模糊PID控制在单相光伏逆变电源中的应用

文章对PID控制和模糊控制的优缺点进行了分析,并提出了将两者结合组成模糊自整定PI双闭环控制,并对控制器进行了设计,最后通过仿真,证明了该方法的有效性。     

模糊自整定PID控制在在线流变仪温控系统中的应用

在传统的PID控制器中加入模糊控制环节,通过模糊推理的方法实现了对PID参数的自整定,并将该控制策略应用于在线流变仪的温控系统中,结果表明:模糊自整定PID控制应用于该系统中具有良好的控制效果。     

模糊自适应控制算法在配料系统中的应用

介绍模糊自适应控制算法在配料系统中的应用。模糊自适应控制器以误差E和误差变化量Ee作为输入，可以满足不同时刻E和Ee对PID参数自整定的要求；利用模糊控制规则在线对PID参数进行修改，消除了生产过程中人为因素的影响，提高了产品质量的均一性和稳定性。     

锦纶聚合釜温度模糊自适应PID控制系统研究与仿真

锦纶聚合釜温度具有非线性、大滞后等特点,使用传统PID控制器的控制效果不够理想。在建立聚合釜温度数学模型的基础上,采用模糊算法与PID控制相结合的模糊自适应PID控制算法,实现PID参数自整定,提高控制精度。基于MATLAB平台构建釜温控制系统模型,仿真结果表明所提模糊自适应PID控制算法效果优于传统PID控制。     

基于T-S模型的CFB床温模糊控制策略

设计一种基于T-S模型的循环流化床床温控制的三维模糊控制器,在制定模糊规则时参考PID参数的整定方法,将PID策略与模糊控制进行互补结合.仿真结果表明,该控制器具有较好的抗干扰能力以及鲁棒性,从控制精度上以及快速性上都比PID控制有了一定的提高.     

PVC胶槽温度模糊PID控制系统

为提高PVC胶槽温度控制的可靠性和精度,将模糊算法与PID算法结合并应用于PVC胶槽温度控制系统中,通过两种控制算法的优势互补,利用模糊控制算法求得PID参数修正系数,实现PID控制器3个参数的在线自调整。仿真结果表明:PVC胶槽温度模糊PID控制器的控制性能明显地优于常规PID控制器,温度控制精度达到±1℃,满足了PVC胶槽温度参数动态变化实时调节控制的需要,具有较好的自适应调节和鲁棒性。     

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。     

纯碱装置碳化塔温度的模糊控制仿真研究

针对纯碱装置碳化塔温度的控制过程,利用参数自整定PID模糊控制方案进行了该控制过程的仿真研究。结果表明过程参数发生变化时,该类模糊控制器能有效地进行PID参数的修正,较好满足了工程控制要求。其控制效果优于常规PID控制器。     

二次风温与篦速的协调控制技术研究

以某水泥生产线的窑系统和篦冷机系统为研究对象,针对篦速的变化对二次风温产生的复杂影响,传统的PID难以实现对其有效控制的特点,运用常规PID控制和模糊控制理论,设计了模糊自整定PID控制器。仿真结果表明,该模糊PID控制器实现了二次风温与篦速的协调控制,改善了系统的静、动态性能,提高了系统的适应能力和鲁棒性。     

变论域模糊PID控制器的设计与仿真

双容水箱是典型的非线性时滞系统,采用常规模糊PID控制时,超调量大、振荡剧烈、控制效果不理想。本文将变论域模糊控制和PID控制结合起来形成变论域模糊PID控制器解决了上述问题。介绍了变论域模糊PID控制器的结构设计及伸缩因子的选择方法,并给出了具体的控制算法。使用Matlab建模仿真,结果表明该控制器的自适应能力强,且无超调量。     

Fuzzy Control Of A Fluidized Bed Dryer

Fluidized bed dryers are utilised in almost every area of drying applications and therefore improved control strategies are always of great interest. The nonlinear character of the process, exhibited in the mathematical model and the open loop analysis, implies that a fuzzy logic controller is appropriate because, in contrast with conventional control schemes, fuzzy control inherently compensates for-process nonlinearities and exhibits more robust behaviour. In this study, a fuzzy logic controller is proposed; its design is based on a heuristic approach and its performance is compared against a conventional PI controller for a variety of responses. It is shown that the fuzzy controller exhibits a remarkable dynamic behaviour, equivalent if not better than the PI controller, for a wide range of disturbances. In addition, the proposed fuzzy controller seems to be less sensitive to the nonlinearities of the process, achieves energy savings and enables MIMO control.     

I.D.S'90, PRAGUE,CZECHOSLOVAKIA

ABSTRACT

Fluidized bed dryers are utilised in almost every area of drying applications and therefore improved control strategies are always of great interest. The nonlinear character of the process, exhibited in the mathematical model and the open loop analysis, implies that a fuzzy logic controller is appropriate because, in contrast with conventional control schemes, fuzzy control inherently compensates for-process nonlinearities and exhibits more robust behaviour. In this study, a fuzzy logic controller is proposed; its design is based on a heuristic approach and its performance is compared against a conventional PI controller for a variety of responses. It is shown that the fuzzy controller exhibits a remarkable dynamic behaviour, equivalent if not better than the PI controller, for a wide range of disturbances. In addition, the proposed fuzzy controller seems to be less sensitive to the nonlinearities of the process, achieves energy savings and enables MIMO control.     

MODELLING AND CONTROL OF AN ALFALFA ROTARY DRYER

A dynamic model of an alfalfa rotary dryer was developed and used to test the performance of two different feedback controllers. One controller is a conventional PI (Proportional-Integral) controller with fixed tuning parameters whereas the other is a gain-scheduled PI controller with automatically adjusted tuning parameters. The performance of the two controllers was compared with the performance of the dryer under manual control. The gain-scheduled PI controller was found to be superior in the sense that it used less control action and achieved the same control performance as the fixed tuning parameter PI controller. The use of the gain-scheduled controller was shown to reduce energy consumption, increase dryer throughput and had an estimated pay-back time of nine months.     

Adaptive type-2 fuzzy logic control of a bioreactor

Two adaptive type-2 fuzzy logic controllers with minimum number of rules are developed and compared by simulation for control of a bioreactor in which aerobic alcoholic fermentation for the growth of Saccharomyces cerevisiae takes place. The bioreactor model is characterized by nonlinearity and parameter uncertainty. The first adaptive fuzzy controller is a type-2 fuzzy-neuro-predictive controller (T2FNPC) that combines the capability of type-2 fuzzy logic to handle uncertainties, with the ability of predictive control to predict future plant performance making use of a neural network model of the nonlinear system. The second adaptive fuzzy controller is instead a self-tuning type-2 PI controller, where the output scaling factor is adjusted online by fuzzy rules according to the current trend of the controlled process. The performance of a type-2 fuzzy logic controller with 49 rules is used as reference.     

MODELLING AND CONTROL OF AN ALFALFA ROTARY DRYER

ABSTRACT

A dynamic model of an alfalfa rotary dryer was developed and used to test the performance of two different feedback controllers. One controller is a conventional PI (Proportional-Integral) controller with fixed tuning parameters whereas the other is a gain-scheduled PI controller with automatically adjusted tuning parameters. The performance of the two controllers was compared with the performance of the dryer under manual control. The gain-scheduled PI controller was found to be superior in the sense that it used less control action and achieved the same control performance as the fixed tuning parameter PI controller. The use of the gain-scheduled controller was shown to reduce energy consumption, increase dryer throughput and had an estimated pay-back time of nine months.     

Temperature control of highly exothermic batch polymerization reactors

In this article a highly exothermic batch polymerization reactor is considered. The reactor is simplified as a mixing tank with the internal heat generation treated as a disturbance. A fuzzy-hybrid-PID-feedback (FH-PID) control structure is developed in which the output of fuzzy hybrid portion is used to adjust the set point of a PID controller to compensate for the effect of the major disturbance, the heat of reaction. In this way, the hybrid portion of the controller does not influence the stability of the original PID control system. A fuzzy model was constructed to estimate the heat of reaction inside the fuzzy hybrid block. The fuzzy parameters of the hybrid portion do not depend on the process model and can be estimated from the transient response obtained with a conventional PID controller. This FH-PID control strategy has been applied to the temperature control of batch solution and batch inverse emulsion polymerizations of acrylamide in a 1 gallon pilot scale reactor. The results show that this fuzzy hybrid—PID-feedback control strategy improves the control performance of the batch polymerization reactor.     

模糊PID控制器的发展

将模糊控制技术和PID控制相结合，可克服常规PID控制器的不足，使PID控制器具有参数自适应能力。介绍了模糊控制器和PID控制器间的关系，从一维、二维及三维模糊控制器的输入－输出形式上，以及解析分析角度出发，可以说模糊控制器人本质上讲是一种非线性PID控制器。还总结了模糊PID控制器的结构。模糊PID控制器可分成两大类，一类由参数可变的PID控制器及一个模糊控制器共同构成，模糊控制器用于完成PID参数的在线整定，控制信号仍由PID控制器生成；另一类直接用模糊控制器来构造、实现PID控制功能。分别归纳了这两类控制器的多种结构形式。最后还对模糊PID控制器的发展进行了展望，提出了模糊PID 控制器结构的选择、控制器参数调整及控制器评价标准的确定这几方面是今后工作的重点。     

Controller adjustment for improved nominal performance and robustness—II. Robust geometric control of a distillation column

In Part I of this paper a general frequency domain method to adjust multivariable controllers with respect to both nominal performance and robustness are presented. In Part II this method is used to examine and improve the control of a binary distillation column. The selected control strategies are conventional PI control and geometric control. The PI controller is adjusted in order to obtain satisfactory robustness properties. The basic geometric controller is extended with feedback from state variables which do not alter the nominal disturbance rejection. Both constant gain feedback and integration are examined. The included control parameters are adjusted for improved nominal performance and for robustness. The major result is that the adjusted geometric controller has a robustness which equals that of the adjusted conventional PI controller. However, the nominal performance of the geometric controller is superior to that of conventional PI control. Thus we expect the adjusted geometric controller to have improved performance on a real column compared to that of conventional PI control.     

Integrating data‐based modeling and nonlinear control tools for batch process control

A data‐based multimodel approach is developed in this work for modeling batch systems in which multiple local linear models are identified using latent variable regression and combined using an appropriate weighting function that arises from fuzzy c‐means clustering. The resulting model is used to generate empirical reverse‐time reachability regions (RTRRs) (defined as the set of states from where the data‐based model can be driven inside a desired end‐point neighborhood of the system), which are subsequently incorporated in a predictive control design. Simulation results of a fed‐batch reactor system under proportional‐integral (PI) control and the proposed RTRR‐based design demonstrate the superior performance of the RTRR‐based design in both a fault‐free and faulty environment. The data‐based modeling methodology is then applied on a nylon‐6,6 batch polymerization process to design a trajectory tracking predictive controller. Closed‐loop simulation results illustrate the superior tracking performance of the proposed predictive controller over PI control. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

DYNAMIC FUZZY ADAPTIVE CONTROLLER FOR pH

Control of pH processes is very difficult due to nonlinear dynamics, high sensitivity at the neutral point, and changes in the concentrations of known or unknown chemical species. In this study, a dynamic fuzzy adaptive controller (DFAC) with a new inference mechanism is proposed and applied for the control of pH processes. The DFAC consists of a low-level basic control phase with a minimum rule base and a high-level dynamic learining phase with an updating mechanism to interact and modify the control rule base. The DFAC can self-adjust its fuzzy control rules using information from the process during on-line control and create new fuzzy control rules or modify the present control rules using its learning capability from past control trends. The controller is evaluated by applying it to a weak acid-strong base pH process with input disturbances and to another pH process that involve that has changes in acidic/buffering streams. The results of the DFAC with the new inference mechanism are compared with the known inference mechanisms, the fuzzy controller, the conventional PI controller, and also with an adaptive PID controller. The proposed DFAC provides better performance for set point tracking of the pH and rejection of load disturbances and buffering affects.