Nonlinear Networked Control Systems Research Articles

Attack-Dependent Adaptive Event-Triggered Security Fuzzy Control for Nonlinear Networked Cascade Control Systems Under Deception Attacks

This article investigates the issue of H∞ security output feedback control for a nonlinear networked cascade control system with deception attacks. First, to further reduce the amount of communication data, reasonably schedule network resources, and alleviate the impact of multi-channel deception attacks, an attack-dependent adaptive event-triggered mechanism is introduced into the primary network channel, and its adaptive triggered threshold can be adjusted according to the random attack probability. Secondly, the output dynamic quantization of the secondary network channel is considered. Then, a novel security cascade output feedback controller design framework based on the Takagi–Sugeno (T-S) fuzzy networked cascade control system under deception attacks is established. In addition, by introducing the Lyapunov–Krasovskii stability theory, the design conditions of the controller are given. Finally, the effectiveness and superiority of the proposed design strategies are verified by two simulation examples of power plant boiler–turbine system and power plant boiler power generation control system.

Fault-tolerant control of nonlinear networked control systems based on a dynamic event-triggered mechanism under dual cyber attacks

In this paper, the fault-tolerant control problem of actuator failures for nonlinear networked control systems under deception attacks and denial-of-service attacks is investigated based on a dynamic event-triggered mechanism. Firstly, denial-of-service attacks and deception attacks are modeled as two independent variables that conform to the Bernoulli probability distribution, and the fault-tolerant control problem of nonlinear networked control systems is considered under the possible actuator failures. Then, an appropriate Lyapunov-Krasovskii functional is constructed, and sufficient conditions for ensuring asymptotic stability of the closed-loop systems are obtained using Jensen inequality. Meanwhile, the designed fault-tolerant controller can handle possible actuator failures. Finally, the effectiveness of the proposed method is proved through an example.

Event‐Triggered Generalized Extended State Observer‐Based Control for Nonlinear Networked Systems Under Gain Variation and Multi‐Channel Attacks

ABSTRACTThis paper investigates the event‐triggered generalized extended state observer‐based estimation issue for a class of nonlinear networked control systems with unmodeled dynamics, external disturbances and multi‐channel attacks. In order to resist different forms of attack threats on multiple communication channels from sensors to the observer, Markov chain is introduced to describe the stochastic switching or jumping behavior between different attack modes. Considering the limited network resources, the measured outputs are transmitted to the observer only when the triggering conditions are met. Moreover, the parameters modeled by the Bernoulli process are adopted to help analyze potential random gain variations of the generalized extended state observer. By employing Lyapunov stability theory and stochastic systems analysis, sufficient conditions are derived to ensure that the augmented system is exponentially bounded in mean square, and the expected observer gains are further determined through linear matrix inequalities. Finally, a numerical example and a simulation related to the RLC series circuit system are conducted, illustrating the proposed method's effectiveness.

Data-Driven Control for Nonlinear Networked Systems on Basis of Two Description Coding Scheme

Data-Driven Control for Nonlinear Networked Systems on Basis of Two Description Coding Scheme

Observer-Based Fuzzy Control for Nonlinear Networked Systems Under Multichannel Attacks With Indirectly Accessible Mode Information

Observer-Based Fuzzy Control for Nonlinear Networked Systems Under Multichannel Attacks With Indirectly Accessible Mode Information

Dynamic event‐triggered model‐free adaptive control for networked control systems with random packet loss

AbstractA novel dual‐channel dynamic event‐triggered model‐free adaptive control method with compensation is proposed for nonlinear networked control systems (NCSs) subjected to random packet loss. In order to tackle the issue of redundant data transmission, a dual‐channel triggering control framework is designed, where data transmission only occurs upon meeting the triggering conditions. Compared with the traditional static event‐triggered schemes with fixed triggering thresholds, the proposed method allows for dynamically adjustable triggering thresholds. This means that the number of data transmissions in the forward and feedback channels can be further reduced. In addition, considering the detrimental impact of random packet loss and output event‐triggered on the system, compensation is applied to the system's output data using the linear data model of the nonlinear system, which is directly derived from I/O data without incorporating any other mechanistic model information, thereby ensuring optimal control performance. In contrast to existing results, the proposed control strategy can enhance system performance while conserving network communication resources. The effectiveness and superiority of the proposed scheme have been validated through two simulations.

Observer-based self-triggered adaptive neural network control for nonlinear systems with prescribed time

This paper is concerned with the adaptive neural networks (NNs) prescribed-time control problem for a class of strict-feedback nonlinear systems subject to unmeasured states via the self-triggered control (STC). By developing a new state observer with prescribed-time function, an adaptive NNs self-triggered controller is designed to solve the problem of prescribed-time performance (PTP). Due to the initiative of the STC, it has excellent practical significance in terms of contracting computing resources and network communication resources. With the proposed new strategy, the PTP of the closed-loop system can be guaranteed, and all the signals within the closed-loop system are bounded. Finally, the practicability and effectiveness of the above prescribed-time STC algorithm are verified via some physical simulations.

Observer-Based Adaptive Event-Triggered Control for Nonlinear Networked Systems Under Multiple Cyber Attacks

Observer-Based Adaptive Event-Triggered Control for Nonlinear Networked Systems Under Multiple Cyber Attacks

Complete Controllability of Nonlinear Neural Network Control Systems

Complete Controllability of Nonlinear Neural Network Control Systems

Asynchronous Quantizer-Dependent Event-Triggered Control for Nonlinear Networked Control System With Observer-Based Controller

Asynchronous Quantizer-Dependent Event-Triggered Control for Nonlinear Networked Control System With Observer-Based Controller

Asynchronous encoder-based event-triggered control of nonlinear networked control system under DoS attacks

This paper discusses the stability of a nonlinear networked control system with limited channels under denial-of-service (DoS) attacks. The transmitted signals in both plant-to-controller and controller-to-plant channels are asynchronous, and each channel is under the threat of DoS attacks. To overcome the negative effects raised by imperfect channels and DoS attacks, the relationship among encoder structure, DoS attacks and triggering characteristics is thoroughly analyzed and the asynchronous encoder-based event-triggered control mechanisms are novelly proposed. Moreover, both encoding and decoding schemes are designed to ensure the transmission efficiency. Furthermore, the QSR-dissipativity, finite-gain L2 stability and input-feedforward output-feedback passivity of the nonlinear system are analyzed. Finally, experiments are conducted to illustrate the validity and superiority of the proposed scheme.

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

Adaptive Fuzzy Control for Fractional-Order Networked Control Systems with Input Time Delay and Data Loss

This paper considers the adaptive fuzzy control of fractional-order nonlinear networked control systems subjected to network-induced input delay and data loss. To approximate unknown functions, fuzzy logic systems are employed. Furthermore, the Pade approximation method and an intermediate variable are introduced to eliminate the impact of input delay, and an adaptive fuzzy controller is designed using backstepping technology. Based on fractional-order Lyapunov stability theory, the proposed method can ensure that all signals are uniformly ultimately bounded, and the tracking error can converge to a small region of the origin. Two simulation examples are provided to verify the viability of the control method.

Robust Tracking Control Design for a Class of Nonlinear Networked Control Systems Considering Bounded Package Dropouts and External Disturbance

Robust Tracking Control Design for a Class of Nonlinear Networked Control Systems Considering Bounded Package Dropouts and External Disturbance

Parallel event-triggered dynamic output feedback control for nonlinear networked systems with randomly occurring multiple communication delays

This paper investigates the issue of parallel event-triggered (PET) dynamic output feedback control for networked control systems (NCSs) built by the discrete-time T–S fuzzy model. Initially, a novel PET dynamic output feedback controller is designed. Based on saving network resources and enhancing transmission efficiency, the PET strategy makes full use of relative and absolute triggering condition information. And the dynamic output feedback control can not only address unmeasurable states but also provide a better response to the internal information of the system. The random multiple communication delays and the ℓth-order Rice fading model with different channel coefficients, meanwhile, are both applied in the system. It is closer to the actual situation. Subsequently, new sufficient conditions of membership function dependence are proposed via the staircase function approximation method combined with Lyapunov stability. It guarantees that the system is exponentially mean square stable (EMSS) with H∞ performance. Ultimately, the presented results are validated using two examples. In the future, we will explore the correlative research of T–S fuzzy Markov jump NCSs.

Adaptive event‐triggered tracking control for nonlinear networked systems with dynamic quantization and deception attacks

AbstractThe problem of adaptive event‐triggered security tracking controller design is studied for interval type‐2 (IT2) Takagi‐Sugeno (T‐S) fuzzy‐approximation‐based nonlinear networked systems with deception attacks, privacy protection, and dynamic quantization in this paper. To reduce the network burden, the adaptive event‐triggered schemes (AETSs) and dynamic quantization are introduced to reduce unnecessary transmission and the number of network packets, respectively. Under the framework of network security, a novel security co‐design scheme with the tracking controller, AETSs, and the quantizers is developed, in which the privacy protection of the reference model and the deception attacks of the controller are taken into consideration simultaneously. Sufficient conditions for the results system to be mean‐square asymptotically stable with a tracking performance are established. Finally, two practical simulation examples demonstrate the effectiveness and novelty of the proposed design strategy.

Adaptive Optimal Control of Networked Nonlinear Systems With Stochastic Sensor and Actuator Dropouts Based on Reinforcement Learning.

This article investigates the adaptive optimal control problem for networked discrete-time nonlinear systems with stochastic packet dropouts in both controller-to-actuator and sensor-to-controller channels. A Bernoulli model-based Hamilton-Jacobi-Bellman (BHJB) equation is first developed to deal with the corresponding nonadaptive optimal control problem with known system dynamics and probability models of packet dropouts. The solvability of the nonadaptive optimal control problem is analyzed, and the stability and optimality of the resulting closed-loop system are proven. Two reinforcement learning (RL)-based policy iteration (PI) and value iteration (VI) algorithms are further developed to obtain the solution to the BHJB equation, and their convergence analysis is also provided. Furthermore, in the absence of a priori knowledge of partial system dynamics and probabilities of packet dropouts, two more online RL-based PI and VI algorithms are developed by using critic-actor approximators and packet dropout probability estimator. It is shown that the concerned adaptive optimal control problem can be solved by the proposed online RL-based PI and VI algorithms. Finally, simulation studies of a single-link manipulator are provided to illustrate the effectiveness of the proposed approaches.

Event-Triggered State Estimation and Control for Networked Nonlinear Systems Under Dynamic Sparse Attacks

Event-Triggered State Estimation and Control for Networked Nonlinear Systems Under Dynamic Sparse Attacks

Learning-Based Model-Free Adaptive Control for Nonlinear Discrete-Time Networked Control Systems Under Hybrid Cyber Attacks.

A novel learning-based model-free adaptive control (LMFAC) approach is presented in this article for a class of unknown nonaffine nonlinear discrete-time networked control systems (NCSs) subject to hybrid cyber attacks. The aperiodic denial-of-service (DoS) attacks and persistent deception attacks are assumed to arise in feedback channels, which could result in the absence or authenticity lackness of system signals sent to the controller. With the aid of dynamic linearizaton technology, the equivalent dynamic linearized data models of considered NCSs are first established only based on I/O information instead of the knowledge of mathematical models that are commonly used under the model-based control framework. Then, an LMFAC scheme is designed on the basis of occurred maximum DoS attacks interval to adaptively tune the attenuation coefficient of the input signal for improving system performance during the next DoS attacks interval. Finally, the boundedness of tracking error is rigorously proved through the contraction mapping principle and the effectiveness of the proposed pure data-driven LMFAC method is demonstrated via simulations.