Nonlinear Networked Control Systems Research Articles

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.

Further Results on Asynchronous Fuzzy Observer-Based Output Feedback Control for Networked Nonlinear Systems Using Quantized Measurements

Further Results on Asynchronous Fuzzy Observer-Based Output Feedback Control for Networked Nonlinear Systems Using Quantized Measurements

Quantized Input Robust Adaptive Neural Network Control for Nonlinear Systems With Full State Constraints

Quantized Input Robust Adaptive Neural Network Control for Nonlinear Systems With Full State Constraints

LyZNet with Control: Physics-Informed Neural Network Control of Nonlinear Systems with Formal Guarantees

Optimal control for high-dimensional nonlinear systems remains a fundamental challenge. One bottleneck is that classical approaches for solving the Hamilton-Jacobi-Bellman (HJB) equation suffer from the curse of dimensionality. Recently, physics-informed neural networks have demonstrated potential in overcoming the curse of dimensionality in solving certain classes of PDEs, including special cases of HJB equations. However, one perceived limitation of neural networks is their lack of formal guarantees in the solutions they provide. To address this issue, we have built LyZNet, a Python tool that combines physics-informed learning with formal verification. The previous version of the tool demonstrated the capability for stability analysis and region of attraction estimates. In this paper, we present the tool for solving optimal control problems. We expand the functionalities of the tool to support the formulation and solving of optimal control problems for control-affine systems via physics-informed neural network policy iteration (PINN-PI). We outline the methodology that enables the learning and verification of PINN for optimal stabilization tasks. We demonstrate with a classical control example that the learned optimal controller indeed has significantly improved performance and verifiable regions of attraction.

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

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.

Guaranteed cost control via dynamic output feedback for networked interval type-2 T–S fuzzy system with hybrid communication mechanism

This paper aims to investigate the guaranteed cost control via dynamic output feedback for nonlinear networked control systems (NCSs) with consideration of hybrid communication mechanism, data dropout and bounded disturbance. Interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy model is utilized to describe the nonlinear system with parameter uncertainties. To enhance bandwidth utilization and improve control performance, a hybrid communication mechanism involving both event-triggered mechanism (ETM) and time-triggered mechanism (TTM) is proposed. Two Bernoulli processes are invoked to describe the switching between two triggering mechanisms, and the data dropout phenomenon in communication network, respectively. The quadratic boundedness (QB) technique is employed to specify the closed-loop stability of a bounded disturbance networked system. The sufficient conditions for the stability of the system and the presence of a dynamic output feedback guaranteed cost controller are presented. In addition, the problem of controller design is converted to a convex optimization problem that can be tackled by linear matrix inequalities (LMIs) technique. At last, simulation experiment is carried out to explicate the availability and usefulness of the designed controller.

Dynamic Event-Based Control for Stochastic Optimal Regulation of Nonlinear Networked Control Systems.

In this article, a dynamic event-triggered stochastic adaptive dynamic programming (ADP)-based problem is investigated for nonlinear systems with a communication network. First, a novel condition of obtaining stochastic input-to-state stability (SISS) of discrete version is skillfully established. Then, the event-triggered control strategy is devised, and a near-optimal control policy is designed using an identifier-actor-critic neural networks (NNs) with an event-sampled state vector. Above all, an adaptive static event sampling condition is designed by using the Lyapunov technique to ensure ultimate boundedness (UB) for the closed-loop system. However, since the static event-triggered rule only depends on the current state, regardless of previous values, this article presents an explicit dynamic event-triggered rule. Furthermore, we prove that the lower bound of sampling interval for the proposed dynamic event-triggered control strategy is greater than one, which avoids the so-called triviality phenomenon. Finally, the effectiveness of the proposed near-optimal control pattern is verified by a simulation example.

Finite‐time observer‐based fault detection with nonfragile control design for switched nonlinear networked systems with time‐delays

AbstractThe strategy of observer‐based nonfragile control and fault‐detection over finite‐time horizon has been examined for a class of uncertain switched nonlinear networked control systems (NNCSs) contingent with time varying delays inferred by the networks from sensor to controller and controller to actuator. Also, in this study, an observer‐based fault detection control has been designed for the NNCSs. By making use of fault detection control as residual generator, the conveyed fault detection problem is then transformed into attenuation problem. By employing multiple Lyapunov function and average dwell time (ADT) approach, sufficient conditions in terms of linear matrix inequalities (LMIs) ensures the finite‐time boundedness (FTB) criterion of the resulting switched NNCSs with a prescribed disturbance attenuation. Subsequently, the preferred LMIs constraints realizes the controller and observer gains. Finally, two numerical examples are proffered to showcase the efficacity of the proposed technique.

Event-triggered dual-mode predictive control for constrained nonlinear NCSs subject to disturbances and packet dropouts

ABSTRACT In this paper, we propose an event-triggered dual-mode predictive control strategy to deal with two-channel packet dropouts for discrete-time constrained nonlinear networked control systems. The main focus is on the construction of network compensation mechanisms and control algorithms. Firstly, two types of novel event generators are set, respectively, in sensor node and controller node to reduce resource consumption while preserving the desired system performance. In order to establish corresponding event-triggering conditions, two special states, successful transmitted states and reconstructed states, are introduced. Subsequently, a dual-mode control scheme, where the switch of modes depends on reconstructed states, is designed. The recursive feasibility of optimization problems is ensured by determining relationships between actual states and reconstructed states. By constructing actual control laws under two system modes, the stability property of closed-loop systems is guaranteed. Furthermore, effects of packet dropouts and disturbances on system performance are analyzed. At last, the efficiency of the proposed control strategy is illustrated by a cart-damper-spring simulation example.

Fuzzy Secure Event-Triggered Control for Networked Nonlinear Systems Under DoS and Deception Attacks

This article investigates the resilient dynamic event-triggered control issue of interval type-2 T-S fuzzy systems (IT2 T-S FSs) under denial-of-service (DoS) and deception attacks. A novel resilient dynamic event-triggering mechanism (DETM) and the corresponding fuzzy dynamic event-triggered controller are proposed to solve the stochastically exponential stability (SES) problem of concerned IT2 T-S FSs in the presence of DoS and deception attacks. Meanwhile, an aperiodic-sampling-based piecewise loop Lyapunov functional is constructed. It is shown that the resulting closed-loop IT2 T-S FSs can achieve SES, and under the piecewise loop Lyapunov functional, some relaxed conditions are derived. In addition, the derived results can apply to IT2 T-S FSs subject to DoS attacks, static event-triggered control, or dynamic periodic-sampling-based event-triggered control. Finally, the effectiveness of the achieved results is verified by a numerical simulation on one-link manipulator.