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.

Distributed Economic MPC for Synergetic Regulation of the Voltage of an Island DC Micro-Grid

In this paper,distributed model predictive control(DMPC) for island DC micro-grids(MG) with wind/photovoltaic(PV)/battery power is proposed,which coordinates all distributed generations(DG) to stabilize the bus voltage together with the insurance of having computational efficiency under a real-time requirement.Based on the feedback of the bus voltage,the deviation of the current is dispatched to each DG according to cost over the prediction horizon.Moreover,to avoid the excessive fluctuation of the battery power,both the discharge-charge switching times and costs are considered in the model predictive control(MPC) optimization problems.A Lyapunov constraint with a time-varying steady-state is designed in each local MPC to guarantee the stabilization of the entire system.The voltage stabilization of the MG is achieved by this strategy with the cooperation of DGs.The numeric results of applying the proposed method to a MG of the Shanghai Power Supply Company shows the effectiveness of the distributed economic MPC.

Observer-based dynamic event-triggered control for distributed parameter systems over mobile sensor-plus-actuator networks

We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.

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.

基于CNN的OCR技术在核电厂DCS系统测试中的应用和实现

核电厂分布式控制系统(DCS)的输入/输出模块在运行期间,随着电子器件性能下降以及时漂的影响,需要定期进行进度复测和校准。针对传统的DCS输入/输出通道测试覆盖率不足,测试效率低及人因问题,本文提出一种基于卷积神经网络(CNN)的光学字符识别(OCR)技术应用在DCS系统的在线测试方法。通过模拟设备和视频采集设备完成画面的自动切换,并读取画面信息,截取特定画面后对其图像预处理,再使用OCR识别模型识别画面内容,将得到的识别结果与期望值进行比较判断,从而实现自动化测试。测试结果表明,通过CNN训练后,显控设备画面字符识别率能达到100%,该方法可以突破设备厂家的专有通信协议的壁垒,可有效降低操作员的人因失误,提升测试效率和核电厂的经济性。

基于SCADA-DCS通讯技术的油气开采数据自动化采集与控制系统

设计基于SCADA-DCS通讯技术的油气开采数据自动化采集与控制系统。系统以SCADA服务器为上层监控服务器,通过与井站现场仪器仪表实时通信,监控整个开采过程。当油气缓冲罐液位越限时,SCADA服务器自动下达指令至原油处理站DCS,启动基于智能PID的油泵控制器,调节油泵变频器,控制油气液位不溢出。系统使用SCADA-DCS通讯技术的MODBUS通信协议,并根据通信中断优先级分配方法,确保对中断处理时间要求高的通信请求能实时响应。实验结果表明,该系统能提升油气液位控制和紧急信息通讯效率。

基于DCS-惯性权重组合的柔性机械臂运动轨迹控制系统研究

为了有效调控机械臂运动幅度,避免柔性机械臂实际运动轨迹与目标轨迹发生较大的偏差,设计一种基于分散控制系统(DCS)-惯性权重组合的柔性机械臂运动轨迹控制系统,它是一个集合了计算机、通信、显示和控制的计算机系统,该系统由过程控制和过程监控。通过DCS算法控制监测对象行为幅度并计算柔性机械臂惯性权重,利用DCS-惯性权重组合求解素数控制指标的具体数值,实现了对柔性机械臂运动轨迹控制系统的软件设计。实验结果表明,DCS-惯性权重组合算法作用下的机械臂末端坐标在横轴、纵轴、空间轴方向上的运动幅值均可以被控制在10个单位长度之内,并且在0.52 s内进入机械臂运动轨迹最佳控制状态,运动轨迹差值最大仅为0.01 rad,验证了该系统具备可行性和有效性。

DCS控制器中电表接触传感器计量误差检测方法

由于DCS控制器中电表传感器在计量检测过程中,传统的B-MAC-DCS协议能耗和丢包率较高,无法缓解汇聚节点的漏斗效应,导致在远程抄表过程中传感器计量误差增大。提出一种机械电表接触传感器计量误差检测方法。采用小波基函数对DCS控制器中的接触传感器计量数据抗干扰处理,并通过动态选取阈值的方法,对经过小波变换后的数据去除噪声。使用低功耗自适应集簇分层型(low energy adaptive clustering hierarchy,LEACH)协议分簇代替传统的B-MAC协议;根据簇内监测值,引入阈值分析方法获取传感器计量指标,并将其作为判定依据进行误差检测,根据计量指标的变化情况判断是否存在计量误差。实验结果表明,所提方法可以准确且有效检测出机械电表接触传感器计量误差,解决DCS中机械电表的运行隐患问题。

Optimal Cooperative Secondary Control for Islanded DC Microgrids via a Fully Actuated Approach

DC-DC converter-based multi-bus DC microgrids(MGs) in series have received much attention, where the conflict between voltage recovery and current balancing has been a hot topic. The lack of models that accurately portray the electrical characteristics of actual MGs while is controller design-friendly has kept the issue active. To this end, this paper establishes a large-signal model containing the comprehensive dynamical behavior of the DC MGs based on the theory of high-order fully actuated systems, and proposes distributed optimal control based on this. The proposed secondary control method can achieve the two goals of voltage recovery and current sharing for multi-bus DC MGs. Additionally, the simple structure of the proposed approach is similar to one based on droop control, which allows this control technique to be easily implemented in a variety of modern microgrids with different configurations. In contrast to existing studies, the process of controller design in this paper is closely tied to the actual dynamics of the MGs. It is a prominent feature that enables engineers to customize the performance metrics of the system. In addition, the analysis of the stability of the closed-loop DC microgrid system, as well as the optimality and consensus of current sharing are given. Finally, a scaled-down solar and battery-based microgrid prototype with maximum power point tracking controller is developed in the laboratory to experimentally test the efficacy of the proposed control method.

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.

An Optimal Control-Based Distributed Reinforcement Learning Framework for A Class of Non-Convex Objective Functionals of the Multi-Agent Network

This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential equation.Hence, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.

DCS在电除尘系统自动控制的应用研究

传统的电除尘器自动控制系统采用可编程逻辑控制器(PLC)控制,其逻辑复杂、可读性差、可维护性低。PLC系统无法与主控系统完成一体化,导致工艺上的割裂,额外地增加了运维成本。本文提出了一种基于分散控制系统(DCS)的电除尘器自动控制方案。

Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

Research on Scheduling Strategy of Flexible Interconnection Distribution Network Considering Distributed Photovoltaic and Hydrogen Energy Storage

Distributed photovoltaic(PV)is one of the important power sources for building a new power system with new energy as the main body.The rapid development of distributed PV has brought new challenges to the operation of distribution networks.In order to improve the absorption ability of large-scale distributed PV access to the distribution network,the AC/DC hybrid distribution network is constructed based on flexible interconnection technology,and a coordinated scheduling strategy model of hydrogen energy storage(HS)and distributed PV is established.Firstly,the mathematical model of distributed PV and HS system is established,and a comprehensive energy storage system combining seasonal hydrogen energy storage(SHS)and battery(BT)is proposed.Then,a flexible interconnected distribution network scheduling optimization model is established to minimize the total active power loss,voltage deviation and system operating cost.Finally,simulation analysis is carried out on the improved IEEE33 node,the NSGA-II algorithm is used to solve specific examples,and the optimal scheduling results of the comprehensive economy and power quality of the distribution network are obtained.Compared with the method that does not consider HS and flexible interconnection technology,the network loss and voltage deviation of this method are lower,and the total system cost can be reduced by 3.55%,which verifies the effectiveness of the proposed method.

Differential flatness-based distributed control of underactuated robot swarms

This paper proposes a distributed control method based on the differential flatness(DF) property of robot swarms. The swarm DF mapping is established for underactuated differentially flat dynamics, according to the control objective. The DF mapping refers to the fact that the system state and input of each robot can be derived algebraically from the flat outputs of the leaders and the cooperative errors and their finite order derivatives. Based on the proposed swarm DF mapping, a distributed controller is designed. The distributed implementation of swarm DF mapping is achieved through observer design. The effectiveness of the proposed method is validated through a numerical simulation of quadrotor swarm synchronization.

DCS自动控制装置操作与管理中的优化措施

采用DCS仪表控制可以使化工流程达到完全的自动化控制,为企业的高效运行提供有力的保证。DCS自动控制装置的日常维修与管理,是保证化工装置正常运行的重要因素之一,所以对其进行操作管理和优化控制是非常必要的。本文通过对DCS自控系统特性的研究,对该系统常见的故障进行剖析,总结归纳了在生产过程中的操作规程及管理办法及应急处理措施,以使其更好地运用于实际工作中。

某油气平台DCS升级期间燃料气临时控制系统搭建与应用

某海上油气处理平台在生产控制系统升级改造期间,所有生产流程均处于关停状态,透平发电机燃料气供给系统也随之失效,平台无法自主发电。充分分析该平台燃料气供给系统工艺流程中气源、压力、温度等参数,解读原DCS(集散控制系统)底层控制程序及逻辑,利用水下井口平台弃置拆下的西门子S7-200控制器及其组件,自主编写PLC控制程序代替原DCS程序,并加装压力监测装置,铺设临时通讯电缆,组装监控上位机及输入、输出装置,成功搭建燃料气供给临时控制系统。实现生产控制系统升级期间燃料气正常供应,保障了海上平台空气压缩机、造淡水机、办公、照明等方面的用电。通过设计PID选择控制程序,细化控制程序在旧系统、临时系统、新系统三者之间的切换流程,实现了透平发电机在新旧燃气系统之间不停车无扰切换。同时,设计临时燃料气系统多种可切换操作模式,保障了燃料气的稳定供给。燃料气临时系统的应用不仅解决了平台生产、生活供电问题,还避免了外租大型发电设备,从而降低了生产控制系统升级成本。

基于DCS自动化控制的氟化反应工艺研究

氟化反应工艺是精细化工的重要分支,但传统工艺存在效率低、污染重等问题。本章针对氟化反应特点,提出了一种基于DCS自动化控制的新型工艺方案。通过对关键工艺参数进行精确调控和设备的无缝集成,实现了反应过程的智能化优化控制。实证研究表明,该方案可使邻氟苯产率提高10%以上,能耗和三废排放量降低60%,为氟化工业的智能制造升级提供了新思路。

生物质发电厂DCS系统一体化设计

简要论述了生物质发电厂自动控制技术的应用情况,分析了目前生物质发电厂控制系统的现状,对生物质发电厂DCS系统一体化设计原则进行了介绍,并对全厂DCS一体化的优势进行了分析。通过介绍生物质发电厂全厂DCS系统一体化设计的基本设计思路,提出生物质发电厂DCS一体化设计的控制方案,并通过设计案例实现全厂DCS系统一体化对全厂进行监控。

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.