网络控制系统与网络化串级控制系统的结构分析

基于实际工业过程控制,引入了网络化串级控制系统的概念。针对网络控制系统中存在多个网络及多个控制回路,提出了节点设备连接阵和网络传输阵的概念。分析网络控制系统的三种典型结构形式,并用系统配置图、方框图以及节点设备连接阵和网络传输阵等方法描述了这三种不同结构的网络控制系统。在此基础上指出网络化串级控制系统的四种典型结构,并分别采用这三种方法进行描述。对网络控制系统和网络化串级控制系统结构的分析和描述,为系统的进一步分析和设计奠定了坚实的基础。     

非线性Hammerstein模型预测控制策略及其在pH中和过程中的应用

所有实际工业过程都包含一定程度的非线性,如pH中和过程由于其本身的强非线性是工业过程控制中具有挑战性的难题,但至今为止仍缺乏有效的非线性控制方法。将基于差分方程模型的模型预测控制策略(model predictive control,MPC)推广到包含一个静态非线性多项式函数和一个线性差分方程动态环节的非线性Hammerstein系统,详细描述了基于静态非线性多项式函数的最优控制作用求解方法,提出了一套新的非线性Hammerstein MPC 控制策略(nonlinear Hammerstein predictive control,NLHPC)。pH中和过程控制仿真和控制实验表明,NLHPC的控制结果好于工业上常用的非线性 PID(nonlinear PID,NL-PID)控制器。     

模糊神经网络控制在二阶液位控制中的应用

以化工生产中典型的二阶液位控制为背景,针对大多数控制系统中存在的强耦合性,提出模糊神经网络控制(FNNC)与传统PID结合的FNNC-PID复合控制方法.对模糊系统构成、网络层的输入输出关系、网络学习算法等方面作了详细描述.该方法已成功地应用于二阶非线性液位控制系统.     

一类网络化串级控制系统的性能分析

基于工业过程控制实际,引入了网络化串级控制系统的概念,并指出了网络化串级控制系统的四种典型拓扑结构.针对其中的一类具有不同性质网络诱导时延的网络化串级控制系统,研究了将传统的离散时间控制方法应用于网络化串级控制系统闭环系统建模的可行性.将控制方法分为同步和异步两种,提出了系统的性能损失指标,同步和异步控制时的系统仿真结果表明异步控制所得到的性能优于同步控制的性能.从而推导出结论网络化串级控制系统的分析与设计需要更多的异步控制方法.     

网络化动态矩阵控制及在倒立摆控制中的应用

针对不确定有界长时延网络控制系统,研究动态矩阵控制(DMC)算法及其稳定性问题。首先基于多输入多输出状态空间模型,推导针对长时延网络化控制系统的DMC算法;然后,利用Lyapunov稳定性理论和线性矩阵不等式方法,给出闭环控制系统渐近稳定的充分条件;最后,通过对倒立摆的实时控制验证笔者算法的有效性。     

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。     

基于Ms-LWPLS的化工过程网络化性能分级评估方法

针对化工过程输入输出数据间非线性关系问题,提出一种基于多数据空间局部加权潜结构映射(multispace locally weighted projection to latent structures,Ms-LWPLS)的网络化性能分级评估方法。该方法将历史数据分成不同性能等级的集合,利用Ms-LWPLS方法提取不同性能等级训练数据的过程变化,获得训练数据与性能等级标签之间的非线性映射结构,实现输入数据与性能等级之间的网络化"离线建模"。得到模型后,以数据滑动时间窗为评估单元,将滑动窗口数据输入到训练好的神经网络模型中,根据网络输出划分过程当前性能等级,并构造过渡性能系数,将稳态性能等级和过渡性能等级进行识别和区分。最后,将该方法应用到乙烯裂解过程在线性能评估中,说明此性能评估方法的有效性和准确性。     

基于Ms-LWPLS的化工过程网络化性能分级评估方法

针对化工过程输入输出数据间非线性关系问题，提出一种基于多数据空间局部加权潜结构映射(multi-space locally weighted projection to latent structures，Ms-LWPLS)的网络化性能分级评估方法。该方法将历史数据分成不同性能等级的集合，利用Ms-LWPLS方法提取不同性能等级训练数据的过程变化，获得训练数据与性能等级标签之间的非线性映射结构，实现输入数据与性能等级之间的网络化“离线建模”。得到模型后，以数据滑动时间窗为评估单元，将滑动窗口数据输入到训练好的神经网络模型中，根据网络输出划分过程当前性能等级，并构造过渡性能系数，将稳态性能等级和过渡性能等级进行识别和区分。最后，将该方法应用到乙烯裂解过程在线性能评估中，说明此性能评估方法的有效性和准确性。     

基于期望闭环系统响应的网络化串级控制系统PID整定

研究有常数网络诱导时延的一类典型网络化串级控制系统的PID控制器参数整定问题。系统中的网络诱导时延为常数,主对象和副对象均为一阶惯性加纯迟延环节(FOPDT)。主控制器是有超前滞后校正环节的PID,副控制器是带有滤波环节的PID。基于过程模型和期望闭环系统响应整定PID参数,采用一阶P偄de近似纯迟延环节,以解析的形式推导出了主控制器和副控制器整定参数的表达式,并采用双线性变换法得到离散时间PID控制器。提出了网络化串级控制系统的控制性能综合指标,并基于TrueTime工具箱在不同网络诱导时延时对网络化串级控制系统进行了仿真研究。仿真结果和性能指标计算结果均验证了所提算法的可行性和有效性。     

深度融合特征提取网络及其在化工过程软测量中的应用

复杂化工过程的观测数据往往同时包含非线性和强动态特性,而传统的化工过程软测量方法无法准确提取观测数据的非线性动态特征,以至影响数据建模和质量预报的准确性。提出了一种基于变分自编码器的深度融合特征提取网络(deep fusion features extraction network, DFFEN)。在变分自编码器框架下,通过构建潜隐特征信息传递通道,提取非线性动态潜隐变量。并利用自注意力机制(self-attention)融合关键的隐层信息,优化因信息传递通道过长而导致的潜在特征被遗忘的问题。此外,在后端网络构建潜隐变量和关键质量变量之间的回归模型,以实现关键质量变量的预报。最后,通过数值案例和实际的合成氨过程验证了所提出的DFFEN模型的可行性和有效性。     

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.     

Monitoring and handling of actuator faults in two-tier control systems for nonlinear processes

This work focuses on the monitoring and reconfiguration of two-tier control systems applied to general nonlinear processes in the presence of control actuator faults. Specifically, a general class of nonlinear process systems is first considered and is controlled by a two-tier control system integrating a local control system using continuous sensing/actuation with a networked control system using asynchronous sensing/actuation. To deal with control actuator faults that may occur in the closed-loop system and eliminate the ability of the two-tier control system to stabilize the process, a fault detection and isolation (FDI) and fault-tolerant control (FTC) system is designed which detects and isolates actuator faults and determines how to reconfigure the two-tier control system to handle the actuator faults and ensure closed-loop stability. The FDI/FTC system uses continuous measurements of process variables like temperature and asynchronous measurements of variables like species concentrations. We develop reconfiguration-based FTC schemes that effectively deal with faults in the actuators of both the local and networked control systems. A detailed mathematical analysis is carried out to determine precise conditions for the stabilizability of the FDI/FTC system. The method is demonstrated using a reactor-separator process consisting of two continuously stirred tank reactors and a flash tank separator with recycle stream.     

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 cyber-secure control-detector architecture for nonlinear processes

This work presents a detector-integrated two-tier control architecture capable of identifying the presence of various types of cyber-attacks, and ensuring closed-loop system stability upon detection of the cyber-attacks. Working with a general class of nonlinear systems, an upper-tier Lyapunov-based Model Predictive Controller (LMPC), using networked sensor measurements to improve closed-loop performance, is coupled with lower-tier cyber-secure explicit feedback controllers to drive a nonlinear multivariable process to its steady state. Although the networked sensor measurements may be vulnerable to cyber-attacks, the two-tier control architecture ensures that the process will stay immune to destabilizing malicious cyber-attacks. Data-based attack detectors are developed using sensor measurements via machine-learning methods, namely artificial neural networks (ANN), under nominal and noisy operating conditions, and applied online to a simulated reactor-reactor-separator process. Simulation results demonstrate the effectiveness of these detection algorithms in detecting and distinguishing between multiple classes of intelligent cyber-attacks. Upon successful detection of cyber-attacks, the two-tier control architecture allows convenient reconfiguration of the control system to stabilize the process to its operating steady state.     

基于IP网络的NCS中通信网络动态模型的建立

对于一个基于IP网络的控制系统 (NCS),该IP网络上的其它数据流严重地影响着NCS的控制性能。为此,提出了一个动态模型来描述NCS数据传输过程中的时延和丢包。基于该模型,通过计算机仿真考察了不同网络干扰流对单输入单输出常规PIDNCS开环、闭环特性的影响。仿真结果表明:对于采用常规PID控制器的NCS,网络负荷对其控制性能影响很大;当网络负荷过大时,该NCS可能变得不稳定。     

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。     

基于控制器切换的模糊不确定网络化系统的稳定性

基于T-S模糊模型建立模糊网络化控制系统模型,研究了一类存在不确定参数的网络时滞系统在单包传输且没有丢包情况下的稳定性.为了增强其诱导时延的动态性能,假设存在有限个备选的控制增益己知的模糊状态反馈控制器,在每个备选的控制器均不能镇定系统的情况下,使用控制器切换技术及Lyapunov函数方法,设计切换律,得到了系统渐进稳定的一个充分条件,并且此条件可转化为求解线性矩阵不等式(LMIS)问题.最后仿真结果表明所设计策略的可行性和有效性.     

Encrypted model predictive control design for security to cyberattacks

In recent years, cyber-security of networked control systems has become crucial, as these systems are vulnerable to targeted cyberattacks that compromise the stability, integrity, and safety of these systems. In this work, secure and private communication links are established between sensor–controller and controller–actuator elements using semi-homomorphic encryption to ensure cyber-security in model predictive control (MPC) of nonlinear systems. Specifically, Paillier cryptosystem is implemented for encryption-decryption operations in the communication links. Cryptosystems, in general, work on a subset of integers. As a direct consequence of this nature of encryption algorithms, quantization errors arise in the closed-loop MPC of nonlinear systems. Thus, the closed-loop encrypted MPC is designed with a certain degree of robustness to the quantization errors. Furthermore, the trade-off between the accuracy of the encrypted MPC and the computational cost is discussed. Finally, two chemical process examples are employed to demonstrate the implementation of the proposed encrypted MPC design.     

网络控制系统与网络化串级控制系统的统一建模

基于智能化的现场设备提出广义对象和广义控制器的概念,广义对象包括被控对象、传感器以及发送传感信息的网络,广义控制器包括控制器以及发送控制信息的网络。执行器和被控对象直接连接,直接接收广义控制器的控制指令,广义控制器直接接收广义对象发送过来的传感信息。不同结构形式的网络控制系统和网络化串级控制系统都可看作是广义对象、广义控制器和执行器的组合。同时考虑了网络诱导时延和数据包丢失,采用增广状态向量法在离散时间域分别建立了它们的统一模型,为系统进一步的分析和综合奠定了坚实的基础。