This article investigates the finite-time stability (FTS) problem of switched nonlinear systems (SNSs) involving partially unstable modes under hybrid switching signals. In spite of significant challenge caused by unstable modes in SNSs, the qualitative analysis of FTS can still be conducted through the comprehensive utilization of switching information. To be specific, by constructing the discontinuous multiple Lyapunov function (DMLF) coupled with persistent dwell time (PDT)-based hybrid switching, a novel feasible solution for the FTS of SNSs is established. The “discontinuous” property of DMLF implies that certain state attenuation can be artificially designed at the switching instant to compensate for system divergence, and the proposed PDT-based switching employs respectively the τ -portion and theT-portion between stable and unstable subsystems to achieve FTS. Then, the FTS criteria are proposed in terms of stabilizing and destabilizing states during theT-portion, and the hybrid switching design scheme is established for stabilizing or destabilizing state during theT-portion. In particular, the overall settling-time upper bounds depending on both the initial state and the specific dwell time are further derived. Finally, two numerical examples including an application to the finite-time synchronization of switched complex dynamical networks are presented to demonstrate the practicality and effectiveness of the proposed theoretical results.

In this paper, an adaptive sensor fault-tolerant tracking control scheme is developed for state-constrained uncertain nonlinear systems. Unlike most existing methods that directly rely on fault-contaminated sensor measurements for feedback control, an adaptive fault-compensated estimator (AFCE) is developed. By embedding an adaptive compensation term into the estimator feedback channel, the AFCE dynamically counteracts sensor faults and enables the joint estimation of system states and external disturbances. To accommodate state constraints, a generalized intermittent state constraint (GISC) approach is introduced. By constructing a novel switching function and an auxiliary variable, the proposed GISC achieves smooth transitions between constrained and unconstrained phases, while eliminating the conventional requirement that the upper and lower bounds have opposite signs. Then, building upon the estimated results, a generalized barrier Lyapunov function is incorporated into the fault-tolerant controller design. Rigorous Lyapunov analysis proves that the actual system output can track the desired reference while all states remain within the prescribed bounds under the established controller. Finally, the effectiveness and superiority of the proposed approach are verified through both simulation and experimental results.

In this paper, a new data-based Q-learning algorithm is proposed to address the optimal control issue for a class of discrete-time switched affine systems (SASs). The algorithm shifts the emphasis onto learning the optimal switching law directly from system input-output data, employing a neural-network-approximated Q-function as the key learning element. Firstly, the optimal control issue is transformed into solving the corresponding Bellman’s optimality equation based on the Q-function. Then, a new Q-learning algorithm is developed to find the optimal solution of system switching based entirely on the system input-output data, and a fully connected neural network is borrowed as the Q-function approximator. Moreover, considering the affine properties of SASs, the sequence of Q-functions generated remains bounded in proximity to the precise optimal solution. Finally, both the advantage and effectiveness of the proposed Q-learning based optimal control approach are verified by three examples, including a case study of DC-DC buck-boost converter.

This work addresses the adaptive prescribed-time performance platoon control (PTP-PC) problem for heterogeneous connected autonomous vehicles, which faces two key challenges: invading vehicle tracking and unknown overall disturbance. An information protection spacing policy is proposed to tackle the issue of tracking platoon by the invading vehicle. Besides, the variable gain nonlinear extended state observers (NESOs) and variable gain hyperbolic tangent tracking differentiators (HTTDs) are designed to effectively address the overall disturbance and complexity explosion issues, respectively. To obtain the specified transient and steady-state performance for the entire platoon, a prescribed-time performance function is established. On the basis of the above presented techniques, a distributed adaptive PTP-PC scheme is developed to ensure the vehicular bistability and superior prescribed performance. For practical considerations, both numerical simulations and co-simulation experiments based on PRESCAN/SIMULINK have been conducted to simulate a traffic scenario involving the transportation of college entrance examination papers, and the results have verified the feasibility of the developed scheme.

This article investigates the security control problem for the heterogeneous autonomous vehicle platoon subjected to dual false data injection attacks, namely position sensor attack and actuator attack, as well as external disturbances. To begin with, an exponential spacing policy is introduced to mitigate the time-lag effects of existing spacing policies and further enhance traffic flow efficiency. Then, dual attacks and matched/unmatched external disturbances are modeled as the lumped disturbance for each channel of the system, and a set of neural-network-based extended state observers is developed to accurately estimate these lumped disturbances. Furthermore, to overcome initial error boundary constraints and improve platoon transient/steady-state performance, a modified prescribed-time performance security control method is proposed via a novel switch-driven error transformation function. Based on the methods mentioned above and relying on the backstepping technique, this study ensures the stable and safe driving of the heterogeneous autonomous vehicle platoon. Ultimately, the effectiveness and superiority of the presented approach are validated by simulation cases and comparative analysis results.

In this paper, the fault estimation and fault-tolerant control problems are addressed for a class of continuous-time switched affine systems with actuator faults and bounded disturbances. Two novel observer-based approaches are developed to address the fault estimation problem for switched affine systems. The first one refers to a dynamic proportional-integral observer design method, where the presented fault estimate constitute proportional and integral terms to enhance the accuracy of the fault estimation, the common assumption that the measurement output derivative needs to be measurable is eliminated. The second one is an intermediate variable observer, which relaxes the observer matching condition. The output estimation error feedback term is added to the intermediate variable observer to improve the estimation performance. Then, by introducing a switching multi-shifted-point-dependent Lyapunov functional, both a fault-tolerant controller and a new robust output-dependent switching law are jointly designed to compensate the fault effects in the closed-loop switched affine systems and to ensure the practical exponential stability of augmented system, where the convergence region consists of multiple regions and the center point is around some shifted points. The traditional switching quadratic Lyapunov function method is generalized by the designed method. A practical study of a DC-DC boost converter and a numerical example are provided to illustrate effectiveness and validity of the developed fault-tolerant control design method. Note to Practitioners—Power electronics are very common in practical systems, which are usually modeled as a class of switched affine systems. In real applications, faults inevitably occur, which may lead to undesirable behavior and damage to the system. Therefore, how to achieve better fault-tolerant control objectives to guarantee the normal operation of the system with faults is a hot topic. It is practically important to address the fault-tolerant control problem for switched affine systems, where actuator faults and bounded disturbances exist simultaneously. In addition, on account of the existence of affine terms, the controller synthesis of switched affine systems is more complicated than switched linear systems. Based on the special structure of switched affine systems, two kinds of novel fault observers are proposed, where the dynamic proportional-integral observer is proposed to improve the estimation accuracy and speed by utilizing the current output information, and the common supposition that the output derivative needs to be measurable is eliminated. Furthermore, to avoid the limitation of observer matching conditions, an improved intermediate variable observer is designed to estimate faults. Different from the conventional method, the intermediate variable parameters can be selected separately for the corresponding fault channel of each subsystem and the error feedback term of output estimation is added to the intermediate variable observer to enhance the estimation performance. In addition, it is challenging to design a fault-tolerant controller and an output-dependent switching law to guarantee that the augmented system is practically stable and robust to bounded disturbances. The results demonstrate that the designed fault-tolerant control scheme has a definitive practical value.

This article focuses on the stabilization issue of inertial neural networks (INNs) with actuator saturation. Two novel sampled-data stabilization controller design methods are proposed without resorting to traditional variable transformation. The nonlinearity of the activation function significantly influences system performance. Different from the simple bounding techniques used to handle the activation function in the existing sampled-data results for INNs, in this paper, the activation function is represented as a weighted form with weight functions based on the parameterized approach. The designed controller gains depend on affinely transformed weight parameters, leveraging the sector nonlinearity information of the activation function. Constraints on these weight parameters are also considered in the form of linear matrix inequalities (LMIs). By constructing new looped functionals and inequality estimation techniques, two sufficient local stability criteria guaranteeing$H_{\infty }$performance are proposed in the form of LMIs using parameterized techniques. Additionally, corresponding optimization algorithms for enlarging the basin of attraction are presented. Finally, the feasibility of the obtained results is validated through a numerical example with two different classes of common activation functions.

This article focuses on the fault reconstruction and bumpless transfer fault-tolerant (FT) control problems for switched linear systems with magnitude-bounded disturbances and actuator faults in continuous-time domain. A new learning-based robust unknown input observer (UIO), not requiring fault differentiability and completely decoupled disturbances, is developed to accomplish fault reconstruction and state estimation. The fault reconstruction value is updated by one iteration learning on the timeline, i.e., the fault at the current moment is reconstructed by learning historical information from the previous moment. Based on the obtained estimation information, an efficient bumpless transfer FT controller is designed to counteract the fault effects and suppress the control bumps. The bumpless transfer constraint is guaranteed via a new inequality transformation method, which improves the anti-disturbance capability of the controller and also decreases the switching bumps. The solvability conditions for the bumpless transfer controller and learning-based UIO are developed under the condition of average dwell time switching. Finally, an application of the inverted pendulum controlled by a direct current motor is presented to reveal the effectiveness and applicability of the developed methods.

The fundamental cause of severe output fluctuations in photovoltaic (PV) power plants is the abrupt change in ground irradiance due to cloud cover. To enhance the accuracy of PV power forecasting under cloud cover conditions, this article proposes a second level power forecasting method for PV power plants based on an improved Pix2PixHD image restoration algorithm. First, the PV plant model is constructed based on the actual layout by applying Newton Raphson method. Second, the data characteristics of PV power output from the inverters are deeply explored to analyze the mapping relationship between PV power and irradiance. A virtual cloud image is constructed to represent the cloud cover (power loss) situation by describing the shape, thickness, and movement direction of the clouds. Subsequently, the virtual cloud images were preprocessed using the Canny edge detector, followed by restoration of the processed defective virtual cloud images using the Improved Pix2PixHD image restoration algorithm. Finally, a high-precision PV power forecasting at second level is achieved based on the linear relationship between PV power, irradiance, and pixel values of the virtual cloud image.

It is quite desirable yet challenging to plan specified motion behaviors from cooperation to opposition in terms of practical human–robot interaction tasks if the settling time, symmetric/asymmetric constraint, limited resolution, and less bandwidth occupation are involved. Based on this fact, this work is devoted to the assimilation control of human–robot interaction, which can flexibly reshape the physical trajectory in practical interaction work. Then, the adaptive parameter estimation terms are designed to address the effect of limited resolution and unknown robotic dynamics. In particular, a novel easy-to-implement self-triggered quantized mechanism is developed, which can better balance the relationship between system performance and resource utilization. Meanwhile, benefiting from the piecewise exponential function and bias state transformation, the practically prescribed-time stability of the controlled robotic dynamics can be guaranteed. The prominent feature of this design lies in that the settling time and convergence precision can be decoupled into separately user-preassigned parameters, and the symmetric/asymmetric output constraints can be implemented in a unified framework. Afterward, experiment results on a robot system verify the benefits and efficacy of the resultant scheme.

This study addresses the sampled-data control issue for a class of continuous-time switched affine systems (SASs). Specifically, this research aims to relax the constraints on the number of equilibrium points to better characterize the convergence region of continuous-time SASs under the framework of the nonuniform sampling. A sampled-data switching signal is designed to drive the system state toward a union of convergence regions encompassing the shifted points. The shifted points refer to specific points in the state space toward which the system's trajectory is directed or guided. By introducing a switching multishifted-point-dependent Lyapunov function, sufficient conditions are derived for the practical stability of the presented SASs. Moreover, the number of shifted points is determined by the designer, whose value is calculable by resolving the derived stability conditions. Finally, the effectiveness and advancement of the proposed approach are illustrated via a dc–dc buck–boost converter.

It is greatly desirable to carry out the secure practical prescribed-time human-robot co-transportation task. The implementation of such application becomes even more theoretical and practical challenge if uncertainties in the robot model, unmeasured velocity vector and multiple-dynamic-obstacles environment are involved, yet certain behavior indices are also pursued. In this work, a settling time regulator is introduced and it is integrated with the dynamic surface-based backstepping design embedded with specific system transformation. This results in a solution that both constrained and unconstrained cases can be accommodated uniformly, concurrently, the settling time and tracking precision can be preset by user as required. Furthermore, a fuzzy velocity observer is designed with aid of the fuzzy logic technique, which is nontrivial to perform a control design of robot dynamics with unmeasured velocity vector and modeling uncertainties. In particular, benefiting from integral multiplicative barrier-Lyapunov function, an improved adaptive obstacle-avoiding controller is designed, which, without control singular issue, is capable of achieving desired tracking while avoiding obstacles encountered. The validity and benefits of the resultant control strategy are eventually substantiated via the simulation results of a two-DOF robotic manipulator.

This article studies the bumplessH∞control issue for a class of continuous-time switched linear systems with mode-dependent average dwell time (MDADT) switching. A new two-step approach is proposed to suppress the control input bumps. First, the predesigned stabilizing controller is acquired via imposing the common-matrix-based bump limitation constraints. Second, the transition bumpless transfer (BT) controller is designed to be activated at the subsystem switching instants and combined with the stabilizing controller to constitute the transition-dependent piecewise BT controller. By applying the new transition-dependent piecewise Lyapunov function and the control amplitude limitation strategy, sufficient conditions are derived for the existence of a piecewise BT controller under MDADT switching. Finally, a tunnel diode circuit system is provided to highlight the feasibility and superiority of the developed BT control method.

This paper concerns with the fault estimation problem for a class of unmanned marine vehicles with sensor faults and non-differentiable actuator faults. Firstly, the presented unmanned marine vehicles are modeled as a class of continuous-time switched systems. In order to resolve the non-differentiable actuator faults, two kinds of learning observer methods are proposed to allow the augmented systems independent of the time-derivative of actuator faults. The first one is a kind of timeline-based learning observer, which produces the satisfactory estimation results by using the estimated information from the previous moment and the measured output estimation error information. The second one is an iterative learning observer, where the actuator faults are accurately estimated by the iterative process. The monotonic convergence of the iterative process is guaranteed based on the derived linear matrix inequality conditions. Under arbitrary switching signals, the simultaneous estimations of states, faults and disturbances can be achieved via the proposed observer design approaches. Finally, simulation results are provided to illustrate the effectiveness of the developed two kinds of learning observers.Note to Practitioners—The mass of unmanned marine vehicles (MVs) is switched back and forth as the unmanned MV performs the tasks, such as loading, dispatching and retrieving detectors. Hence, in order to better describe the dynamic behaviour, the unmanned MVs are described as the mass-switched systems. In general, any actual systems with switching properties can be characterised as the switched systems. At present, the fault estimation problem of the mass-switched unmanned MVs has not yet been investigated to date. This paper presents a theoretical study. Existing approaches to fault estimation of switched systems generally require differentiable actuator faults, which may be difficult to guarantee. In order to relieve this restriction, this paper proposes the new learning observer-based estimation method for switched linear systems. Based on the physical data of the unmanned MV, simulation results show that the proposed method is feasible.

This paper focuses on the sampled-data control for a DC-DC buck-boost converter, which is modeled as a class of continuous-time switched affine systems (SASs). The controller of the closed-loop system, in which both the control inputs and switching signals are sampled-data-dependent, is designed to ensure convergence of the system state to a specified region. This region comprises multiple ellipsoids centered around some shifted points that need to be determined, which can better characterize the convergence region under non-uniform sampling intervals. By introducing a switching multi-shifted-point-dependent Lyapunov functional, sufficient conditions are derived to ensure practical stability of the presented SASs. The proposed design scheme generalizes the switching quadratic Lyapunov function based method, and provides a smaller and more accurate invariant set. Moreover, the method is extended to SASs with uncertainties, and the corresponding robust stability conditions are provided. Finally, the superiority of the proposed approach is demonstrated through a case study of a DC motor driven by a buck-boost converter.

This article investigates the singularity-free finite-time adaptive optimal control problem for coordinated robots, where the position and velocity are constrained within the asymmetric yet time-varying ranges. Different from the existing results concerning constrained control, the imposed feasibility conditions are relaxed by skillfully integrating a nonlinear state-dependent function into the backstepping design procedure. Therein, the typical feature of the designed finite-time controller lies in the application of the modified smooth switching function, rendering the designed controller powerful enough to eliminate singularity problem. Notably, with the aid of the constructed optimal cost function and neural network-based critic architecture, the optimal control law is established under the backstepping design framework. It is theoretically verified that the designed controller is of satisfied optimization and finite-time tracking ability, and desired constrained objective in the meanwhile. The validity of the resulting control algorithm is eventually substantiated via two robotic manipulators.

A predefined-time adaptive consensus control strategy is developed for a class of multi-agent systems containing unknown nonlinearity. The unknown dynamics and switching topologies are simultaneously considered to adapt to actual scenarios. The time required for tracking error convergence can be easily adjusted using the proposed time-varying decay functions. An efficient method is proposed to determine the expected convergence time. Subsequently, the predefined time is adjustable by regulating the parameters of the time-varying functions (TVFs). The neural network (NN) approximation technique is used to address the issue of unknown nonlinear dynamics through predefined-time consensus control. The Lyapunov stability theory testifies that the predefined-time tracking error signals are bounded and convergent. The feasibility and effectiveness of the proposed predefined-time consensus control scheme are demonstrated through the simulation results.

This article addresses the simultaneous actuator and sensor fault estimation (FE) problem for a class of Markovian jump systems (MJSs) with nondifferentiable actuator failures. In order to overcome the difficulties brought by the nondifferentiable actuator failures, we construct an extended vector composed of states, sensor faults, and disturbances, where the derivatives of actuator failures are not required in this augmented system. Then, two novel observer-based approaches are developed for the augmented descriptor system to cope with the FE problem. The first one is a reduced-order FE observer, where the actuator failures can be estimated by the algebraic input reconstruction strategy. The second one refers to an iterative learning observer (ILO) design method, which can obtain the accurate FE result by integrating the estimations in the iterative processes. The two proposed FE observer design methods can avoid the sliding surface switching problem produced by sliding-mode observers in the area of MJSs. Finally, a practical example of the F-404 aircraft engine system is presented to show the validity of the proposed FE observer design techniques.

This article investigates the fault reconstruction problem for a class of continuous-time switched systems subject to persistent dwell-time (PDT) switching. In order to relax the rank condition of the perturbations' coefficient matrices in fuzzy fault reconstruction results, a novel adaptive fuzzy design approach is developed by decomposing the perturbations into virtual faults and states. Then, the PDT switching is first considered in the adaptive fuzzy observer to overcome the restricted switching frequency problem, which can guarantee the stability of the estimation error systems with fast and slow switching characteristics simultaneously. Moreover, the unknown nonlinearities, actuator faults and disturbances can be reconstructed by the designed adaptive mechanism, where the switching problem of sliding surfaces in sliding-mode observers can be avoided. Finally, a circuit system example is provided to demonstrate the effectiveness of the fault reconstruction scheme.

This study is concerned with the adaptive neural network (NN) observer design problem for continuous-time switched systems via quantized output signals. A novel NN observer is presented in which the adaptive laws are constructed using quantized measurements. Then, persistent dwell time (PDT) switching is considered in the observer design to describe fast and slow switching in a unified framework. Accurate estimations of state and actuator efficiency factor can be obtained by the proposed observer technique despite actuator degradation. Finally, a simulation example is provided to illustrate the effectiveness of the developed NN observer design approach.

This article investigates the command filter-based decentralized prescribed performance adaptive fault-tolerant compensation control strategy for uncertain nonlinear large-scale systems subject to asymmetric time-varying full-state constraints. Via integrating the performance function with command filter-based backstepping technique, the prescribed performance control problem is addressed, under which the complexity of controller design is reduced. Under the prescribed performance control framework, the nonlinear transformed function is constructed so as to ensure that the asymmetric time-varying full-state constraints free from feasibility conditions imposed on virtual control signals are not violated. Besides, the effect of infinite number of time-varying actuator faults is compensated with the aid of projection adaption design. Furthermore, based on the piecewise Lyapunov function analysis, it is rigorously testified that entire involved signals are bounded, desired constraints are not breached and tracking errors within the predefined domains. Finally, the effective performances of the developed control algorithm are confirmed by some simulation results.

In this article, the active fault-tolerant control (FTC) issue is addressed for switched linear parameter varying (LPV) systems in the discrete-time domain. A general dwell-time property is considered in the system setup to govern the switching dynamics between subsystems, and the parameter variations within each subsystem are subjected to the polytopic uncertainties. The main innovation of the developed active FTC approach is to effectively cope with switched LPV models where both input and output matrices can be of parameter-dependent form. By virtue of a novel Lyapunov function approach depending on both dwell time and parameter variations, the joint task for fault detection, estimation, and compensation is fulfilled via an integrated design scheme. The input-to-state stability (ISS) conditions are obtained for the augmented faulty system satisfying a given ISS-gain performance requirement. The desired effect of active FTC is achieved in three critical steps: 1) construct a fault detection filter in terms of the$\mathcal {H}_{\infty }$norm; 2) design an observer-based estimator to estimate the system states and faults simultaneously; and 3) synthesize a fault-tolerant controller with the LPV structure using the estimated states and faults for fault compensation. Finally, an application example is utilized to illustrate the efficiency and availability of the proposed control approach.

In this paper, the stability and stabilization issues are investigated for a class of discrete-time switched piecewise affine systems with modal average dwell time switching. Both the autonomous (state-partition-dependent) switching and the controlled (modal average dwell time) switching appear concurrently in the studied system. At first, the exponential stability criterion is derived using the proposed piecewise quadratic Lyapunov function approach. Then the state feedback controller in the piecewise affine form is designed to achieve stabilization for the resulting closed-loop switched piecewise affine system. In the end, the effectiveness and accuracy of the theoretical findings are testified via a numerical example.

This paper investigates the problem of quasi-synchronization for a class of discrete-time Lur'e-type switched systems with parameter mismatches and transmission channel noises. Different from the previous studies referring to the persistent dwell-time (PDT) switching signals, the average dwell-time (ADT) constraints combined with the PDT are considered simultaneously in this paper to relax the limitation of dwell-time requirements and to improve the flexibility of the PDT switching signal design. By virtue of the semi-time-varying (STV) Lyapunov function, the synchronization criteria for transmitter-receiver systems in a switched version are obtained to satisfy a prescribed synchronization error bound. An estimate of the synchronization error bound is provided via the reachable set approach and, further, an explicit description of the error bounds is given. Then, sufficient conditions on the existence of STV observers are derived with a predetermined error bound, and the corresponding observer gains are calculated via solving a group of linear matrix inequalities. Finally, the effectiveness and validness of the developed theoretical results are demonstrated via a numerical example.

This paper studies the fault detection problem for a class of discrete-time switched linear systems under dwell-time (DT) constraints, using the parity space-based approach. The DT-dependent Lyapunov function is employed to investigate the asymptotic stability with less conservatism and to solve the constant l2-gain performance analysis problem, and its advantage is verified compared to the time-independent Lyapunov function approach. The corresponding switching residual generation is made to carry out the desired fault detection in the framework of parity space-based model. Then, by means of solving a generalized eigenvalue-eigenvector problem, the parity space matrices design is implemented. A quantitative relationship is established between the optimization performance and the choice of the parity space order. Two numerical examples are utilized to demonstrate effectiveness of the developed fault detection approach, including an application to switched RLC circuits.

In this paper, we study the exponential stabilization problem for continuous-time Takagi-Sugeno fuzzy systems subject to aperiodic sampling. By aiming to transmission reduction, an appropriate aperiodic event-triggered communication scheme with adaptive mechanism is put forward, which covers the existing periodic mechanisms as special cases. For the sake of reduction in design conservativeness, both the available information of sampling behavior and threshold error are fully acquired by constructing a novel time-dependent Lyapunov functional. Then, a new exponential stability criterion is presented to establish the quantitative relationship among the adaptive adjusted event threshold, the decay rate, the upper bound, and the lower bound of variable sampling period, simultaneously. By resorting to a matrix transformation, the corresponding stabilization criterion is further derived by which the sampled-data controller can be obtained. Finally, two illustrative examples are provided to demonstrate the virtue and applicability of proposed design 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 examines the problem of distributed event-triggering output-feedback control for discrete-time large-scale fuzzy systems in the network scenario. A novel distributed event-triggering control scheme is proposed, where each subsystem transmits its output information and premise variables only when its local output error exceeds a specified threshold. The resulting closed-loop fuzzy control system subject to nonsynchronous grades of membership is explored under such circumstances. In virtue of a piecewise Lyapunov function together with some matrix inequality convexification techniques, the solution to the distributed event-triggering output-feedback control problem is derived in the form of linear matrix inequalities. It will be shown that the closed-loop control system is asymptotically stable while significantly reducing the communications via networks. Finally, a direct current microgrid with solar photovoltaic arrays is utilized to illustrate the validity and practicability of the proposed results.

In this paper, the filtering problem is investigated for a class of discrete-time Markov jump linear parameter varying systems with packet dropouts and channel noises in the network surroundings. The partial accessibility of system modes with respect to the designed filter is described by a hidden Markov model (HMM). A typical behavior characterization mechanism is proposed in the communication channel including data losses and additive noises, which occurs in a probabilistic way based on two mutually independent Bernoulli sequences. With the aid of a class of Lyapunov function subject to parameter-dependent and mode-dependent constraints, sufficient conditions ensuring the existence of HMM-based filters are obtained such that the filtering error system is stochastically stable with a guaranteed H∞error performance. The influence of monotonicity on the performance index is explored while changing the degree of both additive noise and mode inaccessibility. The effectiveness and applicability of the obtained results are finally verified by two numerical examples.

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.

In this paper, an adaptive neural output-feedback tracking controller is designed for a class of multiple-input and multiple-output nonstrict-feedback nonlinear systems with time delay. The system coefficient and uncertain functions of our considered systems are both unknown. By employing neural networks to approximate the unknown function entries, and constructing a new input-driven filter, a backstepping design method of tracking controller is developed for the systems under consideration. The proposed controller can guarantee that all the signals in the closed-loop systems are ultimately bounded, and the time-varying target signal can be tracked within a small error as well. The main contributions of this paper lie in that the systems under consideration are more general, and an effective design procedure of output-feedback controller is developed for the considered systems, which is more applicable in practice. Simulation results demonstrate the efficiency of the proposed algorithm.

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.

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 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.