Uncertain Networked Systems Research Articles

Fixed-time and predefined-time group-bipartite consensus for uncertain networked Euler-Lagrange systems

This paper aims to investigate the fixed-time and predefined-time group-bipartite consensus (FTGBC, PTGBC) problems for uncertain networked Euler-Lagrange systems (NELSs) using neural network (NN). An auxiliary sliding mode variable describing the local communication topology is first introduced within the fixed-time and predefined-time frameworks, and the NN technique is used to estimate online uncertainty of NELSs, which ensures the achievement of the desired FTGBC and PTGBC goals, respectively. Moreover, the allowable upper bound expressions for fixed-time and predefined-time are derived by analyzing the convergence of the closed-loop control systems, thus ensuring the adaptability, stability, and robustness of the proposed FTGBC and PTGBC control schemes, which are further verified by numerical simulation examples.

Quantized feedback integral sliding mode control for uncertain networked linear systems via event-triggered approach

This paper investigates the design problem of quantized event-triggered (ET) integral sliding mode (ISM, SM) controller for uncertain networked linear systems. Firstly, this paper proposes a novel ISM surface constructed using only quantized ET states and applied to the design of the controller. The quantized ET ISM surface in this paper divides the integrals from t0 to t into the sum of adjacent ET intervals and replaces ET states with quantized ET states. Secondly, based on state error and quantized ET SM error, novel ET conditions are constructed to decide whether to update the current control signal or not. Thirdly, this paper proves the existence of minimum inter-event times under “zoom-out” and “zoom-in” phases respectively, avoiding Zeno phenomenon. Finally, to prove the validity of the proposed method, two comparative simulation results are given.

Distributed Optimal Control With Recovered Robustness for Uncertain Network Systems: A Complementary Design Approach

Distributed Optimal Control With Recovered Robustness for Uncertain Network Systems: A Complementary Design Approach

Fault-tolerant adaptive event-triggered integral sliding mode control for uncertain networked systems under actuator failures

This paper investigates the design of fault-tolerant adaptive event-triggered integral sliding mode control (FTAETISMC) for a class of uncertain systems in the presence of partial actuator failures. The proposed control scheme guarantees the closed-loop system to operate properly in case of partial actuator failures and ensures the reachability of the sliding mode surface. In order to reduce the consumption of system resources, an adaptive event-triggered (ET) mechanism is introduced, where the threshold is based on a function of a non-negative adaptive parameter. By choosing the parameter of the integral sliding mode manifold, the asymptotic stability of the system can be achieved. To ensure that the system does not suffer from Zeno phenomenon, there exists a positive lower bound of the inter-event time. Finally, simulation results of a numerical example are presented to prove the benefits of the proposed method.

Neural network-based robust consensus tracking for uncertain networked Euler-Lagrange systems with time-varying delays and output constraints

This paper mainly focuses on the cooperative robust consensus tracking problem of uncertain networked Euler-Lagrange systems (NELSs) with time-varying delays and output constraints. By systematically integrating the neural network (NN) adaptive technique and the logarithmic type Barrier Lyapunov Function (BLF) in combination with the additional robust control law, two distributed robust consensus schemes for uncertain NELSs are proposed for two cases of time-varying communication and input delays respectively, which can fully guarantee to constrain the output consensus error within a safety region simultaneously. Furthermore, numerical simulation examples are provided to demonstrate the comparable potential advantages of the proposed robust control law over some existing algorithms, including adaptability, stability, and robustness, as well as delay effects.

Resilient distributed secure consensus control for uncertain networked agent systems under hybrid DoS attacks

In this paper, the problem of resilient distributed secure consensus control for uncertain networked agent systems under hybrid denial-of-service (DoS) attacks is investigated. The current situation is that there exists a cyber layer connecting the network control units and a physical layer with specific physical links in the networked agent systems. Both the communication networks connecting the two layers and within the cyber layer may be attacked by DoS maliciously. These two attack scenarios have different impacts on the networked system. The former focuses on updating the control inputs timely, while the latter influences the connection weight of the cyber communication topology. Firstly, a distributed security consensus framework is proposed for the case that updates of signals are destroyed by DoS attacks between two layers, in which an acknowledgment (ACK)-based attack detection and a recovery mechanism are introduced, and a self-triggered based distributed control protocol is designed. On the premise of avoiding Zeno behavior, the relationship between trigger intervals and DoS attack characteristics is revealed. Secondly, corresponding asynchronous switching topology method is developed for secure consensus of the networked systems when DoS attacks are launched within the cyber layer. In addition, we found that large signals jump during switching and triggering will generate pulses and affect system stability. Therefore, a saturation function is introduced to constrain the fluctuation range of the signal. Finally, the effectiveness of the design scheme is verified by the simulation results of multi-robot systems.

Low-Computation Adaptive Saturated Self-Triggered Tracking Control of Uncertain Networked Systems

In this paper, a low-computation adaptive self-triggered tracking control scheme is proposed for a class of strict-feedback nonlinear systems with input saturation. By introducing two novel error transformation functions, the designed low-computation adaptive control scheme can overcome the problem of complexity explosion in the absence of any filters, such that the developed control scheme is more applicable. In addition, to save communication resources in networked systems, a self-triggered communication strategy is proposed which can predict the next trigger point based on the current information. Compared with traditional event-triggered mechanisms, the computational burden arising from continuous monitoring of threshold conditions was successfully avoided. Furthermore, the input saturation problem considered in this paper prevents the overload phenomenon caused by signal jumps, and the adverse effects are compensated by introducing an auxiliary system. The effectiveness of the developed control scheme is verified through a simulation example.

Adaptive Event-triggered H2/H∞ Control for Uncertain Networked Systems With Hybrid Cyber Attacks and Imperfect Network

Adaptive Event-triggered H2/H∞ Control for Uncertain Networked Systems With Hybrid Cyber Attacks and Imperfect Network

Fuzzy-Based Robust Precision Consensus Tracking for Uncertain Networked Systems With Cooperative–Antagonistic Interactions

In bipartite consensus tracking (BCT) tasks for nonlinear multiagent systems, stochastic disturbances and actuator faults are regarded as essential factors that hamper effective controller formulation and tracking precision improvement. To address these difficulties, we design an improved finite-time performance function (FTPF) for a fuzzy fault-tolerant distributed cooperative control scheme to achieve finite-time robust precision BCT tasks for nonlinear multiagent systems. The parameter selection range of the improved FTPF is relaxed, which renders systems to achieve better transient performance. Benefitting from stochastic Lyapunov stability theory, it is shown that all signals of systems are semi-global uniformly ultimately bounded in probability, and bipartite consensus errors can satisfy the arbitrary precision with probability in the predefined time. Finally, to verify its effectiveness, the proposed control scheme is applied to BCT tasks of a group of vehicles, which manifests anticipated control performance under various uncertainties.

SMC for Discrete-Time Networked Semi-Markovian Switching Systems With Random DoS Attacks and Applications

This article concentrates on the discrete-time sliding mode control (DTSMC) problem for uncertain networked semi-Markovian switching systems (S-MSSs) under random denial-of-service (DoS) attacks. The semi-Markovian kernel (SMK) approach is utilized such that the switching among different modes is mutually governed by the transition probability and the sojourn-time distribution function. Considering randomly occurring DoS attacks, a sliding mode function related to the attack probability is constructed to analyze the impact of malicious attacks. Then, sufficient conditions under the equivalent DTSMC law are derived in view of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> -error mean square stability criterion. Furthermore, the synthesis problem of the proposed DTSMC law ensures that the resulting closed-loop system dynamics can be driven onto the prespecified sliding region within a limited time. Finally, an electronic throttle control system is shown to validate the proposed algorithm.

Event-triggered integral sliding mode control for uncertain networked linear control systems with quantization.

In this paper, the integral sliding mode (ISM, SM) controller is designed to address the problem of implementing non-periodic sampled data for a class of networked linear systems with matched and unmatched uncertainties. Due to the redesigned gain of the nominal controller, the feedback control used by the nominal controller guarantees the asymptotic stability of the uncertain networked linear system. The discontinuous control uses intermittent control based on the reaching law to achieve the finite-time reachability of practical SM band. Based on the defined measurement error, the event-triggered (ET) condition can be derived, and furthermore, it guarantees a sufficient condition for the existence of the actual SM. On this basis, a quantization scheme is added to further decrease the network transmission burden of the linear system. No Zeno behavior occurs in the system owing to the existence of a positive lower bound of inter-event time. Compared with the conventional integral sliding mode control (ISMC, SMC), the proposed control law can not only relieve the network burden, but also decrease the transmission energy loss. Finally, simulation results of a numerical example and a mass-spring damping system demonstrate the effectiveness of the proposed method.

Robust Sequential Fusion Estimation Based on Adaptive Innovation Event-Triggered Mechanism for Uncertain Networked Systems.

In order to reduce the transmission pressure of the networked system and improve its robust performance, an adaptive innovation event-triggered mechanism is designed for the first time, and based on this mechanism, the robust local filtering algorithm for the multi-sensor networked system with uncertain noise variances and correlated noises is presented. To avoid calculating the complex error cross-covariance matrices, applying the sequential fusion idea, the robust sequential covariance intersection (SCI) and sequential inverse covariance intersection (SICI) fusion estimation algorithms are proposed, and their robustness is analyzed. Finally, it is verified in the simulation example that the proposed adaptive innovation event-triggered mechanism can reduce the communication burden, the robust local filtering algorithm is effective for the uncertainty generated by the unknown noise variances, and two robust sequential fusion estimators show good robustness, respectively.

Adaptive Appointed-Time Consensus Control of Networked Euler–Lagrange Systems With Connectivity Preservation

With consideration of motion control performance and efficient information communication, the synchronization problem on communication connectivity preservation and guaranteed consensus performance for networked mechanical systems has attracted considerable attention in recent years. Different from the existing works, this article investigates a brand-new appointed-time consensus control approach for uncertain networked Euler-Lagrange systems on a directed graph via exploring the prescribed performance control structure. First, a two-layer prescribed performance envelope is formulated via using an appointed-time convergent function for position-related and velocity-related consensus errors, respectively. Then, a simple state-feedback virtual controller with online adaptive performance adjustment is developed to preserve the communication connectivity. Moreover, to guarantee the velocity consensus of the networked systems and improve the position consensus accuracy, an appointed-time adaptive controller is designed by applying the norm inequality to the system uncertainties and external disturbances. Compared to the existing consensus control approaches, the prime advantage of the proposed one is that the constraints generated from the communication ranges are approximated by a time-varying contractive performance envelope, wherein, the appointed-time convergence and steady-state tracking accuracy are preassigned a priori. Meanwhile, no repeated logarithmic error transformations are required in the relevant controller design, which implies that the complexity of the devised control laws has decreased dramatically. Finally, two groups of illustrative examples are organized to validate the effectiveness of the proposed consensus control approach.

Centralized fusion robust filtering for networked uncertain systems with colored noises, one-step random delay, and packet dropouts

This paper studies the estimation problem for multisensor networked systems with mixed uncertainties, which include colored noises, same multiplicative noises in system parameter matrices, uncertain noise variances, as well as the one-step random delay (OSRD) and packet dropouts (PDs). This study utilizes the centralized fusion (CF) algorithm to combing all information received by each sensor, which improve the accuracy of the estimation. By using the augmentation method, de-randomization method and fictitious noise techniques, the original uncertain system is transformed into an augment model with only uncertain noise variances. Then, for all uncertainties within the allowable range, the robust CF steady-state Kalman estimators (predictor, filter, and smoother) are presented based on the worst-case CF system, in light of the minimax robust estimation principle. To demonstrate the robustness of the proposed CF estimators, the non-negative definite matrix decomposition method and Lyapunov equation approach are employed. It is proved that the robust accuracy of CF estimator is higher than that of each local estimator. Finally, the simulation example applied to the uninterruptible power system (UPS) with colored noises and multiple uncertainties illustrates the effectiveness of the proposed CF robust estimation algorithm.

Adaptive Neural Security Control for Networked Singular Systems Under Deception Attacks

This paper studies the issue of the adaptive neural security controller design for uncertain networked singular systems in the presence of deception attacks. Considering that the attack signal is unknown, the neural networks technique is exploited to approximate the attack signal, which eliminates the assumption that the attack signal has a known upper bound. By combining the state feedback with the estimated information of the attack, the impact of the attack is effectively compensated. Furthermore, a novel Lyapunov function, including the decomposed state vector and the weight matrix estimation error, is established to evaluate the bounded area of the system state. Finally, a numerical example substantiates the validity of the theoretical results.

Quadratic Stabilization of Takagi-Sugeno Fuzzy Discrete-time Uncertain Networked Systems

For one class of Takagi-Sugeno uncertain time-delay discrete-time systems this paper proposes an extended way to account related constraints in problem solving of the networked control system quadratic stability reflecting sensor drifts and communication delays. The new design conditions are proposed, exploiting a set of linear matrix inequalities to guaranty the closed-loop system quadratic stability where a specific Lyapunov-Krasovskii functional is exploited. Combining into the functional the disturbance transfer function H∞ norm upper bound, the design task is defined as a multi-objective robust control problem, while only the set of time-invariant control law gains are used in the fuzzy parallel structure of the controller realization. A numerical example is included to assess the feasibility of the technique and its applicability.

Robust Fault Detection for Uncertain Delayed Systems With Measurement Outliers Under Stochastic Communication Protocol

In this paper, the robust fault detection (RFD) problem is studied for uncertain networked systems with time delay and measurement outliers under limited communication condition, where the time delay is presumed to occur randomly and the occurrence probability is considered to be uncertain. Specifically, in order to reduce the communication burden, the stochastic communication protocol (SCP) is introduced to adjust the transmission of measurement signals. Moreover, a fault detection filter (FDF) with saturation constraints is constructed to achieve the purpose of properly eliminating possible measurement outliers. Subsequently, a generalized sector condition is utilized to derive the stability criteria of the system on the basis of appropriate Lyapunov functional. In addition, a traditional FDF is also designed to compare with the saturation-constrained FDF. It is shown that the FDF with saturation constraints can properly avoid the influence of outliers on the detection effect. Finally, two simulations are presented to show the superiority and the practicality of the developed saturation-constrained fault detection (FD) filtering algorithm.

Multiple delay-dependent event-triggered finite-time [formula omitted] filtering for uncertain networked random systems against state and input constraints

In this article, multiple delay-dependent event-triggered finite-time H∞ filtering as well as fault-tolerant control is discussed for networked random systems subject to state as well as input constraints. This is the first time to explore fault detection issue via filtering for networked random models. First, an event-triggered scheme is designed. Secondly, a zero order holder is studied. According to this event-triggered mechanism, a fault detection filter, a fault weighting system and a controller are investigated for the networked random system. And then an augmented system can be got. Sufficient conditions of mean-square finite-time boundedness with input-output finite-time mean square stabilization can be provided through the framework of linear matrix inequalities. Finally, these effectiveness and merits are verified by an example.

A Two-Phase Distributed Filtering Algorithm for Networked Uncertain Systems with Fading Measurements under Deception Attacks.

In this paper, the distributed filtering problem is addressed for a class of discrete-time stochastic systems over a sensor network with a given topology, susceptible to suffering deception attacks, launched by potential adversaries, which can randomly succeed or not with a known success probability, which is not necessarily the same for the different sensors. The system model integrates some random imperfections and features that are frequently found in real networked environments, namely: (1) fading measurements; (2) multiplicative noises in both the state and measurement equations; and (3) sensor additive noises cross-correlated with each other and with the process noise. According to the network communication scheme, besides its own local measurements, each sensor receives the measured outputs from its adjacent nodes. Based on such measurements, a recursive algorithm is designed to obtain the least-squares linear filter of the state. Thereafter, each sensor receives the filtering estimators previously obtained by its adjacent nodes, and these estimators are all fused to obtain the desired distributed filter as the minimum mean squared error matrix-weighted linear combination of them. The theoretical results are illustrated by a simulation example, where the efficiency of the developed distributed estimation strategy is discussed in terms of the error variances.

Moving Horizon Estimation for Uncertain Networked Control Systems with Packet Loss

This paper studies a moving horizon estimation approach to solve the constrained state estimation problem for uncertain networked systems with random packet loss. The system model error range is known, and the packet loss phenomena are modeled by a binary switching random sequence. Taking the model error, the packet loss, the system constraints, and the network transmission noise into account, a time-varying weight matrix is obtained by solving a least-square problem. Then, a robust moving horizon estimator is designed to estimate the system state by minimizing an optimization problem with an arrival cost function. The proposed estimator ensures that the optimal estimated state can be obtained in the worst case. Furthermore, the asymptotic convergence of the estimator is analyzed and some sufficient conditions for convergence are given. Finally, the validity of the proposed approach can be demonstrated by numerical simulations.