Nonlinear Networked Control Systems Research Articles

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.

Improved nonlinear model‐free adaptive iterative learning control in DoS attack environment

AbstractThis paper investigates the design of a model‐free adaptive iterative learning controller(MFAILC) based on sampled‐data under the presence of denial‐of‐service(DoS) attacks in nonlinear networked control systems. First, the MFAILC is presented only using I/O data, where a compensation mechanism for DoS attacks is proposed. With dynamic linearization techniques, the nonlinear system is transformed into a linear system in the iteration domain. Then an improved MFAILC is designed to actively compensate the lost data caused by DoS attacks, where the estimation of pseudo‐partial derivative (PPD) is improved by establishing the AR model. The proposed algorithm can weaken the adverse effects of the DoS attacks and ensure the excellent tracking performance of the system. Finally, the stability of the method is proved, and the effectiveness of the proposed algorithm is demonstrated by a numerical example.

Event‐based adaptive resilient control for networked nonlinear systems against unknown deception attacks and actuator saturation

AbstractThis paper investigates an event‐triggered adaptive resilient control problem for nonlinear systems against unknown false data injection and actuator saturation. The sensors of the controlled system are subject to unknown false data injection attacks so that all original states can not be directly applied in the controller design process. To address the negative effects of the false data injection attacks, attack compensators are introduced in control design. Simultaneously, a dynamic event‐triggering mechanism is set up to reduce the communication burden of the network. Furthermore, an auxiliary system is constructed to cope with the actuator saturation that commonly exists in practical systems. Based on the Lyapunov stability theory, it is demonstrated that the designed adaptive controller can guarantee the stability of the controlled system and the boundedness of all signals. The validity of the proposed control scheme is evidenced by simulation examples.

Nonfragile [formula omitted] observer-based fuzzy control for nonlinear networked control systems with multipath packet dropouts

This work considers the design of nonfragile H∞ observer-based controller for nonlinear networked control systems (NCSs) subject to multipath packet dropouts. The nonlinear NCSs are modeled through the Takagi–Sugeno (T–S) fuzzy model. The occurrences of packet dropouts in the measurable output and the control input indicate the multipath packet dropouts. Two random sequences which are mutually independent and obey the Bernoulli distribution are adopted to represent the multipath packet dropouts. Unlike most existing works about the observer-based controller design, a novel observer design scheme is developed in this paper for the T–S fuzzy NCSs with packet dropouts, which does not require the observer to be designed to share the same local system parameters “Al” as the fuzzy plant. Then, by means of the Lyapunov function approach, sufficient conditions are developed to make certain that the controlled T–S fuzzy NCSs are stochastically stable with a H∞ performance index. Through adopting a two-step design method, the search for the local system parameters and gains of the observer and the controller gains is formulated as a feasibility problem of linear matrix inequalities (LMIs). Eventually, the effectiveness of the derived results is verified by an example.

Performance recovery-based fuzzy robust control of networked nonlinear systems against actuator fault: A deferred actuator-switching method

This paper concentrates on the performance recovery-based fuzzy robust control of networked nonlinear systems via a deferred actuator-switching approach. The main design procedure of this paper is divided into three parts: 1) nominal controller design; 2) monitoring function and shifting function design; 3) reconfigurable controller design. With the aid of low-computation method and fuzzy logic systems, a structurally and computationally efficient nominal controller is designed for fault-free. Then, an actuator-switching rule is established based on monitoring the stabilization error. Furthermore, a new error transformation is constructed via designing the shifting function, by which tangent barrier Lyapunov functions are effective at the replacement instant. Finally, the stabilization error recovers into the prescribed performance within a specified period of time, and a reconfigurable controller is designed for the backup actuator. By employing the event-triggering mechanism with the relative threshold strategy, communication loads from controllers to actuators are reduced. Simulation examples are given to confirm the effectiveness of the proposed control approach.

Quantized guaranteed cost dynamic output feedback control for uncertain nonlinear networked systems with external disturbance

In this article, the guaranteed cost control problem for a class of nonlinear networked control systems (NCSs) with dynamic quantization, external bounded disturbance, and data dropout is investigated via the dynamic output feedback. The Takagi–Sugeno (T-S) fuzzy model is used to represent the discrete-time nonlinear system discussed in this paper. Under the dynamic quantization strategy, two quantizers with dynamic parameters are used to quantize the measured output and control input, and the data dropout process is described by introducing the Bernoulli stochastic variable. By using the quadratic boundedness technique, the quadratic stability of the quantized NCS with external disturbance is described. Furthermore, the sufficient condition for the global design approach are proposed, that is, not only the dynamic output feedback controller but parameters of the quantizers are designed synchronously with strict linear matrix inequalities. Eventually, a simulation of nonlinear mass-spring-damper mechanical system is carried out to verify the effectiveness of the provided algorithm.

Data-Driven $H_{\infty }$ Control of Networked Nonlinear Systems With External Disturbances and Random Communication Packet Losses

Data-Driven $H_{\infty }$ Control of Networked Nonlinear Systems With External Disturbances and Random Communication Packet Losses

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

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

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

An adaptive event‐triggered H∞ control framework for nonlinear networked control systems with dual‐channel quantization

AbstractNetworked control system (NCS) is widely used in various fields, but the nonlinear of the controlled plants and the limited network bandwidth bring stability issues, which will affect the performance or even instability of the NCS. To address these aforementioned challenges, this paper proposes a novel nonlinear NCS framework, by considering network‐induced delay, adaptive event‐triggered mechanism, state quantization, control input quantization, and external disturbance based on T‐S fuzzy model. Also, based on this framework, we derive an stability criterion for NCS using linear matrix inequality and propose a collaborative design method for event triggers and controllers. Finally, we verify the superiority and effectiveness of the method by giving two numerical examples.

Output-Based Dynamic Event-Triggered Control for Networked Control Systems With Delays and Packet Losses Without Acknowledgements

Event-triggered control (ETC) has been proposed in the networked control systems (NCSs) literature to reduce the need of data transmissions, and the main focus has been on the design of ETC mechanisms with a guaranteed positive minimum of inter-event time (MIET) and hence avoiding Zeno phenomena. In this paper, we propose a design of an aperiodic ETC mechanism for general nonlinear NCSs. In our setup, the communication network could introduce variable transmission delays, while data packets could get lost without acknowledgements. For this, we first introduce auxiliary variables dependent upon the transmission situations to capture the relationship between the number of successive packet dropouts, MIETs, transmission delays and the number of successful packets being transmitted. Meanwhile, by designing appropriate jump rules of these variables, the changing process of the network-induced errors can be tracked. Then, we present a novel output-based dynamic ETC strategy that can simultaneously guarantee a finite <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{p}$</tex-math></inline-formula> -gain and a strictly positive lower bound despite the presence of packet losses and transmission delays. In particular, the mandatory limit of short delays can be avoided, and the transmission delays exceeding the positive MIETs are allowed. Finally, a numerical example is given to verify the correctness and effectiveness of the proposed control approach.

Self-Triggered $$H_\infty $$ Control for Uncertain Nonlinear Networked Control Systems Under Dynamic Quantization and Packet Dropout

Self-Triggered $$H_\infty $$ Control for Uncertain Nonlinear Networked Control Systems Under Dynamic Quantization and Packet Dropout

Robust control design of a nonlinear liquid-level networked control system: a comparative study between STR and Kharitonov analysis

Robust control design of a nonlinear liquid-level networked control system: a comparative study between STR and Kharitonov analysis

Observer‐based event‐triggered security control for nonlinear networked control systems with cyber attacks

SummaryThis paper investigates the observer‐based security control problem for interval type‐2 nonlinear networked control systems with deception attacks and replay attacks. Firstly, to alleviate the communication burden of the network, a novel adaptive event‐triggered communication (ETC) mechanism is proposed. Compared with existing observer‐based adaptive ETC security control techniques, the threshold of the proposed adaptive ETC scheme can be adjusted dynamically depending on the value of adjustable parameters and estimation error. In particular, the adjustable parameters can change the sensitivity of the threshold flexibly. Then, taking the influence of the adaptive ETC mechanism and cyber attacks into consideration, an observer‐based security controller is developed under the co‐designed method. Furthermore, by utilizing the Lyapunov stability theory, sufficient conditions for asymptotically mean‐square stable of the closed‐loop system are obtained. Ultimately, the feasibility of the designed control approach can be proved via a simulation example.

A Differential Error Based Self-Triggered MPC With Adaptive Prediction Horizon For Discrete Systems

Abstract For discrete time nonlinear networked control systems, a novel self-triggered adaptive model predictive control (MPC) strategy is developed. Different from the existing self-triggered MPC methods that determine the triggering instants based on the difference between the optimal and real states at one single instant, the proposed approach updates the MPC system according to the differential form of the state error of two consecutive sampling moments to effectively reduce the computation and communication burden while maintaining the ideal control performance. In addition, this paper introduces a new adaptive prediction horizon mechanism to the self-triggered MPC, so that the amplitude of prediction horizon contraction is sufficiently large to further reduce the computational burden of the MPC method. Finally, the recursive feasibility and robust stability of this proposed strategy are proved strictly by theoretical analysis, and the simulation comparison results are shown to verify the proposed framework.