一种基于遗传算法的模糊内模控制方法

针对一类非线性过程,提出了基于T-S模糊模型的非线性内模控制方法.使用遗传算法和模糊聚类方法进行模糊建模,解决了非线性内模控制方法中建立精确的模型及其逆模型困难的问题.通过模糊辨识获得过程的T-S模型及逆模型,并以此设计了内模控制器.最后,将该方法应用于一类非线性过程的控制,仿真结果表明该方法的有效性.     

多变量非线性系统的模糊内模控制

大多数的先进控制器是基于线性模型的,它们对化学工业中常见的非线性过程的控制效果并不能达到最优.因此,考虑使用非线性模型,以使控制性能获得改善.用基于T-S模型的自适应模糊聚类辨识算法对系统进行辨识.T-S模型是用线性的方程来描述非线性系统,从而利于求出模型的逆.而模型逆又是IMC的关键一步,因此选用这种基于T-S模糊模型的控制器(FIMC)来实现对非线性多变量系统的控制.对2输入2输出的非线性系统进行仿真,结果表明FIMC在多变量系统中可以实现好的控制.     

基于模糊神经网络α阶逆系统的发酵过程多变量解耦控制

将逆系统方法与模糊神经网络相结合, 提出一种基于模糊神经网络®阶逆系统的发酵过程解耦控制方法. 在分析了系统可逆性的基础上, 利用模糊神经网络建立发酵过程的非线性逆模型, 然后将得到的模糊神经α阶逆系统与发酵过程串联复合成伪线性系统, 最后设计专家控制器实现高性能闭环解耦控制. 仿真结果表明, 提出的解耦控制方法能够适应发酵过程模型的不确定性和参数的时变性, 具有较强的鲁棒性, 克服了解析逆系统解耦控制方法依赖于过程模型和对模型参数的变化很敏感的缺点, 且结构简单, 易于实现.     

基于模糊专家模型的神经控制器及应用研究

针对具有严重非线性特性的pH中和过程,提出了一种基于模糊专家模型的神经控制策略,这种方法将神经网络逆控制器与神经元PID控制器相结合,并利用模糊专家模型所得到的预报结果来调整神经元PID的权值。仿真试验表明该方法能有效改善控制性能,所提出的方法实现了对pH过程的有效控制,并且有很强的适应性。     

基于模糊模型的非线性通用模型控制

通用模型控制是一种有效的非线性控制方法,但该方法的应用条件是过程一阶微分模型必须要有显式解.为了克服这一局限性,提出一种基于模糊模型的通用模型控制策略,将非线性过程模型应用逆系统的方法在控制算法中直接嵌入过程模型,从而保证了通用模型控制策略的可实现性.该控制器参数物理意义明显,整定方便,仿真结果表明了该控制策略的有效性和鲁棒性.     

基于逆系统方法的综放工作面控制系统的模糊控制器

对逆系统方法作为反馈线性化方法及其非线性本质进行深入的研究后,认知到逆系统方法建立的模糊控制器是一种变增益的非线性控制器,它与PID控制器有许多相似之处,进而设计出模糊PID复合控制器。模糊PID控制器在综放工作面中的应用的关键是模糊控制器的各个参数的整定。因此,我们用传统的方法首先设计一个PID控制器,在稳定时使模糊PID控制器的参数与PID控制器的对应参数相等,逐步调节、修改各个参数,从而可以得出模糊PID控制器的参数,模糊PID控制器在煤矿综放工作面上运用的结果令人满意。     

基于模糊树模型的非线性系统内模控制

本文提出一种基于模糊树模型的非线性系统的内模控制方法,该方法采用模糊树建立非线性系统的内部模型和逆模型.仿真结果表明模糊树方法建立的非线性系统内部模型和逆模型均具有较高的建模精度,所提内模控制方法对非线性系统具有较好的控制性能、较强的抗干扰能力和鲁棒性能.     

基于模糊逆模型的磁力轴承逆控制系统的设计与仿真

磁力轴承是一种通过磁场力使转子稳定悬浮的新型支撑部件,针对磁力轴承系统零阻尼、负刚度和非线性特性,在分析其受力特点的基础上,提出一种基于模糊逆模型的逆控制方案。该方案将具有模糊器和解模糊器的模糊系统当作一种插值器,将模糊逆模型与系统串联构成广义伪线性对象,采用线性PID控制该对象。其中,模糊逆模型和PID控制器共同组成逆控制器以实现对系统的闭环逆控制。仿真结果表明逆控制明显减小了超调量,缩短了调节时间,使转子工作范围扩大。     

基于T-S模糊模型的非线性预测控制策略

提出了一种新的基于T-S模糊模型的非线性预测控制策略. T-S模糊模型用于描述对象的非线性动态特性, 通过将模糊模型的输出反馈回来作为模型输入, 从而构成了模糊多步预报器. 由于T-S模糊模型每条规则的结论部分是一个线性模型, 因此整个模糊模型可以看作一个线性时变系统, 从而将模糊预测控制器中的非线性优化问题转化为一个线性二次寻优问题, 以方便求解. pH中和过程的仿真结果表明其性能优于传统的动态矩阵控制器.     

基于支持向量机的船舶锅炉智能控制研究

在非线性系统逆控制的思想基础上,采用SVM辨识系统的逆模型,设计并实现了基于SVM的逆控制.为克服基于SVM输入输出数据辨识的逆模型不精确引起的逆控制不足,提出了一种SVM逆控制与模糊控制相结合的复合控制策略.在复合控制中,模糊控制器主要用来实现前期控制快速响应的要求.在此期间,逆辨识器在线调整其参数并完全传递给逆控制器.SVM逆控制器在后期起主要作用,以消除对被控系统的稳态误差.由于此复合控制系统中增加了反馈环节,因而比直接逆控制具有更优的性能和鲁棒性,有效解决了逆控制模型精确度要求的问题.将其应用于用锅炉蒸汽压力控制系统中,仿真证明了该复合智能控制策略是有效和实用的.     

基于模糊树模型的自适应直接逆控制

基于模糊树模型, 结合神经网络中的逆向学习和专门化学习, 提出了自适应直接逆控制方法. 首先离线辨识对象的逆模型作为初始的控制器, 然后与对象串联, 用最小均方差 (Least mean square, LMS) 算法在线调节控制器中的线性参数. 本方法辨识得到的逆模型控制器可以减少需要的模糊规则数目, 同时达到较好的跟踪控制效果. 仿真结果表明了方法的有效性.     

Interval type-2 fuzzy inverse controller design in nonlinear IMC structure

In the recent years it has been demonstrated that type-2 fuzzy logic systems are more effective in modeling and control of complex nonlinear systems compared to type-1 fuzzy logic systems. An inverse controller based on type-2 fuzzy model can be proposed since inverse model controllers provide an efficient way to control nonlinear processes. Even though various fuzzy inversion methods have been devised for type-1 fuzzy logic systems up to now, there does not exist any method for type-2 fuzzy logic systems. In this study, a systematic method has been proposed to form the inverse of the interval type-2 Takagi-Sugeno fuzzy model based on a pure analytical method. The calculation of inverse model is done based on simple manipulations of the antecedent and consequence parts of the fuzzy model. Moreover, the type-2 fuzzy model and its inverse as the primary controller are embedded into a nonlinear internal model control structure to provide an effective and robust control performance. Finally, the proposed control scheme has been implemented on an experimental pH neutralization process where the beneficial sides are shown clearly.     

一类非线性大滞后系统的智能前馈控制策略与算法

针对工业控制中一类常见的非线性大滞后对象,本文提出一种基于仿人智能策略的前馈组合控制器.前馈控制部分通过在线优化滞后参数估计值来获得被控对象的静态逆模型,逆模型辨识与直接逆控制由两个神经网络分别实现,从而可以在线调整网络权值.基本模糊控制器可在模糊PID和模糊PD控制算法之间进行转换,适用于控制系统的各工作状态.仿人智能控制策略根据输出误差变化的不同阶段调整所使用的控制组合,并具有自寻优功能.仿真表明,这种控制方法可取得较好的控制效果.     

基于逆系统方法的内模控制在吊车消摆运动中的应用

针对非线性、欠驱动的桥式吊车水平运动系统，先运用非线性逆系统方法将其转化为伪线性系统，然后按照线性系统理论设计内模控制器来控制吊车的水平定位．同时设计了一个角度反馈控制器来确保摆角迅速衰减．最后和常规PID、模糊控制做了比较．实验结果表明，本方案能保证定位无静差、摆角迅速衰减，同时具有良好的抗干扰能力和较强的鲁棒性，控制效果优于PID和模糊控制．     

Intelligent modeling and control for nonlinear systems with rate-dependent hysteresis

A new modeling approach for nonlinear systems with rate-dependent hysteresis is proposed. The approach is used for the modeling of the giant magnetostrictive actuator, which has the rate-dependent nonlinear property. The models built are simpler than the existed approaches. Compared with the experiment result, the model built can well describe the hysteresis nonlinear of the actuator for input signals with complex frequency. An adaptive direct inverse control approach is proposed based on the fuzzy tree model and inverse learning and special learning that are used in neural network broadly. In this approach, the inverse model of the plant is identified to be the initial controller firstly. Then, the inverse model is connected with the plant in series and the linear parameters of the controller are adjusted using the least mean square algorithm by on-line manner. The direct inverse control approach based on the fuzzy tree model is applied on the tracing control of the actuator by simulation. The simulation results show the correctness of the approach. Supported by the National Natural Science Foundation of China (Grant No. 60534020), the National Basic Research Program of China (Grant No. G2002cb312205-04), the Research Fund for the Doctoral Program of Higher Education (Grant No. 20070006060), and the Key Subject Foundation of Beijing (Grant Nos. XK100060526, XK100060422)     

Hybrid control of a pneumatic artificial muscle (PAM) robot arm using an inverse NARX fuzzy model

We investigated the possibility of applying a hybrid feed-forward inverse nonlinear autoregressive with exogenous input (NARX) fuzzy model-PID controller to a nonlinear pneumatic artificial muscle (PAM) robot arm to improve its joint angle position output performance. The proposed hybrid inverse NARX fuzzy-PID controller is implemented to control a PAM robot arm that is subjected to nonlinear systematic features and load variations in real time. First the inverse NARX fuzzy model is modeled and identified by a modified genetic algorithm (MGA) based on input/output training data gathered experimentally from the PAM system. Second the performance of the optimized inverse NARX fuzzy model is experimentally demonstrated in a novel hybrid inverse NARX fuzzy-PID position controller of the PAM robot arm. The results of these experiments demonstrate the feasibility and benefits of the proposed control approach compared to traditional PID control strategies. Consequently, the good performance of the MGA-based inverse NARX fuzzy model in the proposed hybrid inverse NARX fuzzy-PID position control of the PAM robot arm is demonstrated. These results are also applied to model and to control other highly nonlinear systems.     

An offset-free neural controller based on a non-extrapolating scheme for approximating the inverse process dynamics

This work presents a novel control scheme based on approximating the inverse process dynamics with a radial basis function (RBF) neural network model, trained with the fuzzy means algorithm. The produced RBF network constitutes an inverse model of the process, which can be applied as an explicit control law. In order to avoid extrapolation in the RBF model predictions, a concept borrowed from chemometrics, namely the applicability domain, is incorporated to the proposed framework. Moreover, an error correction term is added, allowing the inverse neural controller to account for modeling errors and process uncertainty and eliminate offset. The proposed approach is applied to the control of a nonlinear Continuous Stirred Tank Reactor (CSTR) exhibiting multiple equilibrium points, including an unstable one. A comparison with other control schemes on various tests, including set-point tracking, unmeasured disturbance rejection and process uncertainty highlights the advantages of the proposed controller.     

非线性动态模糊系统过程控制与稳定性分析

针对自动控制领域中存在的大量的非线性动态模糊系统,提出了非线性动态模糊系统过程控制模型,并给出了动态模糊控制器的设计算法和该模型的稳定性分析,很好地解决了模糊控制系统所不能解决的动态性问题.