Fuzzy Neural Network Control System Research Articles

Stability of tracking wheel mobile robot with teleoperation fuzzy neural network control system

The stability of the Tracking Wheel Mobile Robot with Teleoperation System and Path Following Method is discussed in this study. The path is to be tracked by the host computer which is the master robot. The response from the robot is captured on camera. As the slave robot approaches the target position, the camera captures the response robot’s position and as well as moving trajectory. The host computer receives all of the images, enabling mobile robot deviation recoveries. The slave robot can use teleoperation to follow the sensor based on the decisions made by the master robot. The Lyapunov function in the Fuzzy Neural Network (FNN) control structure assures the system’s stability and satisfactory performance. It supports a mobile robot’s ability to adhere to a reference trajectory without deviating from it. Finally, the outcome of the simulation demonstrates that our controller is capable of tracking different environmental conditions and maintaining stability.

Modeling of Mobility as a Service (MaaS) Collaborative Dispatching System of Railway Passenger Transport Hub Based on Neural Network Algorithm

In order to solve the modeling problem of travel as a service (MaaS) collaborative dispatching system of railway passenger transport hub based on neural network algorithm, meet people’s needs, make up for the lack of high traffic travel pressure, and improve people’s living standards, through 30 random questionnaires, it is found that 28 people think that travel convenience improves their quality of life, and 2 people think that owning a car has little effect on travel convenience. Travel has always been people’s basic living needs. In recent years, with the improvement of people’s living standards, there are more and more urban vehicles. At the same time, the increase of vehicles also directly leads to the increasing pressure of urban traffic travel, and the problems of vehicle emission pollution and traffic congestion are becoming more and more obvious. With the national low-carbon environmental protection policies, green transportation has become the theme of the times. With the continuous development of shared transportation mode and intelligent information technology, the new transportation concept of “Mobility as a Service” based on this technical mode will highly integrate the existing transportation modes and travel services. With the support of MaaS system, people’s travel modes will change greatly. Starting with the MaaS cooperative dispatching system of railway passenger transport hub, a multiparameter fuzzy neural network control system dispatching algorithm is proposed to better help the modeling of MaaS cooperative dispatching system of railway passenger transport hub.

Adaptive Type-2 Fuzzy Neural Network Inherited Terminal Sliding Mode Control for Power Quality Improvement

This article proposes an adaptive type-2 fuzzy neural network control system to enhance the performance of power quality improvement. First, the dynamic model of APF with lumped uncertainties caused by parameter perturbation of ac inductor and dc capacitor is briefly introduced. Then, an integral-type terminal sliding mode control (TSMC) is developed for the finite-time reference signal tracking. Meanwhile, in terms of the considered chattering problem, saturation function is utilized in the proposed TSMC. Moreover, an adaptive type-2 fuzzy neural network (T2RFSFNN) is derived to achieve the model-free design, by applying the recurrent feature selection algorithm in the type-2 fuzzy neural network. To enhance the capacity to represent the uncertainties, adaptive learning mechanisms for updating the parameters of T2RFSFNN are derived by the Lyapunov theorem. Furthermore, a robust compensator with an adaptive uncertainty estimation law is investigated to relax the requirement for lumped uncertainties. Finally, the control performance using the developed T2RFSFNN is evaluated by some comparative experimental results.

Evaluation of the effect of music education on improving students’ mental health based on intelligent fuzzy system

Traditional control methods and modern accurate mathematical model control methods do not perform well in the evaluation of students’ mental health. In order to improve the evaluation effect of students’ mental health, this paper takes the intelligent fuzzy system as the control center and proposes an evaluation system to evaluate the effect of music education in promoting students’ mental health based on fuzzy neural network. Moreover, according to the working characteristics of the music education system, this paper interprets the design requirements of its control system in detail, and has an in-depth understanding of the fuzzy principle, neural network principle and fuzzy god network principle. Secondly, this paper completes the design of the actual orthosis control algorithm applied to the fuzzy neural network control system and the optimization of the fuzzy neural network algorithm. Finally, this paper realizes the intelligent processing of the non-linear pressure signal output by the corresponding strain, and uses music education to evaluate the students’ mental health and manage the rehabilitation effect. From the experimental research results, it can be seen that the system constructed in this paper has a certain effect.

Self-Organizing Recurrent Wavelet Fuzzy Neural Network-Based Control System Design for MIMO Uncertain Nonlinear Systems Using TOPSIS Method

The major objective of this study is to design an effective control algorithm for dealing with multiple-input–multiple-output uncertain nonlinear systems. Novelty advantages of the proposed method include: (1) The network has the maximum initial rules; it helps to increase the responsiveness of the system; (2) the network has two dynamic thresholds: One dynamic threshold is utilized to consider whether to retain or to delete the existing rules and the other is used for generating a new rule; (3) the fuzzy neural network-based system can automatically construct the network structure and adjust the parameters of the system; (4) the network uses multiple combination techniques, such as sliding mode control, adaptive control, recurrent unit, wavelet function, fuzzy logic, neural network, and technique for order of preference by similarity to ideal solution multi-criteria decision analysis method. Based on the advantages of the above techniques, a self-organizing recurrent wavelet fuzzy neural network control system is designed comprising a main controller and a robust compensator. The gradient descent method is used to online tune the parameters for the main controller, and a Lyapunov stability theorem is applied to guarantee the system’s stability. Finally, the proposed control system is applied to a nonlinear chaotic system, an inverted double-pendulum system, and an unmanned aerial vehicle motion control to verify the effectiveness of the proposed control scheme. The simulation results show that the proposed control scheme can achieve favorable control performance.

Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation

Abstract This study investigates an adaptive fuzzy neural network control system for the multiobjective operation of wastewater treatment process (WWTP) with standard effluent quality (EQ) as well as low energy consumption (EC). The control system consists of an optimization module with the adaptive multiobjective differential evolution (AMODE) algorithm and a control module with the adaptive fuzzy neural network (AFNN). First, an AMODE algorithm, using the adaptive adjustment strategies for selecting the suitable scaling factor and crossover rate, is developed to optimize all objectives simultaneously. Then, the optimal set-points of the dissolved oxygen concentration in the fifth tank ( S O5 ) and the nitrogen nitrate concentration in the second anoxic tank ( S NO2 ) of WWTP can be obtained by the AMODE algorithm. Second, an AFNN controller, based on an adaptive second order algorithm, is employed to trace the set-points of S O5 and S NO2 for achieving the process performance. Finally, the proposed control system is applied on the Benchmark Simulation Model 1 (BSM1). The performance comparison with other algorithms indicates that the proposed control system yields better effluent qualities and lower average operation consumption.

MR-SAS variable universe fuzzy control in steering situation based on wheelbase preview

In order to make the vehicle meet the requirements of complex conditions, the dynamic model of 1/2 vehicle magneto-rheological (MR) semi-active suspension (SAS) based on wheelbase preview in steering situation was established, and the variable universe adaptive fuzzy controller based on fuzzy neural network (FNN) was designed. The universe size of the system fuzzy controller can be adjusted automatically by FNN controller according to system deviation, avoided subjectivity of universe division, improved the fuzzy control accuracy. The damping of MR damper can be adjusted by controlling the current size, so the vehicle will remain the ride comfort and safety in steering situation, and the vehicle dynamic performance can be improved. Through the comparison of simulation, the vehicle performance of variable universe FNN control system based on wheelbase preview were superior to passive system and fuzzy control system and without preview system in the same situation.

Design of Adaptive Control and Fuzzy Neural Network Control for Single-Stage Boost Inverter

This study mainly focuses on the development of two newly designed control strategies including an adaptive control scheme and a fuzzy neural network (FNN) control system for a single-stage boost inverter. First, the dynamic model of a single-stage boost inverter is analyzed and built for the later control manipulation. Then, a model-based adaptive control scheme and a model-free FNN control system with varied learning rates are designed sequentially. The effectiveness of the proposed adaptive control scheme and the proposed FNN control system is verified by experimental results of a 1-kW single-stage boost inverter prototype, and their merits are indicated in comparison with a traditional double-loop proportional-integral (PI) control framework. Experimental results show that the superior FNN control system has significant improvements of 45.2% total harmonic distortion and 37.1% normalized mean square error compared to the conventional double-loop PI control framework under nonlinear loads.

Study of Active Suspension Based on Fuzzy Neural Network Control

Based on the established full-car active suspension model, fuzzy control theory was combined with neural network control, the fuzzy neural network control system of vehicle active suspension was designed, simulation and analysis of random road input and sine wave input were carried on. The results show that, by comparison with the traditional suspension system, the peak and standard deviation of vehicle mass vertical acceleration decreased by 55.38% and 59.04%, the peak of vehicle mass vertical acceleration decreased by 49.96% when vehicle go through the sine wave at the speed of 5m/s, the ride comfort was improved obviously.

Self-organizing adaptive wavelet CMAC backstepping control system design for nonlinear chaotic systems

A novel self-organizing wavelet cerebellar model articulation controller (CMAC) is proposed. This self-organizing wavelet CMAC (SOWC) can be viewed as a generalization of a self-organizing neural network and of a conventional CMAC, and it has better generalizing, faster learning and faster recall than a self-organizing neural network and a conventional CMAC. The proposed SOWC has the advantages of structure learning and parameter learning simultaneously. The structure learning possesses the ability of on-line generation and elimination of layers to achieve optimal wavelet CMAC structure, and the parameter learning can adjust the interconnection weights of wavelet CMAC to achieve favorable approximation performance. Then a SOWC backstepping (SOWCB) control system is proposed for the nonlinear chaotic systems. This SOWCB control system is composed of a SOWC and a fuzzy compensator. The SOWC is used to mimic an ideal backstepping controller and the fuzzy compensator is designed to dispel the residual of approximation errors between the ideal backstepping controller and the SOWC. Moreover, the parameters of the SAWCB control system are on-line tuned by the derived adaptive laws in the Lyapunov sense, so that the stability of the feedback control system can be guaranteed. Finally, two application examples, a Duffing–Holmes chaotic system and a gyro chaotic system, are used to demonstrate the effectiveness of the proposed control method. The simulation results show that the proposed SAWCB control system can achieve favorable control performance and has better tracking performance than a fuzzy neural network control system and a conventional adaptive CMAC.

Application of Fuzzy Neural Network Controller for Cement Rotary Kiln Control System

This paper presents the design and application of fuzzy neural network control system for the rotary cement kiln system. Due to the dynamic characteristics and reaction process parameters are with features of large inertia, pure hysteresis, nonlinearity and strong coupling, the fuzzy neural network controller combining both the advantages of neural network and fuzzy control was applied. The fuzzy neural network is an adaptive control process whose parameters can be adjusted by learning algorithms automatically which is aimed to eliminate the shortcomings of conventional control methods. The main control system structure includes mainly the pressure control loop, the burning zone control loop and the back-end of kiln temperature control loop. Simulation results show the effectiveness of the control scheme with satisfied dynamic performances on response time and overshoot.

Application of Fuzzy Neural Network Controller for Cement Rotary Kiln Control System

This paper presents the design and application of fuzzy neural network control system for the rotary cement kiln system. Due to the dynamic characteristics and reaction process parameters are with features of large inertia, pure hysteresis, nonlinearity and strong coupling, the fuzzy neural network controller combining both the advantages of neural network and fuzzy control was applied. The fuzzy neural network is an adaptive control process whose parameters can be adjusted by learning algorithms automatically which is aimed to eliminate the shortcomings of conventional control methods. The main control system structure includes mainly the pressure control loop, the burning zone control loop and the back-end of kiln temperature control loop. Simulation results show the effectiveness of the control scheme with satisfied dynamic performances on response time and overshoot.

Modeling and Simulation of Fuzzy Neural Network Control System of Penetration in Titanium-alloy Electron Beam Welding

The control system modeling of penetration is studied in titanium-alloy electron beam welding.On the basis of analysing the characteristics of electron beam welding,the orthogonal test of three factors and five levels is designed,the test is done to obtain the values of molten width and penetration under different welding parameters,the values of molten width and penetration are used as the training sample to train the neural network,thus a BP neural network model between molten width and penetration is established,the molten width is input,the penetration is output.The model consists of a S-function hidden layer plus a linear output layer.Because the mathematical model for penetration is difficult to obtain,so the fuzzy controller is designed,its input variables are bias and change rate of bias,its output variables are the change of welding current,The controller has nine fuzzy-control rules.The BP neural network model and fuzzy controller are combined to establish fuzzy neural network control-system model of penetration of titanium-alloy electron beam welding,and unit step signal is used to carry out the simulation experiment of the model,the results show that the dynamic performance and steady-state performance of designed control system are eminent.

SynRM 드라이브의 고성능 제어를 위한 RFNN 제어기 설계

Since the fuzzy neural network(FNN) is universal approximators, the development of FNN control systems have also grown rapidly to deal with non-linearities and uncertainties. However, the major drawback of the existing FNNs is that their processor is limited to static problems due to their feedforward network structure. This paper proposes the recurrent FNN(RFNN) for high performance and robust control of SynRM. RFNN is applied to speed controller for SynRM drive and model reference adaptive fuzzy controller(MFC) that combine adaptive fuzzy learning controller(AFLC) and fuzzy logic control(FLC), is applied to current controller. Also, this paper proposes speed estimation algorithm using artificial neural network(ANN). The proposed method is analyzed and compared to conventional PI and FNN controller in various operating condition such as parameter variation, steady and transient states etc.

순환 퍼지뉴로 제어기를 이용한 IPMSM 드라이브의 고성능 속도제어

Abstract - Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. Since the fuzzy neural network(FNN) is recognized general approximate method to control non-linearities and uncertainties, the development of FNN control systems have also grown rapidly. The FNN controller is compounded of fuzzy and neural network. It has an advantage that is the robustness of fuzzy control and the ability to adapt of neural network. However, the FNN has static problem due to their feed-forward network structure. This paper proposes high performance speed control of IPMSM drive using the recurrent FNN(RFNN) which improved conventional FNN controller. The RFNN has excellent dynamic response characteristics because of it has internally feed-back structure. Also, this paper proposes speed estimation of IPMSM drive using ANN. The proposed method is analyzed and compared to conventional FNN controller in various operating condition such as parameter variation, steady and transient states etc.Key Words : IPMSM drive, Speed control, RFNN, FNN, ANN, Speed estimation*정 회 원 : 순천대 공대 전기공학과 박사과정†교신저자, 정회원 : 순천대 공대 전기공학과 정교수․공박E-mail : hwa777@sunchon.ac.kr 접수일자 : 2011년 5월 31일 최종완료 : 2011년 7월 13일

Electric Arc Furnace Based on Fuzzy Neural Network Control System and MATLAB Simulation

EAF steelmaking is a complex physical and chemical reactions，has Highly nonlinearity, time variability and strong coupling. As the electrode's contact with the charging circuit, charging the collapse of the gasification furnace components and liquid metal boiling and other factors, often result in dramatic fluctuations in arc current. At this point we need to quickly adjust the location of electrodes, so that arc current stable base on this within a certain range. This article in the fuzzy control and neural network’s foundation,will study a kind based on three-phase consciousness electric arc furnace predictive control new method, Using the fuzzy neural network control adjusting electrode, further enhancing the effectiveness of electric arc furnace’synthesis movement.

Adaptive hybrid type-2 intelligent sliding mode control for uncertain nonlinear multivariable dynamical systems

A new adaptive hybrid interval type-2 fuzzy neural network (FNN) controller incorporating sliding mode and Lyapunov synthesis approaches is proposed in this paper to handle the training data corrupted by noise or rule uncertainties for a class of uncertain nonlinear multivariable dynamic systems. The hybrid adaptive FNN controller, the free parameters of which can be tuned on-line by an output feedback control law and adaptive laws, is a combination of interval type-2 indirect and direct adaptive FNN controllers to meet the requirement of sufficient reach for the sliding mode control. A weighting factor, which can be adjusted based on the trade-off between plant knowledge and control knowledge, is included when combining the control efforts of the indirect adaptive FNN controller and the direct adaptive FNN controller. The overall adaptive control scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. The mass–spring–damper nonlinear system is fully illustrated to track sinusoidal signals. The resulting adaptive hybrid interval type-2 FNN control system shows better performance than the adaptive hybrid type-1 FNN control system; it reduces both the tracking error and the control effort and it is more flexible in the design process.

Adaptive Fuzzy Neural Network Control System in Cylindrical Grinding Process

An adaptive fuzzy neural network control system in cylindrical grinding process was proposed. In this system, the initial cylindrical grinding parameters were decided by the expert system based on fuzzy neural network. Multi-feed and setting overshoot optimization methods were also adopted during the grinding process, and a human machine cooperation system (composed of human and two fuzzy – neural networks) could revise the process parameters in real-time. The experiment of the cylindrical grinding was implemented. The results showed that this control system was valid, and could greatly improve the cylindrical grinding quality and machining efficiency.

Indirect Adaptive Self-structuring Fuzzy Neural Network Control System

To obtain a high control performance of adaptive fuzzy control systems, the stability guarantee should be made stricter to reduce the approximation error of the system. Avoiding the system becomes complex, this study aims to design a kind of adaptive fuzzy control system that realizes compatibility of concise stability guarantee and high control performance, and it proposes the adaptive fuzzy control system design method of the indirect controller approximation type designed based on the self-structuring fuzzy neural network. It is evaded that the approximation error element that was not able to be removed by adaptive law influences the entire control input directly, and influences are distributed, by composing an indirect type control system. Additionally, it aims at the improvement of the control performance by compensating this remaining approximation error by the robust control input element in the control input. Finally, the effectiveness of this method is verified by the computation simulation of the cart-pole system control.

Neuro-fuzzy speed control of traveling-wave type ultrasonic motor drive using frequency and phase modulation

This paper presents a Fuzzy Neural Network (FNN) control system for a traveling-wave ultrasonic motor (TWUSM) driven by a dual mode modulation non-resonant driving circuit. First, the motor configuration and the proposed driving circuit of a TWUSM are introduced. To drive a TWUSM effectively, a novel driving circuit, that simultaneously employs both the driving frequency and phase modulation control scheme, is proposed to provide two-phase balance voltage for a TWUSM. Since the dynamic characteristics and motor parameters of the TWUSM are highly nonlinear and time-varying, a FNN control system is therefore investigated to achieve high-precision speed control. The proposed FNN control system incorporates neuro-fuzzy control and the driving frequency and phase modulation to solve the problem of nonlinearities and variations. The proposed control system is digitally implemented by a low-cost digital signal processor based microcontroller, hence reducing the system hardware size and cost. The effectiveness of the proposed driving circuit and control system is verified with hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a conventional proportional-integral control system.