Fuzzy Basis Function Network Research Articles

FBFN-Based Pointing Error Correction Architecture in Wind-Force Environments

Beam pointing error caused by wind force significantly affects the performance of the wireless communications. We develop a fuzzy basis function network (FBFN) architecture to estimate the angle error of planar array antenna. The wind force model measured in Taiwan is related to the beam pointing error model for the validation of accuracy and processing time of the proposed FBFN correction architecture.

Adaptive Fuzzy Sliding Mode Control for a Class of Nonlinear Dynamical Systems

This paper presents a FBFN-based (Fuzzy Basis Function Networks) adaptive sliding mode control algorithm for nonlinear dynamic systems. Firstly, we designed an perfect control law according to the nominal plant. However, there always exists discrepancy between nominal and actual mode, and the FBFN was applied to approximate the uncertainty. After that, the adaptive law was designed to update the parameters of FBFN to alleviate the approximating errors. Based on the theory of Lyapunov stability, the stability of the adaptive controller was given with a sufficient condition. Simulation example was also given to illustrate the effectiveness of the method.

A self-tuning fuzzy controller for a class of multi-input multi-output nonlinear systems

This paper presents a systematic design procedure of a multivariable fuzzy controller for a general Multi-Input Multi-Output (MIMO) nonlinear system with an input–output monotonic relationship or a piecewise monotonic relationship for each input–output pair. Firstly, the system is modeled as a Fuzzy Basis Function Network (FBFN) and its Relative Gain Array (RGA) is calculated based on the obtained fuzzy model. The proposed multivariable fuzzy controller is constructed with two orthogonal fuzzy control engines. The horizontal fuzzy control engine for each system input–output pair has a hierarchical structure to update the control parameters online and compensate for unknown system variations. The perpendicular fuzzy control engine is designed based on the system RGA to eliminate the multivariable interaction effect. The resultant closed-loop fuzzy control system is proved to be passive stable as long as the augmented open-loop system is input–output passive. Two sets of simulation examples demonstrate that the proposed fuzzy control strategy can be a promising way in controlling multivariable nonlinear systems with unknown system uncertainties and time-varying parameters.

FUZZY RADIAL BASIS FUNCTION (FRBF) NETWORK BASED TOOL CONDITION MONITORING SYSTEM USING VIBRATION SIGNALS

Thriving automation in industries leads to more research on the tool condition monitoring systems for better accuracy and fast recognition/evaluation of tool wear. Research on the applicability of the new advances in the soft-computing as well as in the signal processing fields is the inevitable consequence. In this work, a new soft-computing modeling technique, fuzzy radial basis function (FRBF) network has been applied to the prediction of drill wear using the vibration signal features. This work presents the wear prediction performance comparison of this new model with three other already tried and established soft-computing models, such as back propagation neural network (BPNN), radial basis function network (RBF) and normalized radial basis function network (NRBF), for both time-domain as well as wavelet packet approaches of feature extraction. Experimental results show that FRBF model with wavelet packet approach produces the best performance of predicting flank wear.

구동기 동역학을 가지는 이동 로봇에 대한 FBFN을 이용한 강인 적응 퍼지 추종 제어

This paper proposes a robust adaptive fuzzy tracking control scheme for a nonholonomic mobile robot with external disturbances as well as parameter uncertainties in the robot kinematics, the robot dynamics, and the actuator dynamics. In modeling a mobile robot, the actuator dynamics is integrated with the robot kinematics and dynamics so that the actuator input voltages are the control inputs. The presented controller is designed based on a FBFN (Fuzzy Basis Function Network) to approximate an unknown nonlinear dynamic function with the uncertainties, and a robust adaptive input to overcome the uncertainties. When the controller is designed, the different parameters for two actuator models in the actuator dynamics are taken into account. The proposed control scheme does not require the kinematic and dynamic parameters of the robot and actuators accurately. It can also alleviate the input chattering and overcome the unknown friction force. The stability of the closed-loop control system including the kinematic control system is guaranteed by using the Lyapunov stability theory and the presented adaptive laws. The validity and robustness of the proposed control scheme are shown through a computer simulation.

A Study on an Adaptive Robust Fuzzy Controller with GAs for Path Tracking of a Wheeled Mobile Robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. Genetic algorithms are employed to optimize the fuzzy rules of FBFN. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations.

Fuzzy-Basis-Function-Network-Based $H_\infty$ Tracking Control for Robotic Manipulators Using Only Position Feedback

This paper presents an H infin fuzzy output-feedback tracking-control scheme for robotic manipulators without measuring joint velocities. The developed controller and observer are based on a fuzzy basis function network (FBFN), which is employed to approximate nonlinear functions in the dynamics of controller and observer. The FBFN-based observer that estimates joint velocities can remove the needs of full-state measurements. According to the inevitable approximation errors and external disturbances, an H infin auxiliary control signal is used to suppress the effects of the uncertainties. Moreover, all parameters of the fuzzy basis functions (FBFs) and FBF-to-output weights can be tuned online. The proposed controller requires no prior knowledge about the dynamics of the robot manipulator and no offline learning phase. Finally, comparative simulations on a three-link robot manipulator are provided to illustrate the tracking performance of the H infin FBFN-based output-feedback control approach.

Adaptive robust fuzzy control for path tracking of a wheeled mobile robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations.

Robust adaptive critic control of nonlinear systems using fuzzy basis function networks: An LMI approach

This paper proposes an adaptive critic tracking control design for a class of nonlinear systems using fuzzy basis function networks (FBFNs). The key component of the adaptive critic controller is the FBFN, which implements an associative learning network (ALN) to approximate unknown nonlinear system functions, and an adaptive critic network (ACN) to generate the internal reinforcement learning signal to tune the ALN. Another important component, the reinforcement learning signal generator, requires the solution of a linear matrix inequality (LMI), which should also be satisfied to ensure stability. Furthermore, the robust control technique can easily reject the effects of the approximation errors of the FBFN and external disturbances. Unlike traditional adaptive critic controllers that learn from trial-and-error interactions, the proposed on-line tuning algorithm for ALN and ACN is derived from Lyapunov theory, thereby significantly shortening the learning time. Simulation results of a cart–pole system demonstrate the effectiveness of the proposed FBFN-based adaptive critic controller.

Mixed [formula omitted] autopilot design of bank-to-turn missiles using fuzzy basis function networks

This paper presents a novel mixed H 2 / H ∞ autopilot design for bank-to-turn missiles. Based on sliding control theory, the proposed autopilot utilizes a fuzzy basis function network to approximate the unknown nonlinear functions of bank-to-.turn missile dynamics, enabling a mixed H 2 / H ∞ controller with weight updating laws to be applied to attenuate the effects of approximation errors and external disturbances. Moreover, the mixed H 2 / H ∞ autopilot design can minimize H 2 performance criteria under a desired H ∞ disturbance rejection constraint. Unlike general mixed H 2 / H ∞ control problems, the proposed H 2 / H ∞ autopilot with self-tuning fuzzy basis function network can avoid solving coupled Riccati equations by adequately selecting diagonal weighting matrices in the H 2 performance criterion and H ∞ constraint. Therefore, the proposed H 2 / H ∞ control approach can be implemented efficiently. Computer simulation results are presented to illustrate the effectiveness of the proposed mixed H 2 / H ∞ autopilot for BTT missiles.

Interaction analysis for MIMO nonlinear systems based on a fuzzy basis function network model

This paper describes an interaction analysis method for a general multi-input multi-output (MIMO) nonlinear system based on its fuzzy rule-based model. Most real-world multivariable systems are nonlinear, complex and time varying. The multiple system input–output variables interact and sometime even fight against each other, causing significant challenges for system control and optimization. Especially in many practical applications, the accurate quantitative system model is not available or difficult to obtain, which subsequently makes it impossible to analyze the system multivariable interaction characteristics. In this paper, the nonlinear MIMO system is first modeled by fuzzy basis function networks (FBFN) based on a set of linguistic IF–THEN rules or data. The multivariable interaction property is analyzed locally around the operating point based on the relative gain Array (RGA), which is systematically formulated using the system steady-state gain values. Two simulation examples of chemical distillation columns illustrate the system interaction degree obtained by the proposed method is in a sufficient accuracy compared with the result calculated from the system explicit mathematical model, which indicates the applicability of the proposed method as an alternative way to derive the system multivariable interaction when the system mathematical model is not available.

DEVELOPMENT, IMPLEMENTATION AND PERFORMANCE ANALYSIS OF INTELLIGENT CONTROLLERS FOR IPM SYNCHRONOUS MOTOR DRIVE SYSTEMS

This paper provides a design, implementation, and performance investigation of different intelligent controllers, particularly, fuzzy logic controller (FLC), artificial neural network (ANN), and neuro-fuzzy (NF) controllers for interior permanent magnet (IPM) synchronous motor (IPMSM) drive systems. A specific FLC is designed for the IPMSM drive based on motor dynamics and nonlinear load characteristics. A radial basis function network (RBFN) is utilized as an ANN. For NF control a fuzzy basis function network (FBFN) is developed in which the fuzzy logic concepts are embedded. In order to provide a demonstrative evidence of comparison, a closed loop vector control scheme for IPMSM incorporating intelligent controllers is successfully implemented in real-time using a digital signal processor (DSP) board DS1102. The performance of various intelligent controllers are investigated and compared both in simulation and experiment. A review of intelligent controller applications for motor drive systems is also presented in this paper. This paper intends to provide useful information for researchers and practicing engineers regarding intelligent controller applications in IPM motor drives.

Chaotic synchronization problem of finite-time convergence based on fuzzy sliding mode

The problem of finite-time synchronization for chaotic system is proposed. By applying the global sliding mode control technique and selecting the exponential fast terminal sliding mode reaching law, a global sliding mode controller is designed and applied to synchronization of a class of chaotic systems. Aimed at the chaotic system with parameter uncertainties and disturbance, fuzzy basis function network is introduced to on-line estimate the parameter bound. The design scheme eliminates the reaching phase of sliding mode control. The tracking error stays on the sliding mode surface and tends to zero in finite time. To verify the feasibility and effectivness of this control strategy, the synchronization for Duffing chaotic system is illustrated.

Adaptive extended fuzzy basis function network

The structure of the extended fuzzy basis function network (EFBFN) is firstly proposed, and the least squares (LS) method is used to design it by fixing the widths of the hidden units in EFBFN. Then, to enhance the performance of the obtained EFBFN ulteriorly, a novel evolutionary algorithm based on LS and the hybrid of evolutionary programming and particle swarm optimization (LS-EPPSO) is proposed, in which we use EPPSO to tune the parameters of the premise part in EFBFN, and the LS algorithm to decide the consequent parameters in it simultaneously. The enhanced EFBFN whose parameters are refined automatically using LS-EPPSO is thus called adaptive EFBFN. In the simulation part, the proposed method to construct AEFBFN is employed to model a three input nonlinear function and to predict a chaotic time series. Comparisons with some typical fuzzy modeling methods and artificial neural networks are presented and discussed.

Modeling of Complex Manufacturing Processes by Hierarchical Fuzzy Basis Function Networks With Application to Grinding Processes

A framework for modeling complex manufacturing processes using fuzzy neural networks is presented with a novel training algorithm. In this study, a hierarchical structure that consists of fuzzy basis function networks (FBFN) is proposed to construct comprehensive models of the complex processes. A new adaptive least-squares (ALS) algorithm, based on the least-squares method and genetic algorithm (GA), is proposed for autonomous learning and construction of FBFNs without any human intervention. Simulation studies are performed to demonstrate advantages of the proposed modeling framework with the training algorithm in modeling complex manufacturing processes. The proposed method is implemented for the surface grinding processes based on the hierarchical structure of FBFNs. Process models for surface roughness and residual stress are developed based on the available grinding model structures with a small number of experimental data to demonstrate the concept. The accuracy of developed models is validated through independent sets of grinding experiments.

Fuzzy radial basis function network: a parallel design

The fuzzy radial basis function (FRBF) network comprises an integration of the principles of a radial basis function (RBF) network and the fuzzy c-means (FCM) algorithm. A programmable paral...

An expert system based on FBFN using a GA to predict surface finish in ultra-precision turning

Ultra-precision turning is an important operation used to generate a high surface finish in precision component and its input–output relationships are highly non-linear. Surface finish of a turned surface depends on the selection of cutting parameters, such as cutting speed, feed and depth of cut. But, it is difficult to obtain the exact mathematical expression for surface finish in terms of the input variables. Realizing the fact that fuzzy logic controller (FLC) is a potential tool for dealing with impression and uncertainty, an attempt was made by the authors previously to model the grinding operation using a combined GA–fuzzy approach. In that approach, an optimal FLC was developed using a GA-based tuning. But, the main drawback of that approach lies in the fact that it does not keep track on the number of times a particular rule is getting fired during training. Thus, the resulting optimized rule base (RB) of an FLC may contain some redundant rules. To overcome this, an expert system is developed in this paper, based on the fuzzy basis function network (FBFN) to predict surface finish in ultra-precision turning. An approach for automatic design of RB and the weight factors (WFs) for different rules is developed using a GA, based on error reduction measures. Results of the proposed expert system have been compared to the experimental results available in the literature.

Development and Implementation of a Hybrid Intelligent Controller for Interior Permanent-Magnet Synchronous Motor Drives

A hybrid neuro-fuzzy scheme for online tuning of a genetic-based proportional-integral (PI) controller for an interior permanent-magnet synchronous motor (IPMSM) drive is presented in this paper. The proposed controller is developed for accurate speed control of the IPMSM drive under various system disturbances. In this work, initially different operating conditions are obtained based on motor dynamics incorporating uncertainties. At each operating condition a genetic algorithm is used to optimize the PI controller parameters in a closed-loop vector control scheme. In the optimization procedure a performance index is developed to reflect the minimum speed deviation, minimum settling time and zero steady-state error. A fuzzy basis function network (FBFN) is utilized for online tuning of the PI controller parameters to ensure optimum drive performance under different disturbances. The proposed FBFN-based PI controller provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed controller is successfully implemented in real time using a digital signal processor board DS 1102 for a laboratory 1-hp IPMSM. The effectiveness of the proposed controller is verified by simulation as well as experimental results at different dynamic operating conditions. The proposed controller is found to be robust for applications in an IPMSM drive.

The car following collision prevention controller based on the fuzzy basis function network

The car following collision prevention controller based on the fuzzy basis function network (FBFN) to non-linearly control the car speed is presented. The distance and speed relative to the car in front are measured by a radar sensor and applied to the controller. The output acceleration or deceleration rate of the controller is based on the characteristics of the vehicles. The initial input and output membership functions and five rules of FBFN are constructed by a fuzzy inference system. The design method of the reference signals, which is used to on-line update the consequent parameters according to the recursive least-squares algorithm, is proposed. The presented FBFN controller can solve the oscillation problems for final relative distance between the leading vehicle (LV) and the following vehicle (FV) and relative speed. The required processing time to achieve safe distance between the LV and the FV is about 6– 7 s that is faster than those proposed by other schemes. The FBFN car following collision prevention controller proposed in the paper can provide a safe, reasonable and comfortable drive.

Construction of fuzzy systems using least-squares method and genetic algorithm

The fuzzy basis function network which was proposed in Wang and Mendel (IEEE Trans. Neural Networks 3(5) (1992b) 807) provides a way of representing fuzzy inference systems in a simple structure similar to those of radial basis function networks. In this paper, two new algorithms based on the least-squares method and genetic algorithm are proposed for autonomous learning and construction of fuzzy basis function networks when training data are available. The proposed algorithms add a significant fuzzy basis function node at each iteration during training, based on error reduction measures. The first, a least-squares algorithm, provides a way of sequentially constructing meaningful fuzzy systems which are not possible to achieve with the orthogonal least-squares algorithm, while the second, an adaptive least-squares algorithm based on the combined least-squares and genetic algorithm, realizes hybrid structure-parameter learning without human intervention. Simulation studies are performed with numerical examples for comparison of its performance against the orthogonal least-squares algorithm, backpropagation algorithm, and conventional genetic algorithm. The adaptive least-squares algorithm is also applied to a real world problem to construct a fuzzy basis function network model for surface roughness in a grinding process using experimental data.