多回路网络控制系统的模糊动态调度

本文首先阐述了采样周期对于网络控制系统性能的影响,然后针对多回路网络控制系统中,回路之间争抢网络资源,导致系统性能恶化的情况,在原有EDF调度算法的基础上,提出含有模糊调度器的多回路模糊动态调度算法。该算法根据系统中各回路的误差和误差变化率,利用模糊控制的方法实时调整各回路的优先级,从而实现对网络控制系统的调度。最后,利用TrueTime工具箱建立了包含模糊动态调度器的网络控制系统仿真模型,并将其与无调度器的网络控制系统进行对比。仿真结果表明,本文所设计的多回路模糊动态调度算法能够合理地安排各回路使用网络的先后顺序,具有较好的控制性能。     

CAN网络下实时控制系统的调度研究

在CAN网络的基础上,分析了静态调度算法的可调度条件、优缺点及其对系统性能的影响。通过搭建一个包含4个控制回路的网络控制系统模型,利用TrueTime工具箱进行仿真,比较两种算法的仿真结果,验证了在网络负载较重且资源不充足的情况下,动态调度算法对系统性能的改善,提高了网络的利用率。     

单控制器多任务网络化控制系统采样点调度研究

针对单控制器多任务的交换式以太网控制系统数据传输和控制性能优化问题,提出了传感器主动退避冲突采样时间点的调度优化方法。使用True Time2.0工具箱搭建了单控制器多任务的交换式以太网控制系统仿真平台进行仿真研究,并与未采用此方法的传统系统进行了对比。仿真结果验证了其可行性。     

基于调度与控制协同设计的网络控制系统研究

分析网络控制系统中调度与控制的关系,对调度与控制协同设计的研究现状做了综述,从动态调度和静态调度角度总结了目前这一领域的研究进展,并提出了考虑调度与控制协同设计的反馈控制调度基本结构.     

基于双轨图的网络控制系统稳定性分析

网络控制系统中网络时延的引入,影响系统性能,甚至引起不稳定.为了便于分析网络时延对控制系统稳定性的影响,采用简单的比例控制器,利用双轨图,分析了单摆网络控制系统存在网络时延时的稳定性问题,给出了不稳定范围和稳定范围.同时也给出了单摆网络控制系统稳定性与阻尼系数之间的关系.结果显示利用双轨图进行有时间延迟的网络控制系统稳...     

网络控制系统在线时延预估策略与实现

分析网络时延的产生以及影响其取值规律的原因,提出网络时延在线预估法以预估将来时刻可能产生的控制器与执行器之间的时延.在CAN bus网络控制实验平台上对该预估法的有效性进行验证,实验结果表明,该方法准确地预估出下一时刻网络时延的大小,并且基于预估到的网络时延设计的控制器很好地保证了控制系统的稳定性.     

基于频域稳定性分析的换热网络旁路控制

在换热网络的运行过程中,其操作条件时常变化。针对过程系统安全平稳运行的基本要求,基于频域稳定性分析方法,得出影响过程系统动态性能与稳定性的关键点,提出一种换热网络旁路控制系统的设计方法。从频域的角度分析潜在旁路与被控变量之间的耦合关系,采用频域相对增益阵方法实现换热网络的旁路设计。并在此基础上,对设计的控制系统进行控制器参数设计,使换热网络控制系统满足一定的稳定裕度要求。结果表明,基于频域稳定性分析设计的换热网络旁路控制系统满足稳定裕度要求,其阶跃响应的绝对误差积分值较小,系统的动态性能更好。     

基于人工免疫PSO算法的球杆系统仿真研究

针对球杆系统非线性不稳定的特性,利用Matlab中的Simulink模块和True Time工具箱设计了人工免疫PSO算法网络控制系统。当网络控制系统存在一定的时延和滞后时,对人工免疫PSO算法的网络控制系统进行仿真分析。结果表明:采用基于人工免疫PSO算法的控制器的系统输出曲线超调量更小,调节时间更短,鲁棒性也优于一般PID控制器。     

时变扰动系统的广义最小方差控制性能评估

针对存在时变扰动的控制系统,提出一种基于广义最小方差的控制性能评估方法。首先,使控制系统的实际输出信号在突变扰动下保持稳定;然后以控制系统广义输出信号方差最小为优化目标,设计得到一个基准控制器;再以该控制器作用下的广义输出信号方差为基准,以此评估时变扰动系统的控制性能。仿真结果验证了该方法的有效性。     

采用辐射能信号的燃烧系统参数自调整模糊PI控制

针对火电厂燃烧过程中主蒸汽压力控制系统的大时滞、大惯性和非线性,采用能迅速反映燃料侧扰动的辐射能信号进行快速补偿,并设计一个参数自调整的模糊PI控制器作为主控制器。该控制器首先通过编写S函数来自动修正量化因子和比例因子,从而改善基本模糊控制器的性能;然后将模糊控制与PI控制相结合,以优化燃烧控制性能;仿真结果表明该方案显著提高了非线性、大时滞燃烧系统的控制品质。     

The control of MSF desalination plants based on inverse model control by neural network

In this paper, a nonlinear inverse model control strategy based on neural network is proposed for MSF desalination plant. Artificial neural networks (ANNs) can handle complex and nonlinear process relationships, and are robust to noisy data. The designed neural networks consist of three layers identified from input–output data and trained with a descent gradient algorithm. The set point tracking performance of the proposed method was studied when the disturbance is present in the MSF system. Three controllers are designed for controlling the top brine temperature, the level of last stage and salinity. These results show that a neural network inverse model control strategy (NNINVMC) is robust and highly promising to be implemented in such nonlinear systems. Also the comparison between the top brine temperature of the proposed model and NN predicted data from the literature supports the accuracy of the model.     

供水管网调度系统信息化建设研究

对供水管网集成化的数据采集与监控系统,水力模型和优化调度3个系统的基本功能进行了分析论述,在此基础上,提出了供水管网调度系统信息化建设的整体解决方案,编制了供水调度系统管理软件,软件由数据模块、决策模块、输出模块组成,实现了供水管网的调度系统的自动化控制。该软件已成功地应用于上海、南京等地的城市供水管网系统。     

基于自适应模糊神经网络的非线性系统模型预测控制

针对非线性动态系统的控制问题,提出了一种基于自适应模糊神经网络(adaptive fuzzy neural network, AFNN)的模型预测控制(model predictive control, MPC)方法。首先,在离线建模阶段,AFNN采用规则自分裂技术产生初始模糊规则,采用改进的自适应LM学习算法优化网络参数;然后,在实时控制过程,AFNN根据系统输出和预测输出之间的误差调整网络参数,从而为MPC提供一个精确的预测模型;进一步,AFNN-MPC利用带有自适应学习率的梯度下降寻优算法求解优化问题,在线获取非线性控制量,并将其作用到动态系统实施控制。此外,给出了AFNN-MPC的收敛性和稳定性证明,以保证其在实际工程中的成功应用。最后,利用数值仿真和双CSTR过程进行实验验证。结果表明,AFNN-MPC能够取得优越的控制性能。     

Resource aware quasi-decentralized control of networked process systems over wireless sensor networks

This paper develops an integrated model-based networked control and scheduling framework for plants with interconnected units and distributed control systems that exchange information using a resource-constrained wireless sensor network (WSN). The framework aims to enforce closed-loop stability while simultaneously minimizing the rate at which each node in the WSN must collect and transmit measurements. Initially, the exchange of information between the local control systems is reduced by embedding, within each control system, dynamic models that provide forecasts of the evolution of the plant units when measurements are not transmitted through the WSN, and updating the state of each model when communication is re-established at discrete time instances. To further reduce WSN utilization, only a subset of the deployed sensor suites are allowed to transmit their data at any given time to provide updates to their target models according to a certain scheduling strategy. By formulating the networked closed-loop plant as a combined discrete-continuous system, explicit characterizations of both the stability and performance properties of the networked closed-loop system under state and output feedback control are obtained in terms of the communication rate, the sensor transmission schedule, the accuracy of the models, as well as the controller and observer design parameters. The results are illustrated using a chemical plant example where it is shown that by judicious management of the interplays between the control, communication and scheduling design parameters, it is possible to enhance the savings in WSN resource utilization beyond what is possible with concurrent transmission configurations.     

A nested online scheduling and nonlinear model predictive control framework for multi-product continuous systems

In the pursuit of integrated scheduling and control frameworks for chemical processes, it is important to develop accurate integrated models and computational strategies such that optimal decisions can be made in a dynamic environment. In this study, a recently developed switched system formulation that integrates scheduling and control decisions is extended to closed-loop operation embedded with nonlinear model predictive control (NMPC). The resulting framework is a nested online scheduling and control loop that allows to obtain fast and accurate solutions as no model reduction is needed and no integer variables are involved in the formulations. In the outer loop, the integrated model is solved to calculate an optimal product switching sequence such that the process economics is optimized, whereas in the inner loop, an NMPC implements the scheduling decisions. The proposed scheme was tested on two multi-product continuous systems. Unexpected large disturbances and rush orders were handled effectively.     

A two-tier architecture for networked process control

Traditionally, process control systems utilize dedicated, point-to-point wired communication links using a small number of sensors and actuators to regulate appropriate process variables at desired values. While this paradigm to process control has been successful, chemical plant operation could substantially benefit from an efficient integration of the existing, point-to-point control networks (wired connections from each actuator/sensor to the control system using dedicated local area networks) with additional networked (wired or wireless) actuator/sensor devices. However, augmenting existing control networks with real-time wired/wireless sensor and actuator networks challenges many of the assumptions made in the development of traditional process control methods dealing with dynamical systems linked through ideal channels with flawless, continuous communication. In the context of control systems which utilize networked sensors and actuators, key issues that need to be carefully handled at the control system design level include data losses due to field interference and time delays due to network traffic. Motivated by the above technological advances and the lack of methods to design control systems that utilize hybrid communication networks, in the present work, we present a novel two-tier control architecture for networked process control problems that involve nonlinear processes and heterogeneous measurements consisting of continuous measurements and asynchronous, delayed measurements. This class of control problems arises naturally when nonlinear processes are controlled via control systems based on hybrid communication networks (i.e., point-to-point wired links integrated with networked wired/wireless communication) or utilizing multiple heterogeneous measurements (e.g., temperature measurements which can be taken to be continuous and species concentration measurements which are fed to the control system at asynchronous time instants and frequently involve delays). While point-to-point wired links are very reliable, the presence of a shared communication network in the closed-loop system introduces additional delays and data losses and these issues should be handled at the controller design level. In the two-tier control architecture presented in this work, a lower-tier control system, which relies on point-to-point communication and continuous measurements, is first designed to stabilize the closed-loop system, and an upper-tier networked control system is subsequently designed, using Lyapunov-based model predictive control theory, to profit from both the continuous and the asynchronous, delayed measurements as well as from additional networked control actuators to improve the closed-loop system performance. The proposed two-tier control architecture preserves the stability properties of the lower-tier controller while improving the closed-loop performance. The applicability and effectiveness of the proposed control method is demonstrated using two chemical process examples.     

A time scale-bridging approach for integrating production scheduling and process control

In this paper, we propose a novel framework for integrating scheduling and nonlinear control of continuous processes. We introduce the time scale-bridging model (SBM) as an explicit, low-order representation of the closed-loop input–output dynamics of the process. The SBM then represents the process dynamics in a scheduling framework geared towards calculating the optimal time-varying setpoint vector for the process control system. The proposed framework accounts for process dynamics at the scheduling stage, while maintaining closed-loop stability and disturbance rejection properties via feedback control during the production cycle. Using two case studies, a CSTR and a polymerization reactor, we show that SBM-based scheduling has significant computational advantages compared to existing integrated scheduling and control formulations. Moreover, we show that the economic performance of our framework is comparable to that of existing approaches when a perfect process model is available, with the added benefit of superior robustness to plant-model mismatch.