Self-adaptive fuzzy controller with formulary rule for servo control of discharge gap in Micro EDM

In micro electrical discharge machining （micro EDM）, it is difficult for servo controlling the narrow discharge gap with the characters of non-linear and quick change. In this paper, aiming at solving the problems above, a self-adaptive fuzzy controller with formulary rule （SAFCFR） is presented based on the dual feedbacks composed by gap electric signal and discharge-ratio statistics. To ensure the properties of self-optimizing and fast stabilization, the formulary rule was designed with a tuning factor. In addition, the fast-convergence algorithms were introduced to adjust control target center and output scale factor. In this way, the normal discharge ratio can tend to the highest value during micro-EDM process. Experimental results show that the proposed algorithms are effective in improving the servo-control performance. According to the drilling-micro-EDM experiments, the machining efficiency is improved by 20% through applying SAFCFR. Moreover, SAFCFR is a prompt way to optimize parameters of discharge-gap servo control.

Instantaneous Active and Reactive Power and Current Strategies for Current harmonics cancellation in 3-ph 4wire SHAF With both PI and Fuzzy Controllers

Control strategies for extracting the three-phase reference currents for shunt active power filters are compared, evaluating their performance under different source conditions with PI and Fuzzy Controllers in MATLAB/Simulink environment When the supply voltages are balanced and sinusoidal, the two control strategies are converge to the same compensation characteristics;However, the supply voltages are distorted and/or un-balanced sinusoidal, these control strategies result in different degrees of compensation in harmonics. The compensation capabilities are not equivalent, with p - q control strategy unable to yield an adequate solution when source voltages are not ideal. Extensive simulations are carried out with PI controller and also with Fuzzy controller for both p-q and Id-Iq control strategies under different main voltages. Extensive Simulations are carried out with PI as well as fuzzy controller for both p-q and Id - Iq control strategies by considering different voltage conditions and adequate results were presented. On owing Id - Iq method with fuzzy logic controller gives away an out-standing performance under any voltage conditions (balanced, un-balanced, balanced and non-sinusoidal).

Fuzzy and PI Controller Based SHAF for Mitigation of Current Harmonics with P-Q Method Using Matlab and RTDS Hardware

The main objective of this paper is to develop PI and Fuzzy logic controllers to analyse the performance of instantaneous real active and reactive power (p-q) control strategy for extracting reference currents of shunt active filters under balanced, un-balanced and balanced non-sinusoidal conditions. When the supply voltages are balanced and sinusoidal, then all controllers converge to the same compensation characteristics. However, when the supply voltages are distorted and/or un-balanced sinusoidal, these control strategies result in different degrees of compensation in harmonics. The p-q control strategy with PI controller is unable to yield an adequate solution when source voltages are not ideal. Extensive simulations were carried out;simulations were performed with balance, unbalanced and non sinusoidal conditions. Simulation results validate the dynamic behaviour of Fuzzy logic controller over PI controller. The 3-ph 4-wire SHAF system is also implemented on a Real Time Digital Simulator (RTDS Hardware) to further verify its effectiveness. The detailed simulation and RTDS Hardware results are included.

DC–DC Converter with Pi Controller for BLDC Motor Fuzzy Drive System

The Brushless DC Motor drive systems are used widely with renewable energy resources.The power converter controlling technique increases the performance by novel techniques and algorithms.Conventional approaches are mostly focused on buck converter,Fuzzy logic control with various switching activity.In this proposed research work,the QPSO(Quantum Particle Swarm Optimization algorithm)is used on the switching state of converter from the generation unit of solar module.Through the duty cycle pulse from optimization function,the MOSFET(Metal-Oxide-Semiconductor Field-Effect Transistor)of the Boost converter gets switched when BLDC(Brushless Direct Current Motor)motor drive system requires power.Voltage Source three phase inverter and Boost converter is controlled by proportional-integral(PI)controller.Based on the BLDC drive,the load utilized from the solar generating module.Experimental results analyzed every module of the proposed grid system,which are solar generation utilizes the irradiance and temperature depends on this the Photovoltaics(PV)power is generated and the QPSO with Duty cycle switching state is determined.The Boost converter module is boost stage based on generation and load is obtained.Single Ended Primary Inductor Converter(SEPIC)and Zeta converter model is compared with the proposed logic;the proposed boost converter achieves the results.Three phase inverter control,PI,and BLDC motor drive results.Thus the proposed grid model is constructed to obtain the better performance results than most recent literatures.Overall design model is done by using MATLAB/Simulink 2020a.

Comparative Analysis between Conventional PI, Fuzzy Logic and Artificial Neural Network Based Speed Controllers of Induction Motor with Considering Core Loss and Stray Load Loss

Most of the controllers of IM （induction motor） for industrial applications have been designed based on PI controller without consideration of CL （core loss） and SLL （stray load loss）. To get the precise performances of torque as well as rotor speed and flux, the above mentioned losses should be considered. Conventional PI controller has overshoot effect at the transient period of the speed response curve. On the other hand, fuzzy logic and ANN （artificial neural network） based controllers can minimize the overshoot effect at the transient period because they have the abilities to deal with the nonlinear systems. In this paper, a comparative analysis is done between PI, fuzzy logic and ANN based speed controllers to find the suitable control strategy for IM with consideration of CL and SLL. The simulation analysis is done by using Matlab/Simulink software. The simulation results show that the fuzzy logic based speed controller gives better responses than ANN and conventional PI based speed controllers in terms of rotor speed, electromagnetic torque and rotor flux of IM.

A Novel Asymmetric Three-Phase Cascaded 21 Level Inverter Fed Induction Motor Using Multicarrier PWM with PI and Fuzzy Controller

Multilevel inverters are gaining popularity in high power applications. This paper proposes a new ladder type structure of cascaded three-phase multilevel inverter with reduced number of power semiconductor devices which is used to drive the induction motor. The ultimate aim of the paper is to produce multiple output levels with minimum number of semiconductor devices. This paper uses only 11 switches along with 3 diodes and 4 asymmetrical sources to produce an output voltage of 21 levels. The modulation technique plays a major role in commutation of the switches. Here we implement the multicarrier level shifting pulse width modulation technique to produce the commutation signals for the inverter. The proposed multilevel inverter is used to drive the three-phase induction motor. The mathematical modeling of three-phase induction motor is done using Simulink. Furthermore the PI and fuzzy logic controllers are also used to produce the reference waveform of the level shifting technique which in turn produces the commutation signals of the proposed multilevel converter. The controllers are used to control the speed of the induction motor. The effectiveness of the proposed system is proved with the help of simulation. The simulation is performed in MATLAB/Simulink. From the simulation results, it shows that the proposed multilevel inverter works properly to generate the multilevel output waveform with minimum number of semiconductor devices. The PI and fuzzy logic controller performances are evaluated using the results which indicate that with the help of controllers the harmonics has been reduced and the speed control of induction motor is achieved under different loading conditions.

Performance Analysis of PI and Fuzzy Control for Modified LCC Resonant Converter Incorporating Boost Converter

In this paper, the modified LCC type of series-parallel Resonant Converter (RC) was designed and state-space modeling analysis was implemented. In this proposed converter, one leg of full bridge diode rectifier is replaced with Synchronous Rectifier (SR) switches. The proposed LCC converter is controlled using frequency modulation in the nominal state. During hold-up time, the SRswitches control is changed from in-phase to phase-shifted gate signal to obtain high DC voltage conversion ratio. Furthermore, the closed loop PI and fuzzy provide control on the output side without decreasing the switching frequency. The parameter such as conduction loss on primary and secondary side, switching loss, core and copper also reduced. Simultaneously, the efficiency is increased about 94.79 is realized by this scheme. The proposed converter with an input of 40 V is built to produce an output of 235 V with the help of ZVS boost converter [1] even under line and load disturbances. As a comparison, the closed loop fuzzy controller performance is feasible and less sensitive than PI controller.

Comparative Study of Different Decoupling Schemes for TITO Binary Distillation Column via PI Controller

This paper presents a comparative study of different decoupling control schemes for a two-input, two-output(TITO) binary distillation column via proportional-integral(PI)controller. The key idea behind this paper is designing two novel fuzzy decoupling schemes that depend on human knowledge,instead of the system mathematical model used in conventional decoupling schemes. Based on conventional and inverted decoupling schemes, fuzzy and inverted fuzzy decoupling schemes are developed. The control effect is compared using simulation results for the proposed two schemes with conventional decoupling and inverted decoupling. The proposed fuzzy decoupling schemes are easy to realize and simple to design, besides they have a good decoupling capability. Two methods are used to prove asymptotic stability of each loop and the entire closed-loop system by applying the proposed fuzzy decoupling-based PI controller.The Wood and Berry model of a binary distillation column is used to illustrate the applicability of the proposed schemes.

PI control based on fuzzy set-point weighting tracking for hydraulic crane boom system

A PI control strategy based on fuzzy set-point weighting following was proposed for the active damping control of a hydraulic crane boom system （HCBS）. Two valve-controlled PI controllers, which include a proportional feedforward controller based on fuzzy set-point weighting following and a limited semi-integrator（LSI）, are designed respectively. LSI is used to limit output signal and to prevent wind up at the low frequency of the spectrum. By using a range camera and an electronic feedback control, the tip damping on the HCBS can be adjusted artificially. A collaborative control simulation technique of HOPSAN and MATLAB/SIMULINK is applied to the controller design. Simulation results show that the proposed PI control system has less overshoot as well as faster response. The tip damping on the HCBS during operation is improved.

Adaptive Fuzzy Logic Controller for Harmonics Mitigation Using Particle Swarm Optimization

An excessive use of non-linear devices in industry results in current harmonics that degrades the power quality with an unfavorable effect on power system performance.In this research,a novel control techniquebased Hybrid-Active Power-Filter(HAPF)is implemented for reactive power compensation and harmonic current component for balanced load by improving the Power-Factor(PF)and Total–Hormonic Distortion(THD)and the performance of a system.This work proposed a soft-computing technique based on Particle Swarm-Optimization(PSO)and Adaptive Fuzzy technique to avoid the phase delays caused by conventional control methods.Moreover,the control algorithms are implemented for an instantaneous reactive and active current(Id-Iq)and power theory(Pq0)in SIMULINK.To prevent the degradation effect of disturbances on the system’s performance,PS0-PI is applied in the inner loop which generate a required dc link-voltage.Additionally,a comparative analysis of both techniques has been presented to evaluate and validate the performance under balanced load conditions.The presented result concludes that the Adaptive Fuzzy PI controller performs better due to the non-linearity and robustness of the system.Therefore,the gains taken from a tuning of the PSO based PI controller optimized with Fuzzy Logic Controller(FLC)are optimal that will detect reactive power and harmonics much faster and accurately.The proposed hybrid technique minimizes distortion by selecting appropriate switching pulses for VSI(Voltage Source Inverter),and thus the simulation has been taken in SIMULINK/MATLAB.The proposed technique gives better tracking performance and robustness for reactive power compensation and harmonics mitigation.As a result of the comparison,it can be concluded that the PSO-basedAdaptive Fuzzy PI system produces accurate results with the lower THD and a power factor closer to unity than other techniques.

Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing unit.Renewable energy sources are playing a significant role in the modern energy system with rapid development.In renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental changes.To develop energy resources,electric power generation involved huge awareness.The power and output voltages are plays important role in our work but it not considered in the existing system.For considering the power and voltage,Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier(GPIC-MDCNNC)Model is introduced for the grid-connected renewable energy system.The input information is collected from two input sources.After that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC Model.Hidden layer 1 is transferred to hidden layer 2.Gaussian activation is employed for determining the output voltage with help of the controller.At last,the output layer offers the last value in GPIC-MDCNNC Model.The designed method was confirmed using one and multiple sources by stable and unpredictable input voltages.GPIC-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art works.The control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

Fuzzy Logic Controlled Induction Motor Based Intelligent Controller

The indirect vector controlled IM （induction motor） drive involves decoupling of the stator current into torque and flux producing components. This paper proposes the implementation of a fuzzy logic control scheme applied to a two d-q current components model of an induction motor. An intelligent based on fuzzy logic controller is developed with the help of knowledge rule base for efficient control. The performance of fuzzy logic controller is compared with that of the proportional integral controller in terms of the settling time and dynamic response to sudden load changes. The harmonic pattern of the output current is evaluated for both fixed gain proportional integral controller and the fuzzy logic based controller. The performance of the IM drive has been analyzed under steady state and transient conditions. Simulation results of both the controllers are presented for comparison.

新型静止无功发生器的Fuzzy-PI控制

论述ASVG的工作原理,根据控制原理设计ASVG的模糊PI(Fuzzy-PI)控制器.采用模糊控制器获得良好的动态性能,同时引入PI控制改善模糊控制器的静态性能,增强其鲁棒性.该控制器使系统在电流跟踪误差较大时比例控制占主导,误差减小速度较快,在误差减小到一定范围内时积分控制占主导,实现稳态无差.并且该控制器具有较强的自适应控制能力,增强了对电力系统稳定性的控制,具有满意的控制精度,易于实现数字控制,比传统的PI控制具有更好的控制效果.数字仿真验证了该控制方法的有效性和正确性.

基于Fuzzy-PI双模控制的控制系统

叙述了孵化装置中有关温度与湿度的控制方法。为了提高控制精度、改善系统的动态、稳态性能,引入了基于Fuzzy-PI双模控制方法,并利用Matlab进行仿真。通过仿真和实际运行表明,采用了Fuzzy-PI双模控制以后,加快了动态响应,提高了控制精度,有效地降低了超调量,孵化率达到了规定的技术指标。对于实际中存在的非线性、大滞后、大惯性、难以建立数学模型的系统,采用该控制方法,不失为一种好的策略。

FUZZY-PI控制器在水温控制系统中的应用

针对水温控制系统存在非线性、时滞的问题,采用FUZZY—PI复合控制器对其进行控制,既利用了模糊控制适应能力强、动态特性好、抗干扰能力强的特点,又利用了PI控制可消除系统静差的特点,从而使控制系统的动态和静态品质都得到了提高,在水温控制实验中取得了较理想的效果．

P-FUZZY-PI控制器在水工模型水位控制系统中的应用

水工模型水位控制系统是一个典型的非线性、大时滞控制系统, 采用P-FUZZY-PI复合控制器对其进行控制, 既利用了模糊控制适应能力强, 动态特性好, 抗干扰能力强的特点, 又利用PID控制消除了系统静差, 使控制系统的动态和静态品质都得到极大提高, 取得了较理想的控制效果。

基于Fuzzy-PI混合控制的无刷直流电动机系统

采用了一种基于模糊规则推理的Fuzzy-PI混合控制器用于无刷直流电动机的转速控制中。在分析无刷直流电动机数学模型的基础上,构造了无刷直流电动机控制仿真模块。仿真结果表明,系统超调量小,响应速度快,具有较高的控制精度和较好的鲁棒性。实验结果显示,较之传统PI控制,采用Fuzzy-PI混合控制在无刷直流电动机实时控制中取得了较好的实验效果,具有较好的动态和静态性能。

基于fuzzy-PI双模控制的光伏发电系统最大功率点跟踪仿真研究

为了提高太阳电池阵列的工作效率和整个光伏发电系统的稳定性,在光伏发电系统中需要对光伏电池的最大功率点进行跟踪。为了消除常规模糊跟踪算法在最大功率点附近出现的振荡问题,在分析光伏电池伏安特性的基础上,提出了fuzzy-PI双模控制策略,分析了该控制算法的原理,并对控制系统做了设计。Matlab/Simulink仿真表明fuzzy-PI双模控制能够快速、准确地跟踪最大功率点,避免了最大功率点处的振荡,提高了系统稳定性和能量转换效率,从而使整个双模控制兼有了MPPT精确性与快速性。

Bang-Bang+Fuzzy-PI自适应控制器的应用研究

针对电液伺服系统模型不精确、参数时变和负载干扰大的特点,提出了一种Bang-Bang+Fuzzy-PI的自适应复合控制器,利用模糊开关在不同控制方式间切换,并采用遗传算法对Fuzzy控制器的量化因子和PI控制器的积分系数进行在线优化。比较了复合控制器与Fuzzy控制器、Fuzzy-PI控制器和PID控制器的主要动态及静态指标,仿真结果表明这种复合控制器上升时间短、稳态精度高、鲁棒性强,具有优良的控制性能。

啤酒发酵过程P-Fuzzy-PI结合Smith预估补偿控制

针对啤酒生产线原系统运行时温度稳定性差等缺点,通过改用P-Fuzzy-PI控制,并结合在线参数自适应Smith补偿方案,对温度进行实时控制。结果表明,改造后温度稳定性提高,效果优于传统PID控制和单纯模糊控制。