电子束快速成型温度自适应模糊PID控制系统

将自适应模糊PID控制技术应用于电子束快速成型温度控制系统中,通过温度采集装置实时得到被加工件的温度信号,与设定值进行对比从而得到温度偏差及偏差的变化率,将温度偏差及偏差的变化率作为模糊控制器的2个输入变量,以自适应模糊PID控制器输出控制量调节电子束流大小,实现电子束快速成型温度的闭环控制.仿真结果表明,该控制系统具有调整时间短、稳态误差小、超调量小,即在电子束快速成型过程中,采用自适应模糊PID控制器比采用传统的PID控制器或模糊控制器可以得到更好的动态响应性能和控制精度.     

烟草薄片线烘箱温度自动控制系统的改进

针对烟草薄片线烘箱自动温度控制系统,采用传统PID控制升温慢,滞后较大,温度控制难以满足生产工艺需要的情况,通过改进,将原来的3块远红外板参与控制改为9块全部受控;在控制上采用模糊自适应PID控制,根据偏差和偏差变化的需要实时调整PID参数;该模糊PID控制器既具有常规PID控制器高精度的优点,又具有模糊控制器快速及适应性强的特点,在实践中取得了较好的效果。     

回转窑煅烧带温度控制器的设计与仿真

针对传统的回转窑煅烧带温度PID控制系统存在温度稳定性差、无法在线调整PID参数等问题,文章提出了一种采用模糊自整定PID参数控制方式设计回转窑煅烧带温度控制器的方案,介绍了该控制器的结构、设计步骤及回转窑煅烧过程系统的建模等,并采用Matlab中的Simulink模糊工具箱对模糊自整定PID温度控制器进行了仿真。仿真结果表明,该控制方法无超调量、调节时间短,能够实现参数的在线自调整。实际应用也证明了该控制方法的优越性。     

模糊PID控制算法改进及在温控系统中的应用

研究了母管制蒸汽锅炉控制系统大延迟、大滞后的工作特性。分析了一般模糊．PID控制器的控制特点,在此基础上给出了一种改进算法。通过在线调整参数。实现模糊．自调整比例常数PID控制。该算法比例常数随着偏差大小而变化,增强了控制性能,有效地解决了在小偏差范围内,一般的模糊．PID控制器无法实现的静态无偏差问题,提高了蒸汽温度控制系统的控制精度。系统采用集散控制系统（DCS）实现系统控制。在沈阳某电厂动力车间锅炉改造工程中的应用表明,锅炉蒸汽温度系统的控制品质得到了相当大的提高。     

基于动态矩阵模糊PID算法的空调房间温度控制

根据中央空调系统中房间温度控制系统的时滞、惰性以及非线性等特点,分析了自整定模糊PID控制和动态矩阵预测控制方法,提出了将两种方法相结合的观点,设计出动态矩阵模糊PID控制器,并建立房间温度控制系统的数学模型,对其进行仿真研究;结果显示该控制器较模糊PID控制器的调节时间快约2 000s、超调量小约0.5、抗干扰能力更强,控制效果更明显。     

时滞系统的模糊自适应PID控制研究

将模糊控制和常规PID控制相结合,设计了一种模糊自适应PID控制器,该控制器根据偏差和偏差变化率的要求实时调整PID参数。通过仿真表明：该控制器既具有常规PID控制器高精度的优点,又具有模糊控制器快速、适应性强的特点,并可以迅速消除系统误差,保证了系统具有良好的动、静态特性。     

基于模糊PID大型电力机组温度过热控制

随着高精密技术的发展,高精度、超高精度的温控系统越来越多被人们运用于大型电力机组中。传统的温度控制系统对各异的真实系统适配性较差,不能保证系统性能,适应性较差。针对这些问题,提出了一种模糊自适应PID大型电力机组温度过热控制算法,即专家-模糊PID控制器方法,根据温度偏差选择采用专家控制还是模糊PID控制,克服适应性差的问题。用专家控制避免设定值震荡,并通过模糊PID温度控制较快达到设定初始目标温度值,并稳定在该值上。最后通过仿真实验证明这种模糊PID超精度温度控制系统对于大型电力机组的温度过热控制问题具有控制精度高、响应速度快、温度波动小等优点。     

复合FUZZY-PID焊缝跟踪控制算法

本文设计了一种用于水下焊接机器人焊缝跟踪的复合FUZZY-PID控制器,采用模糊控制和PID并联的控制模式,在大的误差范围内采用FUZZY-PID控制,进行快速响应调整,在小的误差范围内采用传统线性PID控制进行小偏差的调整.两种控制模式采用基于阈值的切换方法,当焊缝偏差小于2mm时,采用PID控制,当偏差大于2mm时,采用模糊PID控制.通过试验可以看出,这种复合模糊PID控制器动态响应快,超调量小,稳态精度高,能够满足焊缝跟踪的要求.     

一种复合控制器在过热汽温中的应用研究

在电厂中,必须严格控制过热器出口蒸汽温度,蒸汽温度过高或过低,都将给安全生产带来不利影响。针对过热汽温被控对象的特点,充分利用模糊控制的动态特性好和PID调节能消除静态偏差的特性,设计了一种带自调整因子的模糊控制器,研究了Fuzzy-PID复合串级控制在电厂过热汽温控制系统中的应用,该复合控制器的切换采用一种简单的基于偏差量的模糊切换方法。仿真结果表明,Fuzzy-PID复合串级控制系统具有更好控制品质。     

一种改进的三维模糊控制设计方法

针对传统三维模糊控制器模糊规则多,在实际应用中不易实现,输入量之间相互耦合的问题,提出一种PID与模糊混合(M)型控制器.该控制策略是把三个输入量分别为偏差、偏差变化、偏差的偏差变化的一维模糊控制器加权融合实现混合(M)型控制器,解决了多变量模糊控制中的规则数量几何爆炸问题,具有更大的可控范围.针对传统模糊控制不能消除系统静差的缺陷,吸收增量式PID控制递推累加的形式,对偏差进行积分,混合(M)型控制器实现了对系统静差的消除.此外介绍了M型控制器的参数整定方法,并对其稳定性进行了分析.仿真表明,M型控制器控制效果比二维模糊控制和PID控制都好.     

压电自适应桁架结构智能振动控制

介绍了采用模糊神经网络模型进行振动主动控制的压电自适应桁架结构设计、应用及实验结果. 设计了一种具有自适应结构技术的压电主动构件结构, 并提出了具有5层结构能够自调整隶属函数的模糊神经网络控制模型. 为了验证控制模型的有效性, 搭建了配置压电主动构件的双跨桁架结构试验平台, 通过检测误差信号, 由模糊神经网络控制模型确定主动构件的驱动输出. 试验结果证实了模糊神经网络控制模型在振动抑制方面的有效性.     

Synchronization of uncertain chaotic systems based on adaptive type-2 fuzzy sliding mode control

A novel direct adaptive interval type-2 fuzzy neural network (FNN) controller in which linguistic fuzzy control rules can be directly incorporated into the controller is developed to synchronize chaotic systems with training data corrupted by noise or rule uncertainties involving external disturbances, in this paper. By incorporating direct adaptive interval type-2 FNN control scheme and sliding mode approach, two non-identical chaotic systems can be synchronized based on Lyapunov stability criterion. Moreover, the chattering phenomena of the control efforts can be reduced and the external disturbance on the synchronization error can be attenuated. The stability of the proposed overall adaptive control scheme will be guaranteed in the sense that all the states and signals are uniformly bounded. From the simulation example, to synchronize two non-identical Chua’s chaotic circuits, it has been shown that type-2 FNN controllers have the potential to overcome the limitations of tpe-1 FNN controllers when training data is corrupted by high levels of uncertainty.     

Intelligent tracking control of a dual‐arm wheeled mobile manipulator with dynamic uncertainties

This paper presents an intelligent control approach that incorporates sliding mode control (SMC) and fuzzy neural network (FNN) into the implementation of back‐stepping control for a path tracking problem of a dual‐arm wheeled mobile manipulator subject to dynamic uncertainties and nonholonomic constraints. By using the back‐stepping technique, the system equations are reformulated into two levels: the kinematic level and the dynamic level. A sliding manifold is constructed by considering the disturbance free kinematic level equations only. With all the system uncertainties concentrated in the dynamic level, an FNN controller associated with a switching type of control law is employed to enforce sliding mode on the prescribed manifold. All parameter adjustment rules for the proposed controller are derived from the Lyapunov theory such that uniform ultimate boundedness for both the tracking error and the FNN weighting updates is ensured. A simulation study, which compares different control design approaches, is included to illustrate the promise of the proposed SMC–FNN method. Copyright © 2012 John Wiley & Sons, Ltd.     

A fuzzy neural network controller with adaptive learning rates for nonlinear slider-crank mechanism

A fuzzy neural network (FNN) controller with adaptive learning rates is proposed to control a nonlinear mechanism system in this study. First, the network structure and the on-line learning algorithm of the FNN is described. To guarantee the convergence of the tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the adaptive learning rates of the FNN. Next, a slider-crank mechanism, which is driven by a permanent magnet (PM) synchronous motor, is studied as an example to demonstrate the effectiveness of the proposed control technique; the FNN controller is implemented to control the slider position of the motor-slider-crank nonlinear mechanism. The robust control performance and learning ability of the proposed FNN controller with adaptive learning rates is demonstrated by simulation and experimental results.     

Robust tracking control of a unicycle-type wheeled mobile manipulator using a hybrid sliding mode fuzzy neural network

This article presents a robust tracking controller for an uncertain mobile manipulator system. A rigid robotic arm is mounted on a wheeled mobile platform whose motion is subject to nonholonomic constraints. The sliding mode control (SMC) method is associated with the fuzzy neural network (FNN) to constitute a robust control scheme to cope with three types of system uncertainties; namely, external disturbances, modelling errors, and strong couplings in between the mobile platform and the onboard arm subsystems. All parameter adjustment rules for the proposed controller are derived from the Lyapunov theory such that the tracking error dynamics and the FNN weighting updates are ensured to be stable with uniform ultimate boundedness (UUB).     

Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems

A new hybrid direct/indirect adaptive fuzzy neural network (FNN) controller with a state observer and supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The hybrid adaptive FNN controller, the free parameters of which can be tuned on-line by an observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which can be adjusted by the tradeoff between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive FNN controller and direct adaptive FNN controller. Furthermore, a supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be deactivated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Two nonlinear systems, namely, inverted pendulum system and Chua's (1989) chaotic circuit, are fully illustrated to track sinusoidal signals. The resulting hybrid direct/indirect FNN control systems show better performances, i.e., tracking error and control effort can be made smaller and it is more flexible during the design process.     

AUV深度的模糊神经网络滑模控制

本文设计了一个模糊神经网络滑模变结构控制器，通过模糊神经网络对滑模控制律 的控制增益进行在线调整，并在海浪干扰条件下，用此控制器对AUV进行深度控制．仿真结 果验证了该智能控制方法具有很好的控制性能和鲁棒牲．     

A diagonal recurrent neural network-based hybrid direct adaptiveSPSA control system

A direct adaptive simultaneous perturbation stochastic approximation (DA SPSA) control system with a diagonal recurrent neural network (DRNN) controller is proposed. The DA SPSA control system with DRNN has simpler architecture and parameter vector size that is smaller than a feedforward neural network (FNN) controller. The simulation results show that it has a faster convergence rate than FNN controller. It results in a steady-state error and is sensitive to SPSA coefficients and termination condition. For trajectory control purpose, a hybrid control system scheme with a conventional PID controller is proposed     

Evaluation of fuzzy neural network run-to-run controller using numerical simulation analysis for SISO process

During the past decade, a variety of run-to-run (R2R) control techniques have been proposed and extensively used to control various semiconductor manufacturing processes. The R2R control methodology combines response surface modeling, engineering process control, and statistical process control, with the main objective of fine-tuning the recipe so that the process output of each run can be maintained as close to the nominal target as possible. In this paper, the single-input single-output (SISO) model is addressed. To overcome the shortcomings in the traditional R2R EWMA controller, a fuzzy neural network (FNN) control strategy is proposed. When a process has large autoregressive parameters, traditional EWMA control methods cannot establish stable SISO process control. To solve this problem, an SISO process control model based on an FNN was used to build an SISO process control procedure. The analysis results from a numerical simulation indicated that when the coefficient of autocorrelation  > 0.6, the MSE ratio when using the FNN controller was 97.11% lower than when using the EWMA controller and 61.12% lower than when using an adaptive EWMA controller. This showed that the FNN control method established better SISO process control than the EWMA and adaptive EWMA control methods.     

基于CAFSA-FNN的扭矩加载系统控制研究*

针对磁粉制动器扭矩加载系统的非线性和滞后性,提出了一种基于混沌人工鱼群-模糊神经网络(CAFSA-FNN)PID控制器。该控制器采用基于Mamdani模型的模糊神经网络来整定PID控制器的控制参数,并结合混沌人工鱼群算法离线粗调和BP算法在线细调来学习和调整模糊神经网络的参数。利用Matlab进行离线仿真优化,在此基础上使用PID控制器、模糊神经网络控制器、人工鱼群-模糊神经网络控制器以及本文设计的控制器进行磁粉制动器扭矩加载实验,实验结果证明了该控制器的稳定性、快速性和有效性,能够解决滞后性问题。