Model Reference Adaptive Fuzzy Control Research Articles

Model reference fuzzy cascade controller: a novel method for attaining desired mass flow in a wind tunnel

Hypersonic wind tunnels can hit hypersonic speeds and is used to test newly designed space crafts, airplanes, etc. The prototype of the vehicle is made and it is tested in the wind tunnel. While performing the experiment the pressure in the settling chamber has to be maintained constant, to get the desired Mach number speed and mass flow rate. Here, a novel non-linear control method called model reference fuzzy cascade MRFC controller is proposed and simulated for hypersonic wind tunnels. It consists of a cascade controller along with model reference and fuzzy controllers. Fuzzy controller is used instead of other neural controllers, since it is easy to implement and train them. The performance of MRAC controller is analysed with that of model reference fuzzy adaptive PI MRFA-PI controller. The simulation results shows that the MRFC controller is performing well compared to MRFA-PI for this process in terms of settling time and IAE.

Simulation of Permanent Magnet Linear Synchronous Motor’s Self-Organizing Rule and Self-Regulared Parameter Fuzzy Control

In view of the permanent magnet linear synchronous motor vector control system, this paper proposes a model reference adaptive speed controller gain fuzzy self-tuning neurons. The simulation results show that when a sudden load disturbance or parameter mutation, the gain of model reference adaptive fuzzy self-tuning neuron speed control has quick response, strong robustness, good dynamic and static characteristic, etc.

Adaptive fuzzy logic-controlled surface mount permanent magnet synchronous motor drive

Permanent magnet synchronous machines (PMSMs) are becoming popular in industries due to their fast transient response, high power density and high efficiency. This paper presents rotor field-oriented control of a surface mount permanent magnet synchronous motor drive with a fuzzy logic-based speed controller. A model reference adaptive fuzzy logic controller is utilised in the place of a conventional speed proportional plus integral (PI) controller. Simulation and experimental results are provided to show the applicability of artificial intelligence-based speed control of PMSMs. Results are presented for both conventional PI-based control and proposed adaptive fuzzy logic control and compared. The novelty of the paper lies in the experimental implementation of the adaptive fuzzy logic controller in vector-controlled PMSM drive.

Fuzzy model reference adaptive control of velocity servo system

The Implementation of fuzzy model reference adaptive control of a velocity servo system is analysed in this paper. Designing the model reference adaptive control (MRAC) and the problem of choosing adaptation gain is considered. Tuning the adaptation gain by fuzzy logic subsystem and a simple synthesis procedure of fuzzy MRAC are proposed. Several simulation runs show the advantages of fuzzy MRAC approach. Experimental validation on laboratory speed servo is realized by the acquisition system. The results confirm benefits of the proposed controller in comparison with the standard MRAC.

Robust Evolving Fuzzy Adaptive Control With Input-domain Clustering

The paper proposes a fuzzy model reference adaptive control approach with evolving antecedent part. The proposed algorithm has the possibility of controlling a plant with poorly known and/or time-varying nonlinearity which is an advantage over approaches with fixed antecedent part. It is intended for control of a large class of nonlinear plant models with the dominant dynamics of the first order. Such plants occur quite often in process industries.

Engineering Vehicle ABS Model Reference Self-Adaptive Control Strategy Analysis and Simulation

Taking the wheel slip-ratio as a control object, the basic theory of fuzzy control is analyzed, the fuzzy controller of ABS is constructed, and the model reference fuzzy adaptive control algorithm is also adopted, the simulation of ABS characteristic is performed under three different road surface conditions. Simulation results show that the simulations to model reference adaptive control are feasible and vehicle can brake at the optimal slip-ratio on different surfaces. The safety performance required by ABS is realized.

Application of Disturbance Rejecting Fuzzy Controller in Electric Arc Furnace

In this paper, the design of a disturbance rejecting fuzzy controller (DRFC) is applied to the electric arc furnace electrode regulation system. In order to improve traditional PID which is slow response and vulnerable to disturbance, we introduce two model reference fuzzy adaptive control mechanism, one of which is designed to improve response speed and reduce the sine disturbance of the length of arc, and the other of which is designed to reduce the current coupling random disturbance. Simulation results showed that by using a DRFC can acquire better dynamic characteristics in the electric arc furnace electrode regulation system, and has stronger suppression capacity.

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.

Fuzzy Model Reference Adaptive Controller Design for Multivariable Nonlinear System

This paper describes the fuzzy model reference adaptive controller design for nonlinear system based on TSK model. The stability of closed-loop system can be assured by Lyapunov function and the adaptive law is developed. The parameters of the controller can be regulated by the adaptive rules and the control scheme is applied on the automatic gauge control system and spherical motor system as two examples. The analysis of dynamic performance for ordinary PID controller and fuzzy adaptive controller is performed in detail with simulation software. Simulation results verify the systems with strong adaptive ability and can adapt to the changes and disturbances of the controlled object. This study provides a theoretical guide for the configuration design, optimization and control research of multivariable nonlinear systems.

SPI 제어기를 이용한 IPMSM 드라이브의 효율최적화 제어

This proposes an online loss minimization algorithm for series PI(SPI) based interior permanent magnet synchronous motor(IPMSM) drive to yield high efficiency and high dynamic performance over wide speed range. The loss minimization algorithm is developed based on the motor model. In order to minimize the controllable electrical losses of the motor and thereby maximize the operating efficiency, the d-axis armature current is controlled optimally according to the operating speed and load conditions. For vector control purpose, a SPI is used as a speed controller which enables the utilization of the reluctance torque to achieve high dynamic performance as well as to operate the motor over a wide speed range. Also, this paper proposes current control of model reference adaptive fuzzy controller(MFC), and estimation of speed using artificial neural network(ANN) controller.BR The proposed efficiency optimization control, SPI, MFC, ANN in this paper is applied to IPMSM drive system, the validity of this paper is proved by analyzing response characteristics in variety operating conditions.

Fuzzy Adaptive Control for a UAV

In this paper a combination of Fuzzy Logic Control (FLC) and Model Reference Adaptive Control (MRAC) will be developed to stabilize and control a fixed-wing unmanned aerial vehicle (UAV). The control must be able to direct the airplane towards different waypoints while at the same time maintaining the UAV stable. Also, the control should be transferable to similar UAV models with little to no change to the algorithm.

다중 적응 퍼지제어기를 이용한 유도전동기 드라이브의 고성능 제어

유도전동기의 벡터제어는 고성능 적용에서 광범위하게 사용되고 있다. 그러나 이러한 드라이브 성능은 파라미터 변동에 의한 동조는 여전히 한계가 있다. 다양한 속도영역에서 운전하기 위하여 종래에는 PI과 같은 제어기를 보통 사용하였다. 이러한 제어기들은 이상적인 벡터제어 조건에서 광범위한 운전에 대하여 제한된 양호한 성능을 나타낸다.<BR> 본 논문은 다중 적응 제어기를 사용하여 유도전동기 드라이브의 고성능 제어를 제시한다. 이 제어기는 FAM(Fuzzy Adaptation Mechanism)에 의해 속도제어, MFC(Model Reference Adaptive Fuzzy Control)에 의해 전류제어 그리고 ANN을 이용하여 속도추정을 수행한다. 제시한 제어 알고리즘은 FAM, MFC 및 ANN 제어기를 사용하여 유도전동기 드라이브 시스템에 적용한다. 제시한 제어기의 성능은 유도전동기의 파라미터를 사용하여 다양한 동작조건에서 해석으로 평가한다. 또한, 본 논문은 제어기의 효용성을 입증하기 위하여 해석결과를 제시한다.

A New Decentralized Fuzzy Model Reference Adaptive Controller for a Class of Large-scale Nonaffine Nonlinear Systems

This paper proposes a new method to design a decentralized fuzzy adaptive controller (FAC) for a class of large-scale nonaffine nonlinear systems in which functions of the systems and interactions are unknown. Comparing to previous paper which mainly concentrates on affine large-scale system (LSS), the proposed method is on nonaffine nonlinear LSS. The stability of the closedloop system is guaranteed based on Lyapunov theory. The proposed controller is robust against uncertainty and external disturbance. To show the effectiveness of the proposed method, an illustrative example is given. The simulation results are very promising.

Fuzzy MRAC controller design for vane-type air motor systems

Air motors are widely used in the automation industry due to special requirements, such as spark-prohibited environments, the mining industry, chemical manufacturing plants, and so on. The purpose of this paper is to analyze the behavior of a vane-type air motor and to design a model reference adaptive control (MRAC) with a fuzzy friction compensation controller. It has been noted that the rotational speed of the air motor is closely related to the compressed air’s pressure and flow rate, and due to the compressibility of air and the friction in the mechanism, the overall system is actually nonlinear with dead-zone behavior. The performance of the previous controllers implemented on an air motor system demonstrated a large overshoot, slow response and significant fluctuation errors around the setting points. It is important to eliminate the dead-zone to improve the control performance. By considering the effects of the dead-zone behavior, we have developed an MRAC with fuzzy friction compensation controller to overcome the effect of the dead-zone. The following experimental results are given to validate the proposed speed control strategy.

Application of advanced soft control strategies into the dairy industry

This paper outlines the application of soft control strategies in the thermal treatment in the dairy industry. The milk pasteurizer is the most important process for guarantee the quality of the milk. The article describes with details the application of fuzzy logic and neural network in a plate heat exchanger (PHE). Recently the application of artificial intelligent has increased sharply, especially, fuzzy logic due to its easy implementation and low mathematical level. This article discusses technical features of three advanced control technologies: Fuzzy control, Model Reference Adaptive Fuzzy Control and neural networks, their implementation in food processes including a comparative study. Existing control does not permit to introduce the experience of the operator for an adequate control of the milk pasteurization in industrial applications. Consequently, it was the aim of this very relevant to test the efficiency of soft techniques (fuzzy logic and neural networks) to control the temperature during milk pasteurization. The uses of soft techniques do not require complex mathematical models and complex tuning control methods. Since the controller can be realized by simple arithmetic operations, it can be improved continuously.

MIMO Takagi-Sugeno 퍼지 모델을 위한 모델참조 적응 퍼지 제어기의 설계

In this paper, a direct model reference adaptive fuzzy control (MRAFC) scheme is developed for the plant model whose structure is represented by the MIMO Takagi-Sugeno fuzzy model. The MRAFC scheme is proposed to provide asymptotic tracking of a reference signal lot the systems with uncertain or slowly time-varying parameters. The developed control law and adaptive law guarantee that all signals in the closed-loop system are bounded. In addition, the plant state tracks the state of the reference model asymptotically with time tot any bounded reference input signal.

Fuzzy rule: Based model reference adaptive control for PMSM drives

The objective of the model reference adaptive fuzzy control (MRAFC) is to change the rules definition in the direct fuzzy logic controller (FLC) and rule base table according to the comparison between the reference model output signal and system output. The MRAFC is composed by the fuzzy inverse model and a knowledge base modifier. Because of its improved algorithm, the MRAFC has fast learning features and good tracking characteristics even under severe variations of system parameters. The learning mechanism observes the plant outputs and adjusts the rules in a direct fuzzy controller, so that the overall system behaves like a reference model, which characterizes the desired behavior. In the proposed scheme, the error and error change measured between the motor speed and output of the reference model are applied to the MRAFC. The latter will force the system to behave like the signal reference by modifying the knowledge base of the FLC or by adding an adaptation signal to the fuzzy controller output. In this paper, the MRAFC is applied to a permanent magnet synchronous motor drive (PMSM). High performances and robustness have been achieved by using the MRAFC. This will be illustrated by simulation results and comparisons with other controllers such as PI classical and adaptive fuzzy controller based on gradient method controllers.

Takagi-Seguno Fuzzy Systems Based Nonlinear Adaptive Control

This paper develops a fuzzy model reference adaptive control of continuous-time nonlinear systems described by Takagi-Seguno (TS) fuzzy model. The tracking performance is assured using a direct TS fuzzy controller. Both full state information and observer-based control schemes are investigated. It is proved, using Lyapunov stability tools, that this adaptive scheme is asymptotically stable and the tracking and observation errors converge to zero. Simulation results illustrate the performance of the proposed approach.

Sistem Kendali Fuzzy Bertipe-2 Interval dengan Struktur Adaptif Beracuan Model

Adaptive fuzzy controller is a fuzzy controller that has ability to change its parameters when the plant’s operating conditions vary. In this paper, design and implementation of model reference adaptive fuzzy control are presented. Interval type-2 fuzzy logic controller with PD-like action is employed and its performance is studied. The fuzzy controller structure is applied to control an inverted pendulum. Simulation and experimental study shows that by using similar membership function, fuzzy rules and scaling, interval type-2 adaptive fuzzy logic controller provides better control system performance compared to type-1 fuzzy controller.

Power System Stabilizer Based on Model Reference Adaptive Fuzzy Control

A model reference adaptive fuzzy controller (MRAFC) consisting of a reference model and self-learning fuzzy logic controller and its application as a power system stabilizer (PSS) is described in this article. Off-line model identification is used to obtain a dynamic equivalent model for the synchronous machine with respect to the rest of the system. This model is used as the basis for defining a reference model for the generating unit. A fuzzy controller with self-learning capability is then used to adapt the system performance to track the reference model. The self-learning ability of the fuzzy controller is based on the steepest descent algorithm. The effectiveness of the proposed adaptive PSS based on this technique is demonstrated. Results obtained show improvement in the overall system damping characteristics using the proposed adaptive PSS.