scholarly journals A Self-Tuning Proportional-Integral-Derivative-Based Temperature Control Method for Draw-Texturing-Yarn Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Rong Song ◽  
Shuting Chen
Keyword(s):  
Parameter Identification ◽  
Temperature Control ◽  
Control Method ◽  
Fast Time ◽  
Fuzzy Pid ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Self Tuning ◽  
Set Up ◽  
Control Accuracy

Owing to the fast time-varying characteristics, the temperature control for draw-texturing-yarn (DTY) machine has higher technical difficulties and results in challenges for system energy optimization. To address the matter, a self-tuning proportional-integral-derivative- (ST-PID-) based temperature control method is proposed. Referring to the technical procedures of DTY machine, a thermodynamic model is set up. Then, a ST-PID minimum phase control system is constructed by the pole-point placement method. Subsequently, an artificial neural network based forgetting factor searching (ANN-FFS) algorithm is developed to optimize the system parameter identification. The numerical cases show that the proposed ANN-FFS algorithm can improve the parameter identification process, and the average identifying efficiency (K>15) can increase by more than 50%; compared with the fuzzy PID controller, the proposed ST-PID method can increase the control accuracy nearly 3 times for the static temperature ascending. The experimental results prove that the proposed ST-PID method has better abilities of characteristics tracing and anti-interference and can restrain the temperature fluctuation caused by objective switching and the factual control accuracy reaches 3 times that of fuzzy PID method.

Designed of Wireless Intellectualized Temperature Control System in Tobacco Preserve Based on Fuzzy-PID

2013 ◽  
Vol 416-417 ◽  
pp. 890-894
Author(s):  
Xiao Hui Guo
Keyword(s):  
Temperature Control ◽  
Control Method ◽  
Control Function ◽  
Temperature Control System ◽  
Pid Controllers ◽  
Fuzzy Pid ◽  
Single Chip ◽  
Control Effect ◽  
Set Up ◽  
The Individual

Tobacco Warehousing is chiefly applied to preserve the tobacco that is separated into leaf and stem so that the tobacco moisture is controlled at the range of technology demand.The present control method of tobacco save is that the references of every PID control link are set up and adjusted by human experience. So, the control effect varies with the individual and the output tobacco moisture can't maintain stable.The fuzzy-PID temperature system is based on CC2430 single chip. It includes the power source, the manipulative algorithm, the temperature examination , the correspondence of up PC and the output-control of the switch value and so on. Computer takes the parameter deviation and the deviation change as input, and the PID controllers parameters of ΔKp, Δki, ΔKd as output. The sub program realized the corresponding events by completing zone bit and zone bit judgment. The main program realized temperature control function by calling the wireless micro-controller sends a signal to the charged unit

Improved Self-Tuning Fuzzy Proportional–Integral–Derivative Versus Fuzzy-Adaptive Proportional–Integral–Derivative for Speed Control of Nonlinear Hybrid Electric Vehicles

2016 ◽  
Vol 11 (6) ◽  
Author(s):  
Anil Kumar Yadav ◽  
Prerna Gaur
Keyword(s):  
Hybrid Electric Vehicle ◽  
Speed Control ◽  
Pid Controllers ◽  
Vehicle Speed ◽  
Fuzzy Pid ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Self Tuning ◽  
Hybrid Electric ◽  
Nonlinear Hybrid

The objective of this paper is to identify the suitable advance controller among optimized proportional–integral–derivative (O-PID), improved self-tuning fuzzy-PID (ISTF-PID), advanced fuzzy nonadaptive PID (AF-NA-PID), and AF-adaptive PID (AF-A-PID) controllers for speed control of nonlinear hybrid electric vehicle (HEV) system. The conventional PID (C-PID) controller cannot tackle the nonlinear systems effectively and gives a poor tracking and disturbance rejection performance. The performances of HEV with the proposed advance controllers are compared with existing C-PID, STF-PID, and conventional fuzzy PID (C-F-PID) controllers. The proposed controllers are designed to achieve the desired vehicle speed and rejection of disturbance due to road grade with reduced pollution and fuel economy.

A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system

2019 ◽  
Vol 26 (13-14) ◽  
pp. 1187-1198 ◽  
Author(s):  
Li-Xin Guo ◽  
Dinh-Nam Dao
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Deep Learning ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral

This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers.

scholarly journals An improved neuroendocrine–proportional–integral–derivative controller with sigmoid-based secretion rate for nonlinear multi-input–multi-output crane systems

2019 ◽  
Vol 39 (4) ◽  
pp. 1172-1186
Author(s):  
Mohd Riduwan Ghazali ◽  
Mohd Ashraf Ahmad ◽  
Raja Mohd Taufika Raja Ismail ◽  
M Osman Tokhi
Keyword(s):  
Hormone Secretion ◽  
Secretion Rate ◽  
Control Input ◽  
Sigmoid Function ◽  
Hormone Regulation ◽  
Proportional Integral Derivative ◽  
Accuracy Control ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Control Accuracy

This paper proposes an improved neuroendocrine–proportional–integral–derivative controller for nonlinear multi-input–multi-output crane systems using a sigmoid-based secretion rate of the hormone regulation. The main advantage of the sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative is that the hormone secretion rate of neuroendocrine–proportional–integral–derivative can be varied according to the change of error. As a result, it can provide high accuracy control performance, especially in nonlinear multi-input–multi-output crane systems. In particular, the hormone secretion rate is designed to adapt with the changes of error using a sigmoid function, thus contributing to enhanced control accuracy. The parameters of the sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative controller are tuned using the safe experimentation dynamics algorithm. The performance of the proposed sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative controller-based safe experimentation dynamics algorithm is evaluated by tracking the error and the control input. In addition, the performances of proportional–integral–derivative and neuroendocrine–proportional–integral–derivative controllers are compared with the proposed sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative performance. From the simulation work, it is discovered that the sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative design provides better control performances in terms of the objective function, the total norm of error and the total norm of input compared to proportional–integral–derivative and neuroendocrine–proportional–integral–derivative controllers. In particular, it is shown the proposed sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative controller contributes 5.12% of control accuracy improvement by changing the fixed hormone secretion rate into a variable hormone secretion rate based on the change of error.

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