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.

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.

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.

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.

FINDING THE OPTIMAL LENGTH OF DATA FRAME IN PLC COMPUTER NETWORK

The length of data frame is an important parameter in Medium Access Control(MAC) layer protocol. A method of finding the optimal length of data frame when impulsive disturbance is involved in PowerLine Communications (PLC) is proposed. Having analyzed the characteristics of impulsive disturbance in powerline and the collision mechanism between the disturbance and data frame, this letter gives the function of the network channel utilization with the length of data frame in MAC layer and the impulsive disturbance. Our stochastic process simulation shows that it is feasible to get the optimal length of data frame.

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.

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.

Fundamental Issues in Networked Control Systems

Abstract--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, vari- able 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 decentral- ized and distributed NCS, cloud control system, and co-design of NCS are reviewed. Index Terms--Decentralized networked control systems （NCS）, distributed networked control systems, network constraints, net- worked control system, quantization, time delays.

基于PLC控制器的医院网络通信故障自适应监控系统

传统的医院网络通信故障监控系统未对故障警报信息进行分类,通信故障点定位不准确。为此,提出基于PLC控制器的医院网络通信故障自适应监控系统。采用BP网络对医院网络通信故障警报信息分类处理,通过人工神经网络对医院网络通信故障数据进行训练,依据BP神经网络计算动量项、速率以及抽象处理等不同的数据训练结果,对故障点定位分析。构建以UPnP技术为核心设计系统网络构架,为系统提供有力的实时数据支持,实现大数据量的远程实时在线监测。结果表明,所设计系统监控故障监控时延较短,能够实现故障精准定位,能够快速发现故障并及时报警。

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).

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.

基于PLC的工业机器人搬运系统现状研究

近年来,中国在科技、经济与电子信息技术飞速发展,机器人在工业生产以及产品搬运中得到了广泛地运用。在工业自动化领域中,基于可编程控制器(PLC)的机器人技术已经成为自动化控制技术核心。随着工厂对智能化设备要求的提升,传统工业控制技术已经无法满足生产需求,伴随着PLC技术应用的快速发展,特别是在运动控制至以及网络通讯方面功能加强后,基于PLC的机器人不断满足了各式产品的搬运控制。本文主要对基于PLC工业机器人搬运系统的现状展开研究,对其主要应用场景进行了概述。

基于PLC的工程塑料自然环境-载荷应力联合作用装置控制系统开发

目的 实现工程塑料自然环境-载荷应力联合作用装置的自动控制,以考核和验证工程塑料制品在环境-应力联合作用下的环境适应性。方法 基于PLC及物联网设备,设计开发控制系统硬件和软件。结果 成功模拟了工程塑料制品在湿热海洋和寒冷气候2种典型大气环境下的自然环境与外加载荷的协同损伤作用,并实时监控记录了相关试验参数与数据。结论 基于PLC的工程塑料自然环境-载荷应力联合作用装置控制系统有效地确保了试验装置的正常运行,并通过实时监控和数据记录,实现了对工程塑料制品环境损伤的深入研究和客观评价。

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.

中压PLC技术应用于配网自动化通讯中方案论证研究

缩短配电电路修复时间,提高城市配电安全运行水平,是供电企业正在积极和开展的领域。在配网自动化的建设过程中,通讯方式是有效实施配网自动化的基础,是非常重要的一个环节。随着通讯技术的发展,如何解决好通讯通道是一个非常重要的问题。本文分析中压PLC技术应用于配电网自动化通讯的可行性,并对中压PLC技术用于配网自动化通讯进行探讨,提出中压PLC技术用于配电网自动化通讯的意义,旨在利用中压PLC技术应用为电网企业提供实际推广依据。