Development of Distributed Control System for Neutral Beam Injector on EAST

A distributed control system of Neutral Beam Injector （NBI） on the Experimental Advanced Superconducting Tokamak （EAST-NBI） is briefly presented in this paper. The control system is developed in accordance with the experimental operational characteristics of the EAST- NBI. The NBI control system （NBICS）, which is based on the computer network technologies and classified according to the control levels, consists of three levels： a remote monitoring layer, a server control layer, and a field control layer. The 3-layer architecture is capable of extending the system functions and upgrading devices. The timing system provides the reference clock of the synchronization and interlock for the EAST-NBI system. An interlock system ensures the safety of the experiment operators and field devices. Both of the ion sources of the beamline are designed to operate independently. This lays an important foundation for developing a control system for the second beamline on EAST. Experimental results demonstrate that the NBICS meets functional requirements of the EAST-NBI control, and makes experimental operations visual and automatic.

A distributed control system for picosecond accelerator at SINAP

The picosecond accelerator (PA) is a low energy electron linear accelerator facility under commissioning, which is built for the experiment of ps level pulse radiolysis in Shanghai Institute of Applied Physics (SINAP). A practical distributed DA&C system for this facility has been developed. In view of the upgrading-ability and main-tainability of the control system and controlled devices, Advantech? distributed intelligent DA&C products are adopted into the control system. ADAM 5000/TCPs with the protocol of Modbus/TCP are employed to accomplish data acquisition and device control. The PC-compatible programmable logic controller, ADAM-5511, is also adopted to handle the interlocks and the emergency events. On the software side, the integrated software package King-view?V6.5, which friendly supports all Advantech products, has been used to develop the upper layer control logic and process the data. This paper describes the control system design and system architecture. The intelligent ADAM controllers and the software platform are also discussed in detail.

Implementation of Distributed Control System for Rice Mill Using C#

The paper presents the distributed control system for rice mill using C#language.The real-time manufacturing system can be implemented by utilizing the signal from the real time control units that is more operative than other old-fashioned control systems in the extent of modern industrial days.The software-based Distributed Control System(DCS)is novel fashionable than any other conventional control systems in the state-ofthe-art manufacturing developments.This research study emphasizes on the implementation of the DCS-based rice mill using visual C#.net.The Industrial Ethernet(IE)is realized between the top level controller for the operator and the controlled station for the remote devices.The model of client-server approach is more appropriate for the automation and manufacturing research purposes.In this study,the computer graphical simulation of the complete control development is depicted in real-time status quo by visual C#language under Visual Studio 2008 software.The parallel ports in the computers of remote terminal level and the master terminal level controllers have been interconnected with port interface coding by visual C#program.

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.

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.

Controlled Radical Polymerization of Styrene in the Presence of Different Copper Complexes and Organic Halides

The polymerization behaviors of Styrene (St) in the presence of CuX/L [X=Cl or Br; L= 2,2 bipyridine (bpy), 1,10 phenanthroline (phen) or 4,7 diphenyl 1,10 phenanthroline (DPP) ] and R X (R=trichloromethyl, benzyl or allyl; X=Cl or Br) have been studied and examined. In a CuCl/bpy/RCl/St system, a bimodal GPC peak at the early stage of polymerization was observed, and a concept of multi active species was proposed to explain this phenomenon. In a CuCl/phen (DPP)/RCl/St system, the \%M\%\-n of polystyrene (PS) increased linearly with St conversion and ln[M] o/[M] also increased linearly with time, indicating the living nature of this system. Furthermore, the stability of the propagating active species in a CuBr/phen/RBr/St system is higher than that in the CuBr/phen/RBr/St system.

CONTROLLED RADICAL COPOLYMERIZATION OF STYRENE AND MALEIC ANHYDRIDE UNDER GAMMA RADIATION IN THE PRESENCE OF BENZYL DITHIOBENZOATE

The copolymerization of styrene (St) with maleic anhydride (MAh) under gamma radiation at room temperature in the presence of benzyl dithiobenzoate (BDTB) was found to display 'living' nature evidenced by constant concentration of chain radicals during the copolymerization, linear evolution of molecular weights with conversion and narrow molecular weight distribution (M-w/M-n = 1.23-1.35). The compositional analysis and the sequence structural information of the copolymers obtained from DEPT (Distortionless Enhancement by Polarization Transfer) experiments demonstrate that the copolymers obtained also possess strictly alternating structure.

MACROPROCESS OF PARTICLE FORMATION IN SUSPENSION POLYMERIZATION OF STYRENE WITH INORGANIC STABILIZER SYSTEM

The macroprocess of particle formation from suspension droplets of styrene in a pdymerizationsystem was investigated.Inorganic hydroxyapatite or its mixture with polyvinyl alcohol as thepolymerization system was used.Those items such as the effects of the Weight fraction of dispersed-phase,the amount of the inorganic stabilizer and the agitation speed on the breakup and coalescence of thetransient dispersed drops etc.Were examined.Results showd that the dynamic behavior of the transi-ent polymer droplets changed in the presence of the suspension stabilizer during the reaction.

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.

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.

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.

A PI+R Control Scheme Based on Multi-Agent Systems for Economic Dispatch in Isolated BESSs

Battery energy storage systems(BESSs)are widely used in smart grids.However,power consumed by inner impedance and the capacity degradation of each battery unit become particularly severe,which has resulted in an increase in operating costs.The general economic dispatch(ED)algorithm based on marginal cost(MC)consensus is usually a proportional(P)controller,which encounters the defects of slow convergence speed and low control accuracy.In order to solve the distributed ED problem of the isolated BESS network with excellent dynamic and steady-state performance,we attempt to design a proportional integral(PI)controller with a reset mechanism(PI+R)to asymptotically promote MC consensus and total power mismatch towards 0 in this paper.To be frank,the integral term in the PI controller is reset to 0 at an appropriate time when the proportional term undergoes a zero crossing,which accelerates convergence,improves control accuracy,and avoids overshoot.The eigenvalues of the system under a PI+R controller is well analyzed,ensuring the regularity of the system and enabling the reset mechanism.To ensure supply and demand balance within the isolated BESSs,a centralized reset mechanism is introduced,so that the controller is distributed in a flow set and centralized in a jump set.To cope with Zeno behavior and input delay,a dwell time that the system resides in a flow set is given.Based on this,the system with input delays can be reduced to a time-delay free system.Considering the capacity limitation of the battery,a modified MC scheme with PI+R controller is designed.The correctness of the designed scheme is verified through relevant simulations.

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 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.

REVERSE ATOM TRANSFER RADICAL POLYMERIZATION OF STYRENE WITH COPPER-BASED CATALYST

'Living'/controlled radical polymerization of styrene was carried out with diethyl 2,3-dicyano-2,3-diphenylsuccinate (DCDPS)/CuCl2/bipyridine (bipy) initiation system at 120 degreesC. The molecular weights of resultant PSt increased with the monomer conversion and the polydispersities were in the range of 1.37 similar to1.52. A linear ln([M](o)/[M]) versus time plot was also obtained indicating the constant concentration of growing radicals during the polymerization with this initiation system. End group analysis by H-1-NMR spectroscopic studies showed that the end groups of the polymer obtained is omega -functionalized by a chlorine group from the catalyst and alpha -functionalized by a (carbethoxy-cyano-phenyl)methyl group from the fragments of the initiator. Having CI atom at the chain end, the PSt obtained can be used as a macroinitiator to promote a chain-extension reaction with fresh St and block copolymerization reaction with a second monomer, such as methyl methacrylate, in the presence of CuCl/bipy catalyst via a conventional ATRP process.

Molecular Weight Distribution of Polystyrene Produced in a Starved Feed Reactor

A starved feed reactor (SFR) is a semi-batch polymerization reactor where initiator and monomer are fed slowly into a fixed amount of solvent. The polymerization is carried out isothermally at elevated temperatures. The added initiator decomposes instantaneously and the added monomer polymerizes immediately. The molecular weight (MW) and molecular weight distribution (MWD) of the product polymer can be effectively controlled by the feed ratio of monomer to initiator. This paper presents a study on the MWD of styrene polymerization in a SFR. The MWD model parameters are regressed with experimental data. Although the solids fraction in the SFR is high (higher than 50%), viscosity is not too high and the 'gel effect' is weak due to the low molecular weight of the products. It is found that the termination rate constant is a power function of molecular weight, radicals terminate via 100% combination, the thermal initiation can be neglected even at high reaction temperature studied. And calculated results indicate that in the SFR, the validity of the long chain assumption becomes doubted. It appears that other alterative assumption should be found for an improved model.

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.

Robust Consensus Tracking Control of Uncertain Multi-Agent Systems With Local Disturbance Rejection

In this paper,a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed graph.It is of two-degree-of-freedom nature.Specifically,a robust distributed controller is designed for consensus tracking,while a local disturbance estimator is designed for each agent without requiring the input channel information of disturbances.The condition for asymptotic disturbance rejection is derived.Moreover,even when the disturbance model is not exactly known,the developed method also provides good disturbance-rejection performance.Then,a robust stabilization condition with less conservativeness is derived for the whole multi-agent system.Further,a design algorithm is given.Finally,comparisons with the conventional one-degree-of-freedombased distributed disturbance-rejection method for mismatched disturbances and the distributed extended-state observer for matched disturbances validate the developed method.

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.