Networked Control Systems Research Articles

Prediction-Based Control of Large-Scale Networked Control Systems

Prediction-Based Control of Large-Scale Networked Control Systems

H∞ stabilization of singular networked cascade control systems subject to periodic DoS attack

In this paper, a [Formula: see text] controller with an event-triggered mechanism is designed for a singular networked cascade control system (SNCCS) under periodic denial-of-service (DoS) attacks. The SNCCS is treated as a system of differential-algebraic equations (DAEs) with an index of two and is modeled as a switched system. The paper first establishes sufficient stability conditions, which are formulated as linear matrix inequalities based on multiple Lyapunov functions. These conditions ensure that the system remains regular, free from impulse modes, and stable. Next, state feedback controllers are designed for both the inner loop and the outer loop. However, the outer loop is subjected to periodic DoS cyber-attacks. Finally, the proposed method’s effectiveness is demonstrated through simulation results on a practical thermal cascade control system.

Memory‐based event‐triggered control for networked control system under cyber‐attacks

AbstractThis article focuses on the problem of stability for a class of linear networked control systems (NCSs) subjected to network communication delays and random deception attacks. A new memory event‐triggered mechanism (METM) is proposed to reduce the unnecessary transmitted data through the communication channel and then enhance the network resources. In this context, a new memory stochastic state feedback controller is proposed to stabilize the closed‐loop networked control system. A new randomly occurring deception attacks model is employed to deal with the security problem of NCSs. Sufficient stability conditions are derived based on a suitable Lyapunov‐Krasovskii functional (LKF). The designed methodology is proposed in terms of linear matrix inequality to synthesize both event‐triggered parameters and controller gains, and to reduce the conservatism of the system some integral lemma are exploited to bind the time derivative of the LKF. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed method which provides a maximal upper bound value of the network‐induced delay and less transmitted packet regarding the maximal value delay obtained in other works, so less conservatism results are obtained, compared to previous ones in the literature.

Dynamic event-triggered dual-channel quantized sliding mode control for uncertain networked control systems under DoS attacks

This paper introduces a dynamic event-triggered sliding mode control method to address the issue of quantized stability in uncertain networked control systems (NCSs) subjected to random Denial-of-Service (DoS) attacks. Firstly, a dynamic event-triggered mechanism is utilized to compensate for the damage caused by DoS attacks while effectively reducing network bandwidth pressure. Subsequently, a sliding mode controller is designed to ensure the robustness of uncertain NCSs with external disturbances. Additionally, we construct a novel control framework that considers network delays and incorporates quantizers for state and control signals, respectively. Based on this framework, both a sufficient condition for exponential stabilization of NCSs and a co-design method for dynamic event trigger and controller are given. Finally, the effectiveness of the proposed method is verified with two generalized simulation examples.

Dynamic periodic event-triggered control for nonlinear systems with output dynamic quantization

This paper is concerned with the design of stabilizing event-triggered controllers for nonlinear systems subject to dynamic quantization. The plant state is assumed to be partially known, and the feedback signal is transmitted to the controller at discrete-time instants via a digital channel. To that end, an event-triggered controller is synthesized based solely on the available plant measurement. In addition, to better adapt it to practical implementation, the event-triggering law we construct is only verified at periodic time instants, i.e., periodic event-triggering mechanism. Since the network is digital, the transmitted output signal must be quantized to be encoded using a finite number of bits. Furthermore, the initial output value is assumed to be unknown, and the plant is affected by external disturbance, which can result in quantizer saturation. To overcome these challenges, a dynamic quantization policy is developed for the output measurement such that the quantization regions are dynamically updated according to the value of the quantized signal. The combined approach of periodic event-triggering mechanism and dynamic quantization ensures the stability of the networked control system under mild conditions. The stability is studied using appropriate Lyapunov functions. Numerical simulations have been conducted to justify the proposed technique.

The rhythmic mind: brain functions of percussionists in improvisation.

Percussionists stand out for their expertise in rhythm, with the network for musical rhythm (NMR) serving a vital neurological function in their improvisation, which is deeply rooted in comprehensive musical knowledge. Our research examines the central representations of various improvisation tactics used by percussionists and investigates the interactions between the NMR and other relevant neural networks. Twenty-five percussionists participated in functional magnetic resonance imaging (fMRI) sessions, which included two cognitive strategies of improvisation. Structural improvisation (SIMP) emphasized rhythmic patterns, while free improvisation (FIMP) focused on musical spontaneity. Sight-reading scenario served as the reference condition. Paired t-tests were utilized for comparative analyses. The findings revealed a dynamic interplay characterized by increased activity in the executive control network and NMR, along with decreased activity in the default mode network during SIMP. During FIMP, heightened activity was observed in the executive control network, NMR, limbic, and memory systems. In both SIMP vs. sight-reading and FIMP vs. sight-reading comparisons, the visual network's activity decreased, a trend also observed in the comparative analysis of FIMP vs. SIMP. In SIMP, percussionists leverage external rhythmic signals, resulting in heightened NMR and ECN activity and reduced DMN activity. In contrast, FIMP is characterized by a rise in activity within the NMR, ECN, limbic system, memory system, and reward system, underscoring the vital roles of motivation and memory in the rapid production of spontaneous musical ideas within set frameworks. The diminished activity in the visual network during FIMP compared to SIMP suggests less reliance on visual stimuli in FIMP. These findings suggest that various improvisational tactics may engage different neural pathways.

Observer‐based networked predictive controller with bounded and unknown time‐varying delay

AbstractThis paper investigates a networked control system with bounded and time‐varying delays. Also, an observer‐based predictive controller is developed for the active compensation of the network communication delay since it may lead to poor performances or even unstable dynamics for the systems. The existence of the state observer can be established by choosing an appropriate Lyapunov function, and to do this, Luenberger observer gain is computed with Linear Matrix Inequality (LMI) based on the stability conditions of the system. The practical aspect of this research, different from previous works, is the accessibility of the states that are not always available for the system. Controller and observer gains and other essential variables are derived through LMI. In the closed‐loop system, which is modeled as a switching system, the switches are based on communication delays. Using the Lyapunov stability method for switching systems, sufficient LMI conditions are derived to guarantee the stability of the closed‐loop system. Finally, the results of the simulation have demonstrated the performance of the methodology presented.

Event‐triggered fault detector design in networked fuzzy control systems under denial‐of‐service attacks

AbstractThis article addresses the problem of designing an event‐triggered fault detector within a networked nonlinear system described by a Takagi–Sugeno (T–S) fuzzy model, accounting for time‐varying delays and denial‐of‐service (DoS) attacks characterized by a binary Markov chain. Our focus is on constructing an event‐triggered residual system, which aims to detect malfunctions in the original fuzzy system while optimizing communication resource allocation. Specifically, it is tailored to combat DoS attacks in the event generator‐to‐fault detector channel and mitigate network‐induced time‐varying delays. Utilizing the Lyapunov–Krasovskii function approach, we formulate linear matrix inequalities to establish the necessary conditions ensuring the asymptotic mean‐square stability of the residual system, meeting a predefined performance criterion. Finally, the simulation results are presented to validate the effectiveness of the proposed algorithms.

Novel Dynamic Defense Strategies in Networked Control Systems under Stochastic Jamming Attacks

In contemporary networked control systems (NCSs), ensuring robust and adaptive security measures against dynamic threats like jamming attacks is crucial. These attacks can disrupt the control signals, leading to degraded performance or even catastrophic failures. This paper introduces a novel approach to enhance NCS security by applying stochastic game theory to model and resolve interactions between a defender and a jammer. We develop a two-player zero-sum game where the defender employs mixed strategies to minimize the expected cost of maintaining system stability and control effectiveness in the face of potential jamming. Our model discretizes the state space and employs backward induction to dynamically update the value functions associated with various system states, reflecting the ongoing adjustment of strategies in response to the adversary’s actions. Utilizing linear programming in MATLAB, we optimize the defender’s mixed strategies to systematically mitigate the impact of jamming. The results from extensive simulations demonstrate the efficacy of our proposed strategies in attack scenarios, indicating a substantial enhancement in the resilience and performance of NCSs against jamming attacks. Specifically, the proposed method improved network state stability by 75%, reducing the fluctuation range by over 50% compared with systems without defense mechanisms. This study not only advances the theoretical framework for security in NCSs but also provides practical insights for the design of resilient control systems under uncertainty.

Learning predictive control of wheeled mobile robot with communication delays

Abstract This paper proposed a learning predictive control method for wheeled mobile robots with communication delays. In consideration of the property in the Networked Control System (NCS), communication delay was divided into two types: delay from sensor to controller and delay from controller to actuator. For each type of delay, state prediction and state augmentation techniques were utilized respectively to diminish their passive impact on the control of WMR. Then receding horizon reinforcement learning approach was adopted to learn the optimal policy based on the delay compensation. Finally, simulations were performed applying the proposed method to the trajectory tracking of WMR and showed the validity of the proposed scheme.

Automated Design of Fault Diagnosis CNN Network for Satellite Attitude Control Systems.

Despite the dominance of unsupervised and self-supervised anomaly detection methods in the current satellite fault diagnosis domain, supervised anomaly detection offers a superior alternative for high-sensitivity detection and lightweight deployment requirements specific to subsystems or components, such as attitude control systems (ACSs). This article addresses the issues of over-design and insufficient accuracy in the CNN network design for satellite ACS fault diagnosis by introducing the modified particle swarm optimization-advanced convolution blocks-based CNN (MPSO-ACBCNN) method. First, we present the ACBCNN, a lightweight, flexible-layer CNN architecture. This architecture leverages advanced convolution blocks (ACBs), which incorporate numerous efficient design elements to enhance feature extraction capabilities within power spectral density (PSD) graphs of various fault samples, and employs classical dense connection methods to prevent the issue of gradient vanishing. Second, we devise the MPSO-ACBCNN algorithm to optimize the ACBCNN fault diagnosis architecture for specified ACS using MPSO. In MPSO-ACBCNN, several optimizations to the canonical PSO are implemented, including the fitness design that balances the tradeoff between total parameter quantity and the training effectiveness, and methods to ensure feasible solutions, etc. Finally, numerical experimental results demonstrate the effectiveness and superiority of MPSO-ACBCNN in fault diagnosis for ACS.

Asymptotical Mean-Square Stabilization of Networked Control Systems Operating Over Multipath Routing Networks

Asymptotical Mean-Square Stabilization of Networked Control Systems Operating Over Multipath Routing Networks

Secure Reachable Set Synthesis and Reliable Event-Driven Mechanism for T–S Fuzzy Semi-Markovian Networked Control Systems Against Deception Attacks

Secure Reachable Set Synthesis and Reliable Event-Driven Mechanism for T–S Fuzzy Semi-Markovian Networked Control Systems Against Deception Attacks

Periodic event-triggered controller design with Bayesian optimization: An emulation-based approach

In recent years, event-triggered control has emerged as a promising strategy for addressing resource constraints in networked control systems (NCSs), such as limited life-time of battery capacity. This paper explores the development of a periodic event-triggered controller through a model-free design, assuming unknown plant dynamics. The design of the event-triggered controller employs an emulation-based approach, which is divided into solving two sub-problems: the problem for designing parameters for the time-triggered (periodic) control law, and the problem for designing parameters for the event-triggered condition. In particular, both problems will be solved through the usage of two distinct Bayesian optimization algorithms. The first problem is addressed by adapting basic Bayesian optimization to include network utilization by considering the number of communication time steps during optimization. The second problem employs constrained Bayesian optimization to incorporate explicit performance constraints within the optimization process. A numerical example is provided to demonstrate the effectiveness of the proposed method.

Sampled-data event-triggered control with sampling-independent positive guarantees on inter-event times

To fit with a digital environment in networked control systems, sampled-data event-triggered control (ETC) has been proposed where the event-triggering mechanisms sense and process information only at discrete sampling instants (not necessarily periodically). One deficiency in the previous study on sampled-data ETC is the lack of analysis on transmission performance, which yields the potential occurrence of an undesirable phenomenon that the minimum inter-event time is always equal to the minimum inter-sampling interval, no matter how small the latter is. To overcome this drawback, in this paper, we propose a novel sampled-data ETC scheme such that the lower bounds of inter-event times have sampling-independent positive guarantees. A linear networked control system is considered under observer-based controllers, external disturbances, and multiple communication channels. The improved transmission performance is due to the utilization of dynamic event-triggering conditions and some mean-rate error signals, which are the ratios between network-induced errors and some time-increasing functions. After modeling the closed-loop dynamics into a hybrid system, sufficient conditions on the upper bound of inter-sampling intervals and parameters in ETC are provided to ensure input-to-state stability (rather than its practical version) and sampling-independent positive guarantees simultaneously. Furthermore, a new tradeoff relationship between the sampling and transmission performance is revealed: a faster sampling frequency is conducive to improving inter-event times. Finally, a linearized model of an inverted pendulum is simulated to illustrate the efficiency and feasibility of the obtained results.

Spiking neural networks in intelligent control systems: a perspective

Spiking neural networks in intelligent control systems: a perspective

Dynamic memory-based event-triggered security control for networked control systems subjected to false-injection-data attack

This study addresses the security control problem for networked control systems that are subjected to false-data-injection attacks. The designed controller rests on a dynamic memory-based event-triggered mechanism, which exploits historically transmitted data and an auxiliary term to improve transmission efficiency and ensure satisfactory control performance. Then, a controller that defends against the false-data-injection attacks is proposed by making full use of the historical information. With the aid of Cauchy–Schwarz and Gronwall–Bellman inequalities, the existence of such a controller is validated through a novel Lyapunov-based method, which allows the Lyapunov function to be nonpositive. Further, a test on a dynamic power system is provided to verify the effectiveness and superiority of the proposed method.

Decentralized periodic event‐triggered control for smart water distribution systems via digital twin approach

AbstractDecentralized periodic event‐triggered control can reduce the working time and data transmissions in the sensor nodes for feedback control loops, making it suitable for wireless networked control systems, especially those with large physical scales, such as smart water distribution systems. However, the design of this control approach requires accurate system model, which is normally difficult to obtain. In this work, we assume a digital twin of the original system can be obtained by some modeling and simulation tools, for example, Simscape in MATLAB/Simulink for water distribution systems, and thus present an event‐triggered controller design approach. We first present the design of such an event‐triggered controller for the digital twin; and then analyze the condition under which if both the original system and its digital twin can be satisfied, the same controller can work for the original system and some type of stability can be guaranteed at the same time. The proposed approach is applied in a smart water distribution system to show the feasibility.

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.

The Performance Comparison of Multiloop PID Controller on NCS Temperature Plant Based on UDP and TCP

Hardware implementations of networked control systems (NCS) are still rarely found in various research publications so that technical issues, such as how to measure random delay and substitute it into the control equation, still need to be studied further. This study is therefore aimed to compare the performance of NCS based on the used protocol in the ethernet network, i.e. user datagram protocol (UDP) or transmission control protocol (TCP), by applying it in room temperature plant. In this study, the controlled plant is influenced by the fan speed, heating, and window position plant. The heating plant is employed as the main control, while the others are set as sub-plants. The three plants use proportional–integral–derivative (PID) controller where they are regulated by fuzzy logic as the master control unit (MCU). The MCU and three PID controllers are located in the master terminal unit (MTU) while the actuators and sensors are located in the three different remote terminal units (RTU). The verification experiment shows that there is no overshoot on TCP-based NCS, while UDP has 0.725%. For the risetime, the response on UDP is faster than TCP (110.22 as compared to 138.18 s). The same thing also happened to the settling time, where the time with UDP was 101.86 s and 128.44 s for TCP.