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Journal articles on the topic 'Network based control systems'

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Author: Grafiati

Published: 17 July 2024

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1

Jadidi, Zahra, Shantanu Pal, Mukhtar Hussain, and Kien Nguyen Thanh. "Correlation-Based Anomaly Detection in Industrial Control Systems." Sensors 23, no. 3 (February 1, 2023): 1561. http://dx.doi.org/10.3390/s23031561.

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Industrial Control Systems (ICSs) were initially designed to be operated in an isolated network. However, recently, ICSs have been increasingly connected to the Internet to expand their capability, such as remote management. This interconnectivity of ICSs exposes them to cyber-attacks. At the same time, cyber-attacks in ICS networks are different compared to traditional Information Technology (IT) networks. Cyber attacks on ICSs usually involve a sequence of actions and a multitude of devices. However, current anomaly detection systems only focus on local analysis, which misses the correlation between devices and the progress of attacks over time. As a consequence, they lack an effective way to detect attacks at an entire network scale and predict possible future actions of an attack, which is of significant interest to security analysts to identify the weaknesses of their network and prevent similar attacks in the future. To address these two key issues, this paper presents a system-wide anomaly detection solution using recurrent neural networks combined with correlation analysis techniques. The proposed solution has a two-layer analysis. The first layer targets attack detection, and the second layer analyses the detected attack to predict the next possible attack actions. The main contribution of this paper is the proof of the concept implementation using two real-world ICS datasets, SWaT and Power System Attack. Moreover, we show that the proposed solution effectively detects anomalies and attacks on the scale of the entire ICS network.

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Zhang, Haitao, and Zhen Li. "Fuzzy Immune Control Based Smith Predictor for Networked Control Systems." International Journal of Engineering and Technology 3, no. 1 (2011): 81–84. http://dx.doi.org/10.7763/ijet.2011.v3.204.

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3

Zou, Y., T. Chen, and S. Li. "Network-based predictive control of multirate systems." IET Control Theory & Applications 4, no. 7 (July 1, 2010): 1145–56. http://dx.doi.org/10.1049/iet-cta.2008.0577.

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4

Efrati, T., and H. Flashner. "Neural Network Based Tracking Control of Mechanical Systems." Journal of Dynamic Systems, Measurement, and Control 121, no. 1 (March 1, 1999): 148–54. http://dx.doi.org/10.1115/1.2802435.

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A method for tracking control of mechanical systems based on artificial neural networks is presented. The controller consists of a proportional plus derivative controller and a two-layer feedforward neural network. It is shown that the tracking error of the closed-loop system goes to zero while the control effort is minimized. Tuning of the neural network’s weights is formulated in terms of a constrained optimization problem. The resulting algorithm has a simple structure and requires a very modest computation effort. In addition, the neural network’s learning procedure is implemented on-line.

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Dovbnya, Vitaly G., Sergey N. Frolov, Konstantin P. Sulima, and Alexey N. Schitov. "SPECIFICS OF IMPLEMENTATION OF CONTROL SYSTEMS BASED ON LORAWAN TECHNOLOGY." T-Comm 14, no. 9 (2020): 24–30. http://dx.doi.org/10.36724/2072-8735-2020-14-9-24-30.

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In the context of the rapid growth of various areas of the Internet of things, there is currently no unified approach to building networks based on low-power Wide-area Network (LPWAN) wireless networks, taking into account the general requirements for them as automated control systems (ACS). There are the following areas of use of the Internet of things: industry and production; transport and transportation; control of the technical condition of building structures, air quality, background noise and energy consumption; waste management; smart Parking and providing data on traffic jams; smart street lighting and use in everyday life. Networks based on LoRaWAN technology provide low-cost energy-efficient wireless communications for modern ACS in a variety of industries. It is cost-effective for designing hardware and software for telemetry and controlling, such as a system of control and monitoring engineering systems of buildings and facilities (SMES) and automated outdoor lighting control systems. The article presents a structural and functional analysis of approaches to the construction of hardware and software complex elements based on LoRаWAN, taking into account the specifics and logic of the SMES and ASUS. It also provides calculations of network bandwidth and capacity for a single LoRaWAN gateway in a different mode of operation of ACS. A parametric analysis of existing implementations was carried out to design the management server (SU), which is the main element of the LoRaWAN network. The results allowed to obtain seventeen indicators that determine the functionality of a network server (NS). Network server software development. Major structures and the mechanisms of interaction of its elements are determined during the process of designing the original implementation of NS software.

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Abdulrab, Hakim, Fawnizu Azmadi Hussin, Azrina Abd Aziz, Azlan Awang, Idris Ismail, and P. Arun Mozhi Devan. "Reliable Fault Tolerant-Based Multipath Routing Model for Industrial Wireless Control Systems." Applied Sciences 12, no. 2 (January 6, 2022): 544. http://dx.doi.org/10.3390/app12020544.

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Communication in industrial wireless networks necessitates reliability and precision. Besides, the existence of interference or traffic in the network must not affect the estimated network properties. Therefore, data packets have to be sent within a certain time frame and over a reliable connection. However, the working scenarios and the characteristics of the network itself make it vulnerable to node or link faults, which impact the transmission reliability and overall performance. This article aims to introduce a developed multipath routing model, which leads to cost-effective planning, low latency and high reliability of industrial wireless mesh networks, such as the WirelessHART networks. The multipath routing model has three primary paths, and each path has a backup node. The backup node stores the data transmitted by the parent node to grant communication continuity when primary nodes fail. The multipath routing model is developed based on optimal network planning and deployment algorithm. Simulations were conducted on a WirelessHART simulator using Network Simulator (NS2). The performance of the developed model is compared with the state-of-the-art. The obtained results reveal a significant reduction in the average network latency, low power consumption, better improvement in expected network lifetime, and enhanced packet delivery ratio which improve network reliability.

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7

Nwazor, Nkolika Ogechukwu, and Eliezar Elisha Audu. "Data communications network for real-time industrial control systems." Nigerian Journal of Technological Development 19, no. 1 (June 6, 2022): 48–58. http://dx.doi.org/10.4314/njtd.v19i1.6.

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The advancements in network technologies and the evolution of the Internet of Things (IoT) have made supporting industrial control systems over probabilistic data networks promising. However, control systems’ communication over the traditional data networks is faced with problems of instability in feedback control and poor quality of performance due to time-varying data propagation delay. This paper presents two approaches that can enable real-time industrial control over non-deterministic computer networks allowing control system designers to take advantage of the existing communication infrastructures. The first approach is based on system-level interaction over two wires network called the collaboration network. The second approach is based on the implementation of the virtual local area network (VLAN). This method allows real-time control of industrial equipment or systems over IP-based networks while other computers are connected. Nodes providing real-time control services have the same PortID on the VLAN switch. This approach minimizes data traffic and reduces time-varying delay in system control over IP networks. The first approach was modeled and simulated using Proteus ISIS software. Two PIC16F877A microcontrollers were used to represent two nodes. CISCO packet tracer was used in the second approach to model and simulate IP-based control system communications over the traditional data network. Results indicate that the use of a two-wire collaborative network approach to a real-time control system is effective but requires an additional network alongside the main data traffic channel. VLAN, therefore, presents a more flexible approach that relies on the same infrastructures.

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8

Zhang, Yan. "Network Optimization-Based MPC for Distributed Control Systems." Advanced Materials Research 482-484 (February 2012): 2485–88. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.2485.

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In this paper, a novel network optimization-based MPC scheme is proposed for on-line optimization and control of a class of distributed control systems, in which the on-line optimization of the whole system is decomposed into that of several small-scale sub-systems in distributed structures. Under network environment, the connectivity of the communication network is assumed to be sufficient for each sub-system to exchange information with other sub-systems. An iterative algorithm for networked MPC with ideal information model is developed for DCS. Finally, the simulation study of the fuel feed flow control for the walking beam reheating furnace is provided to test the effectiveness and practicality of the proposed networked MPC algorithm.

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Masoumzadeh, Amirreza, and James Joshi. "Ontology-based access control for social network systems." International Journal of Information Privacy, Security and Integrity 1, no. 1 (2011): 59. http://dx.doi.org/10.1504/ijipsi.2011.043731.

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Hong Seong Park, Yong Ho Kim, Don-Sung Kim, and Wook Hyun Kwon. "A scheduling method for network-based control systems." IEEE Transactions on Control Systems Technology 10, no. 3 (May 2002): 318–30. http://dx.doi.org/10.1109/87.998012.

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11

Fujii, Teruo, and Tamaki Ura. "Neural-network-based adaptive control systems for AUVs." Engineering Applications of Artificial Intelligence 4, no. 4 (January 1991): 309–18. http://dx.doi.org/10.1016/0952-1976(91)90045-8.

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Gücüyener, İsmet. "Fuzzy Neural-Network-Based Controller." Solid State Phenomena 220-221 (January 2015): 407–12. http://dx.doi.org/10.4028/www.scientific.net/ssp.220-221.407.

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Using a controller is necessary for any automation system. The controller must be cheap, reliable, user friendly and not cause any problems for inputs and outputs. Classical control systems like proportional integral derivative (PID) put adequate results of linear systems and continuous-time. In fact, real control systems are time-variant, with non-linearity and poorly calculated dynamic variables. For this reason, conventional control systems need an expert person to adjust controller parameters in general. Sometimes an operator is required to solve control problems. Human control is not completely reliable. Also, it does not include any electronic communication. In modern factories, every point must be monitored and electronically controlled from remote points when necessary. In this study, including every electronic communication channel, a simplified handling, low-cost, reliable, Fuzzy Neural Network Controller (FNNC) is designed.

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Esbati, M., M. A. Khanesar, and A. Shahzadi. "Improving the quality of service in network-based control systems." Transactions of the Institute of Measurement and Control 40, no. 8 (July 24, 2017): 2694–702. http://dx.doi.org/10.1177/0142331217714863.

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The use of data networks in control loops has received much attention recently due to its flexibility and economical advantages. In addition, mutual network usage has raised new challenges such as delay and data loss. This paper aims to reduce undesired effects of network by reducing the required traffic of the network. An estimation framework for network control system is introduced, in which estimations of local Kalman filter is sent to remote estimator based on the logic decided by a novel fuzzy communication logic. In order to do so, there exist two estimators, a remote estimator which estimates the states of the plant and its local copy that gives the same output. The output of the local estimator is compared with the real states of the system, if the states of the system are estimated with small error, there is no need to send data, hence, the probability of sending data is decreased using a fuzzy decision system. In order to optimize this fuzzy system, a particle swarm optimization (PSO) algorithm is used. The proposed method is applied to control a pair of overhead crane systems with non-linear dynamics. Since the two overhead cranes need to work synchronously and their synchronization is performed over a network, the control of this system lies within the scope of the proposed controller. Simulation results show that the communication load is reduced and the purposed fuzzy communication logic is able to control the non-linear dynamical systems over a network with a sufficient performance.

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Sarjas, Andrej, and Dusan Gleich. "Event-triggered sliding mode control for constrained networked control systems." Facta universitatis - series: Electronics and Energetics 35, no. 4 (2022): 557–70. http://dx.doi.org/10.2298/fuee2204557s.

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The paper describes a Non-linear Control (ETNC) approach for constrained Networked Feedback Control Systems (NFCS). The real-time controller execution is implemented based on the Event-triggering paradigm. A nonlinear variable structure is used for the controller design. The nonlinear approach is based on the predefined sliding variable defined by the system states with a nonlinear switching function. The system's stability is analyzed regarding the evolution of the sliding variable. The Event-Triggered operation of the nonlinear controller is based on the prescribed triggering rule. The stability boundary of the sliding variable is subject to the preselected triggering condition, whose selection is a tradeoff of system performance, networks constraints and transmission capabilities. The main focus of the Event triggering approach is lowering network resources utilization in the steady-state behavior of the NFCS. The presented approach ensures a non-zero inter-event time of controller execution, which enables scheduling and optimization of the network operation regarding the network constraints and real-time system performance. The efficiency of the presented method is presented with a comparison of the classical time triggering approach. The real measurement supports the results.

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15

Díaz-Cacho Medina, Miguel, Emma Delgado Romero, and Antonio Barreiro Blas. "Control/Network Codesign Basics for IP-Based Shared Networks." Mathematical Problems in Engineering 2011 (2011): 1–23. http://dx.doi.org/10.1155/2011/239512.

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Network and control relationship is an essential aspect in the design of networked control systems (NCSs). The design parameters are mainly centered in the transmission rate and in the packet structure, and some studies have been made to determine how transmission rate affects the network delay and consequently the stability of the control. In Internet, these analysis are mathematically complex due to the large number of different potential scenarios. Using empirical methods, this work deduces that the transmission scheduling problem of an NCS can be solved by designing an appropriate transport protocol, taken into account high and periodic sampling rates. The transport protocol features are determined by simulation, using a new test platform based on the NS2 network simulation suite, to develop control/network codesign solutions. Conclusions of this paper are that the transport features are packet-loss-based flow control, best effort, and fairness, supplemented by a packet priority scheme.

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16

Jing, Yuanwei, Zanhua Li, and Georgi Dimirovski. "Minimax based congestion control for TCP network systems with UDP flows." MATEC Web of Conferences 210 (2018): 03005. http://dx.doi.org/10.1051/matecconf/201821003005.

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The congestion control problem for TCP network systems with UDP flows is considered. A nonlinear TCP network model with strict-feedback structure is established. The unknown UDP flow is considered as the disturbance to the system, and the maximum UDP flow is calculated by using the minimax approach. And then, a congestion control algorithm is proposed by using backstepping approach. Further, a state-feedback congestion controller is presented to make the TCP networks asymptotically stable. The simulation results show the superiority and feasibility of the proposed method.

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17

Jiang, Furong, Zhaoning Zhang, and Xiaoxu Dai. "Ground-Air Traffic Congestion Propagation Model Based on Hierarchical Control Interdependent Network." Journal of Advanced Transportation 2023 (May 5, 2023): 1–22. http://dx.doi.org/10.1155/2023/4602148.

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A multilayer network approach to model and analyze air traffic networks is proposed. These networks are viewed as complex systems with interactions between airports, airspaces, procedures, and air traffic flows (ATFs). A topology-based airport-airspace network and a flight trajectory network are developed to represent critical physical and operational characteristics. A multilayer traffic flow network and an interrelated traffic congestion propagation network are also formulated to represent the ATF connection and congestion propagation dynamics, respectively. Furthermore, a set of analytical metrics, including those of airport surface (AS), terminal controlled airspace (TCA), and area-controlled airspace (ACA), is introduced and applied to a case study in central and south-eastern China. The empirical results show the existence of a fundamental diagram of the airport, terminal, and intersections of air routes. Moreover, the dynamics and underlying mechanisms of congestion propagation through the AS-TCA-ACA network are revealed and interpreted using the classical susceptible-infectious-removed model in a hierarchical network. Finally, a high propagation probability among adjacent terminals and a high recovery probability are identified at the network system level. This study provides analytical tools for comprehending the complex interactions among air traffic systems and identifies future developments and automation of layered coupled air traffic management systems.

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18

Plonka, Leslaw, and Korneliusz Miksch. "ANN-Based Short-Term Wastewater Flow Prediction for Better WWTP Control." Chemistry and Chemical Technology 4, no. 2 (June 15, 2010): 159–62. http://dx.doi.org/10.23939/chcht04.02.159.

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This paper presents an approach to predict the amount of the wastewater which enters wastewater treatment plant, using artificial neural network. The method presented can be used to give short-term predictions of wastewater inflow-rate. The described neural network model uses a very tiny set of data commonly collected by WWTP control systems.

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Florencio, Heitor, Adrião Dória Neto, and Daniel Martins. "ISA 100.11a Networked Control System Based on Link Stability." Sensors 20, no. 18 (September 21, 2020): 5417. http://dx.doi.org/10.3390/s20185417.

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Wireless networked control systems (WNCSs) must ensure that control systems are stable, robust and capable of minimizing the effects of disturbances. Due to the need for a stable and secure WNCS, critical wireless network variables must be taken into account in the design. As wireless networks are composed of several links, factors that indicate the performances of these links can be used to evaluate the communication system in the WNCS. This work presents a wireless network control system composed of ISA 100.11a sensors, a network manager, a controller and a wired actuator. The system controls the liquid level in the tank of the coupled tank system. In order to assess the influence of the sensor link failure on the control loop, the controller calculates the link stability and chooses an alternative link in case of instability in the current link. Preliminary tests of WNCS performance were performed to determine the minimum stability value of the link that generates an error in the control loop. Finally, the tests of the control system based on link stability obtained excellent results. Even with disturbances in the network links, the control system error remained below the threshold.

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Zhou, Yang. "Research on Network Control Based on QoS of the Network." Advanced Materials Research 989-994 (July 2014): 4265–68. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4265.

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With the technology improvement of computer communication and multimedia coding, real time communication such as audio and video is introduced to networks and become a dominant way of communication.In the control of network, the optimization problem of the network controller based on network Quality of service (QoS) is a very important problem in the research of network control. Considering the influence of network quality—of service (QoS) on the control performance,a system model combining the network parameters and the control parameters is established for networked control systems (NCSs). Based on this, the condition dependent on the network parameters and control parameters is presented for the existence of guaranteed cost controllers.LMI).Within the scope of QoS perturbation,the designed controller can not only make the system as hypnotically stable but also guarantee that the system performance index is not greater than the upper bound.

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21

Liang, Geng, and Wen Li. "A novel industrial control architecture based on Software-Defined Network." Measurement and Control 51, no. 7-8 (July 2, 2018): 360–67. http://dx.doi.org/10.1177/0020294018784310.

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Traditionally, routers and other network devices encompass both data and control functions in most large enterprise networks, making it difficult to adjust the network infrastructure and operation to large-scale addition of end systems, virtual machines, and virtual networks in industrial comprehensive automation. A network organizing technique that has come to recent prominence is the Software-Defined Network (SDN). A novel SDN based industrial control network (SDNICN) was proposed in this paper. Intelligent network components are included in a SDNICN. Switches in SDNICN provided fundamental network interconnection for the whole industrial control network. Network controller is used for data transmission, forwarding and routing control between different layers. Service Management Center (SMC) is essentially responsible for managing various services used in industrial process control. SDNICN can not only greatly improve the flexibility and performance of industrial control network but also meet the intelligence and informatization of the future industry.

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22

DESHPANDE, ANIKET, PUSHPAK JAGTAP, PRASHANT BANSODE, ARUN MAHINDRAKAR, and NAVDEEP SINGH. "COMPLEX LAPLACIAN-BASED DISTRIBUTED CONTROL FOR MULTI-AGENT NETWORK." Advances in Complex Systems 21, no. 05 (August 2018): 1850015. http://dx.doi.org/10.1142/s0219525918500157.

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This paper, proposes a complex Laplacian-based distributed control scheme for convergence in the multi-agent network. The proposed scheme has been designated as cascade formulation. The proposed technique exploits the traditional method of organizing large scattered networks into smaller interconnected clusters to optimize information flow within the network. The complex Laplacian-based approach results in a hierarchical structure, with the formation of a meta-cluster leading other clusters in the network. The proposed formulation enables flexibility to constrain the eigenspectra of the overall closed-loop dynamics, ensuring desired convergence rate and control input intensity. The sufficient conditions ensuring globally stable formation for the proposed formulation are also asserted. Robustness of the proposed formulation to uncertainties like loss in communication links and actuator failure have also been discussed. The effectiveness of the proposed approach is illustrated by simulating a finitely large network of 30 vehicles.

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Bao, Feng, Haoyang Yu, and Hao Wang. "TSN-Based Backbone Network of Train Control Management System." Wireless Communications and Mobile Computing 2022 (March 27, 2022): 1–12. http://dx.doi.org/10.1155/2022/5789444.

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Time-sensitive network (TSN), as one of the latest real-time Ethernet techniques, has achieved great success in several scenarios that require strict communication latency and packet loss. This paper reviews the state-of-the-art in time-sensitive networks and investigates the drawbacks of current vehicle networks. By introducing TSN into the TCMS backbone network, next-generation smart vehicles can achieve high-quality data transmissions. This paper also analyzes the priority of the vehicle data and calculates the time-aware shaper slot to ensure that the control data is not affected by the multimedia data. Using the proposed structure, experimental results suggest that the network efficiency can be improved in the rail transit systems.

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Huang, Li-lian, and Jin Chen. "Fuzzy PD Control of Networked Control Systems Based on CMAC Neural Network." Mathematical Problems in Engineering 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/347217.

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The network and plant can be regarded as a controlled time-varying system because of the random induced delay in the networked control systems. The cerebellar model articulation controller (CMAC) neural network and a PD controller are combined to achieve the forward feedback control. The PD controller parameters are adjusted adaptively by fuzzy reasoning mechanism, which can optimize the control effect by reducing the uncertainty caused by the network-induced delay. Finally, the simulations show that the control method proposed can improve the performance effectively.

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Gu, Dongbing, and Huosheng Hu. "Distributed network-based formation control." International Journal of Systems Science 40, no. 5 (May 2009): 539–52. http://dx.doi.org/10.1080/00207720902750029.

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Allibhoy, Ahmed, and Jorge Cortes. "Data-Based Receding Horizon Control of Linear Network Systems." IEEE Control Systems Letters 5, no. 4 (October 2021): 1207–12. http://dx.doi.org/10.1109/lcsys.2020.3021050.

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Guillen, Luis, Satoru Izumi, Toru Abe, Hiroaki Muraoka, and Takuo Suganuma. "SDN-based Network Control Method for Distributed Storage Systems." Advances in Science, Technology and Engineering Systems Journal 3, no. 5 (September 2018): 140–51. http://dx.doi.org/10.25046/aj030518.

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Šimandl, Miroslav, Ladislav Král, and Pavel Hering. "NEURAL NETWORK BASED BICRITERIAL DUAL CONTROL OF NONLINEAR SYSTEMS." IFAC Proceedings Volumes 38, no. 1 (2005): 58–63. http://dx.doi.org/10.3182/20050703-6-cz-1902.01088.

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Xun-Lin Zhu and Youyi Wang. "Stabilization for Sampled-Data Neural-Network-Based Control Systems." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 41, no. 1 (February 2011): 210–21. http://dx.doi.org/10.1109/tsmcb.2010.2050587.

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Tranchero, Bruno, and Cosimo Latorre. "Neural Network-Based Virtual Sensors In Flight Control Systems." IFAC Proceedings Volumes 34, no. 15 (September 2001): 416–21. http://dx.doi.org/10.1016/s1474-6670(17)40763-4.

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Chang, Wei-Der, Li-Chen Fu, and Jung-Hua Yang. "Adaptive Robust Neural-Network Based Control for Siso Systems." IFAC Proceedings Volumes 29, no. 1 (June 1996): 2562–67. http://dx.doi.org/10.1016/s1474-6670(17)58060-x.

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Liu, G. P., and S. Daley. "Neural network based active control of unstable combustion systems." IFAC Proceedings Volumes 32, no. 2 (July 1999): 5123–28. http://dx.doi.org/10.1016/s1474-6670(17)56872-x.

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Zhang, Yanjun, Gang Tao, and Mou Chen. "Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems." IEEE Transactions on Neural Networks and Learning Systems 27, no. 9 (September 2016): 1864–77. http://dx.doi.org/10.1109/tnnls.2015.2461001.

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Banos, Alfonso, Felix Perez, and Joaquin Cervera. "Network-Based Reset Control Systems With Time-Varying Delays." IEEE Transactions on Industrial Informatics 10, no. 1 (February 2014): 514–22. http://dx.doi.org/10.1109/tii.2013.2273434.

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Chang, Y. C. "Neural network-based H∞ tracking control for robotic systems." IEE Proceedings - Control Theory and Applications 147, no. 3 (May 1, 2000): 303–11. http://dx.doi.org/10.1049/ip-cta:20000257.

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Katić, Katarina, Rongling Li, Jacob Verhaart, and Wim Zeiler. "Neural network based predictive control of personalized heating systems." Energy and Buildings 174 (September 2018): 199–213. http://dx.doi.org/10.1016/j.enbuild.2018.06.033.

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Ma, Dan, and Jian-Chang Liu. "Robust exponential stabilization for network-based switched control systems." International Journal of Control, Automation and Systems 8, no. 1 (February 2010): 67–72. http://dx.doi.org/10.1007/s12555-010-0109-8.

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Patan, Krzysztof, and Maciej Patan. "Neural-network-based iterative learning control of nonlinear systems." ISA Transactions 98 (March 2020): 445–53. http://dx.doi.org/10.1016/j.isatra.2019.08.044.

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Yue, Dong, Qing-Long Han, and James Lam. "Network-based robust H∞ control of systems with uncertainty." Automatica 41, no. 6 (June 2005): 999–1007. http://dx.doi.org/10.1016/j.automatica.2004.12.011.

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KARIMI, H. R., B. LOHMANN, B. MOSHIRI, and P. JABEHDAR MARALANI. "WAVELET-BASED IDENTIFICATION AND CONTROL DESIGN FOR A CLASS OF NONLINEAR SYSTEMS." International Journal of Wavelets, Multiresolution and Information Processing 04, no. 01 (March 2006): 213–26. http://dx.doi.org/10.1142/s0219691306001178.

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In this paper, we extend the wavelet networks for identification and H∞ control of a class of nonlinear dynamical systems. The technique of feedback linearization, supervisory control and H∞ control are used to design an adaptive control law and also the parameter adaptation laws of the wavelet network are developed using a Lyapunov-based design. By some theorems, it will be proved that even in the presence of modeling errors, named network error, the stability of the overall closed-loop system and convergence of the network parameters and the boundedness of the state errors are guaranteed. The applicability of the proposed method is illustrated on a nonlinear plant by computer simulation.

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Boukabou, A., and N. Mansouri. "Neural Predictive Control of Unknown Chaotic Systems." Nonlinear Analysis: Modelling and Control 10, no. 2 (April 25, 2005): 95–106. http://dx.doi.org/10.15388/na.2005.10.2.15125.

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In this work, a neural networks is developed for modelling and controlling a chaotic system based on measured input-output data pairs. In the chaos modelling phase, a neural network is trained on the unknown system. Then, a predictive control mechanism has been implemented with the neural networks to reach the close neighborhood of the chosen unstable fixed point embedded in the chaotic systems. Effectiveness of the proposed method for both modelling and prediction-based control on the chaotic logistic equation and Hénon map has been demonstrated.

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Meng, Xiangyu, James Lam, and Huijun Gao. "Network-basedH∞control for stochastic systems." International Journal of Robust and Nonlinear Control 19, no. 3 (February 2009): 295–312. http://dx.doi.org/10.1002/rnc.1307.

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SHAO, Qi-Ke, Li YU, Lin-Lin OU, and Duan ZHANG. "Co-design Methodology for Network-based Control Systems Based on QoS." Acta Automatica Sinica 36, no. 9 (December 21, 2010): 1356–60. http://dx.doi.org/10.3724/sp.j.1004.2010.01356.

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Yang, Xinhao, Sheng Xu, and Ze Li. "Consensus Congestion Control in Multirouter Networks Based on Multiagent System." Complexity 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/3574712.

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Due to the unbalance distribution of network resources and network traffic, congestion is an inherent property of the Internet. The consensus congestion controller based on the multiagent system theory is designed for the multirouter topology, which improves the performance of the whole networks. Based on the analysis of the causes of congestion, the topology of multirouter networks is modeled based on the graph theory and the network congestion control problem is described as a consensus problem in multiagent systems. Simulation results by MATLAB and Ns2 indicate that the proposed algorithm maintains a high throughput and a low packet drip ratio and improves the quality of the service in the complex network environment.

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LIU, MEIQIN. "DYNAMIC OUTPUT FEEDBACK STABILIZATION FOR NONLINEAR SYSTEMS BASED ON STANDARD NEURAL NETWORK MODELS." International Journal of Neural Systems 16, no. 04 (August 2006): 305–17. http://dx.doi.org/10.1142/s0129065706000706.

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A neural-model-based control design for some nonlinear systems is addressed. The design approach is to approximate the nonlinear systems with neural networks of which the activation functions satisfy the sector conditions. A novel neural network model termed standard neural network model (SNNM) is advanced for describing this class of approximating neural networks. Full-order dynamic output feedback control laws are then designed for the SNNMs with inputs and outputs to stabilize the closed-loop systems. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. It is shown that most neural-network-based nonlinear systems can be transformed into input-output SNNMs to be stabilization synthesized in a unified way. Finally, some application examples are presented to illustrate the control design procedures.

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Tao, Qingguang, Min Jiang, Xiaofeng Wang, and Bo Deng. "A cloud-based experimental platform for networked industrial control systems." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 04 (August 2018): 1850024. http://dx.doi.org/10.1142/s1793962318500241.

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Today, a large number of information and communication technologies (ICT) and networking technologies are being used in industrial control systems. Thus, networked industrial control systems (NICS) are exposed to many security threats. Moreover, new technologies for NICS also need to be tested. This paper presents a cloud-based experimental platform for NICS to test new technologies and security threats. A cloud platform is used to emulate network devices and Simulink is used to simulate the physical layer. To build this testbed, we modify the cloud platform and add three modules to the testbed. One module is used so that the cloud platform can connect to real devices. By using this module, real devices can be added to the networks in the cloud platform. The second module is used for network connection configurations in the testbed. By using this module, the bandwidth, delay and packet loss rate for networks in the testbed can all be set. The third module is used to connect the Simulink to the testbed. The main features of the proposed platform are high flexibility, high authenticity, and low cost. Advanced persistent threat (APT) attacks are a common threat for NICS nowadays. In order to prove the feasibility of the proposed testbed, a common NICS is established and an APT attack is executed on it.

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Zhang, Jianhua, and Junghui Chen. "Neural PID Control Strategy for Networked Process Control." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/752489.

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A new method with a two-layer hierarchy is presented based on a neural proportional-integral-derivative (PID) iterative learning method over the communication network for the closed-loop automatic tuning of a PID controller. It can enhance the performance of the well-known simple PID feedback control loop in the local field when real networked process control applied to systems with uncertain factors, such as external disturbance or randomly delayed measurements. The proposed PID iterative learning method is implemented by backpropagation neural networks whose weights are updated via minimizing tracking error entropy of closed-loop systems. The convergence in the mean square sense is analysed for closed-loop networked control systems. To demonstrate the potential applications of the proposed strategies, a pressure-tank experiment is provided to show the usefulness and effectiveness of the proposed design method in network process control systems.

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Park, Bongsang, Junghyo Nah, Jang-Young Choi, Ick-Jae Yoon, and Pangun Park. "Robust Wireless Sensor and Actuator Networks for Networked Control Systems." Sensors 19, no. 7 (March 29, 2019): 1535. http://dx.doi.org/10.3390/s19071535.

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The stability guarantee of wireless networked control systems is still challenging due to the complex interaction among the layers and the vulnerability to network faults, such as link and node failures. In this paper, we propose a robust wireless sensor and actuator network (R-WSAN) to maintain the control stability of multiple plants over the spatial-temporal changes of wireless networks. The proposed joint design protocol combines the distributed controller of control systems and the clustering, resource scheduling, and control task sharing scheme of wireless networks over a hierarchical cluster-based network. In particular, R-WSAN decouples the tasks from the inherently unreliable nodes and allows control tasks to share between nodes of wireless networks. Our simulations demonstrate that R-WSAN provides the enhanced resilience to the network faults for sensing and actuation without significantly disrupting the control performance.

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Zhang, Qi-Zhi, You-Chun Tang, and Wei-Dong Zhang. "ADAPTIVE DYNAMIC MATRIX CONTROL FOR NETWORK-BASED CONTROL SYSTEMS WITH RANDOM DELAYS." Asian Journal of Control 8, no. 1 (October 22, 2008): 45–49. http://dx.doi.org/10.1111/j.1934-6093.2006.tb00250.x.

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Zhang, Chi, Wenjie Ruan, and Peipei Xu. "Reachability Analysis of Neural Network Control Systems." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (June 26, 2023): 15287–95. http://dx.doi.org/10.1609/aaai.v37i12.26783.

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Neural network controllers (NNCs) have shown great promise in autonomous and cyber-physical systems. Despite the various verification approaches for neural networks, the safety analysis of NNCs remains an open problem. Existing verification approaches for neural network control systems (NNCSs) either can only work on a limited type of activation functions, or result in non-trivial over-approximation errors with time evolving. This paper proposes a verification framework for NNCS based on Lipschitzian optimisation, called DeepNNC. We first prove the Lipschitz continuity of closed-loop NNCSs by unrolling and eliminating the loops. We then reveal the working principles of applying Lipschitzian optimisation on NNCS verification and illustrate it by verifying an adaptive cruise control model. Compared to state-of-the-art verification approaches, DeepNNC shows superior performance in terms of efficiency and accuracy over a wide range of NNCs. We also provide a case study to demonstrate the capability of DeepNNC to handle a real-world, practical, and complex system. Our tool DeepNNC is available at https://github.com/TrustAI/DeepNNC.

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