竖炉焙烧过程的智能设定模型

为了改善多目标评价案例推理设定模型在竖炉焙烧过程控制中的性能,运用注水原理分配过程变量的权重和群决策修正方法对多目标评价案例推理设定方法进行改进,得到一种新的智能设定模型.首先引入注水原理构造Lagrange函数对过程变量的权重进行优化分配,再通过案例检索和案例重用得到设定值的建议解,并根据多目标评价模型预测建议解对生产指标的影响效果,最后,对不合理的设定值进行群决策修正.将得到的设定模型应用于竖炉焙烧过程控制中,通过实验测试和对比应用说明了本文方法优于其他方法,能够有效提高多目标评价案例推理设定模型的控制性能.     

基于案例推理的竖炉故障预报系统

为降低竖望炉焙烧过程的故障发生率,基于故障机理的分析,将过程参量预报与案例推理技术相集成,提出了竖炉焙烧过程的智能故障预报方法.参量量预报模型对不易在线连续测量但能反映故障征兆的关键工艺参数进行实时预报,在此基础上,采用案例推理技术对焙烧过程进行全面分析并给出一些典型故障发生的概率和操作指导.将所建立的故障预报系统成功应用于竖炉焙烧过程的生产实际中,故障发生率明显降低,取得了显著应用成效.     

竖炉燃烧室温度的智能控制方法及应用

针对工况变化频繁的竖炉焙烧过程,采用智能控制方法设计了燃烧室温度智能控制系统。该系统由温度的串级模糊控制、空气流量比值控智和自适应补偿系统组成。自适应补偿系统通过智能故障预报器预报出燃烧室的运行状况,根据运行工况的变化通过基于案例推理技术的自适应补偿器给出加热煤气补偿用量和空燃比的校正值,从而实现了燃烧室温度的自适应控制。通过在某选矿厂22台竖炉中成功应用,表明了竖炉燃烧室温度智能控制方法的有效性。     

基于参量预报的磁选管回收率智能优化控制

竖炉焙烧过程的关键工艺指标磁选管回收率难以实时测量,因而实现优化控制很困难.将优化设定、参量预报与回路控制技术相结合,提出一种磁选管回收率的智能优化控制方法.基于案例推理的优化设定模型根据工况的变化和磁选管同收率的实时预报值给出基础控制回路的设定值,并通过先进的控制方法实现回路的稳定控制.该方法应用于竖炉焙烧过程的生产实际,使磁选管回收率的实际值保持在其目标值范围内,取得显著应用成效.     

复杂工业过程运行的混合智能优化控制方法

工业过程运行的优化控制的目标是将反应产品在加工过程中的质量、效率、消耗的工艺指标控制在目标值范围内. 由于复杂工业过程的工艺指标难于在线测量且与控制回路输出之间的动态特性具有强非线性、强耦合、难以用精确模型描述、随生产边界条件变化而变化的综合复杂性,因此, 难以采用已有优化控制方法, 运行控制只能采用人工设定的控制方式. 由于人工控制不能及时准确地随运行工况调整设定值, 难以将工艺指标控制在目标值范围内, 甚至造成故障工况.本文提出了根据运行工况实时调整控制回路设定值, 通过控制系统跟踪调整后的设定值, 将工艺指标控制在目标值范围内的过程优化运行的混合智能控制方法. 该方法由控制回路预设定模型、前馈补偿与反馈补偿器、工艺指标预报模型、故障工况诊断和容错控制器组成. 在某选矿厂22台竖炉组成的焙烧过程的应用案例, 证明了所提出方法的有效性.     

基于精简并发潜结构映射的竖炉焙烧过程综合故障诊断

竖炉焙烧过程因运行条件异常变化或操作不当会造成上火、冒火、过还原和欠还原等运行故障.这些故障直接影响过程运行安全和产品质量（比如,磁选管回收率）,但难以采用基于模型和基于知识的方法建模故障与产品质量的关系,以及诊断故障变量.针对上述问题,本文提出数据驱动的基于并发潜结构映射（Concurrent projection to latent structures,CPLS）的竖炉焙烧过程综合故障诊断方法.首先,将并发潜结构映射分解的过程变量共有子空间与残差空间精简合并来建立磁选管回收率相关的过程变化空间,提出基于精简并发潜结构映射模型的竖炉焙烧过程综合监控方法;接下来,定义相应的重构贡献图并与竖炉焙烧过程相结合,提出CPLS精简重构贡献方法用于竖炉焙烧过程故障变量诊断;最后,利用竖炉焙烧过程半实物仿真平台采集的数据进行实验研究,结果表明所提方法不仅可以诊断出质量相关的故障,而且可诊断出回路设定值之外的故障变量.     

Hybrid intelligent control of combustion process for ore-roasting furnace

Because of its synthetic and complex characteristics, the combustion process of the shaft ore-roasting furnace is very difficult to control stably. A hybrid intelligent control approach is developed which consists of two systems： one is a cascade fuzzy control system with a temperature soft-sensor, and the other is a ratio control system for air flow with a compensation model for heating gas flow and air-fuel ratio. This approach combined intelligent control, soft-sensing and fault diagnosis with conventional control. It can adjust both the heating gas flow and the air-fuel ratio in real time. By this way, the difficulty of online measurement of the furnace temperature is solved, the fault ratios during combustion process is decreased, the steady control of the furnace temperature is achieved, and the gas consumption is reduced. The successful application in shaft furnaces of a mineral processing plant in China indicates its effectiveness.     

Modeling and control of quasi-keyhole arc welding process

Quasi-keyhole is a novel approach proposed to operate the keyhole arc welding process. Because the method's effectiveness depends on the amperage of the peak current used to establish the keyhole, this paper proposes adjusting the amperage based on the duration of the peak current, which equals the keyhole establishment time. A nominal model structure has been selected from those identified using experimental data and been used in the design of an adaptive predictive control system. Closed-loop control experiments have been conducted to verify the effectiveness of the developed system under varying set-points and varying travel speeds.     

积分过程预测控制的稳态分析及反馈校正

多变量积分过程的控制,一直是预测控制理论研究与应用过程中的难点问题.现有的研究成果更多的关注于算法的实现上,而很少关注理论依据.本文从积分过程的控制输入平衡关系出发,利用线性代数方程组解的相容性原理,得到了一个适用于判断多变量积分过程设定点是否可达的判据,可以作为算法能否实现多变量积分过程无静差控制的理论依据.同时分析了传统算法无法在存在模型失配情况下对积分过程进行优化与控制的原因,利用补偿因子重新设计反馈校正环节,使改进后的算法能够实现存在模型失配过程的优化与控制,并通过仿真验证了本文提出的结论.     

Nonlinear model predictive control using parameter varying BP-ARX combination model

A novel back-propagation AutoRegressive with eXternal input (BP-ARX) combination model is constructed for model predictive control (MPC) of MIMO nonlinear systems, whose steady-state relation between inputs and outputs can be obtained. The BP neural network represents the steady-state relation, and the ARX model represents the linear dynamic relation between inputs and outputs of the nonlinear systems. The BP-ARX model is a global model and is identified offline, while the parameters of the ARX model are rescaled online according to BP neural network and operating data. Sequential quadratic programming is employed to solve the quadratic objective function online, and a shift coefficient is defined to constrain the effect time of the recursive least-squares algorithm. Thus, a parameter varying nonlinear MPC (PVNMPC) algorithm that responds quickly to large changes in system set-points and shows good dynamic performance when system outputs approach set-points is proposed. Simulation results in a multivariable stirred tank and a multivariable pH neutralisation process illustrate the applicability of the proposed method and comparisons of the control effect between PVNMPC and multivariable recursive generalised predictive controller are also performed.     

A hybrid approach for supervisory control of furnace temperature

The temperature control of the reheat furnace is very difficult due to its complex characteristics. Based on expert knowledge and pyrology mechanism, a hybrid supervisory control system (an optimal setting model) has been developed innovatively to control its temperature. With the help of a statistical process controller, this hybrid supervisory control system can replace the human operator for most of operations in the process. Since an expert compensator is designed to suppress the external disturbance, the proposed model can automatically update the optimal set-point value for the furnace temperature of each zone under the varying boundary conditions. Both simulation and industrial experiment show the viability and effectiveness of the suggested model and its bright application foreground in thermal process.     

磁选管回收率智能混合预报方法

针对衡量竖炉焙烧过程焙烧矿质量好坏的关键工艺指标磁选管回收率难以在线测量、化验结果滞后的难题，采用神经网络、案例推理和专家系统技术，提出了由神经网络预报模型、案例推理预报模型、自校正模型组成的磁选管回收率智能混合预报模型，讨论了模型的结构、主要功能和实现算法，并成功应用于赤铁矿选矿厂竖炉焙烧过程．应用效果表明，在工况正常与异常两种情况下，所提出的方法均能准确预报磁选管回收率．将磁选管回收率预报模型应用于竖炉焙烧过程的优化控制，使磁选管回收率保持在最优工艺指标范围之内，取得了明显的成效．     

A performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control

This paper presents a performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control for large-scale systems. After the fault has been detected and diagnosed, several controller reconfigurations are proposed as candidate corrective actions for fault compensation. The solution of a set of constrained optimization problems with different actuator and setpoint reconfigurations is derived by means of an original approach, exploiting the information on the active constraints in the non-faulty subsystems. Thus, the global optimization problem is split into two optimization subproblems, which enable the online computational burden to be greatly reduced. Subsequently, the performances of different candidate controller reconfigurations are compared, and the better performing one is selected and then implemented to compensate the fault effects. Efficacy of the proposed approach has been shown by applying it to the benzene alkylation process, which is a benchmark process in distributed model predictive control.     

Partially decoupled approach of extended non-minimal state space predictive functional control for MIMO processes

This paper presents a partially decoupled design of the state space predictive functional control for MIMO processes. The multivariable process is first treated into MISO process by a simple Cramer's rule solution to linear equations which provides a balance between model complexity and control system design, and then the derived MISO process based extended state space predictive functional control is presented. The overall design of the controller enables the controller to consider both the process state dynamics and the output dynamics, thus improved control performance for tracking set-points and disturbance rejection is resulted. The proposed controller is tested on both model match and model mismatch cases to demonstrate its superiority. In addition, a closed-form of transfer function representation that facilitates frequency analysis of the control system is provided to give further insight into the proposed method.     

竖炉焙烧过程综合自动化系统

针对竖炉焙烧过程的工艺特点及技术要求，基于智能技术提出了实现综合生产指标优化的竖炉焙烧过程综合自动化系统，讨论了由智能优化、过程控制和过程管理三层结构组成的综合自动化系统的结构、功能和控制策略。用智能优化设定模型、炉况诊断模型、智能预报模型及回路控制，实现了优化综合生产指标的目标。所提出的系统成功应用于桌选矿厂竖炉焙烧生产过程，实现了竖炉焙烧生产过程的优化控制、优化运行和优化管理，取得了明显的应用成效。     

Control of human arm movement in machine-human cooperative welding process

This paper studies accurate control of human arm movement in machine-human cooperative control of GTAW process. An innovative teleoperated virtualized welding platform is utilized to conduct dynamic experiments to correlate the human welder arm movement with the visual signal input. An adaptive ANFIS model is proposed to model the intrinsic nonlinear and time-varying characteristic of the human welder response. A model based predictive control algorithm is then proposed and an analytical solution is derived. Human control experimental results verify that the proposed controller is able to track varying set-points and is robust under measurement and input disturbances.     

基于网络模型的综合多速率采样预测控制器

针对网络控制系统(NCS)中存在的网络延迟和数据丢包问题以及网络控制系统的多采样率特性,将预测控制器和网络延迟补偿器相结合,提出一种基于网络模型的综合多速率采样预测控制器.预测控制器利用多步预测、滚动优化、反馈校正控制策略补偿了传感器-控制器传输延迟,网络延迟补偿器补偿了控制器-执行器传输时延和一些未知网络延迟.仿真试验表明,该算法对网络延迟和数据丢包具有一定的补偿作用,提高了网络资源利用率并且保证闭环网络控制系统渐近稳定.     

阳极焙烧温度的建模与多变量解耦控制

阳极焙烧温度系统是一个含有耦合、大时滞、非线性的多变量控制系统。为实现对阳极焙烧温度的精确控制,依据现场采集的大量温度数据,辨识出阳极焙烧炉温度的二阶惯性滞后控制模型。以此模型为初始预测模型,提出阳极焙烧温度的多变量预测函数解耦控制方法,对焙烧温度进行多变量预测函数解耦控制。仿真和应用结果表明,这种控制方法的控制精度和鲁棒性优于原有的PID方法,具有很好的控制效果。     

Fuzzy modeling and stable model predictive tracking control of large-scale power plants

This paper develops a stable model predictive tracking controller (SMPTC) for coordinated control of a large-scale power plant. First, a Takagi–Sugeno (TS) fuzzy model is established to approximate the behavior of the boiler–turbine coordinated control system (CCS) using fuzzy clustering and subspace identification (SID). Then, an SMPTC is designed based on the fuzzy model to track the power and pressure set-points while guaranteeing the input-to-state stability and the input constraints of the system. An output-based objective function is adopted for the proposed SMPTC so that the controller could be directly applicable for the data-driven model. Moreover, the effect of modeling mismatches and unknown plant variations has been overcome by the use of a disturbance term and steady-state target calculator (SSTC). Simulation results for a 600 MW power plant show that an off-set free tracking performance can be achieved over a wide range load variation.     

Supervisory nonlinear MPC for emergency voltage control using pattern search

The design of a higher-layer controller using model predictive control (MPC) is considered. The higher-layer controller uses MPC to determine set-points for controllers in a lower control layer. In this paper the use of an object-oriented model of the system for making predictions is proposed. When employing such an object-oriented prediction model the MPC problem is a nonlinear, non-smooth optimization problem, with an objective function that is expensive to evaluate. Multi-start pattern search is proposed as approach to solving this problem, since it deals effectively with the local minima and the non-smoothness of the problem, and does not require expensive estimation of derivatives. Experiments in an emergency voltage control problem on a 9-bus dynamic power network show the superior performance of the proposed multi-start pattern-search approach when compared to a gradient-based approach.