间歇式反应釜温度串级预测函数控制

采用先进预测函数控制(PFC)技术实现实验室规模釜式反应器温度的控制。借助于被控对象的参数模型,对在线不可能测量或很难得到的工艺参数进行了估算,设计并在可编程逻辑控制器(PLC)中实现了温度串级控制。通过修正内嵌参数模型,抵消了模型的不确定性和各种扰动对系统稳定性的影响。结果表明:PFC在控制过程中既能很好地兼顾被控系统中子单元的动态性能,又具有很理想的控制响应品质。     

焦化加热炉出口温度的预测函数控制

采用透明控制结构,设计了焦化加热炉出口温度的预测函数控制系统.针对影响出口温度的可测扰动采用前馈控制加以补偿,实际工业应用表明该控制系统具有较好的跟踪性能和较强的鲁棒性.     

氨合成塔温度的预测自动化控制及优化

目前国内对于氨合成塔温度控制多采用PID控制、串级控制、前馈一反馈控制等方法，这些方法虽然实现简单，但难以克服氨合成塔温度控制中的大延时、强干扰和各段间强烈耦合等特性带来的不利影响，控制效果不佳，因此，决定在氨合成塔温度控制中采用广义预测控制策略设计多路前馈广义预测控制器。     

精密注塑机机筒温度控制与抗干扰研究

针对机筒温度控制偏差较大问题,提出时间最优控制与模糊PID控制相结合对精密注塑机机筒温度进行实时控制的方式。利用模糊控制可在线调整PID控制器的参数,兼顾时间最优控制快速消除大偏差和PID控制精度高的优点,获得动态性能指标,达到了升温快速、超调量小和稳态误差小的要求。通讨Matlab对该方案进行仿真,并通过实验进行了验证。结果表明,模糊PID控制算法动态响应好,控制精度高,能够实现温度偏差在±1. 5℃范围的控制要求。     

聚乙烯生产装置关键控制回路的预测控制

针对聚乙烯生产装置的关键工艺参数－－温度的控制要求，采用具有前馈－反馈结构的预测控制（DMC）方案对温度进行控制，并在DCS上实现，取得令人满意的效果。     

基于广义预测控制的间歇生产迭代优化控制

针对间歇生产,提出了一种基于广义预测控制的批次迭代优化控制策略--BGPC,在间歇过程中引入批次间优化的思想,将迭代学习控制ILC和广义预测控制GPC相结合,在GPC实时结构参数辨识的基础上利用前面批次的模型预测误差修正当前批次的模型预测值.该算法能够有效地克服模型失配、扰动和系统参数变化等情况.文章最后以一个数值例子和间歇反应器为对象进行仿真试验,验证了该算法是有效的.     

监控系统的抗干扰设计

本文分析了闭路电视监控系统中的干扰来源与干扰表现方式,探讨了工程中抑制干扰的实用方法。     

燃烧室温度的测量与控制

本文指出燃烧室温度的测量将由红外辐射温度计代替原来的双铂铑热电偶。实现自动化后,不再直接测量其温度,进而燃烧模型自动控制。针对燃烧模型控制出现的一些问题,介绍了本公司用红外辐射温度计的温度信号为控制对象实现燃烧室温度自控的优点。     

应用先控技术实现对牵伸辊温度的精确控制

以预测PI控制算法为核心,选用新型控制器件,实现对具有大滞后的温度对象——牵伸辊温度的精确控制。提出了一类基于大纯滞后过程的预测PI控制器的结构形式,这种控制器结构简单,可调参数少,参数的调节方便、直观。牵伸辊温度控制的实际应用表明:此类控制器具有良好的控制性能和鲁棒稳定性能。     

米根霉在5L发酵罐种子培养过程中的形态控制

发酵过程中控制菌体为适宜的菌球形态对于丝状真菌发酵的工业化放大具有重要的研究意义.本文针对米根霉在5 L发酵罐上种子培养过程中的形态学控制进行了研究,确定了最适宜形成菌球的培养条件分别为:控制发酵罐内的孢子浓度在5×104～8.3×104/mL的范围内,搅拌转速350 r/min,种子液pH值稳定在2.80,以20g/L的葡萄糖浓度作为最佳种龄的经验值确定种子培养时间为52 h.该条件下可以获得表面光滑的直径约为1 mm的结实茵体.发酵结果表明,相比于絮状形态,菌球形态更有利于富马酸的生成.     

乙炔发生温度控制改造

在电石法生产PVC工艺中,乙炔生产最主要的工艺控制指标是乙炔发生温度,为稳定发生温度,将乙炔发生温度由手控改为自控,不但温度稳定,降低工人的劳动强度,而且取得了显著的经济效益。     

基于控制性能比较的非线性不对称系统预测控制

生产过程某些非线性系统常常表现出不对称动态特性,相对于其在工业工程中经常出现的理论研究特别是控制方法研究则十分有限。本文针对基于正反方向上的两个线性模型分别设计PID控制器的缺陷,提出根据正反方向上的线性模型分别设计相应的状态反馈预测控制器。在每一步的控制率计算中,正反方向的控制器分别计算控制作用,并通过比较正反控制器的控制性能指标来确定最终采用的控制作用。通过pH值控制的仿真实验证明其对非线性不对称系统的控制效果明显优于传统的在正反方向分别采用PID控制的控制效果。     

Predictive control of transport-reaction processes

This work focuses on the development of computationally efficient predictive control algorithms for nonlinear parabolic and hyperbolic PDEs with state and control constraints arising in the context of transport-reaction processes. We first consider a diffusion-reaction process described by a nonlinear parabolic PDE and address the problem of stabilization of an unstable steady-state subject to input and state constraints. Galerkin’s method is used to derive finite-dimensional systems that capture the dominant dynamics of the parabolic PDE, which are subsequently used for controller design. Various model predictive control (MPC) formulations are constructed on the basis of the finite dimensional approximations and are demonstrated, through simulation, to achieve the control objectives. We then consider a convection-reaction process example described by a set of hyperbolic PDEs and address the problem of stabilization of the desired steady-state subject to input and state constraints, in the presence of disturbances. An easily implementable predictive controller based on a finite dimensional approximation of the PDE obtained by the finite difference method is derived and demonstrated, via simulation, to achieve the control objective.     

Predictive control of transport-reaction processes

This work focuses on the development of computationally efficient predictive control algorithms for nonlinear parabolic and hyperbolic PDEs with state and control constraints arising in the context of transport-reaction processes. We first consider a diffusion-reaction process described by a nonlinear parabolic PDE and address the problem of stabilization of an unstable steady-state subject to input and state constraints. Galerkin’s method is used to derive finite-dimensional systems that capture the dominant dynamics of the parabolic PDE, which are subsequently used for controller design. Various model predictive control (MPC) formulations are constructed on the basis of the finite dimensional approximations and are demonstrated, through simulation, to achieve the control objectives. We then consider a convection-reaction process example described by a set of hyperbolic PDEs and address the problem of stabilization of the desired steady-state subject to input and state constraints, in the presence of disturbances. An easily implementable predictive controller based on a finite dimensional approximation of the PDE obtained by the finite difference method is derived and demonstrated, via simulation, to achieve the control objective.     

Predictive control of parabolic PDEs with boundary control actuation

This work focuses on predictive control of linear parabolic partial differential equations (PDEs) with boundary control actuation subject to input and state constraints. Under the assumption that measurements of the PDE state are available, various finite-dimensional and infinite-dimensional predictive control formulations are presented and their ability to enforce stability and constraint satisfaction in the infinite-dimensional closed-loop system is analyzed. A numerical example of a linear parabolic PDE with unstable steady state and flux boundary control subject to state and control constraints is used to demonstrate the implementation and effectiveness of the predictive controllers.     

Predictive optimal management method for the control of polygeneration systems

A predictive optimal control system for micro-cogeneration in domestic applications has been developed. This system aims at integrating stochastic inhabitant behavior and meteorological conditions as well as modelling imprecisions, while defining operation strategies that maximize the efficiency of the system taking into account the performances, the storage capacities and the electricity market opportunities.Numerical data of an average single family house has been taken as case study. The predictive optimal controller uses mixed-integer and linear programming where energy conversion and energy services models are defined as a set of linear constraints. Integer variables model the start-up and shut-down operations as well as the load dependent efficiency of the cogeneration unit. The proposed control system has been validated using more complex building and technology models to asses model inaccuracies. Typical demand profiles for stochastic factors have been used.The system is evaluated in the perspective of its usage in Virtual Power Plants applications.     

基于NLQG的分批补料发酵过程预测控制方法

发酵过程具有高度非线性、时滞性和不确定性等特征,直接影响着发酵过程的有效调控。提出了一种基于非线性二次高斯(NLQG)的分批补料发酵过程预测控制方法。该方法由扩展Kalman滤波器(EKF)和NLQR串联构成,EKF给出被控变量的最优状态估计,NLQR获得被控变量的实时状态反馈,以实现分批补料发酵过程的动态预测控制。在LabVIEW软件平台中,利用ActiveX控件调用MATLAB环境下编译生成的COM组件设计了NLQG控制器,并用于青霉素分批补料发酵过程溶氧浓度的预测控制。实验结果表明,所提出的分批补料发酵过程预测控制方法对于被控变量的设定值变化有良好的跟踪效果,在不同的噪声环境下均能获得较高的控制精度,具有较强的鲁棒性。     

Studies on optimal control approach in a fed-batch fermentation

Fed-batch operation of fermentation processes has been receiving a great deal of interest as it offers the possibility to control a substrate concentration at a desired condition. However, control of a fed-batch fermentation reactor has been known to be a difficult task due to its highly nonlinear and complicated behavior. This work addresses an optimization-based control strategy for a fed-batch bioreactor where an ethanol fermentation process is chosen as a case study. The optimal control problem is formulated to determine the optimal feeding rate policy giving the highest product yield. The resulting optimization problem is solved by using an efficient sequential approach with a piecewise constant control parameterization. Due to the limitation of the sequential approach to cope with inequality path constraints, comparative studies of the methods for handling such constraints are carried out. Furthermore, the impact of time interval and switching time on the solution of the optimal control is investigated.