Cosimulation Platform for Distributed Control System via Heterogeneous Network

A cosimulation platform was established for distributed control systems via heterogeneous network,which integrated OPNET and Matlab/Simulink.The communication node in this cosimulation platform was built based on OSI model and UDP protocol,which was adopted as the transportation layer protocol.Data exchanged between the data source module and the specified node.It was fulfilled by revising the corresponding protocol modules based on the characteristics of UDP.The effectiveness of the constructed simulation platform was demonstrated by a numerical example.

Designing Neural Network Model in Distributed Control System

The realizing of Artificial Neural Network(ANN) in Distributed Control System (DCS) is discussed. The model of ANN designed can be called as easily as conventional algorithm. It can act as an ANN controller or as an identifier in adaptive control system.

Developing Environment of Hierarchical and Distributed Real-Time Fire-Control Network

This paper introduces a hierarchical real-time environment for developing ship-bornefire-control system. Advanced computer networks are used to simulate the system with the requiredengagement scenario, including own-ship and parameters, and data processing and transmission,mission calculation, graphical supervision and gunnery ballistics outputting. The simulation systemis able to receive instruction from, or send information to the command-control center. Furthermore,the system can also be used to compare various designed schemes and analyze the accuracy andeffectiveness of the system.

Leader-follower Optimal Selection Method for Distributed Control System in Active Distribution Networks

Aiming at the shortcomings of a traditional centralized control in an active distribution network(AND),this paper proposes a leader-follower distributed group cooperative control strategy to realize multiple operation and control tasks for an ADN.The distributed information exchange protocols of the distributed generation(DG)group devoted to node voltage regulation or exchange power control are developed using a DG power utilization ratio as the consensus variable.On these bases,this study further investigates the leader optimal selection method for a DG group to improve the response speed of the distributed control system.Furthermore,a single or multiple leader selection model is established to minimize the constraints of the one-step convergence factor and the number of leaders to improve the response speed of the distributed control system.The simulation results of the IEEE 33 bus standard test system show the effectiveness of the proposed distributed control strategy.In addition,the response speed of a DG control group can be improved effectively when the single or multiple leaders are selected optimally.

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

Data-Driven Control of Distributed Event-Triggered Network Systems

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(a.k.a.network systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional approach.Marrying the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is established.Meanwhile,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data.Finally,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.

Operation Control Method of Relay Protection in Flexible DC Distribution Network Compatible with Distributed Power Supply

A novel operation control method for relay protection in flexible DC distribution networks with distributed power supply is proposed to address the issue of inaccurate fault location during relay protection,leading to poor performance.The method combines a fault-tolerant fault location method based on long-term and short-term memory networks to accurately locate the fault section.Then,an operation control method for relay protection based on adaptive weight and whale optimization algorithm(WOA)is used to construct an objective function considering the shortest relay protection action time and the smallest impulse current.The adaptive weight and WOA are employed to obtain the optimal strategy for relay protection operation control,reducing the action time and impulse current.Experimental results demonstrate the effectiveness of the proposed method in accurately locating faults and improving relay protection performance.The longest operation time is reduced by 4.7023 s,and the maximum impulse current is limited to 0.3 A,effectively controlling the impact of large impulse currents and enhancing control efficiency.

Resilience Against Replay Attacks:A Distributed Model Predictive Control Scheme for Networked Multi-Agent Systems

In this paper,a resilient distributed control scheme against replay attacks for multi-agent networked systems subject to input and state constraints is proposed.The methodological starting point relies on a smart use of predictive arguments with a twofold aim:1)Promptly detect malicious agent behaviors affecting normal system operations;2)Apply specific control actions,based on predictive ideas,for mitigating as much as possible undesirable domino effects resulting from adversary operations.Specifically,the multi-agent system is topologically described by a leader-follower digraph characterized by a unique leader and set-theoretic receding horizon control ideas are exploited to develop a distributed algorithm capable to instantaneously recognize the attacked agent.Finally,numerical simulations are carried out to show benefits and effectiveness of the proposed approach.

Distributed Fault-Tolerant Containment Control for Nonlinear Multi-Agent Systems Under Directed Network Topology via Hierarchical Approach

This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on the global information about the communication topology consists of two layers.Different from most existing distributed fault-tolerant control(FTC)protocols where the fault in one agent may propagate over network,the developed control method can eliminate the phenomenon of fault propagation.Based on the hierarchical control strategy,the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower layer.Finally,simulation results are given to demonstrate the effectiveness of the proposed control protocol.

Aperiodic Sampled-Data Control of Distributed Networked Control Systems Under Stochastic Cyber-Attacks

This paper examines the stabilization problem of a distributed networked control system under the effect of cyberattacks by employing a hybrid aperiodic triggering mechanism.The cyber-attack considered in the paper is a stochastic deception attack at the sensor-controller end. The probability of the occurrence of attack on a subsystem is represented using a random variable. A decentralized hybrid sampled-data strategy is introduced to save energy consumption and reduce the transmission load of the network. In the proposed decentralized strategy, each subsystem can decide independently whether its state should be transmitted to the controller or not. The scheme of the hybrid triggering mechanism for each subsystem composed of two stages: In the first stage, the next sampling instant is computed using a self-triggering strategy. Subsequently, in the second stage, an event-triggering condition is checked at these sampling instants and the control signal is computed only if the event-triggering condition is violated. The self-triggering condition used in the first stage is dependent on the selection of eventtriggering condition of the second stage. Finally, a comparison of the proposed approach with other triggering mechanisms existing in the literature is presented in terms of the sampling instants,transmission frequency and performance measures through simulation examples.

Strip Thickness Control of Cold Rolling Mill with Roll Eccentricity Compensation by Using Fuzzy Neural Network

In rolling mill, the accuracy and quality of the strip exit thickness are very important factors. To realize high accuracy in the strip exit thickness, the Automatic Gauge Control (AGC) system is used. Because of roll eccentricity in backup rolls, the exit thickness deviates periodically. In this paper, we design PI controller in outer loop for the strip exit thickness while PD controller is used in inner loop for the work roll actuator position. Also, in order to reduce the periodic thickness deviation, we propose roll eccentricity compensation by using Fuzzy Neural Network with online tuning. Simulink model for the overall system has been implemented using MATLAB/SIMULINK software. The simulation results show the effectiveness of the proposed control.

Optimal distributed resource allocation in a wireless sensor network for control systems

Wireless technology is applied increasingly in networked control systems. A new form of wireless network called wireless sensor network can bring control systems some advantages, such as flexibility and feasibility of network deployment at low costs, while it also raises some new challenges. First, the communication resources shared by all the control loops are limited. Second, the wireless and multi-hop character of sensor network makes the resources scheduling more difficult. Thus, how to effectively allocate the limited communication resources for those control loops is an important problem. In this paper, this problem is formulated as an optimal sampling frequency assignment problem, where the objective function is to maximize the utility of control systems, subject to channel capacity constraints. Then an iterative distributed algorithm based on local buffer information is proposed. Finally, the simulation results show that the proposed algorithm can effectively allocate the limited communication resource in a distributed way. It can achieve the optimal quality of the control system and adapt to the network load changes.

Distributed control and optimization of process system networks:A review and perspective

Large-scale and complex process systems are essentially interconnected networks.The automated operation of such process networks requires the solution of control and optimization problems in a distributed manner.In this approach,the network is decomposed into several subsystems,each of which is under the supervision of a corresponding computing agent(controller,optimizer).The agents coordinate their control and optimization decisions based on information communication among them.In recent years,algorithms and methods for distributed control and optimization are undergoing rapid development.In this paper,we provide a comprehensive,up-to-date review with perspectives and discussions on possible future directions.

A novel fractional uplink power control framework for self-organizing networks

Internet of things and network densification bring significant challenges to uplink management.Only depending on optimization algorithm enhancements is not enough for uplink transmission.To control intercell interference,Fractional Uplink Power Control(FUPC)should be optimized from network-wide perspective,which has to find a better traffic distribution model.Conventionally,traffic distribution is geographic-based,and ineffective due to tricky locating efforts.This paper proposes a novel uplink power management framework for Self-Organizing Networks(SON),which firstly builds up pathloss-based traffic distribution model and then makes the decision of FUPC based on the model.PathLoss-based Traffic Distribution(PLTD)aggregates traffic based on the propagation condition of traffic that is defined as the pathloss between the position generating the traffic and surrounding cells.Simulations show that the improvement in optimization efficiency of FUPC with PLTD can be up to 40%compared to conventional GeoGraphic-based Traffic Distribution(GGTD).

Stability Analysis and Control of DC Distribution System with Electric Vehicles

The DC distribution network system equipped with a large number of power electronic equipment exhibits weak damping characteristics and is prone to low-frequency and high-frequency unstable oscillations.The current interpretation of the oscillation mechanism has not been unified.Firstly,this paper established the complete statespace model of the distribution system consisting of a large number of electric vehicles,characteristic equation of the distribution network system is derived by establishing a state-space model,and simplified reduced-order equations describing the low-frequency oscillation and the high-frequency oscillation are obtained.Secondly,based on eigenvalue analysis,the oscillation modes and the influence of the key system parameters on the oscillation mode are studied.Besides,impacts of key factors,such as distribution network connection topology and number of dynamic loads,have been discussed to suppress oscillatory instability caused by inappropriate design or dynamic interactions.Finally,using the DC distribution example system,through model calculation and time-domain simulation analysis,the correctness of the aforementioned analysis is verified.

Flatness Control Based on Dynamic Effective Matrix for Cold Strip Mills

Steel strips are the main of steel products and flatness is an important quality indicator of steel strips. Flatness control is the key and highly difficult technique of strip mills. The bottle-neck restricting the improvement of flatness control techniques is that the research on flatness theories and control mathematic models is not in accordance with the requirement of technique developments. To build a simple, rapid and accurate explicit formulation control model has become an urgent need for the development of flatness control technique. This paper puts forward the conception of dynamic effective matrix based on the effective matrix method for flatness control proposed by the authors under the consideration of the influence of the change of parameters in roiling processes on the effective matrix, and the concept is validated by industrial productions. Three methods of the effective matrix generation are induced： the calculation method based on the flatness prediction model; the calculation method based on the data excavation in rolling processes and the direct calculation method based on the network model. A fuzzy neural network effective matrix model is built based on the clusters, and then the network structure is optimized and the high-speed-calculation problem of the dynamic effective matrix is solved. The flatness control scheme for cold strip mills is proposed based on the dynamic effective matrix. On stand 5 of the 1 220 mm five-stand 4-high cold strip tandem mill, the industrial experiment with the control methods of tilting roll and bending roll is done by the control scheme of the static effective matrix and the dynamic effective matrix, respectively. The experiment result proves that the control effect of the dynamic effective matrix is much better than that of the static effective matrix. This paper proposes a new idea and method for the dynamic flatness control in the rolling processes of cold strip mills and develops the theory and model of the flatness control effective matrix method.

Adaptive Predictive Functional Control for Networked Control Systems with Random Delays

Nowadays, more and more field devices are connected to the central controller through a serial communication network such as fieldbus or industrial Ethernet. Some of these serial communication networks like controller area network （CAN） or industrial Ethernet will introduce random transfer delays into the networked control systems （NCS）, which causes control performance degradation and even system instability. To address this problem, the adaptive predictive functional control algorithm is derived by applying the concept of predictive functional control to a discrete state space model with variable delay. The method of estimating the networkinduced delay is also proposed to facilitate the control algorithm implementing. Then, an NCS simulation research based on TrueTime simulator is carried out to validate the proposed control algorithm. The numerical simulations show that the proposed adaptive predictive functional control algorithm is effective for NCS with random delays.

Fundamental Issues in Networked Control Systems

This paper provides a survey on modeling and theories of networked control systems(NCS). In the first part,modeling of the different types of imperfections that affect NCS is discussed. These imperfections are quantization errors,packet dropouts, variable sampling/transmission intervals, variable transmission delays, and communication constraints. Then follows in the second part a presentation of several theories that have been applied for controlling networked systems. These theories include: input delay system approach, Markovian system approach, switched system approach, stochastic system approach,impulsive system approach, and predictive control approach. In the last part, some advanced issues in NCS including decentralized and distributed NCS, cloud control system, and co-design of NCS are reviewed.

Performance Analysis of WLAN Medium Access Control Protocols in Simulcast Radio-Over-Fiber-Based Distributed Antenna Systems

The performance of three wireless local-area network(WLAN) media access control(MAC) protocols is investigated and compared in the context of simulcast radioover-fiber-based distributed antenna systems(RoF-DASs) where multiple remote antenna units(RAUs) are connected to one access point(AP) with different-length fiber links.The three WLAN MAC protocols under investigation are distributed coordination function(DCF) in basic access mode,DCF in request/clear to send(RTS/CTS) exchange mode,and point coordination function(PCF).In the analysis,the inter-RAU hidden nodes problems and fiber-length difference effect are both taken into account.Results show that adaptive PCF mechanism has better throughput performances than the other two DCF modes,especially when the inserted fiber length is short.