The Study of Fuzzy/PID Compound Control Algorithms in Rubber Sulfuration Process Based on Sugeno Rules

2011 ◽  
Vol 221 ◽  
pp. 571-576
Author(s):  
Chun Tang Zhang ◽  
Zhen Zhu Yu
Keyword(s):  
Mathematical Model ◽  
Control Algorithm ◽  
Fuzzy Inference ◽  
Control Algorithms ◽  
Fuzzy Pid ◽  
Inference System ◽  
Pid Algorithm ◽  
Compound Control ◽  
Simulation Results ◽  
Nonlinear Delay

Aiming at rubber sulfuration of nonlinear, delay and complexity, a Fuzzy/PID compound control algorithm is proposed. The algorithm combined fuzzy inference system and PID algorithm, it has solved well the problem which is difficult to establish a precise mathematical model because of the uncertainties and complexities of rubber sulfuration. The simulation results indicate that the control algorithm is viable and effective.

An Intuitionistic Fuzzy Based Novel Approach to CPU Scheduler

2020 ◽  
Vol 16 (4) ◽  
pp. 316-328
Author(s):  
Supriya Raheja
Keyword(s):  
Fuzzy Inference ◽  
The Other ◽  
Simulation Environment ◽  
Inference System ◽  
Intuitionistic Fuzzy ◽  
Novel Approach ◽  
Continuous Feedback ◽  
Simulation Results ◽  
Effectiveness And Efficiency ◽  
Unique Capability

Background: The extension of CPU schedulers with fuzzy has been ascertained better because of its unique capability of handling imprecise information. Though, other generalized forms of fuzzy can be used which can further extend the performance of the scheduler. Objectives: This paper introduces a novel approach to design an intuitionistic fuzzy inference system for CPU scheduler. Methods: The proposed inference system is implemented with a priority scheduler. The proposed scheduler has the ability to dynamically handle the impreciseness of both priority and estimated execution time. It also makes the system adaptive based on the continuous feedback. The proposed scheduler is also capable enough to schedule the tasks according to dynamically generated priority. To demonstrate the performance of proposed scheduler, a simulation environment has been implemented and the performance of proposed scheduler is compared with the other three baseline schedulers (conventional priority scheduler, fuzzy based priority scheduler and vague based priority scheduler). Results: Proposed scheduler is also compared with the shortest job first CPU scheduler as it is known to be an optimized solution for the schedulers. Conclusion: Simulation results prove the effectiveness and efficiency of intuitionistic fuzzy based priority scheduler. Moreover, it provides optimised results as its results are comparable to the results of shortest job first.

Improved ANFIS based MRAC observer for sensorless control of PMSM

2021 ◽  
pp. 1-13
Author(s):  
Suryakant ◽  
Mini Sreejeth ◽  
Madhusudan Singh
Keyword(s):  
Fuzzy Inference ◽  
Permanent Magnet Synchronous Motor ◽  
Torque Control ◽  
Control Algorithms ◽  
Motor Speed ◽  
Inference System ◽  
Rotor Position ◽  
Improved Performance ◽  
Incremental Encoder ◽  
Model Reference Adaptive

Detection of the rotor position is an important prerequisite for controlling the speed and developed torque in permanent magnet synchronous motor (PMSM). Even though use of incremental encoder and resolver is one of the popular schemes for sensing the rotor position in a PMSM drive, it increases the size and weight of the drive and reduces its reliability. Dynamic modeling of the motor and control algorithms are often used in sensor-less control of PMSM to estimate rotor position and motor speed. Most sensor-less control algorithms use machine parameters like torque constant, stator inductances and stator resistance for estimating the rotor position and speed. However, with accuracy of such estimation and the performance of the motor degrades with variation in motor parameters. Model reference adaptive control (MRAC) provides a simple solution to this issue. An improved Adaptive neuro-fuzzy inference system (ANFIS) based MRAC observer for speed control of PMSM drive is presented in this paper. In the proposed method adaptive model and adaptive mechanism are replaced by an improved ANFIS controller, which neutralize the effect of parametric variation and results in improved performance of the drive. The modeling equations of PMSM are used to estimate the rotor position for speed and torque control of the drive. Simulation studies have been carried out under various operating condition using MATLAB/Simulink. In addition, a comparative analysis of the conventional MRAC based observer and improved ANFIS based MRAC observer is carried out. It is observed that the proposed method results in better performance of the PMSM drive.

scholarly journals An Adaptive Neuro-Fuzzy Control of Pneumatic Mechanical Ventilator

Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 51
Author(s):  
Jozef Živčák ◽  
Michal Kelemen ◽  
Ivan Virgala ◽  
Peter Marcinko ◽  
Peter Tuleja ◽  
...  
Keyword(s):  
Control System ◽  
Control Algorithm ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Mechanical Design ◽  
Mechanical Ventilator ◽  
Mechanical Ventilators ◽  
Inference System ◽  
Control Approach ◽  
Neuro Fuzzy

COVID-19 was first identified in December 2019 in Wuhan, China. It mainly affects the respiratory system and can lead to the death of the patient. The motivation for this study was the current pandemic situation and general deficiency of emergency mechanical ventilators. The paper presents the development of a mechanical ventilator and its control algorithm. The main feature of the developed mechanical ventilator is AmbuBag compressed by a pneumatic actuator. The control algorithm is based on an adaptive neuro-fuzzy inference system (ANFIS), which integrates both neural networks and fuzzy logic principles. Mechanical design and hardware design are presented in the paper. Subsequently, there is a description of the process of data collecting and training of the fuzzy controller. The paper also presents a simulation model for verification of the designed control approach. The experimental results provide the verification of the designed control system. The novelty of the paper is, on the one hand, an implementation of the ANFIS controller for AmbuBag pressure control, with a description of training process. On other hand, the paper presents a novel design of a mechanical ventilator, with a detailed description of the hardware and control system. The last contribution of the paper lies in the mathematical and experimental description of AmbuBag for ventilation purposes.

Intelligent cooperative collision avoidance via fuzzy potential fields

Robotica ◽  
2021 ◽  
pp. 1-20
Author(s):  
Daegyun Choi ◽  
Anirudh Chhabra ◽  
Donghoon Kim
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
Fuzzy Inference ◽  
Potential Fields ◽  
Rule Base ◽  
Local Minima ◽  
Inference System ◽  
Aerial Vehicles ◽  
Different Shapes ◽  
Simulation Results

Summary This paper proposes an intelligent cooperative collision avoidance approach combining the enhanced potential field (EPF) with a fuzzy inference system (FIS) to resolve local minima and goal non-reachable with obstacles nearby issues and provide a near-optimal collision-free trajectory. A genetic algorithm is utilized to optimize parameters of membership function and rule base of the FISs. This work uses a single scenario containing all issues and interactions among unmanned aerial vehicles (UAVs) for training. For validating the performance, two scenarios containing obstacles with different shapes and several UAVs in small airspace are considered. Multiple simulation results show that the proposed approach outperforms the conventional EPF approach statistically.

Control Algorithm of a Multilevel Converter based on a Fuzzy Inference System using MATLAB

2007 ◽  
Author(s):  
Salvador Revelo-Andrade ◽  
Mariano Fernandez-Nava ◽  
Pedro Banuelos-Sanchez ◽  
Felix E. Guerrero-Castro
Keyword(s):  
Fuzzy Inference System ◽  
Control Algorithm ◽  
Fuzzy Inference ◽  
Multilevel Converter ◽  
Inference System

Position and Tilt Control of Two-Wheeled Robot (TWR)

2017 ◽  
Vol 6 (4) ◽  
pp. 17-33 ◽  
Author(s):  
Ashwani Kharola ◽  
Pravin P. Patil
Keyword(s):  
Fuzzy Inference ◽  
Second Law ◽  
Membership Functions ◽  
Inference System ◽  
Control Approach ◽  
Simulink Model ◽  
Neuro Fuzzy ◽  
Wheeled Robot ◽  
Simulation Results ◽  
Training Error

This paper presents a fuzzy based adaptive control approach for stabilization of Two wheeled robot (TWR) system. The TWR consists of a robot chassis mounted on two movable wheels. The objective is to stabilize the proposed system within desired time, minimum overshoot and at desired location. The data samples collected from simulation results of fuzzy controllers were used for training, tuning and optimisation of an adaptive neuro fuzzy inference system(ANFIS) controller. A Matlab Simulink model of the system has been built using Newton's second law of motion. The effect of shape and number of membership functions on training error of ANFIS has also been analysed. The designing of fuzzy rules for both fuzzy and ANFIS controller were carried out using gbell shape memberships. Simulations were performed which compared and validated the performance of both the controllers.

Research on Acceleration Slip Regulation for Vehicle Based on Compound Control of Fuzzy and PID

2012 ◽  
Vol 433-440 ◽  
pp. 4165-4172
Author(s):  
Zhi Yuan Li ◽  
Dan Dan Su ◽  
Hao Dong Li ◽  
Shun Yan Hou
Keyword(s):  
Pid Controller ◽  
Control Algorithm ◽  
Driving Ability ◽  
Fuzzy Pid ◽  
Optimal Range ◽  
Simulation Experiments ◽  
Slip Ratio ◽  
Fuzzy Pid Controller ◽  
Compound Control

In this paper, the compound control algorithm of fuzzy and PID has been applied in Acceleration Slip Regulation for vehicle to prevent the excessive spin of driving wheels on wet or icy roads. Simulation experiments have been done to investigate the performance of Acceleration Slip Regulation. The results show that the provided Fuzzy PID controller can effectively keep the slip ratio of the driving wheels in the optimal range and improve the driving ability of the vehicle.

A Direct Digital Control Algorithm for Control of a Single Environmental Space

1985 ◽  
Vol 107 (4) ◽  
pp. 324-331 ◽  
Author(s):  
D. B. Cherchas ◽  
A. Abdelmessih ◽  
M. Townsend
Keyword(s):  
Mathematical Model ◽  
Moisture Content ◽  
Discrete Time ◽  
Digital Control ◽  
Control Algorithm ◽  
Environmental Space ◽  
Practical Performance ◽  
Simulation Results ◽  
Time Form ◽  
Direct Digital Control

A direct digital control algorithm for control of dry bulb temperature in a single environmental space is developed. The algorithm is based on a bilinear mathematical model, developed in the paper, of the response of the space dry bulb temperature and moisture content. The algorithm is somewhat unique in that it includes feedback and feedforward terms in a manner respecting the bilinear nature of the controlled process and as well, minimizes a practical performance index. The discrete time form of the algorithm is presented and simulation results given.

Research of Several PID Algorithms Based on MATLAB

2013 ◽  
Vol 760-762 ◽  
pp. 1075-1079
Author(s):  
Jin Zeng ◽  
Li Guang Wang ◽  
Meng Jun Ye ◽  
Chang Hui Hu ◽  
Tian Feng Ye
Keyword(s):  
Pid Control ◽  
Control Algorithm ◽  
Dead Zone ◽  
Control Algorithms ◽  
Pid Algorithm ◽  
Digital Pid ◽  
Digital Pid Control

This paper introduces several PID control algorithms and their discretization expression. Compare the performance of positional PID algorithm with incremental PID algorithm, integration separate PID algorithm, incomplete differential PID algorithm and PID algorithm with dead zone. The experiment results show that different digital PID control algorithm could achieve different using.

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