Networked Control Systems Research Articles

Quantized iterative learning control of communication-constrained systems with encoding and decoding mechanism

In practical applications, due to the limited communication bandwidth, the network control systems (NCSs) are prone to data dropouts when the load is high. In this paper, the problem of quantized iterative learning control (ILC) based on encoding and decoding mechanism for such communication-constrained systems is studied. By combining the encoding and decoding mechanism with the uniform quantizer, the network burden and the impact of quantization error on the tracking performance of the systems are significantly mitigated. Meanwhile, data dropouts are represented as the Bernoulli random variable model, and an ILC law based on gradient is designed. When data dropouts occur, the signals maintain the value of the previous trial, otherwise the signals are updated. For this kind of learning framework, the asymptotic zero-error tracking performance has been rigorously proven for the uniform quantizer. To validate the proposed design, a joint motion of an industrial robot in the horizontal plane is simulated as an example.

Dissipativity-based control for networked control systems under bilateral Round-Robin protocols and denial-of-service attacks

A dissipativity-based control strategy adopting bilateral Round-Robin (RR) protocols for networked control systems (NCSs) suffering from denial-of-service (DoS) attacks is investigated in this work. To save limited network resources in multi-packet transmission scenarios, RR protocols are adopted for coordinating the node access on bilateral (i.e., sensor-to-controller and controller-to-actuator) channels. Subsequently, the NCSs integrating considerations of bilateral RR protocols and DoS attacks are further described as the switched time-delay model. The sufficient stability condition and controller design criterion are derived by using Lyapunov theory to ensure the closed-loop exponential mean-square stability and (Q,S,R) dissipative performance under fragile network environment. Furthermore, to maximize the tolerable system delay and reduce the conservativeness of proposed results effectively, an optimization algorithm is also proposed. Finally, the superiority and feasibility of the proposed methods are verified through two practical examples.

A Conceptual Architecture Design - Building Cyber Security Architecture for Industrial Control Systems Networks and for Critical Infrastructures

A Conceptual Architecture Design - Building Cyber Security Architecture for Industrial Control Systems Networks and for Critical Infrastructures

Optimal linear filter design for process state and packet loss estimation in networked control systems

Considering the effect of packet losses on the behavior of networked systems, this work is concerned with estimation of the packet loss occurrences in the input channels possibly together with the system state. For this purpose, the commonly used Markov chain model of the successive packet loss occurrences is transformed to a linear recursive model in which the packet loss occurrence variables appear as new state variables. Two methods are proposed for combining the recursive packet loss model with the plant model to obtain an overall model for the whole networked control system (NCS). In the first method, a state space model of the plant is used which allows for simultaneous estimation of the packet loss occurrences and the plant state. In the second method, an input–output model of the plant is employed which allows for estimating only the packet loss occurrences. Both the zero and the hold packet loss handling strategies are considered and stability of the filters is analyzed. The proposed methods are compared with some existing results during an example to show their advantages.

Automatic Analysis System of Network Incidents

This article presents the automated processing system of network incidents, as developed and implemented by Iberdrola SA. The SASIRE system was developed due to the need by power companies, of the early identification and resolution of incidents occurring in the high and medium voltage power grid. SASIRE detects, processes and analyses system disturbances and automatically generates network incident reports. By acquiring data from the intelligent electronic devices (IEDs) installed on the network, the information from the network control systems and the knowledge of the incident analysts, it was possible to create automatic incident reports, improved and enhanced sharing of information between the various internal stakeholders and the extraction of knowledge from expert analysts. Throughout the development process, the need for new data, features and functionality in protection and control plant was discovered.

Sequential output information based predictive control for event-triggered networked control systems

In a Networked Control System (NCS), event-triggering is often used to reduce the number of network transmission instances and improve network usage. This paper considers transmitting output data rather than the estimated state for such event-triggered schemes. Output transmission facilitates efficient transmission by reducing the size of the packets sent over the network. However, since the state observer utilizes delayed information output transmission deteriorates system performance and requires improved observer-based prediction schemes for closed-loop control. The performance of a new scheme involving the transmission of sequential output information in a single packet to improve the state observer’s performance is demonstrated in this paper. A sequential observer is constructed that uses successive output information to better observe the states, and a prediction scheme is used to take care of the delays due to event-triggering, network delays, and dropouts. Further, event-triggering is employed in both the feedback and forward channels. The demonstrated efficacy of this sequential approach in the networked control of an inverted pendulum system and a DC motor system emphasizes its potential as a practical solution for improved control in NCSs, particularly in the face of network constraints and communication challenges.

A lifting approach to learning-based self-triggered control with Gaussian processes

This paper investigates the design of self-triggered control for networked control systems (NCS), where the dynamics of the plant are initially unknown. Given the nature of the self-triggered control where state measurements are sent to the controller a-periodically, our proposal involves augmenting the continuous-time dynamics to a novel dynamical model that incorporates inter-event time as a supplemental input. Then, this new model is studied through Gaussian processes (GP) regression. Additionally, we propose a learning-based approach where a self-triggered controller is formulated by minimizing a cost function for the newly learned model, ensuring consideration of inter-sample behavior. The usage of the lifting approach facilitates the application of gradient-based policy updates, allowing for the effective optimization of both control and communication policies. Finally, we provide a numerical simulation to illustrate the effectiveness of the proposed approach.

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.

Simultaneous Estimation of Vehicle Sideslip and Roll Angles Using an Event-Triggered-Based IoT Architecture

In recent years, there has been a significant integration of advanced technology into the automotive industry, aimed primarily at enhancing safety and ride comfort. While a notable proportion of these driver-assist systems focuses on skid prevention, insufficient attention has been paid to addressing other crucial scenarios, such as rollovers. The accurate estimation of slip and roll angles plays a vital role in ensuring vehicle control and safety, making these parameters essential, especially with the rise of modern technologies that incorporate networked communication and distributed computing. Furthermore, there exists a lag in the transmission of information between the various vehicle systems, including sensors, actuators, and controllers. This paper outlines the design of an IoT architecture that accurately estimates the sideslip angle and roll angle of a vehicle, while addressing network transmission delays with a networked control system and an event-triggered communication scheme. Experimental results are presented to validate the performance of the IoT architecture proposed. The event-triggered scheme of the IoT solution is used to decrease data transmission and prevent network overload.

An event‐triggered subspace predictive control method for partially unknown linear parameter‐varying systems

AbstractThis article develops a novel event‐triggered subspace predictive control (ET‐SPC) method for discrete‐time linear parameter‐varying (LPV) systems with partially unknown system parameters. An event‐triggered law is developed to alleviate the heavy load of data transmission and computation in the conventional subspace predictive control methods for the LPV systems. The design of the event‐triggered law relies on the input‐to‐state stability (ISS) theory and applies the robust stability condition based on linear matrix inequalities (LMIs), which guarantees the stability of the proposed method. Consequently, the input component can be directly transmitted to the system without receding horizon optimization when the event‐triggered law evaluates that the system satisfies the stability condition. The simulation results illustrate its satisfactory performance, which indicates its potential application involving a considerable amount of data transmission and computation such as the network control systems (NCSs).

Priority-Aware Actuation Update Scheme in Heterogeneous Industrial Networks.

In heterogeneous wireless networked control systems (WNCSs), the age of information (AoI) of the actuation update and actuation update cost are important performance metrics. To reduce the monetary cost, the control system can wait for the availability of a WiFi network for the actuator and then conduct the update using a WiFi network in an opportunistic manner, but this leads to an increased AoI of the actuation update. In addition, since there are different AoI requirements according to the control priorities (i.e., robustness of AoI of the actuation update), these need to be considered when delivering the actuation update. To jointly consider the monetary cost and AoI with priority, this paper proposes a priority-aware actuation update scheme (PAUS) where the control system decides whether to deliver or delay the actuation update to the actuator. For the optimal decision, we formulate a Markov decision process model and derive the optimal policy based on Q-learning, which aims to maximize the average reward that implies the balance between the monetary cost and AoI with priority. Simulation results demonstrate that the PAUS outperforms the comparison schemes in terms of the average reward under various settings.

Event-triggered sliding mode control for networked control systems with actuator faults: Application to gas turbine system

This paper presents an event-triggered sliding mode fault-tolerant control method for networked control systems with actuator faults and external disturbance. First, an event-triggered scheme in the networked control systems is proposed to reduce the number of data transmission. In addition, considering the event-triggering, a sliding mode surface based on the system structure is constructed. Besides, to obtain the controller parameters that meet the [Formula: see text] performance, the bounded real lemma in the form of linear matrix inequality is obtained using the Lyapunov functional. In addition, a sliding mode fault-tolerant control is designed to guarantee that the system can still run stably under the condition of faults and disturbances. Finally, the simulation results of gas turbine system are given to verify the feasibility of the theoretical method.

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.

Sampled-data stabilization for networked control systems under deception attack and the transmission delay

For the stability analysis and stabilization synthesis problems, this paper considers networked control systems (NCSs) with the transmission delay and the deception attack under aperiodic samplings, where the deception attack and its activation function are represented as a sector bound function and a random variable with Bernoulli distribution, respectively. This paper proposes the stability and stabilization criteria for the NCSs by constructing Lyapunov–Krasovskii (L–K) functionals with continuous functionals and looped-functionals. Compared with the literature, the proposed continuous functionals take into account the mixed delay including the transmission delay and the maximum allowable sampling interval, as well as the augmented vector and integral terms. Also, the proposed looped-functionals construct two augmented vectors with integral vectors that are zeros at t=tk and t=tk+1, respectively. By utilizing these two augmented vectors, the looped-functionals fully utilize the sampling patterns compared with the literature. Based on the proposed L–K functionals, this paper derives not only the stability criterion for the NCSs with the transmission delay, but also the stabilization criterion for the NCSs with the transmission delay and the deception attack in terms of linear matrix inequalities (LMIs), respectively. Numerical example demonstrates the validity of the proposed approach.

Enhancing Industrial Control Network Security Through Vulnerability Detection and Attack Graph Analysis

Insufficient communication attack defense capabilities within industrial control networks is a serious problem that is addressed in this study. The author proposes a methodology that focuses on creating attack graphs to ease security and vulnerability studies in industrial control network systems in order to address this issue. The article provides thorough construction guidance and techniques for attack graphs, which are used for penetration testing and vulnerability analysis of networks for industrial control systems. On the created attack graph, experimental evaluations utilizing the ``earthquake net'' virus were carried out. The findings point to four main attack routes where the ``Zhenwang'' virus is most likely going to attack and cause the most damage. With a loss value of 12.2 and an attack success chance of 0.096, the first path involves cumulative attack stages. The second path consists of cumulative attack steps, with a loss value of 10.2 and an attack success probability of 0.072. The third path encompasses cumulative attack steps, with a loss value of 16.6 and an attack success probability of 0.063. The fourth path comprises cumulative attack steps, with a loss value of 18.6 and an attack success probability of 0.084.

Network Analysis and Control System in Mega Projects: The Case of PICASP Erasmus Project

Network Analysis achieved some significant results in recent years thanks to advances in technology and in the approach to organisational networks. Less exciting were the results obtained with Mega Projects. These projects show high risk with regards to results, to the compliance of the budget and to the realization of the entire project. So far, the response of Mega Projects contracting authorities is to ask those presenting the project proposals to increase the number of controls and connected indexes without obtaining, almost until now, an increase in project performance. This paper examines the proposal to transfer some results of network analysis to the performance monitoring of Mega Projects. Starting from the case of Erasmus PICASP Project for the creation of Pilot courses and new didactics for teachers training in cultural tourism for the development of Caspian Area, the paper proposes an evolutionary model of cycles of simplification and complexification of the networks control systems. The approach of Practice Enterprise which allows the members of the project networks to acquire the practice of teamwork right from the early stages of planning, and, once the project starts case risks or emergencies arise, will be applied within his model. Keywords: Network Analysis, Practice Enterprise, MOOCs, Mega Projects, Erasmus Projects

A Control-Recoverable Added-Noise-Based Privacy Scheme for LQ Control in Networked Control Systems

A Control-Recoverable Added-Noise-Based Privacy Scheme for LQ Control in Networked Control Systems

A Counteracting Resilience Strategy for Denial-of-Service Scenarios in Networked Control Systems

A Counteracting Resilience Strategy for Denial-of-Service Scenarios in Networked Control Systems

Security Allocation in Networked Control Systems Under Stealthy Attacks

Security Allocation in Networked Control Systems Under Stealthy Attacks

Privacy Preserving Approximated Optimal Control of Pasteurization Unit Using Homomorphic Encryption

Homomorphic encryption (HE) enables computations to be carried out over encrypted data, ensuring data privacy during processing. This feature is lucrative in various fields, including general process control and network control systems. However, there are some limitations to the applicability of modern noisy HE schemes, especially for computationally more challenging control techniques, such as Model Predictive Control. One of the solutions to this problem is to use homomorphically encrypted neural networks trained to mimic the behavior of MPC. This paper showcases such a neural network and demonstrates its capabilities for data privacy preserving control of the laboratory pasteurization unit.