scholarly journals Comparison of cascade P-PI controller tuning methods for PMDC motor based on intelligence techniques

2022 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Kareem Ghazi Abdulhussein ◽  
Naseer Majeed Yasin ◽  
Ihsan Jabbar Hasan
Keyword(s):  
Classical Method ◽  
Pso Algorithm ◽  
Pi Controller ◽  
Load Effect ◽  
Ga Algorithm ◽  
Tuning Methods ◽  
Parameter Values ◽  
Permanent Magnet Dc Motor ◽  
Tracking Trajectory ◽  
Best Parameters

In this paper, there are two contributions: The first contribution is to design a robust cascade P-PI controller to control the speed and position of the permanent magnet DC motor (PMDC). The second contribution is to use three methods to tuning the parameter values for this cascade controller by making a comparison between them to obtain the best results to ensure accurate tracking trajectory on the axis to reach the desired position. These methods are the classical method (CM) and it requires some assumptions, the genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). The simulation results show the system becomes unstable after applying the load when using the classical method because it assumes cancellation of the load effect. Also, an overshoot of about 3.763% is observed, and a deviation from the desired position of about 12.03 degrees is observed when using the GA algorithm, while no deviation or overshoot is observed when using the PSO algorithm. Therefore, the PSO algorithm has superiority as compared to the other two methods in improving the performance of the PMDC motor by extracting the best parameters for the cascade P-PI controller to reach the desired position at a regular speed.

scholarly journals Comparison between butterfly optimization algorithm and particle swarm optimization for tuning cascade PID control system of PMDC motor

2021 ◽  
Vol 12 (2) ◽  
pp. 736
Author(s):  
Kareem G. Abdulhussein ◽  
Naseer M. Yasin ◽  
Ihsan J. Hasan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Modern Method ◽  
Pid Controllers ◽  
Swarm Optimization ◽  
Permanent Magnet Dc Motor ◽  
Tracking Trajectory

In this paper, two optimization methods are used to adjust the gain values for the cascade PID controller. These algorithms are the butterfly optimization algorithm (BOA), which is a modern method based on tracking the movement of butterflies to the scent of a fragrance to reach the best position and the second method is particle swarm optimization (PSO). The PID controllers in this system are used to control the position, velocity, and current of a permanent magnet DC motor (PMDC) with an accurate tracking trajectory to reach the desired position. The simulation results using the Matlab environment showed that the butterfly optimization algorithm is better than the particle swarming optimization (PSO) in terms of performance and overshoot or any deviation in tracking the path to reach the desired position. While an overshoot of 2.557% was observed when using the PSO algorithm, and a position deviation of 7.82 degrees was observed from the reference position.

scholarly journals Design of Decentralized Fractional Order PIʎ Controller for Pilot Plant Binary Didtillation Column

2020 ◽  
Vol 5 (1) ◽  
pp. 33-39
Author(s):  
KHALFA BETTOU ◽  
ABDELFATEH CHAREF
Keyword(s):  
Fractional Order ◽  
Fractional Integration ◽  
Pso Algorithm ◽  
Pi Controller ◽  
Integral Criterion ◽  
Control Loops ◽  
Tuning Method ◽  
Control Quality ◽  
Basic Ideas ◽  
Tuning Methods

This paper presents the application of fractional order operators to improve the control quality of multivariable systems. The basic ideas of this tuning method are based, in the first place, on the existed tuning methods for setting the parameters of the decentralized fractional order PIʎ controller for ʎ=1, which means setting the parameters of the classical decentralized PI controller, and the minimum integral criterion by using Particle Swarm Optimization (PSO) algorithm for setting the fractional integration action order ʎ. The integral criterion is formulated to improve the dynamic response of the system, while causing a good decoupling between control loops. The Distillation Column, which is a multivariable system with two inputs and two outputs (TITO), in a decentralized control structure, is analyzed. Simulation results are presented to show the control quality improvement of this proposed decentralized fractional order PIʎ controller tuning method compared to the decentralized PI controller tuned using any existed tuning method.

scholarly journals Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 839
Author(s):  
Shaofei Sun ◽  
Hongxin Zhang ◽  
Xiaotong Cui ◽  
Liang Dong ◽  
Muhammad Saad Khan ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Communication Systems ◽  
Optimization Problems ◽  
Classical Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Electromagnetic Analysis ◽  
Swarm Optimization ◽  
Test Board

This paper focuses on electromagnetic information security in communication systems. Classical correlation electromagnetic analysis (CEMA) is known as a powerful way to recover the cryptographic algorithm’s key. In the classical method, only one byte of the key is used while the other bytes are considered as noise, which not only reduces the efficiency but also is a waste of information. In order to take full advantage of useful information, multiple bytes of the key are used. We transform the key into a multidimensional form, and each byte of the key is considered as a dimension. The problem of the right key searching is transformed into the problem of optimizing correlation coefficients of key candidates. The particle swarm optimization (PSO) algorithm is particularly more suited to solve the optimization problems with high dimension and complex structure. In this paper, we applied the PSO algorithm into CEMA to solve multidimensional problems, and we also add a mutation operator to the optimization algorithm to improve the result. Here, we have proposed a multibyte correlation electromagnetic analysis based on particle swarm optimization. We verified our method on a universal test board that is designed for research and development on hardware security. We implemented the Advanced Encryption Standard (AES) cryptographic algorithm on the test board. Experimental results have shown that our method outperforms the classical method; it achieves approximately 13.72% improvement for the corresponding case.

IMPROVED INDIRECT FIELD-ORIENTED CONTROL OF INDUCTION MOTOR DRIVE BASED PSO ALGORITHM

2016 ◽  
Vol 78 (6-2) ◽  
Author(s):  
Jamal Abd Ali ◽  
M A Hannan ◽  
Azah Mohamed
Keyword(s):  
Induction Motor ◽  
Speed Control ◽  
Pso Algorithm ◽  
Pi Controller ◽  
Field Oriented Control ◽  
Variable Speed ◽  
Three Phase ◽  
Indirect Field Oriented Control ◽  
Speed Response ◽  
Variable Speed Control

Optimization techniques are increasingly used in research to improve the control of three-phase induction motor (TIM). Indirect field-oriented control (IFOC) scheme is employed to improve the efficiency and enhance the performance of variable speed control of TIM drives. The space vector pulse width modulation (SVPWM) technique is used for switching signals in a three-phase bridge inverter to minimize harmonics in the output signals of the inverter. In this paper, a novel scheme based on particle swarm optimization (PSO) algorithm is proposed to improve the variable speed control of IFOC in TIM. The PSO algorithm is used to search the best values of parameters of proportional-integral (PI) controller (proportional gain (kp) and integral gain (ki)) for each speed controller and voltage controller to improve the speed response for TIM. An optimal PI controller-based objective function is also used to tune and minimize the mean square error (MSE). Results of all tests verified the robustness of the PSO-PI controller for speed response in terms of damping capability, fast settling time, steady state error, and transient responses under different conditions of mechanical load and speed.

PSO OPTIMIZATION FOR SOLAR SYSTEM INVERTER CONTROLLER AND COMPARISON BETWEEN TWO CONTROLLER TECHNIQUES

2016 ◽  
Vol 78 (6-2) ◽  
Author(s):  
Maher. G. M. Abdolrasol ◽  
M A Hannan ◽  
Azah Mohamed
Keyword(s):  
Solar System ◽  
Controller Design ◽  
Pso Algorithm ◽  
Pi Controller ◽  
Housing Unit ◽  
Current Waveform ◽  
Phase Voltage ◽  
Swarm Optimization ◽  
Three Phase ◽  
Controller Performance

This paper explains a deep comparison between two controller techniques firstly controller control on modulation index and the second controller use dq method. Both of these controller approaches have control on three phase voltage and use the same system unchanged. The system is a solar system together with a backup battery connected to a single housing unit. Particle Swarm Optimization (PSO) algorithm has been utilized to improve the controller performance by automatically finding its parameters in order to reduce the error in the proportional Integral (PI) controller. Optimization process has been done with a real recording data of housing unit demand in Malaka, Malaysia. System has been simulated and tested in MATLAB/Simulink environment with m-file runs PSO algorithm and simulate the system hundreds of times to get the best results showing in this paper. Comparisons were taking place in controller design and in the simulation results that express the strength and weaken points of each controller starts with THD voltage and current waveform and RMS voltage in each controller.  

scholarly journals By using a new iterative method to the generalized system Zakharov-Kuznetsov and estimate the best parameters via applied the pso algorithm

2020 ◽  
Vol 19 (2) ◽  
pp. 1055 ◽  
Author(s):  
Abeer Aldabagh
Keyword(s):  
Differential Equation ◽  
Exact Solution ◽  
Iterative Method ◽  
Numerical Method ◽  
Pso Algorithm ◽  
Accurate Solution ◽  
Generalized System ◽  
A New Technique ◽  
Best Parameters ◽  
New Iterative Method

In this paper, a new iterative method was applied to the Zakharov-Kuznetsov system to obtain the approximate solution and the results were close to the exact solution, A new technique has been proposed to reach the lowest possible error, and the closest accurate solution to the numerical method is to link the numerical method with the pso algorithm which is denoted by the symbol (NIM-PSO). The results of the proposed Technique showed that they are highly efficient and very close to the exact solution, and they are also of excellent effectiveness for treating partial differential equation systems.

scholarly journals A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 738 ◽  
Author(s):  
Łukasz Strąk ◽  
Rafał Skinderowicz ◽  
Urszula Boryczka ◽  
Arkadiusz Nowakowski
Keyword(s):  
Computation Time ◽  
Pso Algorithm ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Discrete Pso ◽  
Parameter Values ◽  
Positive Effect ◽  
Uniform Parameter

This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic traveling salesman problem (DTSP). The DTSP can be modeled as a sequence of static sub-problems, each of which is an instance of the TSP. In the proposed DPSO algorithm, the information gathered while solving a sub-problem is retained in the form of a pheromone matrix and used by the algorithm while solving the next sub-problem. We present a method for automatically setting the values of the key DPSO parameters (except for the parameters directly related to the computation time and size of a problem).We show that the diversity of parameters values has a positive effect on the quality of the generated results. Furthermore, the population in the proposed algorithm has a higher level of entropy. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO.

scholarly journals Application of the genetic algorithm for cascade control of a HVAC system

2019 ◽  
Vol 292 ◽  
pp. 01064 ◽  
Author(s):  
Donka Ivanova ◽  
Nikolay Valov ◽  
Martin Deyanov
Keyword(s):  
Genetic Algorithm ◽  
Control Systems ◽  
Pid Controller ◽  
Classical Method ◽  
Pi Controller ◽  
Cascade Control ◽  
Hvac System ◽  
Cascade System ◽  
Outer Loop ◽  
Cascade Control System

In this article the application of genetic algorithm for tuning of HVAC cascade system is proposed. The tuning procedure for a cascade system is very time-consuming and practice shows that additional controller tuning is needed when classical method is used. The main problem in classical method is the interconnection between the parameters of the two controllers. The proposed optimal tuning procedure overcomes the disadvantages. It is based on the following criteria: minimum integral square error, minimum settling time and minimum overshoot. The best process quality is achieved with PI controller in the inner loop and a PID controller in the outer loop of the cascade HVAC system. The proposed method for simultaneous tuning of controller parameters in a cascade control system can be applied in different control systems.

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