一类化工过程多变量系统的自适应非线性预测控制

针对化工过程的一类多变量非线性系统,提出了一种自适应非线性预测控制(ANMPC)算法。在采用递归最小二乘法进行预测模型参数在线辨识的基础上,将系统的静态非线性关系用一个反向传播(BP)神经网络稳态模型来表示,通过稳态模型求得的动态增益来进一步校正预测模型的参数。详述了ANMPC控制器设计步骤,通过在一个多变量pH中和过程中的仿真验证了本算法的可行性和有效性。     

新型模糊预测PID控制在pH中和过程中的应用

利用自适应学习算法及模糊推理方法在线修正pH过程所得的局部线性化模型，同时基于广义预测控制(GPC)的思想和离散PID算法的相互关系，提出了一种以预测控制这类先进控制方法为思想，以经典PID控制为实现的新型控制器。其中，控制器的参数通过GPC与PID的相互关系递推计算得到。仿真研究表明本文所提出方法的可行性和有效性。     

模糊自适应PID控制在水泥生产中的应用

水泥生产是一个复杂的化学过程,具有大惯性,滞后等特点。由于AQC炉对水泥生产的影响及水泥生产的滞后性,传统的PID控制难以有效的实现对水泥二次风和三次风温度的控制。针对这一情况,提出一种模糊自适应的控制系统,显著提高了系统的跟踪和抗干扰性。     

自适应模糊PID控制在陶瓷压机布料系统液压调速中的应用

受外界环境的影响,陶瓷压机布料系统采用电液比例阀控制液压调速具有非线性、时变性、磁滞性等特点,常规PID控制和常规模糊控制对布料系统的小车速度控制效果不够理想。本文设计了一种自适应模糊PID控制器,通过仿真结果表明,这种自适应模糊PID控制器既具有模糊控制灵活、响应快、适应性强等优点,又具有PID控制精度高的特点,它改善了布料系统速度的控制效果。     

模糊神经网络控制在二阶液位控制中的应用

以化工生产中典型的二阶液位控制为背景,针对大多数控制系统中存在的强耦合性,提出模糊神经网络控制(FNNC)与传统PID结合的FNNC-PID复合控制方法.对模糊系统构成、网络层的输入输出关系、网络学习算法等方面作了详细描述.该方法已成功地应用于二阶非线性液位控制系统.     

浅谈自适应控制在纯碱生产过程中的应用

1问题的提出我厂是以氨碱法生产纯碱、年产100万吨的过程装置,原设计过程控制主要是以电动Ⅲ型仪表构成。近年来,随着微电子技术和信息技术的高速发展,我厂自2000年以来相继对热电、重碱、石灰、煅烧、压缩5个主要工序的仪表实施了DCS化,使生产装置基本实现了生产过程最佳化,系统控制智能化,操作性能柔性化。为实现生产过程最优化,系统控制智能化,我们在主要工序中影响产品质量的关键参数控制上引入了新型控制系统———自适应控制。例如:锅炉水位控制、热负荷控制、碳化塔反应温度及转化率控制、重灰水合机水碱比值控制等等。由于这些生…     

化工过程强非线性系统的变模型自适应预测控制

提出一种变模型自适应预测控制算法 ;基于非线性状态空间模型 ,通过每步在当前工作点 (非平衡点 )线性化获得线性化子模型 ,以此进行状态反馈预测控制 ,线性化子模型随工作点变化 ,且不限于平衡点。通过pH值控制的对比仿真实验 ,证明其对强非线性过程的控制效果优于传统的多模型预测控制。最后分析讨论了该控制算法存在的几个重要问题 ,并指出与之相关的未来研究方向     

基于模糊滑模迭代的注射机液压系统控制

注射机液压控制系统是一个非线性、大时滞性、时变性的复杂过程,传统PID控制精度较低、灵敏性较差,为此设计了一种模糊滑模迭代的注射机液压控制器。其中迭代学习控制算法用于实现目标值的跟踪;滑模控制器采用液压系统的偏差及其变化率对滑模输出进行模糊化和解模糊化处理;通过实时控制调节迭代学习控制器的增量得到理想的控制效果。仿真结果表明,与传统PID控制方法相比,模糊滑模迭代控制算法超调量小、调节速度快,能够满足注射机液压系统控制精度与鲁棒性的要求。     

基于控制规则调整的自适应模糊控制器

工业对象大多是具有非线性、大时滞、高阶次的复杂对象 ,常规的控制方法往往难以适应这些对象的变化。文中提出了一种自适应模糊控制器 ,它能在控制过程中不断调整和修改控制规则 ,以适应对象和环境的变化。对控制方法与常规PID控制、Smith预估控制、基本模糊控制进行了仿真比较。仿真曲线表明 ,自适应模糊控制器的控制性能明显好于其它 3种控制方法     

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。     

Temperature control during microwave heating process by sliding mode neural network

In microwave heating applications, Lambert’s law is a common way to calculate power distribution. However, because of the complex application environment, Lambert’s law is not precise for the unknown power distribution on material surfaces. During the microwave heating process, the system process parameters can only be partly known by experience. Therefore, for such situations, to make the entire heating process safe, a sliding mode combined with a neural network algorithm is proposed. The algorithm is designed to calculate the suitable input power at each control period to make the material temperature follow the reference trajectory, which is determined by experience. The simulation and actual application results demonstrate that the proposed algorithm can commendably control the heating process. The difference between the reference trajectory and the material sampling temperature may exceed 1°C initially. However, as time progresses, the difference gradually decreases. Nonetheless, due to the low conduction coefficient, a single microwave heating process may take a long time. Therefore, many actual applications combine convective heat transfer with microwave. This article also discusses the control method of multiple inputs including microwave power and convective heat transfer with unknown model parameters. Another neural network is constructed to identify the unknown parameters. The algorithm is designed to obtain the suitable input power and input convective heat transfer at each control period. The simulation results show that the control algorithm can work well under multiple inputs. The material temperature on both the surfaces and the interior can follow the reference trajectory with a satisfactory difference, and suitable inputs can be obtained with few fluctuations during the learning process.     

基于分离变量的模糊控制规则的简化设计

针对多输入单输出的倒立摆模糊控制系统的规则爆炸问题,尝试了一种基于分离变量的模糊控制规则设计方法,大大减少了控制规则数,减少了控制器的运算时间。利用该方法设计一级倒立摆模糊控制系统,仿真结果表明,简化控制规则后仍能取得较好的效果。     

An adaptive neuro-fuzzy sliding mode controller for MIMO systems with disturbance

This paper introduces the mathematical model of ammonia and urea reactors and suggested three methods for designing a special purpose controller. The first proposed method is Adaptive model predictive controller, the second is Adaptive Neural Network Model Predictive Control, and the third is Adaptive neuro-fuzzy sliding mode controller. These methods are applied to a multivariable nonlinear system as an ammonia–urea reactor system. The main target of these controllers is to achieve stabilization of the outlet concentration of ammonia and urea, a stable reaction rate, an increase in the conversion of carbon monoxide(CO) into carbon dioxide(CO_2) to reduce the pollution effect, and an increase in the ammonia and urea productions, keeping the NH_3/CO_2 ratio equal to 3 to reduce the unreacted CO_2 and NH_3, and the two reactors' temperature in the suitable operating ranges due to the change in reactor parameters or external disturbance. Simulation results of the three controllers are compared. Comparative analysis proves the effectiveness of the suggested Adaptive neurofuzzy sliding mode controller than the two other controllers according to external disturbance and the change of parameters. Moreover, the suggested methods when compared with other controllers in the literature show great success in overcoming the external disturbance and the change of parameters.     

一种新型模糊液位控制及其应用

在连续生产过程中,工艺上通过设立缓冲容器来解决前后工序之间的物料量突变,以确保生产的平稳性,针对这类容器的液位控制,常规PID定值控制已满足不了上述要求。为解决这个问题,以长期现场实践为基础,根据操作人员的思维特性,提出了一种基于模糊控制和常规控制相结合的液位区域控制方法。该方法以液位的上下限和变化量作为输入模糊量化依据,改变了传统输入模糊量化方法,同时对于模糊输出量到控制输出采用新的转换方法,使模糊输出离散量转化为希望调节的液位量。为提高控制精确度,在实际控制输出与希望调节偏差较大时,增加了输出校正环节以减少输出误差。这种方法允许液位在给定的高低限范围内波动,以保证送出的物料缓慢平稳变化;只有液位超出高低限时,或液位在正常范围内,并且液位变化量超过给定阈值时才进行调节,以确保维持下游工序的负荷平稳。实际应用结果验证了这种方法的有效性。     

Spinline tension control in melt spinning by discrete adaptive sliding‐mode controllers

The spinline tension plays a critical role in the development of fiber structures and the quality of as‐spun yarns in melt spinning. Implementing a controller to adjust the spinline velocity is helpful to maintain the spinline tension at a target level with small fluctuation, enabling as‐spun yarns to possess the desired tenacity and uniform qualities. The spinline tension system is difficult to model and the stochastic disturbance always exists. The discrete adaptive sliding‐mode controller can robustly and adaptively deal with the system with the unknown model and stochastic disturbance, such as the spinline tension system. The algorithm estimates the parameters of the controller in the sense of minimizing the deviation from the sliding surface, thus reducing the variation of the tension response about the desired level. The sliding surface is defined by an asymptotically stable polynomial, and seven stable polynomials are chosen in experiments. The experiments are carried out by using a laboratory type of the melt spinning setup to produce polypropylene as‐spun yarns. Compared with the results without control, the proposed controller can not only maintain the mean of the tension response close to the target level but also reduce the standard deviation to the value, which is generally acceptable to the manufacturer. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 3816–3821, 2006     

基于自适应模糊神经网络的非线性系统模型预测控制

针对非线性动态系统的控制问题,提出了一种基于自适应模糊神经网络(adaptive fuzzy neural network,AFNN)的模型预测控制(model predictive control, MPC)方法。首先,在离线建模阶段,AFNN采用规则自分裂技术产生初始模糊规则,采用改进的自适应LM学习算法优化网络参数;然后,在实时控制过程,AFNN根据系统输出和预测输出之间的误差调整网络参数,从而为MPC提供一个精确的预测模型;进一步,AFNN-MPC利用带有自适应学习率的梯度下降寻优算法求解优化问题,在线获取非线性控制量,并将其作用到动态系统实施控制。此外,给出了AFNN-MPC的收敛性和稳定性证明,以保证其在实际工程中的成功应用。最后,利用数值仿真和双CSTR过程进行实验验证。结果表明,AFNN-MPC能够取得优越的控制性能。     

Adaptive control design for a nonlinear parabolic PDE: Application to water coning

Water coning is usually responsible for the production of undesirable water from oil wells. This phenomenon may cause a decrease in oil production rate, increase in water cut production, and costs, which subsequently leads to early shutdown of the well. Although the boundary control of the production rate was suggested for managing the problem, due to the uncertainty associated with the physical nature of petroleum reservoirs, it failed to be implemented in practice. To overcome this issue, the paper employs the adaptive control approach for the distributed parameter system, which is modelled using a nonlinear partial differential equation (PDE). For this purpose, an adaptive control law and an update law for estimating the uncertain parameter are developed using the direct Lyapunov method. Next, the global stability of the closed‐loop system with the abovementioned laws is proven. Finally, the effectiveness and performance of the proposed idea is demonstrated by numerical simulations. The results show that the thickness of an oil column tends to zero as time tends to infinity for the whole spatial domain. In other words, as time elapses, the whole oil column will be depleted before the cone breakthrough. The numerical simulation demonstrates that though water cone breakthrough is inevitable in the conventional way of production, the adaptive control approach successfully controls the cone growth up, even with no knowledge of reservoir permeability. The results of this study can be applied to any type of reservoir subjected to water coning.

     

Adaptive gain sliding mode observer for state of charge estimation based on combined battery equivalent circuit model

An adaptive gain sliding mode observer (AGSMO) for battery state of charge (SOC) estimation based on a combined battery equivalent circuit model (CBECM) is presented. The error convergence of the AGSMO for the SOC estimation is proved by Lyapunov stability theory. Comparing with conventional sliding mode observers for the SOC estimation, the AGSMO can minimise chattering levels and improve the accuracy by adaptively adjusting switching gains to compensate modelling errors. To design the AGSMO for the SOC estimation, the state equations of the CBECM are derived to capture dynamics of a battery. A lithium-polymer battery (LiPB) is used to conduct experiments for extracting parameters of the CBECM and verifying the effectiveness of the proposed AGSMO for the SOC estimation.