锅炉汽包液位动态前馈控制的研究

本文建立了锅炉汽包液位对象的数学模型，应用模糊控制理论设计出一个锅炉汽包液位模糊控制系统，采用了模糊动态前馈控制。通过在simulink下仿真，获得了较好的控制效果。从而实现了对锅炉汽包液位的最佳实时控制。     

基于DeltaV系统的PID控制算法的实时性能优化

以DeltaV系统中的比例积分微分（Proportional-Integral-Derivative,PID）控制算法为研究对象，以锅炉汽包液位调节为例，针对当前算法的实时性能进行评估与改进。提出了一种改进方法，包括参数优化和控制策略调整。通过提高控制器的响应速度和精度，旨在更好地满足锅炉汽包液位调节的实时要求。实验结果表明，改进后的PID控制算法在锅炉汽包液位调节中具有更好的实时性能，能够更准确地控制液位，提高系统的稳定性和可靠性。     

基于PCS7平台对于锅炉液位的控制系统应用

基于SIMENS的PCS7对于锅炉液位控制过程中应用并且提出先进的控制策略实现。首先深入了解了锅炉液位的生产原理和工艺流程;其次,设计了控制系统,讨论了运用PCS7如何对电厂设备中锅炉液位环节的自动控制和实时监控,最后为了保证锅炉安全和蒸汽的高效,在汽包的液位控制上采用了PID控制算法结合三冲量串级调节,在实际运行期间取得了良好的效果。     

基于定性状态模型的定性控制方法研究

针对某些复杂系统难以获得精确数学模型来实现有效控制这一问题,采用定性状态模型来描述含有不确定性的非线性系统,提出了一种定性控制方法。首先通过对系统状态的定性划分,采用基于定性推理的自动机建立被控系统定性状态模型,然后设计基于系统定性状态模型的定性控制器,并给出了控制器设计的一般步骤。最后以液位控制系统为例,建立了液位对象的定性状态模型并设计了定性控制器,仿真结果表明该方法的有效性及其理论价值和应用前景。     

汽包锅炉的一种非线性串级控制设计

电厂锅炉的非线性和大延迟时间常数等是影响其控制品质主要因素。针对这些问题,提出一种非线性串级控制设计方案,一方面采用非线性补偿能够抵消锅炉的非线性的影响,另一方面采用串级控制能够提高锅炉的动态响应速度。在控制设计中,选择锅炉汽包压力为副控制对象、主蒸汽压力为主控制对象构成串级控制系统。仿真研究表明,在大范围变负荷运行条件下,所设计的机组控制系统具有良好的控制品质并能够有效地提高锅炉的响应速度。     

PID-DMC算法及其在液位控制系统中的应用

提出了一种新的带PID校正环节的DMC算法，论述了其原理并将其应用于实际液位控制系统，实现了系统在模型失配明显的情况下对二阶液位对象的有效控制。由于控制阀的非线性特性使得本实验中的液位对象具有时滞弱非线性特性，常规DMC算法控制存在明显的模型失配问题并影响了系统的控制性能。为此在常规DMC算法中引入PID环节，从而得到了一种新的PID—DMC算法。实验结果表明该算法能有效改善系统在模型失配情况下的控制性能，且算法简单易行，在线计算量小。     

智能PID算法在液位控制系统中的应用

针对自己开发的液位控制系统参数难以调整的问题,本文提出了一种智能PID的液位控制方法。智能PID控制算法是在常规PID控制算法的基础上,根据前人和专家的经验以及操作人员的实际经验,针对具有大滞后、时变、非线性系统对象而提出的控制算法。该算法是分段进行调节的,它既有较好的快速性,又有迟滞(死区)控制的稳定性和抗干扰能力。实验结果表明:这种控制方法不仅简单、精度高,而且具有一定的实用价值。     

基于STM32燃气热水锅炉液位控制系统设计与实现

针对燃气热水锅炉汽包液位采集与虚假水位的控制问题,设计了基于STM32F103-VET6的燃气热水锅炉液位控制系统。该系统以STM32F103-VET6芯片为核心,由采集、控制、显示报警等电路组成,实现了汽包液位信号的采集、锅炉水泵启停控制,同时将锅炉汽包液位的状态通过触摸屏显示。在汽包液位控制中采用了三冲量串级控制方法,分别将模糊PID和PID控制算法运用到控制器,有效防止虚假水位的产生。     

基于预测控制方法的非线性神经网络逆控制

针对一类多输入多输出非线性被控对象,利用前向神经网络逼近原系统的逆系统,将其作为控制器,采用预测滚动优化性能指标训练该神经网络逆控制器,以克服干扰和不确定性影响,实现对多变量非线性对象的解耦控制。对某微型锅炉对象进行了控制算法仿真,结果表明,所提出的控制方法能够克服模型误差的影响,实现稳定解耦控制,且易于实现。在仿真过程中通过实验方法建立该锅炉对象的神经网络预测模型,并注意采用泛化方法采集训练样本数据和训练神经网络,以提高神经网络模型的泛化能力。     

基于MCGS锅炉液位和温度控制系统的设计

分析锅炉液位和温度系统的控制特点，采用VB设计锅炉液位和温度控制器，利用工控组态软件MCGS的OLE扩展功能，将液位和温度控制器嵌入到MCGS组态软件中，实现液位和温度的自动控制。     

A nonlinear state‐feedback state‐feedforward tracking control strategy for a boiler‐turbine unit

A boiler‐turbine unit is a primary module for coal‐fired power plants, and an effective automatic control system is needed for the boiler‐turbine unit to track the load changes with the drum water level kept within an acceptable range. The aim of this paper is to develop a nonlinear tracking controller for the Bell‐Åström boiler‐turbine unit. A Takagi‐Sugeno fuzzy control system is introduced for the nonlinear modeling of the Bell‐Åström boiler‐turbine unit. Based on the Takagi‐Sugeno fuzzy models, a nonlinear tracking controller is developed, and the proposed control law is comprised of a state‐feedforward term and a state‐feedback term. The stability of the closed‐loop control system is analyzed on the basis of Lyapunov stability theory via the linear matrix inequality approach and Schur complement. Moreover, model uncertainties are also considered, and it is proved that with the proposed control law the tracking error converges to zero. To assess the performance of the proposed nonlinear state‐feedback state‐feedforward control strategy, a nonlinear model predictive control strategy and a linear strategy are presented as comparisons. The effectiveness and the advantages of the proposed nonlinear state‐feedback state‐feedforward control strategy are demonstrated by simulations.     

一种基于工况分解的热工过程非线性控制模型建立方法及应用

充分考虑大多数复杂热工控制对象非线性特性与运行工况密切相关的实际特点,采用基于工况分解的多模型建模思路,提出一种面向控制的非线性过程建模方法.将该方法应用于某电厂300MW机组锅炉过热汽温对象,实际考核结果表明采用该方法建立的模型,即使在运行工况大范围变化时也具有满意的动态预测效果,验证了提出的方法的有效性.     

基于Hammerstein模型的多变量动态矩阵解耦控制研究

许多工业过程都具有强耦合、非线性等复杂过程特性；针对具有变量间强耦合和执行机构非线性的一类典型过程对象，提出基于Hammerstein模型的多变量动态矩阵解耦控制算法；并针对实验室电加热锅炉与强制对流换热器组成的广义对象，采用s7—300可编程序控制器，将控制策略应用于该对象，实现了对锅炉内胆水温及强制对流换热器出口水温的控制；实验结果表明，采用基于Hammerstein模型的多变量动态矩阵解耦控制策略，在解耦效果、设定值跟踪及抗干扰响应等方面，都能取得满意的结果，能够解决一类多变量非线性系统的控制问题。     

Robust stabilization of nonlinear interconnected systems with application to an industrial utility boiler

This paper presents a new scheme for robust stabilization of nonlinear interconnected systems, based on linear matrix inequalities (LMIs). The fact that the improvement in stability is significant and the controller uses only the output information of plant leads to the name robust output feedback control. The control design is formulated as a convex optimization problem, which makes it computationally tractable, when the problem size increases. The controller concept is then evaluated on a natural circulation drum boiler (utility boiler), where the nonlinear model describes the complicated dynamics of the drum, downcomer, and riser components. The linearized system is non-minimum phase and has two poles at the origin, which are major sources of interaction, bandwidth limitation and instability. Simulation results are presented which show the effectiveness of the proposed control against instabilities following sudden load variations. The control is also effective for steady state operation.     

Neural‐Adaptive Control and Nonlinear Observer for Waste‐To‐Energy Boilers

This paper looks at the problem of controlling an incinerator that burns waste gas to generate power. The system is modelled as a standard utility boiler using one known and one unknown (waste) fuel input. Standard linear controls have trouble dealing with large variations in the waste input, and in practice boiler shutdowns can occur. In this work, a nonlinear adaptive control design accounts for uncertainty in the plant parameters, and an adaptive neural‐network estimates the effect of the waste input. Since a linear observer design cannot guarantee convergence away from a set point, a novel nonlinear observer design provides estimates of the states. The observer design uses fictitious states to estimate nonlinear terms in the observer dynamics. The analysis guarantees Lyapunov stability, thus the observer bounds depend on the accuracy of the observer initial conditions. Simulation results show the proposed method can obtain accurate performance and stability, improving over results obtained withproportional–integral control.     

A design framework for overlapping controllers and its industrial application

This paper presents a new practical framework for output feedback control design with overlapping information structure constraints. In comparison to the earlier work, the proposed method removes some restrictions in the control design algorithm by utilizing congruence transformations, simplifications, and the reciprocal variant of the projection lemma. This leads to a less conservative solution than previous design methods because the choice of some design parameters by trial and error is eliminated. Moreover, in some cases the structural constraint of having a diagonal Lyapunov function in linear matrix inequalities (LMIs) is removed. The results are extended to capture a more general scenario of output feedback control design for nonlinear interconnected systems. The validity of the proposed approach is demonstrated through applications to an industrial utility boiler and to a multi-area power system. Simulation experiments using a nonlinear simulation package of utility boilers called SYNSIM reveal that the proposed design strategy overcomes control problems in the present plant and maintains stability in the presence of sudden load variations. Furthermore, the performance of the overlapping controllers is found to be better than existing PI controllers in the plant.     

锅炉汽包水位模糊控制系统的仿真与设计

锅炉汽包水位是一种非线性、时变大、强耦合的多变量系统。在建立了锅炉汽包水位为调节对象的数学模型基础上,应用MATLAB simulink软件对汽包水位控制系统进行验证和仿真,给出了传统PID控制和模糊PID控制的比较结果,同时采用PLC进行模糊控制,明显地改善了汽包水位控制系统的静、动态特性,从而实现了对锅炉汽包水位的最佳实时控制。     

Nonlinear control of coal-fired steam power plants

This work proposes a nonlinear control strategy for steam power plants that efficiently controls the superheated steam temperature while accommodating large and frequent variations in power demand. The variables to be controlled are the pressure in the boiler, power generation, and superheater/reheater temperatures. The proposed strategy decomposes the overall plant into three separate subsystems and applies decoupling with deadtime compensation for each one of them. The derived strategy is implemented within a MATLAB/Simulink environment for different setpoint tracking and disturbance rejection cases, showing excellent performance and robustness.     

Learning control of process systems with hard input constraints

In this paper, a novel and simple learning control strategy based on using a bounded nonlinear controller for process systems with hard input constraints is proposed. To enable the bounded nonlinear controller to learn to control a changing plant by merely observing the process output errors, a simple learning algorithm for parameter updating is derived based on the Lyapunov stability theorem. The learning scheme is easy to implement, and does not require any a priori process knowledge except the system output response direction. For demonstrating the effectiveness and applicability of the learning control strategy, the control of a once-through boiler, as well as an open-loop unstable continuously stirred tank reactor (CSTR), were investigated. Furthermore, extensive comparisons of the proposed scheme with the conventional PI controller and with some existing model-free intelligent controllers were also performed. Due to significant features of simple structure, efficient algorithm and good performance, the proposed learning control strategy appears to be a promising and practical approach to the intelligent control of process systems subject to hard input constraints.