Robust control of a distillation column by the method of inequalities

The method of inequalities is applied to design a robust PI controller for the control of distillate composition using the reflux flow as the manipulated variable. The controller design method takes into account wide variations in k,τ and τ_D of the first order plus the delay transfer function of the process. The performance of the controlled system is evaluated for different levels and changes in direction of set point changes on the linear and also on the original non-linear model equations. The performance of the proposed controller is compared with that of a controller with Zieglar-Nichols (Z-N) settings based on a nominal operating point. The closed loop system becomes unstable at other operating points for the Z-N method whereas the present controller gives good response for wide operating points.     

溶解氧浓度的直接自适应动态神经网络控制方法

针对污水处理过程溶解氧浓度的控制问题,提出一种直接自适应动态神经网络控制方法(direct adaptive dynamic neural network control,DADNNC).构建的控制系统主要包括神经网络控制器和补偿控制器.神经网络控制器由自组织模糊神经网络实现系统状态与控制量之间的映射;提出一种基于规则无用率的结构修剪算法,并给出结构调整后网络收敛的理论证明.同时,为保证系统稳定,设计补偿控制器减小网络逼近误差,参数调整由Layapunov理论给出.国际基准仿真平台上的实验表明,与固定结构神经网络控制器、PID和模型预测控制等已有控制方法相比,DADNNC方法具有更高的控制精度和更强的适应能力.     

Multi‐Variable Direct Self‐Organizing Fuzzy Neural Network Control for Wastewater Treatment Process

A multi‐variable direct self‐organizing fuzzy neural network control (M‐DSNNC) method is proposed for the multi‐variable control of the wastewater treatment process (WWTP). In this paper, the proposed control system is an essential multi‐variable control method for the WWTP. No exact plant model is required, which avoids the difficulty of establishing the mathematics model of WWTP. The M‐DSNNC system is comprised of a fuzzy neural network controller and a compensation controller. The fuzzy neural network is used for approximating the ideal control law under a general nonlinear system. Moreover, the neural network is designed in a self‐organizing mode to adapt the uncertainty environment. Simulation results, based on the international benchmark simulation model No.1 (BSM1), demonstrate that the control accuracy is improved under the proposed M‐DSNNC method, and the controller has a much stronger decoupling ability.     

ANN-based estimator for distillation using Levenberg–Marquardt approach

In modern chemical industries the purity of the distillate is the main objective and time to estimate the distillate composition is also the constraint. In the present paper, the Levenberg–Marquardt (LM) approach is proposed for predictive inferential control of distillation process. The developed estimator using LM approach predicts the composition of distillate using column pressure, reboiler duty, and reflux flow along with the temperature profile of the distillation column as inputs.In complex chemical industries where the output depends on many parameters, Steepest Descent Back Propagation (SDBP) algorithm does not work properly for estimating the composition of distillate, which results in saturated outputs and differs from the desired results. To overcome such type of situation, LM approach is used in developed estimator. The estimated results are compared with the simulation results and it is observed that the results obtained from LM approach are significantly improved than the results obtained from SDBP algorithm. To enhance the accuracy of the estimated results, the pressure, reflux flow and heat input with temperature profile of the column are used as input to train the neural network.     

倒立摆系统的高精度控制器设计与实现

对线性二次调节器LQR算法中Q和R矩阵的参数与控制系统反馈矩阵Ⅸ之间的关系进行了实验研究,并将所获得的最佳反馈矩阵作为所设计的神经网络控制器的权值初始值。该神经网络控制器是带有局部递归神经网络并具有PID结构的控制器,因而设计简单,尤其适合用于多变量非线性时变系统。通过对一级和二级直线倒立摆系统的具体控制器的设计以及实验,将LQR控制器与神经网络控制器分别在无干扰和有干扰情况下的控制效果进行了对比分析,设计并实现了具有控制精度以及鲁棒性比最优线性二次调节器更高的一级和二级直线倒立摆系统。     

变结构神经网络自适应鲁棒控制

针对一类不确定非线性系统, 提出一种变结构神经网络自适应鲁棒控制(Variable structure neural network adaptive robust control, VSNNARC)方法. 其中变结构神经网络用于在线辨识系统未知非线性函数, 该网络利用节点激活与催眠技术进行动态调节, 减小网络规模与计算量; 自适应鲁棒控制用于网络权值学习与系统建模误差及外部扰动补偿. 采用Lyapunov稳定性分析法, 给出网络权值自适应律的形式以及鲁棒控制项的设计方法. 该方法不仅能保证系统的稳定性, 也能保证系统具有很好的瞬态性能. 将该方法应用到转台伺服系统的位置跟踪控制中, 实际运行结果表明, 该方法使系统具有很强的鲁棒性及良好的跟踪效果.     

基于可变学习率BP算法的空调控制系统的研究

通过分析控制器参数学习率和控制器性能之间的关系,设计一种基于可变学习速率反向传播算法VLRBP和模糊神经元网络的变频空调控制系统.该系统不仅可以通过反传误差信号训练控制器参数,而且可以根据网络的当前状态朝最优化方向调整控制器参数的学习率.实验结果表明,该控制系统不仅比传统的空调PID控制器和模糊控制器具有更好的控制性能,而且相比基于标准BP算法和动量BP算法的模糊神经网络控制系统,也具有更快的收敛速度和更好的控制精确度.     

Integral variable structure control of nonlinear system using a CMAC neural network learning approach.

This work presents a novel integral variable structure control (IVSC) that combines a cerebellar model articulation controller (CMAC) neural network and a soft supervisor controller for use in designing single-input single-output (SISO) nonlinear system. Based on the Lyapunov theorem, the soft supervisor controller is designed to guarantee the global stability of the system. The CMAC neural network is used to perform the equivalent control on IVSC, using a real-time learning algorithm. The proposed IVSC control scheme alleviates the dependency on system parameters and eliminates the chattering of the control signal through an efficient learning scheme. The CMAC-based IVSC (CIVSC) scheme is proven to be globally stable inasmuch all signals involved are bounded and the tracking error converges to zero. A numerical simulation demonstrates the effectiveness and robustness of the proposed controller.     

一般严格反馈型非线性系统的自适应控制

研究一般严格反馈型非线性系统的控制问题．假设系统的对象模型、状态均未知,只有输出是可测的．应用自适应模糊神经推断系统辨识对象模型,状态观测器设计为Luenberger型,控制器由反步控制、变结构控制和3层神经网络直接控制综合而成．理论分析和仿真研究都说明此方案能够有效地控制只有输出可测的一般严格反馈型非线性系统．     

基于自适应递归模糊神经网络的污水处理控制

针对污水处理过程中具有的非线性、大时变等特征,提出了一种基于自适应递归模糊神经网络(recurrent fuzzy neural network,RFNN)的污水处理控制方法.该方法利用自适应RFNN识别器建立污水处理过程的非线性动态模型,建立的模型可以为RFNN控制器提供污水处理过程中的状态变量信息,保证了控制器根据系统响应调整操作变量的精确性;并且RFNN辨识器及RFNN控制器基于自适应学习率进行学习,确保了递归模糊神经网络的收敛精度和速度,并通过构造李雅普诺夫函数证明了此算法的收敛性;最后,基于基准仿真模型(benchmark simulation model 1,BSM1)平台进行仿真实验.结果表明,与PID、模型预测控制及前馈神经网络相比,该方法对污水处理中溶解氧浓度和硝态氮浓度的跟踪控制精度具有明显的提升.     

一种模糊神经网络控制器参数的混沌优化设计

通过模糊控制与神经网络相串联的方式构成模糊神经网络系统，然后提出一种基于模拟退火策略的混沌优化算法，将该算法引入模糊神经网络参数域中进行优化，实现混沌粗搜索与细搜索相结合优化目的，体现出具有更强的模糊神经网络参数全局最优解的搜索能力。采用该控制器对一个非线性对象进行控制。仿真实验表明，该方法能有效地实现模糊神经网络控制器参数优化，控制具有无振荡、超调小、调节时间短等优点，算法结构简单，容易实现。     

Nonlinear internal model control using neural networks: an application for machining processes

This paper presents a novel approach to the control of the cutting force on the basis of the internal model control (IMC) principle. The main goal is to control a single output variable, the cutting force, by changing a single input variable, the feedrate. A neural model is used as an internal model to determine the control inputs (feedrate) necessary to keep the cutting force constant. Three approaches, the fuzzy logic controller (FLC), the direct inverse controller (DIC) and the IMC, based on artificial neural networks (IMC-NN), are simulated and their performances are assessed in terms of several performance measurements. The results demonstrate that IMC-NN strategy provides a better disturbance rejection than FLC for the cases analysed.     

T-S模糊神经网络应用于变参数系统的控制研究

针对基于模型的传统控制策略在线性时变系统中的应用受到系统的时变性和不确定性限制,通常难以获得理想的控制性能这一问题,提出了线性时变系统的一种变参数系统模型。该模型具有有界性和不确定性特点,利用模糊神经网络具有的自学习能力强、模型依赖性小以及鲁棒性强的优点,提出一种基于遗传算法的T-S模糊神经网络控制器对其进行控制研究,并通过仿真实验证明了该模糊神经网络控制器对变参数系统控制的可行性与有效性,为线性时变系统的控制问题提供了一种新思路。     

Supersonic, variable-throat, blow-down wind tunnel control using genetic algorithms, neural networks, and gain scheduled PID

In this paper the design and application of a control algorithm is discussed to control the test conditions within plenum chamber and the test section of a supersonic blow-down, variable throat wind tunnel at the University of Alabama. The artificially intelligent controller algorithm was designed using a gain scheduled Proportional-Integral-Differential (PID) control approach. The PID controller was augmented to work with time variant properties of the control problem by determining a functional form of the integral term of the controller from the governing equations of the tunnel. The controller was optimized using genetic algorithms (GA) on a neural network (NN) model of the tunnel and was compared to a conventional PID controller using the same NN model. The process was repeated for different throat settings to find the control gains for each setting. The controller algorithm was next applied to the actual wind tunnel at different throat settings and the results were compared. The optimized controller is proven to work very well at every throat setting.     

不确定系统的神经网络积分变结构控制

针对一类不确定系统,在系统上界值未知的情况下,结合神经网络能任意的逼近不确定系统的优点,设计出一种神经网络积分变结构控制器,利用RBF(Radial Basis Function)神经网络来实时估计系统的不确定性界限,从而降低了一般变结构控制研究的条件.在变结构控制器中又引入饱和函数取代符号函数,进一步减弱“抖振”现象...     

基于T-S模型的模糊神经网络PID控制

针对在非线性、时变不确定系统中,常规PID控制器难以获得满意效果的问题,仿照传统PID控制器结构,设计了一种基于T-S模型的模糊神经网络PID控制器。该控制器基于T-S模糊模型,将PID结构融入模糊控制中,充分发挥了模糊系统非线性、可解释性的特点;然后又利用神经网络的学习算法,实现了对模糊控制器的参数调整,使控制器具有了适应时变、不确定系统的自学习和自组织能力。针对非线性、时变系统,将此控制器与传统PID控制器对比进行了仿真研究,并应用于啤酒发酵领域,其结果表明,该控制器取得了令人满意的效果。     

Variable structure controller with neural compensator

This paper proposes a new variable structure controller combined with a multilayer neural network using an error back-propagation learning algorithm. The neural network acts as a compensator for a conventional variable structure controller in order to improve the control performance when the initial assumptions of the uncertainty bounds of the system parameters are violated. Also, the proposed controller can reduce the steady-state error of a conventional variable structure controller using the boundary layer technique. Computer simulation results show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.     

永磁同步电机的自适应反演滑模变结构控制

针对永磁同步电机提出一种基于反演的PMSM自适应滑模控制方案.设计基于反演的滑模变结构位置控制器,通过RBF神经网络实现系统参数变化和外部负载扰动等引起的不确定上界值的在线辨识,减小滑模控制器的控制量,并引入饱和函数来减弱系统的"抖动"现象.理论分析和仿真结果对比表明,基于RBF神经网络的自适应反演滑模控制对参数变化和外部负载扰动具有很好的鲁棒性,永磁同步电动机获得了很好的跟踪效果.     

高速公路可变速度标志神经网络控制

针对高速公路可变速度控制是一个非线性时变系统,难于用数学模型准确建模这一特点,提出了神经网络控制方法.阐述了神经网络学习算法,设计了高速公路可变速度标志神经网络控制器,并对控制器进行了仿真研究.仿真结果表明,该方法切实可行,具有实用价值.     

基于模糊神经网络的冗余度变几何桁架机器人自适应控制

本文提出了一种基于模糊神经网络（FNN）的机器人位置自适应控制方法．利用模糊 神经网络模型来辨识冗余度变几何桁架机器人的逆动力学模型,用常规反馈控制器完成外部 干扰的补偿和闭环控制．并以四重四面体变几何桁架机器人为例进行仿真计算,表明该控制 方法具有良好的轨迹跟踪精度和抗干扰能力．