Adaptive Neuro-fuzzy Control System by RBF and GRNN Neural Networks

Recently, adaptive control systems utilizing artificial intelligent techniques are being actively investigated in many applications. Neural networks with their powerful learning capability are being sought as the basis for many adaptive control systems where on-line adaptation can be implemented. Fuzzy logic, on the other hand, has proved to be rather popular in many control system applications due to providing a rule-base like structure. In this paper, an adaptive neuro-fuzzy control system is proposed in which the Radial Basis Function neural network (RBF) is implemented as a neuro-fuzzy controller (NFC) and the General Regression neural network (GRNN) as a predictor. The adaptation of the system involves the following three procedures: (1) tuning of the control actions or rules, (2) trimming of the control actions, and (3) adjustment of the controller output gain. The tuning method is a non-gradient descent method based on the predicted system response which is able to self-organize the control actions from the initial stage. The trimming scheme can help to reduce the aggressiveness of the particular control rules such that the response is stabilized to the set-points more effectively, while the controller gain adjustment scheme can be applied in the cases where the appropriate controller output gain is difficult to determine heuristically. To show the effectiveness of this methodology, its performance is compared with the well known Generalized Predictive Control (GPC) technique which is a combination of both adaptive and predictive control schemes. Comparisons are made with respect to the transient response, disturbance rejection and changes in plant dynamics. The proposed control system is also applied in controlling a single link manipulator. The results show that it exhibits robustness and good adaptation capability which can be practically implemented.     

Tuning of a neuro-fuzzy controller by genetic algorithm

Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance.     

一种基于模糊径向基函数神经网络的自学习控制器

提出了一种新型的基于模糊径向基函数 (RBF)的神经网络学习控制器 ,并应用于电液伺服系统 .由于RBF网络和模糊推理系统具有函数等价性 ,采用模糊经验值方法选取网络中心值和基函数数目 .与一般的神经网络自学习控制器不同 ,以系统动态误差作为网络输入量 ,RBF神经网络控制器学习的是整个系统的动态逆过程 ,因而控制性能明显提高 .对电液位置伺服系统的仿真和实验结果表明 ,该控制方案可以有效提高系统的控制精度和自适应能力     

Neuro-fuzzy control based on the NEFCON-model: recent developments

 Fuzzy systems are currently being used in a wide field of industrial and scientific applications. Since the design and especially the optimization process of fuzzy systems can be very time consuming, it is convenient to have algorithms which construct and optimize them automatically. One popular approach is to combine fuzzy systems with learning techniques derived from neural networks. Such approaches are usually called neuro-fuzzy systems. In this paper we present our view of neuro-fuzzy systems and an implementation in the area of control theory: the NEFCON-Model. This model is able to learn and optimize the rule base of a Mamdani like fuzzy controller online by a reinforcement learning algorithm that uses a fuzzy error measure. Therefore, we also describe some methods to determine a fuzzy error measure for a dynamic system. In addition we present some implementations of the model and an application example. The presented implementations are available free of charge for non-commercial purposes.     

Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction

Accurate remaining useful life (RUL) prediction is important in industrial systems. It prevents machines from working under failure conditions, and ensures that the industrial system works reliably and efficiently. Recently, many deep learning based methods have been proposed to predict RUL. Among these methods, recurrent neural network (RNN) based approaches show a strong capability of capturing sequential information. This allows RNN based methods to perform better than convolutional neural network (CNN) based approaches on the RUL prediction task. In this paper, we question this common paradigm and argue that existing CNN based approaches are not designed according to the classic principles of CNN, which reduces their performances. Additionally, the capacity of capturing sequential information is highly affected by the receptive field of CNN, which is neglected by existing CNN based methods. To solve these problems, we propose a series of new CNNs, which show competitive results to RNN based methods. Compared with RNN, CNN processes the input signals in parallel so that the temporal sequence is not easily determined. To alleviate this issue, a position encoding scheme is developed to enhance the sequential information encoded by a CNN. Hence, our proposed position encoding based CNN called PE-Net is further improved and even performs better than RNN based methods. Extensive experiments are conducted on the C-MAPSS dataset, where our PE-Net shows state-of-the-art performance.      

Design of Adaptive Robot Control System Using Recurrent Neural Network

The use of a new Recurrent Neural Network (RNN) for controlling a robot manipulator is presented in this paper. The RNN is a modification of Elman network. In order to solve load uncertainties, a fast-load adaptive identification is also employed in a control system. The weight parameters of the network are updated using the standard Back-Propagation (BP) learning algorithm. The proposed control system is consisted of a NN controller, fast-load adaptation and PID-Robust controller. A general feedforward neural network (FNN) and a Diagonal Recurrent Network (DRN) are utilised for comparison with the proposed RNN. A two-link planar robot manipulator is used to evaluate and compare performance of the proposed NN and the control scheme. The convergence and accuracy of the proposed control scheme is proved.     

基于ANFIS的海底采矿车行走控制

针对海底钴结壳采矿车在采矿过程中越过复杂地形时会产生偏离预定路径的问题,提出一种基于ANFIS的海底采矿车直线路径跟踪控制方法,根据训练数据,设计模糊神经网络路径控制器,从而避免专家系统知识库难以获取和精确数学模型难以建立的困难.在此基础上,建立内环采用PID速度控制和外环采用ANFIS控制的机电直线路径行走控制模型,...     

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.     

Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems

Pressure–volume–temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson–Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.     

基于模糊神经元网络的智能车辆个性自动驾驶系统的设计与实现

介绍了一种利用模糊神经元网络实现车辆自动驾驶的设计方案.其基本设计思想 是首先通过模糊逻辑描述驾驶者的驾驶行为,然后利用驾驶者实际驾驶时采集的车辆运行情 况作为训练数据,通过神经元网络的自学习功能修改和改进模糊控制所需的输入/输出信 号的隶属度函数以及模糊推理的运算关系,做到简单控制实现与复杂学习算法的有效结合, 从而实现模糊神经元控制.本方案为智能车辆实现个性化自主或辅助自动驾驶提供了一种非 常有效的机制.     

Adaptive RTRL based neurocontroller for damping subsynchronous oscillations using TCSC

Modern interconnected electrical power systems are complex and require perfect planning, design and operation. Hence the recent trends towards restructuring and deregulation of electric power supply has put great emphasis on the system operation and control. Flexible AC transmission system (FACTS) devices such as thyristor controlled series capacitor (TCSC) are capable of controlling power flow, improving transient stability and mitigating subsynchronous resonance (SSR). In this paper an adaptive neurocontroller is designed for controlling the firing angle of TCSC to damp subsynchronous oscillations. This control scheme is suitable for non-linear system control, where the exact linearised mathematical model of the system is not required. The proposed controller design is based on real time recurrent learning (RTRL) algorithm in which the neural network (NN) is trained in real time. This control scheme requires two sets of neural networks. The first set is a recurrent neural network (RNN) which is a fully connected dynamic neural network with all the system outputs fed back to the input through a delay. This neural network acts as a neuroidentifier to provide a dynamic model of the system to evaluate and update the weights connected to the neurons. The second set of neural network is the neurocontroller which is used to generate the required control signals to the thyristors in TCSC. This is a single layer neural network. Performance of the system with proposed neurocontroller is compared with two linearised controllers, a conventional controller and with a discrete linear quadratic Gaussian (DLQG) compensator which is an optimal controller. The linear controllers are designed based on a linearised model of the IEEE first benchmark system for SSR studies in which a modular high bandwidth (six-samples per cycle) linear time-invariant discrete model of TCSC is interfaced with the rest of the system. In the proposed controller, since the response time is highly dependent on the number of states of the system, it is often desirable to approximate the system by its reduced model. By using standard Hankels norm approximation technique, the system order is reduced from 27 to 11th order by retaining the dominant dynamic characteristics of the system. To validate the proposed controller, computer simulation using MATLAB is performed and the simulation studies show that this controller can provide simultaneous damping of swing mode as well as torsional mode oscillations, which is difficult with a conventional controller. Moreover the fast response of the system can be used for real-time applications. The performance of the controller is tested for different operating conditions.     

模糊神经网络技术的新近发展

本文从模糊系统与神经网络作为自适应模型无关估计器时智能特性的研究，模糊控制器的神经网络实现技术，改善神经网络学习性能的模糊控制技术，面向对象的模糊神经网络开发平台的研究等方面介绍了模糊神经网络技术的研究现状，并针对目前的模糊逻辑，神经网络，子波变换，遗传算法等的集成化技术进行了探讨，并融入了作者关于定性与定量知识有机集成的柔性核理论的基本思想。     

Adaptive recurrent neural network control using a structure adaptation algorithm

This paper proposes an adaptive recurrent neural network control (ARNNC) system with structure adaptation algorithm for the uncertain nonlinear systems. The developed ARNNC system is composed of a neural controller and a robust controller. The neural controller which uses a self-structuring recurrent neural network (SRNN) is the principal controller, and the robust controller is designed to achieve L ² tracking performance with desired attenuation level. The SRNN approximator is used to online estimate an ideal tracking controller with the online structuring and parameter learning algorithms. The structure learning possesses the ability of both adding and pruning hidden neurons, and the parameter learning adjusts the interconnection weights of neural network to achieve favorable approximation performance. And, by the L ² control design technique, the worst effect of approximation error on the tracking error can be attenuated to be less or equal to a specified level. Finally, the proposed ARNNC system with structure adaptation algorithm is applied to control two nonlinear dynamic systems. Simulation results prove that the proposed ARNNC system with structure adaptation algorithm can achieve favorable tracking performance even unknown the control system dynamics function.     

基于自适应模糊神经网络控制器的网络控制系统

网络控制系统中存在着时延、丢包、网络干扰等问题。针对网络控制系统中存在恶化系统的控制性能,甚至导致系统不稳定的因素,提出了一种基于自适应模糊神经网络控制器的网络控制系统,它能根据系统的实际输出与期望输出误差,利用自适应模糊控制和神经网络自学习的原理进行控制参数的自行调整,以符合控制系统的实际要求,同时,分析了网络延时,丢包率及网络干扰因素对系统性能的影响。利用TrueTime工具箱建立了包含自适应模糊神经网络控制器的网络控制系统的仿真模型,并将其分别与基于常规PID控制器的网络控制系统和基于模糊参数PID控制器的网络控制系统进行了比较。实验结果表明,在相同的网络环境下,基于自适应模糊神经网络控制器的网络控制系统的控制效果比基于常规的PID控制器和基于模糊参数PID控制器的要好,且具有较好的抗干扰能力和鲁棒性能。     

基于快速算法的模糊神经网络自适应控制

针对过程控制中被控对象常具有非线性、不确定性及参数时变等复杂因素,而难以建立精确的数学模型的情况,提出了一种基于快速学习算法的模糊神经网络自适应预测控制方案。该方案用神经网络作辨识器,模糊神经网络作控制器来实现非线性系统的自适应预测控制。为了克服传统的梯度下降法收敛速度慢、容易陷入局部极小值的缺点,该方案采用递推最小二乘法训练模糊神经网络。仿真结果表明,该方案可以实现模糊控制和神经网络的优势互补,对不确定非线性系统具有很好的控制效果。     

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.     

无刷直流电机的模糊神经网络控制设计

在无刷直流电机(BLDCM)的控制上,传统PID等控制方法存在或多或少的不足.在模糊PID控制的基础上提出了一种模糊神经网络PI控制器的设计方法.该方法结合了模糊逻辑与神经网络,使得模糊控制器模拟了人的控制功能,不仅对环境变化有较强的适应能力,还拥有自学习能力.相比模糊PID控制,其具有计算量小、稳定性强等特点.对BLDCM进行建模与分析;在BLDCM数学模型的基础上,分别设计模糊PID控制器和模糊神经网络PI控制器;对设计的控制器进行仿真验证并分析.实验结果表明,模糊神经网络PI控制具有跟踪性能好、超调小、响应快、脉动小等优点,其动静态特性均优于模糊PID控制.     

An adaptive neuro-fuzzy model for prediction of student’s academic performance

This paper introduces a systematic approach for the design of a fuzzy inference system based on a class of neural networks to assess the students’ academic performance. Fuzzy systems have reached a recognized success in several applications to solve diverse class of problems. Currently, there is an increasing trend to expand them with learning and adaptation capabilities through combinations with other techniques. Fuzzy systems-neural networks and fuzzy systems-genetic algorithms are the most successful applications of soft computing techniques with hybrid characteristics and learning capabilities. The developed method uses a fuzzy system augmented by neural networks to enhance some of its characteristics like flexibility, speed, and adaptability, which is called the adaptive neuro-fuzzy inference system (ANFIS). New trends in soft computing techniques, their applications, model development of fuzzy systems, integration, hybridization and adaptation are also introduced. The parameters set to facilitate the hybrid learning rules for the constitution of the Sugeno-type ANFIS architecture is then elaborated. The method can produce crisp numerical outcomes to predict the student’s academic performance (SAP). It also provides an alternative solution to deal with imprecise data. The results of the ANFIS model are as robust as those of the statistical methods, yet they encourage a more natural way to interpret the student’s outcomes.     

Neural and Neurofuzzy FELA Adaptive Robot Control Using Feedforward and Counterpropagation Networks

In this paper, the application of neural networks and neurofuzzy systems to the control of robotic manipulators is examined. Two main control structures are presented in a comparative manner. The first is a Counter Propagation Network-based Fuzzy Controller (CPN-FC) which is able to self-organize and correct on-line its rule base. The self-tuning capability of the fuzzy logic controller is attained by taking advantage of the structural equivalence between the fuzzy logic controller and a counterpropagation network. The second control structure is a more familiar neural adaptive controller based on a feedforward (MLP) network. The neural controller learns the inverse dynamics of the robot joints, and gradually eliminates the model uncertainties and disturbances. Both schemes cooperate with the computed torque control algorithm, and in that way the reduction of their complexity is achieved. The ability of adaptive fuzzy systems to compete with neural networks in difficult control problems is demonstrated. A sufficient set of numerical results is included.     

Adaptive Rule Weights in Neuro-Fuzzy Systems

Neuro-fuzzy systems have recently gained a lot of interest in research and application. They are approaches that use learning techniques derived from neural networks to learn fuzzy systems from data. A very simple ad hoc approach to apply a learning algorithm to a fuzzy system is to use adaptive rule weights. In this paper, we argue that rule weights have a negative effect on the linguistic interpretation of a fuzzy system, and thus remove one of the key advantages for applying fuzzy systems. We show how rule weights can be equivalently replaced by modifying the fuzzy sets of a fuzzy system. If this is done, the actual effects that rule weights have on a fuzzy rule base become visible. We demonstrate at a simple example the problems of using rule weights. We suggest that neuro-fuzzy learning should be better implemented by algorithms that modify the fuzzy sets directly without using rule weights.