The stabilization issue of noisy sampled-data networked control systems in the presence of deception attacks and noisy sampling intervals is investigated in this paper. Firstly, networked control systems with deception attacks and noisy sampling intervals are acquired under a consistent structure. Next, feedback interconnection model with zero initial state is established, which facilitates the use of the stochastic discrete-time integral quadratic constraint. Uniform exponential stability conditions are given for networked control systems related to operators expectations with stochastic properties. Subsequently, the difficulties associated with the nonlinear term are overcome and a novel controller algorithm that enables uniform exponential stability of the networked control systems is designed. Lastly, the effectiveness of the designed algorithm is validated by numerical example and direct current motor simulation example.

This article investigates the data-driven control problem of linear time-invariant (LTI) multi-input systems under aperiodic sampling, where the input of the system is determined by the corresponding agent. Focusing on partially unknown system models, we provide a methodology that integrates Integral Quadratic Constraints (IQC) with dissipation inequalities to derive the asymptotic stability criterion and controller design method for such systems. Firstly, addressing the challenge of incomplete system model knowledge, this article develops adaptive system identification algorithm for the above two scenarios, respectively. Each algorithm facilitates accurate model reconstruction directly from the available data. Then, the feedback interconnection between the LTI multi-input system and time-delay operator is established, and the asymptotic stability criterion for the system is demonstrated. In addition, by means of matrix decoupling techniques, a restricted frequency-domain data-driven framework which can effectively address complex coupling dynamics and overcome structural perturbation constraints is proposed, along with two corresponding controller algorithms. Finally, through the simulation analysis and result verification of numerical examples, the effectiveness of the proposed methods are demonstrated.

This article addresses differentially private average consensus for heterogeneous multi-agent systems under aperiodic sampled-data control. A distributed hybrid dynamic controller based on a decomposition-compensation approach is proposed. Individual privacy is safeguarded by integrating non-decaying Laplacian noise into interaction information and decomposing the reference state into interactive and reserved substates, preventing direct exposure of true information to eavesdroppers. Leveraging the Robbins-Monro conditions, a time-varying step-size is designed to relax noise constraints, accelerate convergence, and achieve asymptotically unbiased output average consensus in mean-square sense. Finally, a numerical simulation and a physical experiments involving mobile robots and unmanned aerial vehicles validate the efficacy of the proposed scheme.

This study addresses the output feedback control synthesis problem for a quarter-vehicle active suspension system subject to input delay, using dynamic integral quadratic constraint. To account for the uncertain relationship between the real-time output of the controller and the delayed signal arriving at the actuator, a memory-driven control strategy is proposed. Furthermore, dynamic integral quadratic constraint multipliers are employed to reformulate the output feedback control synthesis into a set of linear matrix inequalities. A simplified memory-free dynamic output feedback controller is also introduced. Corresponding controller design algorithms are developed for both the memory-driven and memory-free schemes. Simulation results demonstrate the effectiveness of the proposed controllers in handling input delays, and comparative studies verify the superiority and feasibility of the proposed control approach.

This article investigates the problem of privacy-preserving average consensus for continuous-time heterogeneous multiagent systems with intermittent information transfer under asynchronous sampled-data interactions. To address the challenges posed by agent-specific asynchronous sampled-data and time-varying communication delays, a time-translation approach incorporating a shared sampling period strategy is introduced, effectively transforming the asynchronous problem into a synchronous framework. Next, integrated distributed hybrid controller with time-varying noise injection is designed, enabling agents to interact with sensitive information only at sampling instants, thereby preserving privacy while maintaining trajectory availability. Then, the time-varying step-size and noise parameters, which are tunable parameters of the dual control mechanism corresponding to the desired ε-differential privacy budget and system convergence accuracy are proposed, and the trade-off between control performance and privacy preservation is thoroughly analyzed. It is shown that the proposed protocol achieves asymptotically unbiased mean-square output consensus with predefined accuracy and privacy budget. Numerical examples validate the theoretical results.

In this article, the problem of data-driven control for Markovian jump systems (MJSs) under aperiodic sampled-data (ASD) control is studied. By constructing a loop-based Lyapunov functional (LBLF), the stability criterion of MJSs based on the model is established. Combining this model-based condition with data-based representations, two distinct stability conditions are formulated. ASD controllers are subsequently designed based on these conditions. The proposed approach is validated by modeling an aircraft system as an MJS, with flight test data showing that the controller effectively stabilizes the system, whether the system matrices are known or unknown. Furthermore, a numerical example highlights that the proposed approach achieves a larger upper bound for the sampled-data period.

This paper is concerned with noisy sampled-data networked control systems with stochastic network-induced delays and noisy sampling intervals using a stochastic discrete integral quadratic constraint (DIQC) approach. Initially, the noisy sampled-data networked control system is discretized into a stochastic discrete-time system. This system is then transformed into a feedback interconnected system with zero initial conditions. Next, a stochastic DIQC method is developed to obtain a novel stability condition for the noisy sampled-data networked control system. Based on this criterion, a two-stage algorithm is presented to design suitable controllers to ensure the closed-loop system under study is uniformly exponentially stable. Finally, a numerical examples and a fourth-order interleaved flyback modular integrated converter circuit are given to validate the efficiency of the proposed results.

This paper investigates the problem of stability analysis for aperiodic sampled-data load frequency control system with multiple time-varying delays. A discrete-time model of the load frequency control system including both generation units and energy storage systems is constructed firstly. Then, the form of feedback interconnection composing of nominal linear system and uncertainty is bulit, where uncertain operators are found to describe the aperiodic sampled-data interval and time-varying delays. Next, a uniformly exponential stability criterion is developed utilizing integral quadratic constraint analysis. Based on the stability criterion, algorithms are presented to obtain more accurate delay margins and maximum allowable sampled-data intervals. Finally, case studies are conducted to demonstrate the validity and benefits of proposed stability criterion.

This article presents an event-triggered neural-network (NN) tracking control scheme, capable of ensuring transient performance for switched nonlinear systems. A mode-dependent event-triggered communication mechanism (MDETCM) is designed, and this significantly saves communication resources without limiting the number of switches between two consecutive triggering instants. Meanwhile, to solve the impact of asynchronous switching on system performance, the information of the switching signal is considered into the event-triggered mechanism (ETM). Also, by introducing a normalized function transformation and a tan-type barrier function, the transient performance is regarded as a tracking error constraint without considering the initial conditions of system output and reference signal required in traditional prescribed performance bound (PPB) control. At the same time, the improved admissible edge-dependent average dwell time (AED-ADT) method is cleverly connected with adaptive backstepping control, and a state-feedback tracking algorithm is proposed, under which all closed-loop signals are bounded and the transient performance of the controlled plant is ensured. Finally, the superiority of the proposed scheme is demonstrated through numerical studies, and the control scheme is available for a single-link robot.

This study mainly investigates the adaptive leader-following consensus tracking control problem for a class of nonlinear multiagent systems (MASs) subjected to unknown control directions, external disturbances, and sensor deception attacks. To start with, an equivalent MAS with known control directions is obtained by introducing a linear state transformation. For the purpose of estimating the unavailable system states caused by malicious attacks, a quantization-based fuzzy state observer is designed, and the fuzzy-logic system (FLS) is utilized to approximate nonlinear functions. Moreover, a dynamic uniform quantizer with scaling function is established to reduce information transmission. With the help of coordinate transformation and available compromised states, a novel compensation mechanism is designed to offset the influence of filter errors while avoiding the problem of "explosion of complexity" in the backstepping design process. In addition, the Nussbaum-type function is considered to eliminate the design obstacle of unknown control gains resulting from the attacks. Under the constructed consensus protocol, it is proved theoretically that the consensus tracking error converges to an adjustable small neighborhood of the origin, and all signals in the closed-loop system are bounded. Finally, the feasibility of the provided secure control scheme is verified through two simulation examples.

In this article, the matter of aperiodic sampled-data$H_{\infty }$control of a quarter vehicle active suspension system with time-varying delay is investigated based on an uncertain discrete-time model. This article mainly solves three problems based on the theory of integral quadratic constraints. First, the system is discretized by resorting to lifting technique, the uncertainties of the discrete-time model are embodied by the operators related to sampled-data and time delay, in this process, a new norm upper bound estimation of operator is given. Second, based on the multipliers of the operator, the conditions for the improved$H_{\infty }$performance constraints are given. Third, with regard to controller design, the difficulties posed by the nonlinear coupling terms for solving the matrix inequalities are addressed by an iterative algorithm. Finally, the simulation platform and a practical example show that the proposed method is effect and can obtain more optimized results.

In this article, the exponential stability and controller design of large-scale networked control systems with prediction-based controller are addressed. A matrix dimension transformation method is proposed, which solves the problem of matrix dimension mismatch due to the complex structure. First, a new Lyapunov–Krasovskii functional (LKF) is introduced under the condition of the backstepping-based partial differential equation (PDE) method, and stability criteria with less conservatism are obtained with the help of free-weight matrixes and the inequality deflation technique. Second, combining with the reduction-based ordinary differential equation (ODE) method, by constructing the two-sided mode-dependent loop-based LKF, the information of sampled-data intervals can be utilized more sufficiently. Meanwhile, with the help of the proposed matrix dimension transformation method, controller design algorithms based on the two prediction methods are given, respectively. Finally, the effectiveness of theoretical analysis are demonstrated by using two coupled cart-pendulum systems, where the proposed method can obtain larger upper bounds of the allowable time delay and sampled-data intervals.

The concept of reachable set and state space ellipsoid is used in this article to determine the optimal safe range of the truck-trailer driving by allowing the constrained variables to move freely within a given range. A method of returning the truck to the desired position is proposed by analysing the movement trajectory of the truck. Two results are certified. The first result considers the issue of reachable set estimation (RSE) for the multirate sampled-data (MRSD) system in the aperiodic sampled-data framework based on the model knowledge. By constructing loop-based Lyapunov functional (LBLF), we obtain the sufficient condition that all the state trajectories are confined to target ellipsoid. This article also provides a computational method for an MRSD controller considering RSE. The second result provides the data-driven control tactics for the unknown sampled-data system to consider the RSE problem for the aperiodic sampled-data system, using only the noisy data. In addition, this article extends the data-driven control scheme to the design of MRSD controllers and ensures the stability of the system in agreement with the measured data. Simulation results show that the MRSD controller under both the model-driven method and the data-driven method is valid and achieves better control effect compared to the single-rate sampled-data (SRSD).

This paper investigates the stability analysis and controller design issues for aperiodic sampled-data networked control systems with time-varying delays. Three main results are obtained. Firstly, the discretized model with delay is constructed as an feedback interconnected system formed by a structural uncertainty operator and a linear time-invariant system. Secondly, an exponential decay rate is introduced as a performance index of the system through model transformation, an equivalent stability condition for the system is also obtained, further, the conclusion is extended to systems with arbitrary initial states. On this basis, the exponential stability conditions with minimal computational complexity are given under an integral quadratic constraint. Thirdly, an iterative algorithm for the solution of the controller is given to deal with the effects of non-linear coupling terms. Finally, two numerical examples and an application example of an interleaved flyback modular integrated converter are used to illustrate the effectiveness and computational simplicity of the stability criterion and the controller design algorithm of this paper.

This article addresses the adaptive fuzzy control problem for switched nonlinear systems with state constraints. The unified barrier function (UBF) is introduced to solve the time-varying state constraints, which removes the feasibility conditions. By integrating command filter into backstepping control to avoid the "explosion of complexity." In addition, a novel event-triggered strategy is designed to deal with the asynchronous switching between subsystems and controllers without limiting the maximum asynchronous time, and mitigate the communication burden. Also, a new threshold function is introduced to overcome the difficulty of discontinuous triggering error at the switching instants. Then, by combining the improved admissible edge-dependent average dwell-time (AED-ADT) method with Lyapunov stability analysis, it is proved that all system signals are bounded and do not violate the predefined constraints under given switching rule. Finally, the numerical simulation results verify the superiority of the proposed algorithm, and the algorithm is applied to a ship maneuvering system.

This article is dedicated to design asynchronous aperiodic sampled-data controller for a class of Markov jump systems with switching transition rate. The sampling controller corresponding switching transition rate matrix mode is not synchronized with the system corresponding switching transition rate matrix mode due to the possible switching of the transfer rate matrix in the sampling interval. With the help of T–S fuzzy models, the state-space representation of the system is turned into a fuzzy Itô stochastic switched Markov jump systems. By constructing novel Lyapunov functionals, mean square exponential stability criteria, and dissipativity criteria are presented. Furthermore, an asynchronous aperiodic sampled-data controller is designed for fuzzy Itô stochastic Markov jump systems. At last, by fuzzing the nonlinear tunnel diode circuit model and nonlinear mass-spring-damper mechanical model, the effectiveness and lower conservativeness of the proposed method are illustrated.

In this article, the problem of reachable set estimation (RSE) and the design of aperiodic sampled-data controller for Takagi–Sugeno fuzzy Markovian jump systems (T–S FMJSs) with unit-energy bounded disturbance (UEBD) and unit-peak bounded disturbance (UPBD) inputs are taken into consideration. First, sufficient conditions that all states of the T–S FMJSs are encompassed by ellipsoids under zero initial conditions are acquired via constructing a mode-dependent two-sided loop-based Lyapunov function and applying a linear matrix inequality approach. Second, the RSE is taken into account in the design of the state feedback aperiodic sampled-data controller with the aim that the resulting ellipsoid encompasses the reachable set of the closed-loop system. Finally, a nonlinear mass-spring model and a tunnel diode circuit model demonstrate the efficiency of the presented approach. In addition, this method is able to obtain a larger sampled-data period than other literature, thus saving bandwidth and reducing communication resources.

In this paper, the adaptive fuzzy control of switched stochastic nonlinear systems with set-time prescribed performance based on event-driven mechanism is studied. The creative part of this paper is that based on the set-time performance function, a modified event-triggered strategy that considers asynchronous switching to deteriorate system performance without strict assumptions is presented, which avoids Zeno behavior and saves communication resources. Then, by using backstepping recursive design technique, It$\hat {o}$’s differential lemma and mode-dependent average dwell time (MDADT) method, a novel adaptive performance control scheme is proposed, which can ensure that all the variables in the system are semiglobally uniformly ultimately bounded (SGUUB) in probability and the tracking error gets into prescribed boundary no later than an arbitrarily adjusted setting time. Finally, the proposed algorithm is applied to a RLC circuit and its practicability is verified via simulation results.

In this article, an aperiodic sampled-data control problem is investigated for polytopic uncertain switched complex dynamical networks subject to actuator saturation. Due to the constraint on the upper bound of the sampling interval being no greater than the dwell time, the issue concerning the asynchronization between the sampled-data controller mode and the system mode is hence considered to be caused by subsystems that may switch in a sampling interval. By considering the sampling interval without switching and the sampling interval with switching, the parameters-dependent loop-based Lyapunov functionals are constructed, respectively. With the help of the constructed functional, mean-square exponential stability criteria for the error polytopic uncertain switched complex dynamical networks are presented under the definition of average dwell time. Furthermore, based on the stability criteria, the asynchronous aperiodic sampled-data controller is designed for polytopic uncertain switched complex dynamical networks subject to actuator saturation. The polytopic uncertain switched complex dynamical networks can be guaranteed to exponentially synchronize with the target node based on the proposed stability conditions and aperiodic sampled-data controller design method. Finally, by transforming the proposed theoretical conditions into the LMI-based objective optimization problem, the domain of attraction of polytopic uncertain switched complex dynamical networks is estimated. An example based on switched Chua's circuit is applied to verify the effectiveness of the proposed method.

The issue of adaptive output-feedback stabilization is investigated for a category of stochastic nonstrict-feedback nonlinear systems subject to unmeasured state and unknown control directions. By combining the event-triggered mechanism and backstepping technology, an adaptive fuzzy output-feedback controller is devised. In order to make the controller design feasible, a linear state transformation is introduced into the initial system. At the same time, the Nussbaum function technology is used to overcome the difficulties caused by unknown control directions, and the state observer solves the problem of the unmeasured state. Based on the fuzzy-logic system and its structural characteristics, the issue of unknown nonlinear function with nonstrict-feedback structure in the system is tackled. The designed controller could not only guarantee all signals of closed-loop systems are bounded in probability but also save communication resources effectively. Finally, numerical simulation and ship dynamics example are given to confirm the effectiveness of the proposed method.

In this article, sampled-data synchronization problem for stochastic Markovian jump neural networks (SMJNNs) with time-varying delay under aperiodic sampled-data control is considered. By constructing mode-dependent one-sided loop-based Lyapunov functional and mode-dependent two-sided loop-based Lyapunov functional and using the Itô formula, two different stochastic stability criteria are proposed for error SMJNNs with aperiodic sampled data. The slave system can be guaranteed to synchronize with the master system based on the proposed stochastic stability conditions. Furthermore, two corresponding mode-dependent aperiodic sampled-data controllers design methods are presented for error SMJNNs based on these two different stochastic stability criteria, respectively. Finally, two numerical simulation examples are provided to illustrate that the design method of aperiodic sampled-data controller given in this article can effectively stabilize unstable SMJNNs. It is also shown that the mode-dependent two-sided looped-functional method gives less conservative results than the mode-dependent one-sided looped-functional method.

In this article, the dissipativity-based sampled-data control problem is studied for the fuzzy switched Markovian jump systems (FSMJSs). By considering the problem of the asynchronous switching caused by the subsystems' switching occurred during the sampling intervals, constructing novel time-dependent Lyapunov functional, and using average dwell time (ADT) approach, sufficient mean-square exponential stability criteria, and strictly (Q,S,R)-γ- dissipativity criteria are proposed under the constraints that the maximum sampling period is no larger than the dwell-time between the transition rates switching instants. Meanwhile, a relationship between ADT and sampling period is revealed for FSMJSs. Based on the strictly (Q,S,R)-γ- dissipativity criteria, the sampled-data controller is designed for FSMJSs. A circuit simulation example is provided to illustrate the effectiveness of the proposed method.

In this paper, for the case of the asynchronous switching caused by that subsystem's switching occuring during a sampling interval, the domain of attraction estimation problem is investigated for aperiodic sampled-data switched delayed neural networks (ASDSDNNs) subject to actuator saturation. A parameters-dependent time-scheduled Lyapunov functional consisting of a novel looped-functional is constructed using segmentation technology and linear interpolation. By employing this novel functional and using an average dwell time (ADT) approach, exponential stability criteria are proposed for polytopic uncertain ASDSDNNs subject to actuator saturation. And a relationship between ADT and sampling period is revealed for ASDSDNNs. As a corollary, exponential stability criteria are proposed for nominal ASDSDNNs subject to actuator saturation. Furthermore, by describing the domain of attraction as a time-varying ellipsoid determined by the time-scheduled Lyapunov matrix, the proposed theoretical conditions are transformed into a linear matrix inequality (LMI)-based multi-objective optimization problem. The dynamic estimates of the domain of attraction for ASDSDNNs are solved. Numerical simulation examples are provided to illustrate the effectiveness of the proposed method.

In this paper, the problem of passivity-based robust sampled-data control is investigated for continuous-time Markovian jump systems (MJSs). First, using parameters-dependent Lyapunov functionals consisting of looped-functionals, stochastic sufficient passivity criteria for continuous-time polytopic uncertain MJSs are proposed. As a corollary, we get the stochastic sufficient passivity criteria for continuous-time nominal MJSs. Based on the obtained stochastic sufficient passivity criteria, time-independent state feedback sampled-data controllers are designed. Then, in order to get a time-dependent state feedback sampled-data controller, parameters-dependent time-scheduled Lyapunov functionals consisting of looped-functionals are developed. New stochastic sufficient passivity criteria are obtained. Furthermore, based on the stochastic sufficient passivity criteria, the time-dependent state feedback sampled-data controllers are designed. Four numerical simulation examples are provided to illustrate the effectiveness of the proposed method.