A supervisory fuzzy neural network control system for trackingperiodic inputs

A supervisory fuzzy neural network (FNN) control system is designed to track periodic reference inputs in this study. The control system is composed of a permanent magnet (PM) synchronous servo motor drive with a supervisory FNN position controller. The supervisory FNN controller comprises a supervisory controller, which is designed to stabilize the system states around a defined bound region and an FNN sliding-mode controller, which combines the advantages of the sliding-mode control with robust characteristics and the FNN with online learning ability. The theoretical and stability analyses of the supervisory FNN controller are discussed in detail. Simulation and experimental results show that the proposed control system is robust with regard to plant parameter variations and external load disturbance. Moreover, the advantages of the proposed control system are indicated in comparison with the sliding-mode control system     

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.     

Diagonal recurrent neural networks for dynamic systems control

A new neural paradigm called diagonal recurrent neural network (DRNN) is presented. The architecture of DRNN is a modified model of the fully connected recurrent neural network with one hidden layer, and the hidden layer comprises self-recurrent neurons. Two DRNN's are utilized in a control system, one as an identifier called diagonal recurrent neuroidentifier (DRNI) and the other as a controller called diagonal recurrent neurocontroller (DRNC). A controlled plant is identified by the DRNI, which then provides the sensitivity information of the plant to the DRNC. A generalized dynamic backpropagation algorithm (DBP) is developed and used to train both DRNC and DRNI. Due to the recurrence, the DRNN can capture the dynamic behavior of a system. To guarantee convergence and for faster learning, an approach that uses adaptive learning rates is developed by introducing a Lyapunov function. Convergence theorems for the adaptive backpropagation algorithms are developed for both DRNI and DRNC. The proposed DRNN paradigm is applied to numerical problems and the simulation results are included.     

Identification of a golf swing robot using soft computing approach

Golf swing robots have been recently developed in an attempt to simulate the ultra high-speed swing motions of golfers. Accurate identification of a golf swing robot is an important and challenging research topic, which has been regarded as a fundamental basis in the motion analysis and control of the robots. But there have been few studies conducted on the golf swing robot identification, and comparative analyses using different kinds of soft computing methodologies have not been found in the literature. This paper investigates the identification of a golf swing robot based on four kinds of soft computing methods, including feedforward neural networks (FFNN), dynamic recurrent neural networks (DRNN), fuzzy neural networks (FNN) and dynamic recurrent fuzzy neural networks (DRFNN). The performance comparison is evaluated based on three sets of swing trajectory data with different boundary conditions. The sensitivity of the results to the changes in system structure and learning rate is also investigated. The results suggest that both FNN and DRFNN can be used as a soft computing method to identify a golf robot more accurately than FFNN and DRNN, which can be used in the motion control of the robot.     

Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction

Delay time, which may degrade the control performance, is frequently encountered in various control processes. The fuzzy neural network sliding mode controller (FNNSMC), which incorporates the fuzzy neural network (FNN) with the sliding mode controller (SMC), is developed to control the long delay system with unknown model based on fuzzy prediction algorithm in the paper. According to the characteristics of the long delay systems, we simulate the manual operating process and predict the delayed error and its derivative based on the information of the input and output variables of the process, and then feedback these prediction values to the FNN and train the FNN with the regulation function by the idea of sliding mode control until the better control results are obtained. The FNNSMC has more robustness due to the abilities of the learning and reasoning and can eliminate the drawbacks of the general SMC, namely the chattering in the control signal and the needing knowledge of the bounds of the disturbances and uncertainties. Simulation examples demonstrate the advantages of the proposed control scheme.     

Neural Network On‐Line Modeling and Controlling Method for Multi‐Variable Control of Wastewater Treatment Processes

A neural network on‐line modeling and controlling method (NNOMC) is proposed in this paper for multi‐variable control of wastewater treatment processes (WWTPs). According to the approximating character of the feedforward neural network (FNN), a modeling FNN is proposed to simulate and decouple WWTPs. Then, an FNN controller for multi‐variable control of WWTPs is designed. Moreover, the stability of the NNOMC method is proven in a general inference via the limitation of the learning rate of the FNN. Finally, this proposed NNOMC method is used in the international benchmark of WWTPs. The results show the NNOMC method owns both better approximating and controlling performance.     

Robust fuzzy neural network control for linear ceramic motor drivevia backstepping design technique

This study presents a robust fuzzy-neural-network (RFNN) control system for a linear ceramic motor (LCM) that is driven by an unipolar switching full-bridge voltage source inverter using LC resonant technique. The structure and operating principle of the LCM are introduced. Since the dynamic characteristics and motor parameters of the LCM are nonlinear and time varying, a RFNN control system is designed based on the hypothetical dynamic model to achieve high-precision position control via the backstepping design technique. In the RFNN control system a fuzzy neural network (FNN) controller is used to learn an ideal feedback linearization control law, and a robust controller is designed to compensate the shortcoming of the FNN controller. All adaptive learning algorithms in the RFNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed RFNN control system is verified by experimental results in the presence of uncertainties. In addition, the advantages of the proposed control system are indicated in comparison with the traditional integral-proportional (IP) position control system     

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).     

Self-organizing adaptive fuzzy neural control for the synchronization of uncertain chaotic systems with random-varying parameters

This paper proposes a self-organizing adaptive fuzzy neural control (SAFNC) for the synchronization of uncertain chaotic systems with random-varying parameters. The proposed SAFNC system is composed of a computation controller and a robust controller. The computation controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principle controller. The SOFNN identifier is used to online estimate the compound uncertainties with the structure and parameter learning phases of fuzzy neural network (FNN), simultaneously. The structure-learning phase consists of the growing of membership functions, the splitting of fuzzy rules and the pruning of fuzzy rules, and thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. The robust controller is used to attenuate the effects of the approximation error so that the synchronization of chaotic systems is achieved.All the parameter learning algorithms are derived based on the Lyapunov stability theorem to ensure network convergence as well as stable synchronization performance. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.     

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。     

基于改进遗传算法的FNNPID控制器在温度控制系统中的应用

根据温度控制系统的特点,为提高其控制质量,设计了一种模糊神经PID控制器。该控制器在温度偏差大时采用模糊神经控制,偏差小时采用模糊神经PID控制,由模糊控制开关保证两种控制方法的平滑过渡,用改进的遗传算法优化网络参数。仿真实验表明采用该控制方法系统响应速度更快、超调更小、精度更高、适应性更强。     

基于模糊神经网络的5连杆双足机器人混杂控制

针对双足机器人单脚支撑期控制问题, 提出了一种新型的模糊神经网络混杂控制方法. 该种方法结合了模糊神经网络、H_∞ 控制及逆系统方法的优点. 应用了一种新的多层模糊CMAC神经网络对系统进行逼近, 一方面将模糊神经网络的构造误差看作系统的干扰, 利用H_∞ 控制对干扰进行抑制. 另一方面利用模糊神经网络对系统模型进行逼近, 为逆系统的构建和H_∞ 控制率的设计提供了有效的系统信息. 并证明了在采用本文提出的模糊神经网络和自适应算法后可以抑制 L₂ 增益.     

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.     

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.     

Fuzzy neural network quadratic stabilization output feedback control for biped robots via H/sub /spl infin// approach

A novel fuzzy neural network (FNN) quadratic stabilization output feedback control scheme is proposed for the trajectory tracking problems of biped robots with an FNN nonlinear observer. First, a robust quadratic stabilization FNN nonlinear observer is presented to estimate the joint velocities of a biped robot, in which an H/sub /spl infin// approach and variable structure control (VSC) are embedded to attenuate the effect of external disturbances and parametric uncertainties. After the construction of the FNN nonlinear observer, a quadratic stabilization FNN controller is developed with a robust hybrid control scheme. As the employment of a quadratic stability approach, not only does it afford the possibility of trading off the design between FNN, H/sub /spl infin// optimal control, and VSC, but conservative estimation of the FNN reconstruction error bound is also avoided by considering the system matrix uncertainty separately. It is shown that all signals in the closed-loop control system are bounded.     

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.     

Direct adaptive fuzzy-neural control with state observer andsupervisory controller for unknown nonlinear dynamical systems

In this paper, an observer-based direct adaptive fuzzy-neural network (FNN) controller with supervisory mode for a certain class of high order unknown nonlinear dynamical system is presented. The direct adaptive control (DAC) has the advantage of less design effort by not using FNN to model the plant. By using an observer-based output feedback control law and adaptive law, the free parameters of the adaptive FNN controller can be tuned on-line based on the Lyapunov synthesis approach. 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 de-activated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Simulation results also show that our initial control effort is much less than those in previous works, while preserving the tracking performance     

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．     

Hybrid Adaptive Type‐2 Fuzzy Tracking Control of Chaotic Oscillation Damping of Power Systems

In this paper a novel hybrid direct/indirect adaptive fuzzy neural network (FNN) moving sliding mode tracking controller for chaotic oscillation damping of power systems is developed. The proposed approach is established by providing a tradeoff between the indirect and direct FNN controllers. It is equipped with a novel moving sliding surface (MSS) to enhance the robustness of the controller against the present system uncertainties and unknown disturbances. The major contribution of the paper arises from the new simple tuning idea of the sliding surface slope and intercept of the MSS. This study is novel because the approach adopted tunes the sliding surface slope and intercept of MSS using two simple rules simultaneously. One advantage of the proposed approach is that the restriction of knowing the bounds of uncertainties is also removed due to the adaptive mechanism. Moreover, the stability of the control system is also presented. The proposed controller structure is successfully employed to damp the complicated chaotic oscillations of an interconnected power system, when such oscillations can be made by load perturbation of a power system working on its stability edges. Comparative simulation results are presented, which confirm that the proposed hybrid adaptive type‐2 fuzzy tracking controller shows superior tracking performance.