一种反应釜温度模糊控制器的设计与实现

反应釜是化工生产过程中的关键设备,准确控制反应釜内温度是提高化工产品的质量和产量,实现优质、稳定和节能生产的有效途径.本文基于模糊控制理论,设计一种反应釜内温度模糊控制器,研究模糊控制器输入和输出变量的确定、精确量的模糊化、模糊控制规则的设计、输出量的模糊判决和基于规则修改的模糊控制方法.以STM32F103RB单片机为核心搭建硬件平台,采用单线数字温度传感器DSl8B20实现多点温度采集,并实现了基于单片机的反应釜温度智能控制硬件系统.     

改进PID在反应釜温度控制系统中的应用研究

反应釜炉温控制是化工生产过程中主要的控制系统之一,其温度控制具有大滞后、时变、非线性等特点.针对常规PID控制效果不佳的缺点,提出一种改进的模糊RBF神经网络智能控制方法.将系统的输入误差及误差变化率进行模糊化,并利用RBF神经网络算法对PID控制参数进行在线学习、运算和整定.在RBF神经网络控制算法中,设定初始权值在一定范围内服从高斯分布和均匀分布,对权值不断优化,使得反应釜温度达到良好的控制效果.经Matlab仿真验证,结果表明和常规PID相比,该方法提高了系统的控制精度并具有较强的鲁棒性.     

腈纶聚合过程连续搅拌反应釜解耦控制

腈纶聚合过程属于多输入多输出(MIMO)非线性系统,聚合连续搅拌反应釜(CSTR)温度和聚合反应转化效率是腈纶聚合生产的重要工艺指标。腈纶聚合过程的非线性、强耦合,参数时变性,使得这两个指标不能同时兼顾。针对腈纶水相聚合过程,采用自适应模糊控制方法,设计了腈纶聚合连续搅拌反应釜转化率和温度的解耦控制器。首先利用反馈线性化方法对腈纶水相聚合模型进行处理,设计系统的等效控制。模糊系统可以较好地逼近非线性函数,等效控制器中的非线性和参数不确定性,可采用模糊系统进行逼近。模糊参数的求解在线实时进行,参数自适应律由Lyapunov综合法获得。针对模糊逼近误差,采用minmax鲁棒最优控制方法,抑制误差干扰项对系统的影响。系统仿真结果表明,解耦控制方法能够在保证聚合过程的转化效率的前提下,调节反应温度。该方法对于提高聚合产品的性能,对提高产品的差别化率具有重要意义。     

基于多目标遗传算法的TS模糊模型设计

提出一种利用遗传算法进行TS模糊模型的优化设计方法。首先定义了TS模糊模型的精确性指标,给出模糊模型解释性的必要条件。然后利用模糊聚类算法和最小二乘法辨识初始的模糊模型;利用多目标遗传算法优化模糊模型;为提高模型的解释性,在遗传算法中利用基于相似性的模糊集合和模糊规则简化方法对模型进行约简。最后利用该方法进行一类二阶合成非线性动态系统的建模,仿真结果验证了该方法的有效性。     

基于模糊-PID的小型反应釜控制系统设计

反应釜中进行化学反应的反应物由于浓度高、反应剧烈、控制灵敏性及散热问题比较突出,而且反应釜具有非线性、多变量、强耦合、大时滞等特点,控制任务比较复杂,用经典的PID控制很难达到理想的控制效果,本文主要对反应釜的温度控制系统进行了研究,根据反应釜的工作原理,对反应釜的过程特性和动态特性进行了计算分析,建立了反应釜的热量平衡方程,并将其线性化,推导出了冷却剂对反应釜温度的传递函数模型,采用了基于查表的模糊控制和不完全微分PID控制算法相结合的控制方法并进行仿真,结果表明,控制效果明显优于改进前的PID控制效果。     

基于TS模糊PID控制模型的发电机控制器设计

研究了对风力双馈感应发电机进行控制的TS模糊PID控制器;针对常规PID控制器难以应对某些对象参数变化大,延时环节较大以及噪声干扰等问题,提出了一种能对发电机控制的TS模糊PID控制器;首先定义了双馈感应发电机的数学模型,在此基础上提出了基于TS模型的PID模糊控制器设计方法,并将其用于双馈感应发电机有功功率控制问题中,最后在加入15%测量噪声干扰的情况下对发电机的TS控制器和TS-PID控制器分别进行了仿真;实验结果表明:采用TS模糊PID控制方法比常规PID具有更强的适应性、鲁棒性和可移植性。     

聚合反应釜温度自适应预估控制

本文根据聚合反应釜温度滞后较大的特点,应用模型参考自适应技术,根据釜内温度和压力的测量值来在线估计反应釜内真实温度.该方法结构简单,可以在可编程调节器上实现.实际运行结果表明,该方法可以大大提高聚合反应釜温度控制精度.     

非线性互联系统的分散混合自适应智能控制

针对一类高阶互联MIMO非线性系统，利用TS模糊系统和神经网络的通用逼近能力，在神经网络控制器中引入模糊基函数，提出一种分散混合自适应智能控制器设计的新方案．基于等价控制思想，设计分散自适应控制器，无需计算TS模型．通过对不确定项进行自适应估计，取消了其存在已知上界的假设．通过理论分析，证明了闭环智能控制系统所有信号有界，跟踪误差收敛到零．     

覆铜板树脂胶液生产自控系统设计

本文针对胶液生产反应釜温度控制具有大惯性、纯滞后等特点,采用模糊控制算法,设计了一种树脂胶液生产自控系统,以西门子S7-300为主控器,成功实现了胶液生产的自动化.     

模糊前馈-反馈控制在药品酯化过程控制的应用

针对制药生产过程控制中间歇式反应釜温度控制非线性和大滞后的特点，提出了模糊前馈—反馈控制的方法，并对模糊控制器的一般设计方法进行了研究，简化了模糊控制器的设计过程，实际控制结果表明本设计方案的可用性和有效性。     

Structure and stability analysis of a Takagi–Sugeno fuzzy PI controller with application to tissue hyperthermia therapy

 In this paper, we first reveal the analytical structure of a simple Takagi–Sugeno (TS) fuzzy PI controller relative to the linear PI controller. The fuzzy controller consists of two linear input fuzzy sets, four TS fuzzy rules with linear consequent, Zadeh fuzzy logic AND and the centroid defuzzifier. We prove that the fuzzy controller is actually a nonlinear PI controller with the gains changing with process output. Utilizing the well-known small Gain Theorem in control theory, we then derive sufficient conditions for global stability of the fuzzy control systems involving the TS fuzzy PI controller. Finally, as an application demonstration, we apply the fuzzy PI controller to control issue temperature, in computer simulation, during hyperthermia therapy. The relationship between heat energy and tissue temperature is represented by a linear time-varying model with a time delay. The sufficient conditions for global stability are used to design a stable fuzzy control system. Our simulation results show that the fuzzy PI control system achieves satisfactory temperature control performance. The control system is robust and stable even when the model parameters are changed suddenly and significantly.     

A new T-S fuzzy model predictive control for nonlinear processes

In this paper, a novel fuzzy Generalized Predictive Control (GPC) is proposed for discrete-time nonlinear systems via Takagi-Sugeno system based Kernel Ridge Regression (TS-KRR). The TS-KRR strategy approximates the unknown nonlinear systems by learning the Takagi-Sugeno (TS) fuzzy parameters from the input-output data. Two main steps are required to construct the TS-KRR: the first step is to use a clustering algorithm such as the clustering based Particle Swarm Optimization (PSO) algorithm that separates the input data into clusters and obtains the antecedent TS fuzzy model parameters. In the second step, the consequent TS fuzzy parameters are obtained using a Kernel ridge regression algorithm. Furthermore, the TS based predictive control is created by integrating the TS-KRR into the Generalized Predictive Controller. Next, an adaptive, online, version of TS-KRR is proposed and integrated with the GPC controller resulting an efficient adaptive fuzzy generalized predictive control methodology that can deal with most of the industrial plants and has the ability to deal with disturbances and variations of the model parameters. In the adaptive TS-KRR algorithm, the antecedent parameters are initialized with a simple K-means algorithm and updated using a simple gradient algorithm. Then, the consequent parameters are obtained using the sliding-window Kernel Recursive Least squares (KRLS) algorithm. Finally, two nonlinear systems: A surge tank and Continuous Stirred Tank Reactor (CSTR) systems were used to investigate the performance of the new adaptive TS-KRR GPC controller. Furthermore, the results obtained by the adaptive TS-KRR GPC controller were compared with two other controllers. The numerical results demonstrate the reliability of the proposed adaptive TS-KRR GPC method for discrete-time nonlinear systems.     

深度确定性策略梯度与模糊PID的协同温度控制

针对现有温度控制系统控温时间长、误差大的问题, 本文提出了一种基于深度确定性策略梯度(DDPG)和模糊自整定PID的协同温度控制. 首先, 模糊PID在控制大滞后系统时, 控制器不能立刻对产生的干扰起抑制作用, 且无法保证大滞后系统的稳定性等问题, 本文建立了模糊PID和DDPG算法相结合的温度控制模型, 该模型将模糊PID作为主控制器, DDPG算法作为辅助控制, 利用双控制器模型实现温度协同控制. 接着, 利用遗传算法对模糊PID的隶属函数和模糊规则进行寻优, 获得模型参数最优解. 最后, 在仿真实验中验证所提方法的有效性. 仿真实验结果表明, 本文提出的算法可有效减少噪声干扰, 减小控制系统的响应时间、误差和超调量.     

Robust Takagi–Sugeno fuzzy control of a spark ignition engine

This work deals with the nonlinear control of a three cylinders spark ignition (SI) engine using a fuzzy control strategy based on a Takagi–Sugeno (TS) modeling and robust control approach. A physical model of the SI engine is presented and transformed into a TS model. Then, a robust nonlinear fuzzy control algorithm is developed and applied to two different engine torque control structures. This approach is tested on a three cylinder SI engine test bench to prove the effectiveness of the method.     

机械手的模糊逆模型鲁棒控制

提出一种基于模糊聚类和滑动模控制的模糊逆模型控制方法,并将其应用于动力学 方程未知的机械手轨迹控制.首先,采用C均值聚类算法构造两关节机械手的高木-关野 (T-S)模糊模型,并由此构造模糊系统的逆模型.然后,在提出的模糊逆模型控制结构中, 离散时间滑动模控制和时延控制(TDC)用于补偿模糊建模误差和外扰动,保证系统的全局 稳定性并改进其动态和稳态性能.系统的稳定性和轨迹误差的收敛性可以通过稳定性定理来 证明.最后,以两关节机械手的轨迹跟随控制为例,揭示了该设计方法的控制性能.     

An Analytical Study on Structure, Stability and Design of General Nonlinear Takagi–Sugeno Fuzzy Control Systems

In this paper, we first study analytical structure of general nonlinear Takagi-Sugeno (TS, for short) fuzzy controllers, then establish a condition for analytically determining asymptotic stability of the fuzzy control systems at the equilibrium point, and finally use the stability condition in design of the control systems that are at least locally stable. The general TS fuzzy controllers use arbitrary input fuzzy sets, any types of fuzzy logic AND, TS fuzzy rules with linear consequent and the generalized defuzzifier which contains the popular centroid defuzzifier as a special case. We have mathematically proved that the general TS fuzzy controllers are nonlinear controllers with variable gains continuously changing with controllers’ input variables. Using Lyapunov’s linearization method, we have established a necessary and sufficient condition for analytically determining local asymptotic stability of TS fuzzy control systems, each of which is made up of a fuzzy controller of the general class and a nonlinear plant. We show that the condition can be used in practice even when the plant model is not explicitly known. We have utilized the stability condition to design, with or without plant model, general TS fuzzy control systems that are at least locally stable. Three numerical examples are given to illustrate in detail how to use our new results. Our results offer four important practical advantages: (1) our stability condition, being a necessary and sufficient one, is the tightest possible stability condition, (2) the condition is simple and easy to use partially because it only needs the fuzzy controller structure around the equilibrium point, (3) the condition can be used for determining system local stability and designing fuzzy control systems that are stable at least around the equilibrium point even when the explicit plant models are unavailable, and (4) the condition covers a very broad range of nonlinear TS fuzzy control systems, for which a meaningful global stability condition seems impossible to establish.     

基于TS模型的增益自校正单神经元PID控制算法

将单神经元自适应PID控制算法和基于TS模型的模糊推理系统相结合,提出了增益模糊自校正单神经元PID控制算法。该算法使得传统单神经元自适应PID控制的神经元增益具备在线自动调整的功能,对于变增益的不确定性系统的控制,获得了很好的控制性能。     

Multiscale fuzzy system identification

One of the biggest challenges in constructing empirical models is the presence of measurement errors in the data. These errors (or noise) can have a drastic effect on the accuracy and prediction of estimated models, and thus need to be removed for improved models accuracy. Multiscale representation of data has shown great noise-removal ability when used in data filtering. In this paper, this advantage of multiscale representation is exploited to improve the accuracy of the nonlinear Takagi–Sugeno (TS) fuzzy models by developing a multiscale fuzzy (MSF) system identification algorithm. The developed algorithm relies on constructing multiple TS fuzzy models at multiple scales using the scaled signal approximations of the input–output data, and then selecting the optimum multiscale model that maximizes the signal-to-noise ratio of the model prediction. The developed algorithm is shown to outperform the time domain fuzzy model, NARMAX model, and fuzzy model estimated from pre-filtered data using an Exponentially weighted Moving Average (EWMA) filter through a simulated shell and tube heat exchanger modeling example. The reason for this improvement is that the developed MSF modeling algorithm improves the model accuracy by integrating modeling and data filtering using a filter bank, from which the optimum filter (for modeling purposes) is selected.     

Robust fusion algorithm based on RBF neural network with TS fuzzy model and its application to infrared flame detection problem

A robust fusion algorithm based on Radial Basis Function (RBF) neural network with Takagi–Sugeno (TS) fuzzy model is proposed in view of the data loss, data distortion or signal saturation which is usually occurred in the process of infrared flame detecting with multiple sensors. To initialize the model, the traditional K-means clustering algorithm is used to obtain the number of the fuzzy rules and the center of the membership function. Compared with the traditional RBF neural network with TS fuzzy model, the output of the node in the proposed model is constructed taking into account the membership degree of the feature components in each item of the output polynomial of the hidden layer nodes in consequent fuzzy network. A new weighted activation degree (WAD) is defined to calculate the firing strength (i.e., fuzzy rule applicability) of the fuzzy node instead of the commonly used Mahalanobis distance. The feature representation coefficients used in the above WAD fully consider the variant representation degree of different features in different fuzzy clusters, thus the developed method can deal with the abnormal outputs of the fuzzy rules caused by the variation of the feature components of the raw data obtained from the complex industrial environments. The robustness of the proposed approach is validated with experimental data obtained from a developed triple-channel infrared flame detector and the experiment results show that the convergence rate, accuracy and generalization ability of the proposed method are improved compared with the traditional RBF neural network with TS fuzzy model in Qiao et al. (2014) and the GA-BP (Genetic Algorithm-Back Propagation) model in Wang et al. (2016). In particular, the required number of the hidden layer nodes in the proposed approach is the least among the aforementioned methods.     

Extracting Takagi-Sugeno Fuzzy Rules with Interpretable Submodels via Regularization of Linguistic Modifiers

In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with the objective of preserving TS submodel comprehensibility, in which linguistic modifiers are suggested to characterize the fuzzy sets. A good property held by the proposed linguistic modifiers is that they can broaden the cores of fuzzy sets while contracting the overlaps of adjoining membership functions (MFs) during identification of fuzzy systems from data. As a result, the TS submodels identified tend to dominate the system behaviors by automatically matching the global model (GM) in corresponding subareas, which leads to good TS model interpretability while producing distinguishable input space partitioning. However, the GM accuracy and model interpretability are two conflicting modeling objectives, improving interpretability of fuzzy models generally degrades the GM performance of fuzzy models, and vice versa. Hence, one challenging problem is how to construct a TS fuzzy model with not only good global performance but also good submodel interpretability. In order to achieve a good tradeoff between GM performance and submodel interpretability, a regularization learning algorithm is presented in which the GM objective function is combined with a local model objective function defined in terms of an extended index of fuzziness of identified MFs. Moreover, a parsimonious rule base is obtained by adopting a QR decomposition method to select the important fuzzy rules and reduce the redundant ones. Experimental studies have shown that the TS models identified by the suggested method possess good submodel interpretability and satisfactory GM performance with parsimonious rule bases.