system is stochastically stable and achieves a prescribed energy-to-peak performance index. Further to the recent study on the class of nonsynchronous estimation problem, a monotonicity is observed in obtaining filtering performance index, while changing the degree of nonsynchronous jumps. A numerical example is presented to verify the theoretical findings.

In this paper, novel stability conditions for Takagi-Sugeno (T-S) fuzzy systems are presented. The so-called nonquadratic membership-dependent Lyapunov function is first proposed, which is formulated in a higher order form of both the system states and the normalized membership functions than existing techniques in the literature. Then, new membership-dependent stability conditions are developed by the new Lyapunov function approach. It is shown that the conservativeness of the obtained criteria can be further reduced as the degree of the Lyapunov function increases. Two numerical examples are given to demonstrate the effectiveness and less conservativeness of the obtained theoretical results.

Networked control systems (NCSs) have, in recent years, brought many innovative impacts to control systems. However, great challenges are also met due to the network-induced imperfections. Such network-induced imperfections are handled as various constraints, which should appropriately be considered in the analysis and design of NCSs. In this paper, the main methodologies suggested in the literature to cope with typical network-induced constraints, namely time delays, packet losses and disorder, time-varying transmission intervals, competition of multiple nodes accessing networks, and data quantization are surveyed; the constraints suggested in the literature on the first two types of constraints are updated in different categorizing ways; and those on the latter three types of constraints are extended.

The articles in this special section focus on advancements in theories and industrial applications of networked control systems in the industrial informatics industry.

This brief is concerned with the robust stability problem for a class of discrete-time uncertain Markovian jumping neural networks with defective statistics of modes transitions. The parameter uncertainties are considered to be norm-bounded, and the stochastic perturbations are described in terms of Brownian motion. Defective statistics means that the transition probabilities of the multimode neural networks are not exactly known, as assumed usually. The scenario is more practical, and such defective transition probabilities comprise three types: known, uncertain, and unknown. By invoking the property of the transition probability matrix and the convexity of uncertain domains, a sufficient stability criterion for the underlying system is derived. Furthermore, a monotonicity is observed concerning the maximum value of a given scalar, which bounds the stochastic perturbation that the system can tolerate as the level of the defectiveness varies. Numerical examples are given to verify the effectiveness of the developed results.