Randomly Occurring Nonlinearities Research Articles

Adaptive impulsive security consensus control of uncertain multi‐agent systems with randomly occurring nonlinearities

AbstractIn practice, deception attacks, uncertainties, and randomly occurring nonlinearities (RONs) in multi‐agent systems (MASs) are ubiquitous. Consequently, this paper settles the problem of secure consensus tracking control for uncertain MASs with randomly occurring nonlinearities under sensor attacks by adopting adaptive impulsive control methods. Compared with the previous works, a class of state‐dependent sensor attack is firstly considered in the impulsive control. Meanwhile, a novel adaptive algorithm is introduced to deal with unknown system matrices, randomly occurring nonlinearities and uncertain parameters caused by sensor attacks. Sufficient conditions are derived to achieve leader‐following consensus for the MASs by adaptive impulsive control under sensor attacks. Lastly, two simulation examples illustrate the validity of theorems in this paper.

Admissibility analysis for networked discrete-time descriptor systems with RONs and ROUs by an arctangent adaptive event-triggering scheme

This paper studies the problem of adaptive event-triggering scheme (AETS) for networked discrete-time descriptor systems subject to randomly occurring nonlinearities (RONs) and uncertainties (ROUs). Firstly, an arctangent adaptive event-triggering scheme (AAETS) is designed to reduce the frequency of communication. This AAETS can be adjusted adaptively based on each sampling instant due to the fact that the triggering thresholds depending on continuous function are updated. Secondly, a networked time-varying delay system model is delicately designed by taking into account the network-induced delay, RONs, ROUs, and the AAETS. According to Lyapunov function method, sufficient admissible criteria to verdict admissibility and stabilization criteria to co-design the triggering matrix and the controller gain are obtained, respectively. Admissible criteria and stability criteria produce a tradeoff between the required communication resources and the desired control performance. Finally, a comparative example is provided to show the effectiveness and the advantageous performances of the designed AAETS.

Non-fragile asynchronous state estimation for Markovian switching CVNs with partly accessible mode detection: The discrete-time case

This article is devoted to the non-fragile asynchronous state estimation problem for Markovian switching complex-valued networks subject to randomly occurring nonlinearities (RONs) and external stochastic disturbances. Two mutually uncorrelated random variables with known statistical properties are introduced to depict the existing RONs. Considering the case that the information concerning to the system modes cannot be completely acquired by the state estimator to be designed, the asynchronous phenomenon is considered via a hidden Markov model with only partial mode detection probabilities being accessible. By resorting to the intensive stochastic analysis as well as an improved complex-valued reciprocal convex inequality, some mode-dependent criteria are provided which ascertain that the estimation error system is asymptotically mean square stable. In addition, the estimator gains desired can be appropriately designed by resorting to feasible solutions of a set of complex matrix inequalities. One numerical example is also provided to demonstrate effectiveness of the estimation scheme proposed.

Robust filtering algorithm against hybrid-attacks and randomly occurring nonlinearities: Application to a quadrotor UAV

This paper addresses the filtering problem for a class of discrete-time cyber-physical system (CPS) in simultaneous presence of physical attack, denial of service attacks (DoS), false data injection (FDI) attacks and randomly occurring nonlinearities (RONs). The malicious attacks can be simultaneously launched at the physical and cyber space to assault/disturb the system's state or block the measurement transmission channels. Firstly, the physical attack is considered as an injected unknown information, which can modify the system's state; then, a unified framework is proposed under which the phenomena of DoS attacks and FDI attacks are depicted by two independent Bernoulli distributed random variables, respectively. Furthermore, the available random probability information of the hybrid attacks and RONs are taken into account in the developed state estimation algorithm. Subsequently, by employing the stochastic analysis and matrix techniques, the exponential boundedness of the state estimation error is discussed from the theoretical aspects. As a typical CPS, a quadrotor unmanned aerial vehicle(UAV) system is provided to demonstrate the effectiveness and practicability of the proposed state estimation method.

Dynamic event-triggered [formula omitted] state estimation for delayed complex networks with randomly occurring nonlinearities

This paper aims to iron out the dynamic event-triggered (ET) H∞ state estimation issue for a class of discrete time-delay complex networks (CNs) with randomly occurring nonlinearities (RONs). In the signal transmission among the nodes, the effect of time-varying delays is examined. The RONs under consideration is modelled by a series of random variables obeying Bernoulli distribution. In the design of state estimators, a dynamic ET scheme is utilized with the hope of improving the energy utilization efficiency. A sufficient condition is first derived to ensure the exponential mean-square (EMS) stability and H∞ performance index of the estimation error systems. Then, by using the matrix inequality technology, the desired state estimators are designed. Lastly, a numerical simulation example is given to show the usefulness of the proposed estimator design algorithm.

Leader-following mean square consensus of stochastic multi-agent systems with ROUs and RONs under periodically variable impulse time windows

In order to bring the theoretical results into harmony or agreement with the reality, this paper copes with the leader-following mean square consensus issue for a class of nonlinear stochastic multi-agent systems with randomly occurring nonlinearities (RONs) and randomly occurring uncertainties (ROUs) under impulse time windows (ITWs). As a majorizing preconfigured mechanism of the windows, the configuration named periodically variable impulse time windows (PVITWs) is put forward. It not only ensures the periodic-based layout of impulse time window (ITW), but also allows the position and width of the window to be arbitrarily adjustable, so that the preset of ITW is more suitable for applications. Based on the Lyapunov stability theory and graph theory, the corresponding sufficient mean square consensus criteria are given. Finally, the feasibility of theoretical analysis is illustrated by numerical simulations.

Resilient state estimation for time‐varying uncertain dynamical networks with data packet dropouts and switching topology: an event‐triggered method

In this study, the recursive state estimation method based on the event-triggered protocol is given for time-varying uncertain complex networks with switching topology (ST), data packet dropouts (DPDs), and randomly occurring non-linearities (RONs). The event-triggered strategy is given to regulate the communications and then save the limited network resources when the measurement information is transmitted. The phenomena of RONs, ST, and DPDs are depicted by some random variables obeying the Bernoulli distribution. Besides, the estimator gain perturbations are dealt with. The major aim of this study is to present a new state estimation algorithm for addressed time-varying uncertain complex networks such that, for all STs, DPDs, and RONs, a minimal upper bound of state estimation error covariance matrix is derived by designing an optimal estimator gain matrix. Finally, a numerical simulation is presented to show the effectiveness of the provided time-varying state estimation method.

Finite-Time Stability and Stabilization of Impulsive Stochastic Delayed Neural Networks With Rous and Rons

This paper mainly tends to investigate finite-time stability and stabilization of impulsive stochastic delayed neural networks with randomly occurring uncertainties (ROUs) and randomly occurring nonlinearities (RONs). Firstly, by constructing the proper Lyapunov-Krasovskii functional and employing the average impulsive interval method, several novel criteria for ensuring the finite-time stability of impulsive stochastic delayed neural networks are obtained by means of linear matrix inequalities (LMIs). Then, some conditions about the state feedback controller are derived to ensure the finite-time stabilization of impulsive stochastic delayed neural networks with ROUs and RONs. Finally, numerical examples are provided to demonstrate the effectiveness and feasibility of the proposed results.

A Unified Mean Square Consensus Criterion for Stochastic Multi-Agent Systems With ROUs and RONs Under Impulse Time Windows

This paper probes into the leader-following mean square consensus problem of stochastic multi-agent systems with randomly occurring uncertainties (ROUs) and randomly occurring nonlinearities (RONs) under impulse time windows (ITWs). A new concept named average left endpoint interval (ALEI) related to ITWs is proposed, which is inspired by the average impulsive interval. Based on ALEI and Lyapunov stability theory, a unified mean square consensus criterion for both stabilizing and interference impulses is attained. The introduction of ALEI allows for a larger upper bound of left endpoint interval and smaller lower bound of left endpoint interval, which makes the preset of ITWs more flexible and less conservative. The validity of the theoretical results is verified by the numerical simulation.

Nonstationary [formula omitted] filtering for Markov switching repeated scalar nonlinear systems with randomly occurring nonlinearities

This paper is concerned with nonstationary l2−l∞ filtering for Markov switching repeated scalar nonlinear systems (MSRSNSs) with randomly occurring nonlinearities (RONs), where measurement output is modeled by a mode-dependent random variable that satisfying Bernouli distribution. The new relationship are proposed to depict multiple mutually independent Markov chains between original MMSRSNSs and nonstationary filters. By constructing a proper Lyapnov function, the MSRSNSs is stochastically stable with l2−l∞ performance level is guaranteed. Accordingly, the nonstationary filters are designed, where filters are characterised by a two-layer structure. The paper provides a numerical example verifying the efficacy of established technique.

Robust Fault Detection for Nonlinear Discrete Systems with Data Drift and Randomly Occurring Faults Under Weighted Try-Once-Discard Protocol

This paper is concerned with the robust fault detection (FD) problem for discrete uncertain systems with data drift, randomly occurring faults (ROFs) and randomly occurring nonlinearities (RONs) under weighted try-once-discard (WTOD) protocol. The phenomena of the RONs are characterized by the Bernoulli distributed variable. The Markov chain with two states is introduced to describe the ROFs in the system. The data drift is depicted by an array of mutually independent stochastic variables with individual probabilistic distribution functions. Moreover, the WTOD protocol is utilized to lessen the incidence of data collisions and enhance the communication efficiency. An FD filter is established, and sufficient conditions are proposed under which the resultant filtering error system is stochastically stable and achieves a prescribed $$H_{\infty }$$ performance irrespective of the simultaneous presence of the WTOD protocol mechanism, ROFs, RONs and data drift. Besides, the form of the desired filter parameters is presented in terms of the solutions to certain matrix inequalities. Finally, two examples are exploited to demonstrate the feasibility of the obtained robust FD algorithm.

Resilient $$H_{\infty }$$ H ∞ Filtering for Stochastic Systems with Randomly Occurring Gain Variations, Nonlinearities and Channel Fadings

In this paper, the resilient $$H_{\infty }$$ filtering problem for a class of stochastically uncertain discrete-time systems with randomly occurring gain variations, nonlinearities (RONs) and channel fadings is investigated. Considering the existences of random parameter fluctuations in both actual plant and filter, a sequence of mutually independent random variables with known probabilistic distribution is utilized to better describe the stochastic characteristics. The phenomenon RONs, which is regulated by random variables with Bernoulli distribution, is used to reflect the reality that actual systems are generally nonlinear. The system measurement with channel fadings is described by the stochastic Rice fading model, where the channel coefficients are a set of mutually independent random variables with any probability density function. It is worthwhile to mention that a series of slack matrix variables are exploited to eliminate the coupling between the plant matrices and the designed filter parameters by providing free dimensions in the solution space. The purpose of this paper is to design a resilient $$H_{\infty }$$ filter such that the filtering error system is asymptotically stable with a prescribed $$H_{\infty }$$ performance level. And sufficient conditions for filter design are eventually converted into solving a convex optimization problem over linear matrix inequalities. A numerical example is presented to illustrate effectiveness of the proposed approach.

Finite-Time and Fixed-Time Consensus of Nonlinear Stochastic Multi-Agent Systems With ROUs and RONs via Impulsive Control

This paper discusses the finite-time and the fixed-time consensus of nonlinear stochastic multi-agent systems (NSMSs) with randomly occurring uncertainties (ROUs) and randomly occurring nonlinearities (RONs) in a leader-following framework. Nonlinear control and impulsive pinning control protocols are designed to guarantee that follower agents realize consensus with the leader agent in finite time(fixed time). Based on the finite-time and fixed-time consensus theory, stochastic analysis technique, comparison system theory and algebra graph theory, some sufficient conditions are proposed to guarantee the finite-time and fixed-time consensus of systems. Then, the setting times of finite-time consensus and fixed-time consensus are estimated. Finally, two simulation examples are presented to illustrate the correctness of our conclusions.

具有随机发生非线性和一步时滞的时变系统的滤波问题

具有随机发生非线性和一步时滞的时变系统的滤波问题

H∞ control for time-delay systems with randomly occurring nonlinearities subject to sensor saturations, missing measurements and channel fadings

The H∞ control problem for a class of time-delay systems with randomly occurring nonlinearities (RONs) is addressed in this paper. Sensor saturations, missing measurements and channel fadings are governed by random variables obeying the Bernoulli distributions. The measurement output is subject to both data missing and randomly occurring sensor saturations (ROSSs) described by sector-nonlinearities as well as the channel fadings caused typically in wireless communication. The aim of the addressed problem is to design a full-order dynamic output-feedback controller such that the closed-loop system is exponentially mean-square stable and satisfies the prescribed H∞ performance constraint. Sufficient conditions are presented by resorting to intensive stochastic analysis and matrix inequality techniques, which not only guarantee the existence of the desired controller for all possible time-delays, RONs, missing measurements and ROSSs but also lead to the explicit expressions of such controllers. Finally, a numerical example is given to demonstrate the applicability of the proposed control scheme.

P.7.b.004 - The impact of attention deficit hyperactivity symptoms on mindfulness

The H∞ control problem for a class of time-delay systems with randomly occurring nonlinearities (RONs) is addressed in this paper. Sensor saturations, missing measurements and channel fadings are governed by random variables obeying the Bernoulli distributions. The measurement output is subject to both data missing and randomly occurring sensor saturations (ROSSs) described by sector-nonlinearities as well as the channel fadings caused typically in wireless communication. The aim of the addressed problem is to design a full-order dynamic output-feedback controller such that the closed-loop system is exponentially mean-square stable and satisfies the prescribed H∞ performance constraint. Sufficient conditions are presented by resorting to intensive stochastic analysis and matrix inequality techniques, which not only guarantee the existence of the desired controller for all possible time-delays, RONs, missing measurements and ROSSs but also lead to the explicit expressions of such controllers. Finally, a numerical example is given to demonstrate the applicability of the proposed control scheme.

Fault estimation for time-varying Markovian jump systems with randomly occurring nonlinearities and time delays

This paper is concerned with the fault estimation problem for a class of time-varying Markovian jump systems including randomly occurring nonlinearities (RONs) and multiple time-varying stochastic communication delays. Both the parameters and the nonlinear function are considered in the time-varying system, where the Markov chain is adopted to reflect the mode transformation phenomenon at different times. The multiple time-varying stochastic communication delays are introduced via stochastic variable sequences following Bernoulli distribution. Such a set of Bernoulli sequences comprising independent variables is utilized to represent the generation of nonlinear interferences in the form of random. The main objective proposed in this paper is to finish a time-varying fault estimator in order to meet the prescribed average filtering performance constraint. Through adopting the Lyapunov–Krasovakii functional as well as the stochastic theory, several corresponding requirements to guarantee such designed estimator are given by LMI approach. A simulation is provided for verifying the feasibility of the addressed methods.

Fuzzy [Formula: see text] output-feedback control for the discrete-time system with channel fadings, sector nonlinearities, and randomly occurring interval delays and nonlinearities.

In this paper, the fuzzy H_{infty} output-feedback control problem is investigated for a class of discrete-time T-S fuzzy systems with channel fadings, sector nonlinearities, randomly occurring interval delays (ROIDs) and randomly occurring nonlinearities (RONs). A series of variables of the randomly occurring phenomena obeying the Bernoulli distribution is used to govern ROIDs and RONs. Meanwhile, the measurement outputs are subject to the sector nonlinearities (i.e. the sensor saturations) and we assume the system output is y(k)=0, kin{-l,ldots, 0}. The Lth-order Rice model is utilized to describe the phenomenon of channel fadings by setting different values of the channel coefficients. The aim of this work is to deal with the problem of designing a full-order dynamic fuzzy H_{infty} output-feedback controller such that the fuzzy closed-loop system is exponentially mean-square stable and the H_{infty} performance constraint is satisfied, by means of a combination of Lyapunov stability theory and stochastic analysis along with LMI methods. The proposed fuzzy controller parameters are derived by solving a convex optimization problem via the semidefinite programming technique. Finally, a numerical simulation is given to illustrate the feasibility and effectiveness of the proposed design technique.

The consensus of multi-agent systems with uncertainties and randomly occurring nonlinearities via impulsive control

In many real-world multi-agent systems, the intrinsic dynamics of the agents are usually dynamic rather than static as the environmental uncertainty and the additive exogenous disturbance input. This paper aims at investigating the consensus problem of multi-agent systems with uncertainties and randomly occurring nonlinearities (RONs). Consider the robust and lost-cost, an effective impulsive control protocol is designed. Based on the Lyapunov stability theory and hybrid control theory, sufficient conditions are obtained to ensure consensus of multi-agent systems. Furthermore, both mathematical investigations and numerical simulations are presented to demonstrate the effectiveness of the proposed approaches.

Finite-Horizon ${\cal H}_{\infty}$ Control for Discrete Time-Varying Systems With Randomly Occurring Nonlinearities and Fading Measurements

This technical note deals with the ${\cal H}_{\infty}$ control problem for a class of discrete time-varying nonlinear systems with both randomly occurring nonlinearities and fading measurements over a finite-horizon. The system measurements are transmitted through fading channels described by a modified stochastic Rice fading model. The purpose of the addressed problem is to design a set of time-varying controllers such that, in the presence of channel fading and randomly occurring nonlinearities, the ${\cal H}_{\infty}$ performance is guaranteed over a given finite-horizon. The model transformation technique is first employed to simplify the addressed problem, and then the stochastic analysis in combination with the completing squares method are carried out to obtain necessary and sufficient conditions of an auxiliary index which is closely related to the finite-horizon ${\cal H}_{\infty}$ performance. Moreover, the time-varying controller parameters are characterized via solving coupled backward recursive Riccati difference equations (RDEs). A simulation example is utilized to illustrate the usefulness of the proposed controller design scheme.