Fuzzy Proportional(퍼지 비례)란 무엇입니까?
Fuzzy Proportional 퍼지 비례 - Thus, in this study, a robust control method for the quadcopter system was proposed to improve system stability and disturbances rejection capability by utilizing a Self- Regulating (SR) Fuzzy Proportional-Integral-Derivative (FPID) control system, to be known as (SR-FPID) scheme. [1] Then, two kinds of adaptive equivalent consumption minimization strategy (ECMS) algorithms based on fuzzy proportional-integral (PI) controller: Fuzzy PA-ECMS and Fuzzy MPGA-ECMS (MGPA: multiple population genetic algorithm), are established to improve the control effect of ECMS with equivalent factor (EF) as the core. [2] The purpose of the paper is to conduct an investigation and obtain a solution through designing a fuzzy proportional-derivative (PD) controller to overcome the uncertainties of a robot manipulator in real-time, like disturbances and other variations. [3] In this paper, a Fuzzy Proportional-Integral-Derivativecontroller is implemented whose parameters are obtainedwith the Spider Monkey Optimization technique taking Integral of Absolute Error as an objective function. [4] First, a new control method of frequency tracking with a Fuzzy proportional-integral (PI) compound controller is proposed, which can eliminate the overshoot of resonant frequency and improve the speed of frequency tracking. [5] The structure of a fuzzy proportional-plus-integral (FPI) position controller based on the Mamdani algorithm is proposed and designed. [6] A fuzzy proportional-derivative controller is designed to provide adequate near real-time control of the inner knee temperature by controlling the cooling temperature. [7] Simulations are conducted on an isolated system to illustrate the effectiveness of the designed observer, and also to examine the advantage of the presented tube-based MPC over a conventional MPC, a fuzzy proportional-integral control, and a linear quadratic regulator control. [8] The susceptible-exposed-asymptomatic but infectious-symptomatic and infectious (severe infected population + mild infected population)-recovered-deceased (SEAI(I 1 + I 2)RD) physical model for unsupervised learning and two types of supervised learning, namely, fuzzy proportional–integral–derivative (PID) and wavelet neural-network PID learning, are used to build a predictive-control system model that enables self-learning artificial intelligence (AI)-based control. [9] The structural scheme of the positional electromechanical system with a fuzzy proportional-plus-differential position controller and the method of control adaptation to the position reference signal change are obtained. [10] As for the control system, a fuzzy proportional-derivative (PD) adaptive impedance control strategy based on the position information is proposed, which can make the device more compliant, avoid secondary injuries caused by excessive muscle tension, and protect the fingers effectively. [11] The three-circuit positional ECS with fuzzy proportional-integral position regulator has been designed. [12] In particular, the inner control loop is designed as a model reference adaptive controller (MRAC) to deal with uncertainties in the system parameters, while the outer control loop utilises a fuzzy proportional-integral controller to reduce the effect of external disturbances on the load. [13] This work presents a novel constant frequency ultrasonicamplitude control (CFUAC) method based on fuzzy proportional-integral-derivative (FPID) and amplitude direct feedback. [14] Finally, to optimize the efficiency of the boiler, a fuzzy proportional-integral controller is designed. [15] The proposed system acquired a rough robot polishing/fettling trajectory and adopted a fuzzy proportional–integral–derivative controller to regulate the trajectory to maintain the desired contact force response from a ceramic object. [16] The results of the proposed method are compared with the fuzzy proportional-integral controller. [17] The system makes use of fast Fourier transform (FFT) combined with fuzzy proportional-integral-derivative (PID) algorithm. [18] In this paper, the autonomous navigation of six-crawler machine is studied, and a visual tracking control method based on machine vision for fuzzy proportional–integral–derivative control of six-crawler machine is proposed. [19] The fuzzy proportional–integral–derivative direct torque control strategy is presented without flux linkage observation. [20] In order to design the upper integrated control algorithm, fuzzy proportional-integral-derivative (PID) is adopted to coordinate the yaw and rollover control, simultaneously. [21] ABSTRACT An effort is made to design the fuzzy proportional-derivative (PD) plus I controller for a nonlinear cruise control system in automobiles, which provides adaptive capability in set-point tracking performance. [22] In particular, the inner controller loop is implemented as a model reference adaptive controller (MRAC) to cope with uncertainties in the system parameters, while the outer control loop adopts a fuzzy proportional-integral controller (FPIC) to reduce the effect of external disturbances on the load. [23] A compound displacement tracking controller could be established, including flow-speed feed-forward with dead-band compensation and displacement feedback by fuzzy proportional–integral (PI) controller with separated integration. [24] It has the following optimal criteria: minimum power consumption, maximum efficiency, maximum quality of consumption current for a given output quality and the fuzzy proportional-integral current control method in magnetization windings electromagnetic separator. [25] The dynamic model is analyzed elementarily and a fuzzy proportional-integralderivative (PID) controller is proposed to control the diving depth and attitude of the vehicle. [26] Here an advanced Mamdani-based fuzzy proportional–integral (MFPI) controller is employed over a two-area solar-thermal interconnected system for LFC. [27] Specifically, a multi-closed-loop control strategy is proposed, where a fuzzy proportional-derivative-like plus an integral action is proposed for controlling the quadrotor system. [28] Finally, the fuzzy Proportional-Integral-Differential Control (PID) control is designed to control the catheter unit which greatly improves the precision of the control model. [29] This work is an early attempt to study the frequency fluctuations because of penetration of Wind and Solar-thermal based renewable powers into a demand response supported isolated hybrid microgrid, coordinating Micro-hydro/Biogas/Biodiesel generators using Fuzzy Proportional, derivative+Integral (PD+I) controller. [30] The fuzzy proportional-integral-derivative (PID) controller combined with the feedforward compensator is implemented to the piezo-actuated stage. [31] This paper is concerned with the H∞ fuzzy proportional-integral-derivative (PID) control problem for delayed Takagi–Sugeno (T-S) fuzzy systems in the discrete-time setting. [32] To overcome these problems, this paper puts forward a novel human attitude solving algorithm based on fuzzy proportional-integral-derivative (PID) controller and complementary filter, which integrates the data collected by accelerometer, magnetometer and gyro. [33] In this paper, the robust $H_{\infty }$ fuzzy proportional-integral-derivative (FPID) control problem is investigated for discrete-time fuzzy systems subject to the Try-Once-Discard (TOD) protocol scheduling effects. [34] In order to design an integrated control algorithm, fuzzy proportional-integral-derivative (PID) methodology is adopted by simultaneous control of the yaw and roll motions. [35]따라서 본 연구에서는 SR(Self-Regulatory) FPID(Fuzzy Proportional-Integral-Derivative) 제어 시스템을 활용하여 시스템 안정성과 외란 제거 능력을 향상시키기 위해 쿼드콥터 시스템에 대한 강력한 제어 방법을 제안했습니다. SR-FPID) 체계. [1] 그런 다음 퍼지 비례 적분(PI) 제어기를 기반으로 하는 두 종류의 적응형 등가 소비 최소화 전략(ECMS) 알고리즘인 퍼지 PA-ECMS 및 퍼지 MPGA-ECMS(MGPA: 다중 모집단 유전 알고리즘)를 설정하여 제어 효과를 향상시킵니다. 등가 계수(EF)를 핵심으로 하는 ECMS. [2] 이 논문의 목적은 외란 및 기타 변동과 같은 로봇 조작기의 불확실성을 실시간으로 극복하기 위해 퍼지 비례 미분(PD) 컨트롤러를 설계하여 조사를 수행하고 솔루션을 얻는 것입니다. [3] 이 논문에서는 퍼지 비례-적분-미분 제어기가 구현된다. 매개변수는 절대값의 적분을 취하는 Spider Monkey Optimization 기술로 얻습니다. 목적 함수로서의 오류. [4] 먼저, Fuzzy PI(Proportional-Integral) 복합 제어기를 사용하여 주파수 추적의 새로운 제어 방법을 제안합니다. 이 제어 방법은 공진 주파수의 오버슈트를 제거하고 주파수 추적 속도를 향상시킬 수 있습니다. [5] Mamdani 알고리즘을 기반으로 하는 퍼지 비례 플러스 적분(FPI) 위치 제어기의 구조를 제안하고 설계합니다. [6] 퍼지 비례 미분 제어기는 냉각 온도를 제어하여 내부 무릎 온도를 거의 실시간으로 적절하게 제어하도록 설계되었습니다. [7] 시뮬레이션은 설계된 관찰자의 효율성을 설명하고 기존 MPC, 퍼지 비례 적분 제어 및 선형 2차 레귤레이터 제어에 비해 제시된 튜브 기반 MPC의 장점을 조사하기 위해 격리된 시스템에서 수행됩니다. [8] 감수성 노출-무증상이지만 감염성-증상 및 감염성(심각한 감염 집단 + 경증 감염 집단)-회복-사망자(SEAI(I 1 + I 2)RD) 비지도 학습 및 지도 학습의 두 가지 유형에 대한 물리적 모델, 즉, 퍼지 비례-적분-미분(PID) 및 웨이블릿 신경망 PID 학습은 자가 학습 인공 지능(AI) 기반 제어를 가능하게 하는 예측 제어 시스템 모델을 구축하는 데 사용됩니다. [9] 퍼지 비례 + 차동 위치 제어기를 포함하는 위치 전기 기계 시스템의 구조적 구조 및 위치 기준 신호 변경에 대한 제어 적응 방법을 얻습니다. [10] 제어 시스템의 경우 위치 정보를 기반으로 하는 퍼지 비례 미분(PD) 적응형 임피던스 제어 전략이 제안되어 장치의 순응도를 높이고 과도한 근육 긴장으로 인한 2차 부상을 방지하며 손가락을 효과적으로 보호할 수 있습니다. [11] 퍼지 비례 적분 위치 조정기가 있는 3회로 위치 ECS가 설계되었습니다. [12] 특히 내부 제어 루프는 시스템 매개변수의 불확실성을 처리하기 위해 모델 기준 적응 제어기(MRAC)로 설계되었으며, 외부 제어 루프는 퍼지 비례 적분 제어기를 활용하여 부하에 대한 외부 교란의 영향을 줄입니다. [13] 이 작업은 퍼지 비례 적분 도함수(FPID) 및 진폭 직접 피드백을 기반으로 하는 새로운 일정 주파수 초음파 진폭 제어(CFUAC) 방법을 제시합니다. [14] 마지막으로 보일러의 효율을 최적화하기 위해 퍼지 비례 적분 제어기가 설계되었습니다. [15] 제안된 시스템은 거친 로봇