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.

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.

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.

Distributed Robust UAVs Formation Control Based on Semidefinite Programming

The formation control of unmanned aerial vehicle(UAV)swarms is of significant importance in various fields such as transportation,emergency management,and environmental monitoring.However,the complex dynamics,nonlinearity,uncertainty,and interaction among agents make it a challenging problem.In this paper,we propose a distributed robust control strategy that uses only local information of UAVs to improve the stability and robustness of the formation system in uncertain environments.We establish a nominal control strategy based on position relations and a semi-definite programming model to obtain control gains.Additionally,we propose a robust control strategy under the rotation setΩto address the noise and disturbance in the system,ensuring that even when the rotation angles of the UAVs change,they still form a stable formation.Finally,we extend the proposed strategy to a quadrotor UAV system with high-order kinematic models and conduct simulation experiments to validate its effectiveness in resisting uncertain disturbances and achieving formation control.

Virtually coupled train set control subject to space-time separation:A distributed economic MPC approach with emergency braking configuration

The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe distance.To guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB process.In this case,the minimal safety distance is usually numerically calculated without an analytic formulation.Thus,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing literature.To solve this problem,we propose a Distributed Economic Model Predictive Control(DEMPC)approach with computation efficiency and theoretical guarantee.Specifically,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved efficiently.For theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient tuning.Moreover,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among VCTS.Finally,experimental results demonstrate the performance and advantages of the proposed approaches.

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.

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.

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 devices based non-linear process control system design for plants with distributed time-delay using particle filter

Remote control process system with distributed time-delay has attracted much attention in different fields.In this paper,non-linear remote control of a single tank process system with wireless network is considered.To deal with the distributed time-delay in a large-scale plant,the time-delay compensation controller based on DCS devices is designed by using operator theory and particle filter.Distributed control system(DCS)device is developed to monitor and control from the central monitoring room to each process.The particle filter is a probabilistic method to estimate unobservable information from observable information.First,remote control system and experimental equipment are introduced.Second,control system based on an operator theory is designed.Then,process system with distributed time-delay using particle filter is carried out.Finally,the actual experiment is conducted by using the proposed time-delay compensation controller.When estimating with the proposed method,the result is close to the case in which the distributed time-delay does not exist.The effectiveness of the proposed control system is confirmed by experiment results.

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.

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.

Distributed Periodic Event-Triggered Optimal Control of DC Microgrids Based on Virtual Incremental Cost

This article presents a distributed periodic eventtriggered(PET)optimal control scheme to achieve generation cost minimization and average bus voltage regulation in DC microgrids.In order to accommodate the generation constraints of the distributed generators(DGs),a virtual incremental cost is firstly designed,based on which an optimality condition is derived to facilitate the control design.To meet the discrete-time(DT)nature of modern control systems,the optimal controller is directly developed in the DT domain.Afterward,to reduce the communication requirement among the controllers,a distributed event-triggered mechanism is introduced for the DT optimal controller.The event-triggered condition is detected periodically and therefore naturally avoids the Zeno phenomenon.The closed-loop system stability is proved by the Lyapunov synthesis for switched systems.The generation cost minimization and average bus voltage regulation are obtained at the equilibrium point.Finally,switch-level microgrid simulations validate the performance of the proposed optimal controller.

STABILITY ANALYSIS AND COOPERATIVE CONTROL OF DISTRIBUTED MULTI-AGENT SYSTEM WITH SAMPLED COMMUNICATION

The cooperative control and stability analysis problems for the multi-agent system with sampled com- munication are investigated. Distributed state feedback controllers are adopted for the cooperation of networked agents. A theorem in the form of linear matrix inequalities（LMI） is derived to analyze the system stability. An- other theorem in the form of optimization problem subject to LMI constraints is proposed to design the controller, and then the algorithm is presented. The simulation results verify the validity and the effectiveness of the pro- posed approach.

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.

The History, Trend, Types, and Mitigation of Distributed Denial of Service Attacks

Over time, the world has transformed digitally and there is total dependence on the internet. Many more gadgets are continuously interconnected in the internet ecosystem. This fact has made the Internet a global information source for every being. Despite all this, attacker knowledge by cybercriminals has advanced and resulted in different attack methodologies on the internet and its data stores. This paper will discuss the origin and significance of Denial of Service (DoS) and Distributed Denial of Service (DDoS). These kinds of attacks remain the most effective methods used by the bad guys to cause substantial damage in terms of operational, reputational, and financial damage to organizations globally. These kinds of attacks have hindered network performance and availability. The victim’s network is flooded with massive illegal traffic hence, denying genuine traffic from passing through for authorized users. The paper will explore detection mechanisms, and mitigation techniques for this network threat.

Distributed Secondary Control and Optimal Power Sharing in Microgrids

We address the control problem of microgrids and present a fully distributed control system which consists of primary controller, secondary controller, and optimal active power sharing controller. Different from the existing control structure in microgrids, all these controllers are implemented as local controllers at each distributed generator. Thus, the requirement for a central controller is obviated. The performance analysis of the proposed control systems is provided, and the finite-time convergence properties for distributed secondary frequency and voltage controllers are achieved. Moreover, the distributed control system possesses the optimal active power sharing property. In the end, a microgrid test system is investigated to validate the effectiveness of the proposed control strategies. © 2014 Chinese Association of Automation.

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。

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。

Distributed cooperative control of autonomous multi-agent UAV systems using smooth control

This paper addresses the cooperative control problem of multiple unmanned aerial vehicles(multi-UAV)systems.First,a new distributed consensus algorithm for second-order nonlinear multi-agent systems(MAS)is formulated under the leader-following approach.The algorithm provides smooth input signals to the agents’control channels,which avoids the chattering effect generated by the conventional sliding mode-based control protocols.Second,a new formation control scheme is developed by integrating smooth distributed consensus control protocols into the geometric pattern model to achieve three-dimensional formation tracking.The Lyapunov theory is used to prove the stability and convergence of both distributed consensus and formation controllers.The effectiveness of the proposed algorithms is demonstrated through simulation results.