基于神经网络自适应PID控制的船舶操纵研究

本文针对船舶操纵这种非线性、时变参数控制对象,提出了一种采用神经网络自适应ＰＩＤ控制方案。该控制结构有两上子神经网络组成,一个三层ＢＰ神经网络用于对被控对象进行在线辨只,另一个两层线性网络构成具有ＰＩＤ结构的控制器。文中给出了神经网络在线训练学习方法,并进行船舶操纵控制仿真研究。     

基于改进Elman网络的最优励磁控制器

本文在线性最优励磁控制的基础上,将线性最优控制理论与改进Elman神经网络有机结合,设计了一种新型的基于改进Elman网络(Modified Elman Neural Network)的最优励磁控制器.由于Elman网络具有特殊结构层,形成有"记忆"能力的神经网络的特点,并在原有结构上将高斯径向基函数引入Elman网络隐含层,因此可以更好地映射系统的非线性和动态特性.对单机无穷大系统进行仿真研究的结果表明,所设计的控制方式能精确地反映系统动态变化过程并提供良好的电压调节性能.     

Development and implementation of an adaptive fuzzy-neural-networkcontroller for brushless drives

This paper introduces a brushless drive system with an adaptive fuzzy-neural-network controller. First, a neural network-based architecture is described for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. Then, the fuzzy rules and input-output of the system are tuned by the supervised gradient decent learning algorithm. Using an experimental setup, the performance of the proposed controller is evaluated under various operating conditions. Test results are presented and discussed. The controller is shown to be robust, adaptive, and capable of learning. The effectiveness of the fuzzy-neural-network controller is demonstrated by its encouraging study results, when compared with those of a proportional-integral controller     

综合智能控制策略在真空退火炉中的应用

真空退火炉退火温度的精确控制是一个典型的大时滞、大惯性、存在强交叉耦合的、时变的复杂控制问题,采用常规的PID控制器很难满足对退火温度的精确控制。通过对退火炉工作机理及退火温度控制系统的分析,提出了一种综合智能控制方案;首先利用人工神经网络对真空退火炉进行建模,之后利用遗传算法对神经网络的权值和阈值进行优化;最后,将此模型作为一个预估模型投入到控制系统中去。在控制器的设计过程中,不再使用单一的控制策略,而是在生产过程中,根据退火工件温度的输出误差大小来随时改变控制器的结构,从而达到对真空退火炉退火状态的最佳控制。     

Self tuning control of wind turbine using neural network identifier

The nonlinear characteristics of the wind turbines and electric generators necessitate that grid connected wind energy conversion systems (WECS) use nonlinear controls. The present paper proposes an adaptive self tuning control strategy with neural network Morlet wavelet for WECS control. The proposed strategy is based on single layer feedforward neural networks with hidden nodes of adaptive Morlet wavelet functions controller and an infinite impulse response recurrent structure. The neuro controller is based on a certain model structure to approximately identify the system dynamics of WECS, and control its response. The proposed controller is studied in three situations: without noise, with measurement input noise and with disturbance output noise. Finally, the results of the performance of the new controller were compared with a multilayer perceptron network proving a more precise modeling and control of WECS.     

前馈神经网络的模糊 PID 算法及其在电力系统负荷预测中的应用

提出了多层前馈神经网络的模糊ＰＩＤ学习算法（ＦＰＢＰ）。这种算法是把多层前馈神经网络的学习过程当作一个动态控制系统来处理,确定出动态控制系统达到稳态时的ＰＩＤ控制器参数,然后再基于模糊控制的思想,对确定出的ＰＩＤ控制器参数进行模糊调整。文中给出了这种算法在电力系统负荷预测中的实际应用,并与标准ＢＰ算法作了比较。结果表明,该算法提高了网络的学习速度和预测的精度。     

A self-organizing knn fuzzy controller and its neural network structure

This paper introduces a fuzzy controller that can be designed without specific information on the membership functions and the fuzzy rules. We show how the membership values of crisp inputs can be determined by K-nearest-neighbour (KNN) distance measures applied to the centres of the input clusters. Based on this new type of membership values, we introduce a KNN defuzzification method that allows the direct estimation of the crisp output of the given input data. the proposed computational model requires a clustering (self-organizing) process. We employ a simple clustering method that can adaptively allocate new clusters as more data become available to the controller. We prove that the resulting controller can uniformly approximate any real and continuous function to any desirable accuracy on a compact set. For hardware implementations we develop a neural network structure of the proposed fuzzy controller and compare it with other types of neural networks. It is shown that the three-layer sigmoid neural network and the Gaussian radial basis function (GRBF) network are special cases of this structure. A learning algorithm for the new structure is provided. the performance of the proposed controller is considered through three application studies: a controller design for truck backer-upper control, the prediction of the S&P 500 index, and the prediction of the Mackey-Glass time series.     

Neural‐hybrid control of systems with sandwiched dead‐zones

The control of systems that have sandwiched nonsmooth nonlinearities, such as a dead‐zone sandwiched between two dynamic blocks, is addressed. An adaptive inverse control scheme using a hybrid controller structure and a neural network based inverse compensator, is proposed for such systems with unknown sandwiched dead‐zone. This neural‐hybrid controller consists of an inner loop discrete‐time feedback structure incorporated with an adaptive inverse using a neural network for the unknown dead‐zone, and an outer‐loop continuous‐time feedback control law for achieving desired output tracking. The dead‐zone compensator consists of two neural networks, one used as an estimator of the sandwiched dead‐zone function and the other for the compensation itself. The compensator neural network has neurons that can approximate jump functions such as a dead‐zone inverse. The weights of the two neural networks are tuned using a modified gradient algorithm. Simulation results are given to illustrate the performance of the proposed neural‐hybrid controller. Copyright © 2002 John Wiley & Sons, Ltd.     

A novel neural network controller and its efficient DSP implementation for vector-controlled induction motor drives

An artificial neural network controller is experimentally implemented on the Texas Instruments TMS320C30 digital signal processor (DSP). The controller emulates indirect field-oriented control for an induction motor, generating direct and quadrature current command signals in the stationary frame. In this way, the neural network performs the critical functions of slip estimation and matrix rotation internally. There are five input signals to the neural network controller, namely, a shaft speed signal, the synchronous frame present and delayed values of the quadrature axis stator current, as well as two neural network output signals fed back after a delay of one sample period. The proposed three-layer neural network controller contains only 17 neurons in an attempt to minimize computational requirements of the digital signal processor. This allows DSP resources to be used for other control purposes and system functions. For experimental investigation, a sampling period of 1 ms is employed. Operating at 33.3 MHz (16.7 MIPS), the digital signal processor is able to perform all neural network calculations in a total time of only 280 /spl mu/s or only 4700 machine instructions. Torque pulsations are initially observed, but are reduced by iterative re-training of the neural network using experimental data. The resulting motor speed step response (for several forward and reverse step commands) quickly tracks the expected response, with negligible error under steady-state conditions.     

基于模糊神经网络的发电机励磁控制器的研究

在分析发电机励磁控制系统的基础上将模糊控制理论和神经网络技术有机结合,提出了基于模糊神经网络(FuzzyNeural Network,FNN)的智能型励磁控制策略,构造了具有双FNN模型结构的励磁控制器.所构建的FNN励磁控制系统不仅保留了模糊控制的功能,而且具有体现励磁控制非线性特征的能力,能更精确地反映系统的动态变化过程,具有更强的鲁棒性和适应性.     

永磁同步电机的神经网络逆动态解耦控制

永磁同步电机是一个非线性、强耦合系统，应用神经网络逆系统方法对永磁同步电机进行动态解耦控制研究。通过对永磁同步电机的数学模型可逆性分析，得出解析逆系统，由解析逆系统与永磁同步电机原系统复合成两个伪线性子系统来构造神经网络逆系统，使永磁同步电机动态解耦成二阶线性转速子系统和一阶线性磁链子系统，并采用鲁棒伺服控制器对伪线性子系统进行线性闭环控制器的设计，实现永磁同步电机转速和定子磁链的动态解耦，仿真表明系统具有良好的动静态性能。     

基于改进学习算法的模糊神经网络控制系统

针对一类复杂非线性系统，提出一种模糊神经网络(FNN)控制方案。系统中采用模糊神经网络控制器和神经网络辨识控制器相结合的结构，介绍一种改进的学习算法，对学习公式进行推导，利用改进的遗传算法来优化已经获得的隶属度函数，并结合误差补偿以提高控制精度。同时将混沌机制引入常规BP算法，利用混沌机制固有的全局游动，逃出权值优化过程中存在的局部极小点，解决了网络训练易陷入局部极小点的问题。用该方法对某非线性动态系统进行辨识和控制，仿真结果表明控制精度和实时性优于常规模糊控制器。     

木材干燥过程温湿度的T-S型模糊神经网络控制器设计

木材干燥过程是一个强耦合、大滞后的非线性动力系统,很难准确建立被控对象的数学模型。为了准确控制木材干燥过程的温度和湿度,提高木材干燥质量,将智能控制引入木材干燥控制系统是必然的发展趋势。结合模糊控制和神经网络优点,设计了一种木材干燥窑内温湿度的Takagi-Sugeno(T-S)型模糊神经网络控制器。该控制器无需对象的精确数学模型,适应性强,利用模糊算法解除木材干燥窑内温度和湿度间的强耦合关系,采用神经网络的自学习和自适应能力来实现整个非线性过程的模糊逻辑推理。仿真和实验结果表明,T-S型模糊神经网络控制器有效解决了木材干燥过程的温湿度控制,控制器响应速度快、超调小、鲁棒性强、控制精确度高,可以满足木材干燥控制系统要求。     

Hardware implementation of an adaptive network-based fuzzy controller for DC-DC converters

A novel control topology of adaptive network-based fuzzy inference system (ANFIS) for control of the dc-dc converter is developed and presented in this paper. It essentially consists of combining fuzzy inference system and neural networks and implementing within the framework of adaptive networks. The architecture of the ANFIS along with the learning rule, which is used to give an adaptive and learning structure to a fuzzy controller, is also described. The emphasis here is on fuzzy-neural-network control philosophies in designing an intelligent controller for the dc-dc converter that allows the benefits of neural network structure to be realized without sacrificing the intuitive nature of the fuzzy system. Specifically, it permits this type of setup to simultaneously share the benefits of both fuzzy control and neural network capabilities. An experimental test bed is designed and built. The components are tested individually and in various combinations of hardware and software segments. Two categories of tests, namely, load regulation and line regulation, are carried out to evaluate the performance of the proposed control system. Experimental results demonstrate the advantages and flexibilities of ANFIS for the dc-dc converter.     

SRM积分滑模变结构与神经网络补偿控制

针对开关磁阻电机非线性动态特性不易控制的缺点,提出了一种积分型滑模变结构与神经网络补偿相结合的复合控制策略.应用一个具有积分型式的滑模切换面的变结构控制器,使用积分补偿技巧降低系统的振动与稳态误差.为减小滑模面的抖动,引入神经网络补偿控制环节.建立滑模变结构控制的数学模型,并给出神经网络补偿滑模面抖动的控制律表达式.利...     

A variable structure adaptive neural network static VAR controller

A variable structure adaptive neural network power system static VAR stabilizer is developed. The static VAR compensator (SVC) controlled by the above proposed controller is used for voltage regulation and enhancing power system stability. The artificial neural network (ANN) is trained off-line using the variable structure control system Benchmark data at different operating conditions and external disturbances. Moreover, the trained ANN parameters (weights and biases) are tuned and updated on-line using the synchronous machine speed deviation state as the ANN output error to increasingly improve the power system performance. A sample digital simulation result of the power system speed deviation state responses when reference voltage, speed deviation state and input power disturbances take place are obtained. The digital simulation results prove the effectiveness and robustness of the present adaptive neural network in terms of a high performance power system.     

神经网络在线学习模糊自适应控制及其应用

基于反馈误差学习法,提出了一种神经网络在线学习模糊自适应控制结构。利用模糊推理机产生的分目标学习误差训练神经网络,避免了控制器的输出产生振荡或进入饱和状态。工程应用表明,该方法将模糊推理引入神经网络学习中,可有效地提高系统的控制品质。     

综合智能控制策略在电弧炉控制中的应用

介绍综合智能控制策略在电弧炉炼钢中对提高性能的作用。简述高阻抗电弧炉系统的特点和基于WindowsNT环境下高阻抗电弧炉系统的组成 ,阐述了瞬时值电量采集到有效值的变换算法。针对电弧炉炼钢过程的高度非线性、时变性和不确定性 ,采用了基于遗传退火算法的神经网络控制策略 ,设计了神经网络预估模型和神经网络控制器 ,并优化了功率设定点 ,实现了高阻抗电弧炉炼钢过程的最优化控制。在石家庄钢铁厂的实际应用表明 ,该系统能较好地适应负荷变化与外部干扰 ,其控制性能优于普通电弧炉系统。     

基于模糊神经网络算法的交流伺服系统DSP控制

将神经网络与模糊逻辑控制结合起来,设计模糊神经网络控制器应用于交流伺服系统中的转速调节器,克服交流调速系统中参数漂移、非线性和耦合等因素的影响.针对模糊神经网络控制器运算量大、收敛慢的特点,硬件采用数字信号处理器(DSP)作为控制器运算单元,并在DSP上实现模糊神经控制算法,提高了系统实时性.实验结果表明,采用该控制器的调速系统具有较快的响应速度、较高的稳态精度和较强的鲁棒性.     

永磁同步电动机的神经网络模糊控制器设计

提出了基于神经网络的自学习模糊控制器的设计方法。在永磁同步电动机(PMSM)矢量控制系统中,使用该控制器作为速度调节器对永磁同步电动机进行精确的速度控制。仿真结果表明,该神经网络模糊控制方法是可行的,具有良好的动态及静态特性。