PID Tuning Method Using Single-Valued Neutrosophic Cosine Measure and Genetic Algorithm

Author(s):  
Jun Ye
Author(s):  
Yasir G. Rashid ◽  
Ahmed Mohammed Abdul Hussain

The point of this paper presents an optimization technique which is flexible and quick tuning by using a genetic algorithm (GA) to obtain the optimum proportional-integral-derivative (PID) parameters for speed control of aseparately excited DC motor as a benchmark for performance analysis. The optimization method is used for searching for the proper value of PID parameters. The speed controller of DC motor using PID tuning method sincludes three types: MATALB PID tunner app., modified Ziegler-Nicholsmethod and genetic algorithm (GA). PID controller parameters (Kp, Ki and Kd) will be obtained by GA to produce optimal performance for the DC motor control system. Simulation results indicate that the tuning method of PID by using a genetic algorithm is shown to create the finest result in system performance such as settling time, rise time, percentage of overshoot and steady state error. The MATLAB/Simulink software is used to model and simulate the proposed DC motor controller system.


Author(s):  
Tengku Ahmad Faris Ku Yusoff ◽  
Mohd Farid Atan ◽  
Nazeri Abdul Rahman ◽  
Shanti Faridah Salleh ◽  
Noraziah Abdul Wahab

Controller tuning is one of the important aspect in industry. With a good tuning method, it can ensure the quality of the process and product produce. Apart from that, it can protect the environment and help the company to reduce the cost. Genetic algorithm is one of the tuning method that increase usage and awareness in industry. Thus, the objective of this research is to compare the performance of the conventional tuning method with the performance of tuning method by using genetic algorithm can be seen. Optimization was done on stripping section of distillation column by using genetic algorithm with population size of 20, 40, 60 and 80 and comparing the result with previous optimization using Ziegler-Nichols method. The result obtain showed large improvement in the process response especially on rise time from 1.33 s to 1.31s and settling time from 4.56 to 4.46. Finally, population size of 40 deliver the fastest rise time and settling time.


2018 ◽  
Vol 51 (4) ◽  
pp. 274-279 ◽  
Author(s):  
M.M. Ozyetkin ◽  
C. Onat ◽  
N. Tan

2020 ◽  
Vol 13 (6) ◽  
pp. 1813-1823 ◽  
Author(s):  
Bin Li ◽  
◽  
Xiaolong Guo ◽  
Xiaodong Zeng ◽  
Songyi Dian ◽  
...  

Author(s):  
O. Tolga Altinoz

In this study, the PID tuning method (controller design scheme) is proposed for a linear quarter model of active suspension system installed on the vehicles. The PID tuning scheme is considered as a multiobjective problem which is solved by converting this multiobjective problem into single objective problem with the aid of scalarization approaches. In the study, three different scalarization approaches are used and compared to each other. These approaches are called linear scalarization (weighted sum), epsilon-constraint and Benson’s methods. The objectives of multiobjective optimization are selected from the time-domain properties of the transient response of the system which are overshoot, rise time, peak time and error (in total there are four objectives). The aim of each objective is to minimize the corresponding property of the time response of the system. First, these four objective is applied to the scalarization functions and then single objective problem is obtained. Finally, these single objective problems are solved with the aid of heuristic optimization algorithms. For this purpose, four optimization algorithms are selected, which are called Particle Swarm Optimization, Differential Evolution, Firefly, and Cultural Algorithms. In total,twelve implementations are evaluated with the same number of iterations. In this study, the aim is to compare the scalarization approaches and optimization algorithm on active suspension control problem. The performance of the corresponding cases (implementations) are numerically and graphically demonstrated on transient responses of the system.


2018 ◽  
Vol 19 (1) ◽  
pp. 137-146 ◽  
Author(s):  
Xuemin Xia ◽  
Simin Jiang ◽  
Nianqing Zhou ◽  
Xianwen Li ◽  
Lichun Wang

Abstract Groundwater pollution has been a major concern for human beings, since it is inherently related to people's health and fitness and the ecological environment. To improve the identification of groundwater pollution, many optimization approaches have been developed. Among them, the genetic algorithm (GA) is widely used with its performance depending on the hyper-parameters. In this study, a simulation–optimization approach, i.e., a transport simulation model with a genetic optimization algorithm, was utilized to determine the pollutant source fluxes. We proposed a robust method for tuning the hyper-parameters based on Taguchi experimental design to optimize the performance of the GA. The effectiveness of the method was tested on an irregular geometry and heterogeneous porous media considering steady-state flow and transient transport conditions. Compared with traditional GA with default hyper-parameters, our proposed hyper-parameter tuning method is able to provide appropriate parameters for running the GA, and can more efficiently identify groundwater pollution.


PID Control ◽  
2005 ◽  
pp. 339-360 ◽  
Author(s):  
K.S. Tang ◽  
G.R. Chen ◽  
K.F. Man ◽  
S. Kwong

Author(s):  
Sheng-Yi Ruan ◽  
Jun Ye ◽  
Wen-Hua Cui

This chapter introduces an improved proportional-integral-derivative (PID) adjusting method by applying a simulated annealing algorithm (SAA) and the cosine, tangent, exponential measures of single-valued neutrosophic sets (SvNSs). For the approach, characteristic values of the unit step response (rise time, peak time, settling time, undershoot ratio, overshoot ratio, and steady-state error) in the control system should be neutrosophicated by the neutrosophic membership functions. Next, one of cosine, tangent, and exponential measures is used to obtain the similarity measure of the ideal SvNS and the response SvNS to assess the control performance of the PID controller by the optimization values of the PID parameters Kp, Ki, and Kd searched by SAA. The results of the illustrative example obtained by these measures and SAA are better than the existing ones and indicate better PID controller performance. Comparative results can demonstrate the rationality and superiority of the improved PID adjusting method.


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