Networked Control Systems Research Articles

Event‐Triggered Integral Sliding Mode Control for Nonlinear Networked Cascade Control Systems With Uncertain Delay

ABSTRACTThis article investigates the problem of event‐triggered integral sliding mode control (ISMC) for a class of nonlinear networked cascade control systems (NCCSs). Due to the limited communication bandwidth between network nodes, uncertain delay can occur during transmission. This may result in a deterioration of the capabilities of NCCS and system instability in certain instances. Thus, an event‐triggered mechanism (ETM) and ISMC are introduced into the system, and a new model is constructed to increase the network bandwidth and reduce the effect of nonlinear perturbation. After that, the design of two controllers and ETM is accomplished through the use of the Lyapunov generalized method and linear matrix inequality (LMI) techniques. Finally, a simulation example of a power plant model is given to verify the effectiveness of the method. The results show that, for the first time, the event‐triggered ISMC is applied to NCCSs, which greatly saves the network bandwidth resources. The states of the system can reach the sliding mode surface (SMS) in finite time and remain stabilized there afterward, which ensures the stable operation of the system.

Resilient Dual-mode Model Predictive Control for Constrained Linear Networked Control Systems With Random DoS Attacks

Resilient Dual-mode Model Predictive Control for Constrained Linear Networked Control Systems With Random DoS Attacks

Stabilization of Interval Type-2 Polynomial Fuzzy Networked Control Systems Under Cyber-Attacks

Stabilization of Interval Type-2 Polynomial Fuzzy Networked Control Systems Under Cyber-Attacks

Iteration-Form Multiplicative Watermarking for Quantization-Involved Networked Control Systems

Iteration-Form Multiplicative Watermarking for Quantization-Involved Networked Control Systems

Stabilization of Networked Control Systems With Time Delays: An Observer‐Based Dynamic Quantized Control Approach

Stabilization of Networked Control Systems With Time Delays: An Observer‐Based Dynamic Quantized Control Approach

Design and analysis of H∞$$ {H}_{\infty } $$ control strategies with dynamic event‐triggered schemes and quantizer in the presence of hybrid attacks

AbstractThis study explores the resilience of networked control systems (NCSs) against hybrid attacks, focusing on enhancing their performance. To mitigate excessive data transmission, the article introduces dynamic event‐triggered schemes and quantizer within the sensor‐to‐observer communication channel. The network environment is susceptible to various attacks, including denial‐of‐service (DoS) attacks and deception attacks. An augmented system model is formulated, and through the application of Lyapunov stability theory, conditions for ensuring the asymptotic stability of the system are established. The collaborative design of quantizer parameters, controller gain, and event‐triggered thresholds is achieved by solving linear matrix inequalities. Simulation‐based theoretical models have effectively demonstrated the reduction of the impact of network intrusions on NCSs, validating the proposed approach.

Networked control systems induced delay modeling and stability analysis using Markov-regime-switching GARCH model

This article focuses on the stability analysis of networked control systems (NCSs). It is well-known that network-induced delays pose many control challenges due to their non-uniform and multifractal nature. With the growing integration of communication networks in control systems, the impact of time-varying and random delays on system performance becomes critical. Hence, it is essential to study their impact on the system’s stability. There are several network-induced delay modeling methods, resulting in constant, random, and probabilistic random delays, which provide the most realistic representation according to recent studies. This article introduces a new network-induced delay modeling method based on the Markov-regime-switching generalized autoregressive conditional heteroskedasticity (MRS-GARCH) process, which captures the intricate temporal dependence of random network delays. The resulting model offers a more accurate depiction of delay volatility behavior, accommodating sudden shifts between volatility regimes. Simulation results demonstrate that MRS GARCH-type modeling enables a more realistic representation of delay dynamics. We also conduct a stability analysis of NCSs modeled with the proposed method using linear matrix inequalities LMIs criteria derived from the Lyapunov-Krasovskii theorem. The simulation results show less conservatism than conventional models and provide a larger maximum allowable upper delay bound MAUDB.

Stability Guarantees for a Class of Networked Control Systems Subject to Stochastic Delays

Stability Guarantees for a Class of Networked Control Systems Subject to Stochastic Delays

An efficient tuning method for networked control systems

Sensors, controllers, and actuators in a networked control system collaborate to execute a distributed closed-loop feedback control system. Currently, NCS includes network components that remain unidentified, such as heightened latency and packet loss, which may be persistent or fluctuate over time. The current predicament stems from the increased complexity of inspection and control systems, which is attributed to the vast expansion of communication networks. Implementing proactive strategies to mitigate the effects of communication network disruptions on control systems is crucial. A comprehensive study has identified various techniques for controller development. Initiatives are underway to mitigate the impact of network-wide random delays and improve the efficiency of the NCS system. To reach this goal, this study uses a meta-heuristic optimization method called the Whale Optimization Algorithm (WOA) along with a fuzzy-PID controller for a Networked Control System (NCS) plant. The proposed work will be evaluated under two operational scenarios of the NCS plant: one incorporating a random delay condition and the other excluding it. The effectiveness of the proposed method is evaluated by measuring and comparing its closed-loop performance to a traditional PID controller. The outcomes for both NCS plant scenarios illustrate the efficacy of the proposed initiative regarding closed-loop performance.

Event-triggered [formula omitted] control for partial unknown Markov jump discrete-time complex networks with distributed delay

Periodic event triggering, an important control mechanism, effectively saves communication resources and energy, making it highly valuable in networked control systems. This study focuses on the comprehensive utilization of periodic sampling pattern and event triggering mechanism to achieve synchronization control of a class of discrete-time Markov jump complex dynamic networks (CDNs) with H∞ performance index. Firstly, a comprehensive CDNs model is established incorporating Markovian jump parameters with incomplete transition probabilities, time-varying delays, and external random disturbances, making it more readily implementable in practical engineering scenarios. Secondly, a novel periodic event-triggered control strategy is proposed. In this strategy, the threshold parameters and weighting matrix in the event-triggering condition are mode-dependent, which can achieve better resource efficiency and effectively avoid the Zeno phenomenon. Thirdly, a discontinuous Lyapunov–Krasovskii functional is designed to capture information about discrete periodic sampling patterns. By applying free-weighting matrix, Jensen’s inequality, and Wirtinger’s inequality, sufficient conditions and controller for CDNs synchronization are derived with lower conservativeness. Finally, a simulation example is provided to demonstrate the feasibility and effectiveness of the theoretical results obtained.

Guaranteed Performance Resilient Security Consensus Control for Nonlinear Networked Control Systems Under Asynchronous DoS Cyber Attacks and Applications on Multi-UAVs Networks

Guaranteed Performance Resilient Security Consensus Control for Nonlinear Networked Control Systems Under Asynchronous DoS Cyber Attacks and Applications on Multi-UAVs Networks

Predictor-based event-triggered learning control of networked control systems with false data injection attacks and output delay

This article is concerned with the predictor-based event-triggered learning control of networked control systems (NCSs) with false data injection attacks (FDIAs) and output delay. Firstly, by applying the prediction method, a new state observer including an output predictor is employed to get the estimation of delayed sampled-data output in the context of sampling. To improve the efficiency of limited networked resources, an intelligent periodic event-triggered scheme (PETS) is established, in which the triggered threshold can be optimized by the asynchronous advantage actor-critic (A3C) algorithm. Then, a predictor-based event-triggered learning control strategy is developed to handle the FDIAs occurring in the controller-to-actuator channel, and the neural network (NN) technique is introduced to approximate the false data. By applying the Lyapunov function, some sufficient conditions are given to guarantee the boundedness of the NCSs. At last, a simulation of a satellite system is given to confirm the superiorities of the presented predictor-based learning control strategy.

Event-triggered [formula omitted] predictive control for networked control systems with sensor and actuator saturation

This paper concerns an event-triggered H∞ predictive control scheme for a networked control system in which sensor and actuator saturation as well as external disturbances are involved. In the scheme, a state estimator is firstly designed to acquire state estimations from the saturated output with measurement noises. An event generator is given to pick out necessary state estimations for saving feedback channel transmission resources. Furthermore, a buffer-based predictive controller is constructed to compensate for network-induced delays and lighten the communication burden of forward channel networks. Then a co-design criterion is shown to find estimator and controller parameters for the resulting closed-loop system with desired H∞ performance. An inverted pendulum model is employed in numerical simulation to verify the proposed control scheme.

Event-triggered anti-disturbance fault tolerant control for switched networked control systems

This work centres on the event-triggered anti-disturbance fault-tolerant (ETADFT) control problem for unknown switched networked control systems (USNCSs) with dual-source disturbances and unknown faults by the multiple Lyapunov functions method. A dynamic memory event-triggered (DMET) mechanism is developed to optimise the utilisation of transmission resources. A composite switching observer is devised to approximate the unmeasurable state, fault and disturbance. By utilising the DMET mechanism, composite switching observer and time-relevant switching regulator, an ETADFT controller is formulated to counteract the impact of the unknown disturbance and tolerant unknown faults. Finally, the suggested ETADFT control strategy is implemented on the switched RLC circuit model to verify the rationality of the control mechanism.

Evaluation of 5G-based closed-loop control on part quality for milling processes

Implementing wireless communication technologies in manufacturing systems shows the potential for a disruptive shift towards wireless networked control systems and edge computing for machine tool control. Especially 5G mobile communications enable wireless closed-loop control with ultra-reliable low-latency communication (uRLLC) and time-sensitive networking (TSN)-features. However, currently available hardware and software cannot yet meet the high requirements for closed-loop control of machine tools. This study evaluates the quality of a manufactured part when produced on a wireless closed-loop machine tool. A setup for emulating network characteristics (latency, jitter) is developed and implemented into a computerized numerical control (CNC) of a machine tool to synthesize the effects of 5G connectivity. The main objective is to evaluate how different network performance characteristics in combination with machine parameters influence surface quality parameters and geometric dimensioning and tolerancing (GD&T) of a milled specimen. Full factorial experimental design paradigm is used in the evaluation. The results show that latency and feed rate have no or insignificant influence on the surface quality of the manufactured part while jitter does have a significant influence. The surface quality deteriorates significantly above a jitter level of 100μs, so that the quality of the manufactured components is no longer sufficient. Moreover, the deviation of quality parameters starts to increase significantly with higher jitter. GD&T are just influenced by the variation of the feed rate, independent of latency and jitter. The results indicate that processes with lower requirements on the control loop (e.g. robot control, control of automated guided vehicles, etc.) can already be implemented using 5G mobile communications. However, closed-loop control of machine tools to achieve high quality manufactured parts, will only be possible with future developments in wireless communications. The results experimentally determine the minimum requirements for the communication architecture and the resulting quality losses for milling if these are not met.

Multilevel modeling and control of dynamic systems

The underlying changes of complex dynamic systems are affected by numerous highly interdependent components with nonlinear interactions and cannot be suitably predicted by merely analyzing their individual parts. Recent technological developments (reducing the size of actuators and sensors, increasing speed and productivity) make it possible to monitor and handle systems on time, even during transient processes. In many practical applications, to effectively study rapidly evolving systems, their structure has to be examined at three levels: Micro-Scale (Short-Term), Meso-Scale (Intermediate-Term), and Macro-Scale (Long-Term). This multiscale framework provides a powerful tool for understanding and managing the intricate dynamics of complex systems. Macro-scale and micro-scale approaches are conventionally utilized in general system modeling and control theory, especially within the fields of networked control and multi-agent systems. By leveraging the meso-scale perspective, it is possible to balance detailed component analysis with overarching system behaviors, thereby enhancing the efficiency and effectiveness of operational strategies in complex systems. This paper presents and discusses a formal meso-level depiction of dynamics and control, assuming a finite number of attractors form and remain stable over time progress. Integral characteristics and dynamics of clusters are defined, simplifying control synthesis while maintaining problem formulation. A significant challenge is the model error due to changing cluster structures linked to randomized estimation and optimization algorithms valid under unknown disturbances. To demonstrate the practicality of this framework, the approach is applied to control tasks of a group of robots, leading to scalable and effective control strategies.

Using Delay for Control

This article reviews two techniques that use delay for control: time-delay approaches to control problems (which initially may be free of delays) and the intentional insertion of delays into the feedback. We begin with a now widely used time-delay approach to sampled-data control. In networked control systems with communication constraints, this is the only method that accommodates transmission delays larger than the sampling intervals. We present a predictor-based design that enlarges the maximum allowable delay, which is important for practical implementations. We then discuss methods that use artificial delays via simple Lyapunov functionals that lead to feasible linear matrix inequalities for small delays and simple sampled-data implementations. Finally, we briefly present a new time-delay approach—this time to averaging. Unlike previous results, this approach provides the first quantitative bounds on the small parameter, making averaging-based control (including vibrational and extremum-seeking control) reliable.

Dynamic quantization of event-triggered adaptive sliding mode control for networked control systems under false data injection attack

The dynamic quantization of event-triggered (ET) adaptive sliding mode control (SM, SMC) for networked control systems (NCS) under false data injection attack (FDIA) is considered in this article. To begin with, to reduce the network transmission burden, dynamic quantizers are used to quantize the states and the input on the channels from the plant to the ET mechanism and from the controller to the plant, respectively. Secondly, the dynamic ET mechanism employs quantized state error, and the existence of the minimum inter-event time demonstrates that the system does not experience the Zeno phenomenon. Thirdly, this paper uses the adaptive parameter to estimate the unknown upper bound of the attack mode. In addition, the range of values for the adaptive gain of the SMC is derived by combining with the Lyapunov stability theory. On the last, the comparative simulation results of different methods for numerical examples are given to verify the superiority of the method proposed in this paper.

Prediction-based controller radial neural network for the traction control system

One of the systems needed to improve vehicle safety is the traction control system (TCS). This control mechanism keeps the wheels from slipping too much when the car accelerates, especially when it moves quickly. Because of the significant nonlinear behavior of the tire during acceleration and the unknown impacts of the road surface, it might be difficult to maintain wheel slip in an acceptable range, especially in bad weather. However, some uncertainties, such as unmodeled dynamics and vehicle parameter uncertainty, should be taken into account when designing the controller. Consequently, TCS requires the existence of a strong nonlinear control law. In this study, an analytical design for a TCS controller is made using the method of nonlinear predictive control. The control system’s resistance is then increased by employing an adaptive radial basis neural network (RFNN) to predict the system’s unknown uncertainties. In this study, the controller was designed using half car and quarter car models, respectively. The behavior of the suggested control system for tracking the reference wheel’s slip in the face of uncertainty for various movements is examined in the simulation results that follow. The resulting results have been compared with the simulation results generated from the nonlinear sliding mode controller response used in valid articles in order to provide a more thorough examination of the suggested control system. The findings demonstrate the effective performance of the suggested control mechanism against nonlinear effects and uncertainties.

An H∞ Memorized Input-dependent Event-triggered Control Scheme for Networked Control System

An H∞ Memorized Input-dependent Event-triggered Control Scheme for Networked Control System