Lead-lag compensator design based on vector margin and steady-state error of the step response via particle swarm optimization

2016 ◽  
Author(s):  
Ying-Sheng Kuo ◽  
Jia-Yu Lin ◽  
Jia-Ci Tang ◽  
Jer-Guang Hsieh
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Particle Swarm ◽  
Step Response ◽  
Swarm Optimization ◽  
Compensator Design ◽  
State Error

scholarly journals Kendali Kecepatan Motor Induksi 3 Fase Berbasis Particle Swarm Optimization (PSO)

2020 ◽  
Vol 8 (3) ◽  
pp. 477
Author(s):  
HANIF HASYIER FAKHRUDDIN ◽  
HANDRI TOAR ◽  
ERA PURWANTO ◽  
HARY OKTAVIANTO ◽  
RADEN AKBAR NUR APRIYANTO ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Real Time ◽  
Induction Motor ◽  
Rise Time ◽  
Low Cost ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Scalar Control ◽  
State Error

ABSTRAKMotor induksi secara struktur dan kendali standarnya dirancang untuk bekerja pada kecepatan nominal, sehingga sulit mengendalikan kecepatan sesuai kebutuhan karena akan mengubah konstruksi motor. Penelitian tentang pengendalian motor induksi agar semudah mengendalikan motor DC sudah banyak dilakukan oleh peneliti, salah satunya adalah dengan kendali skalar. Kendali skalar banyak digunakan karena memiliki keunggulan sederhana, biaya murah, mudah didesain dan diimplementasikan, serta yang paling penting tidak memerlukan parameter dari motor induksi. Penggunaan kendali skalar yang telah dilengkapi pengendali PID penalaan otomatis, dengan parameter yang telah dioptimalkan algoritma Particle Swarm Optimization (PSO), akan memudahkan pengendalian kecepatan motor induksi tiga fase pada kecepatan beragam. Simulasi penalaan otomatis PID menggunakan PSO telah dilakukan dengan LabView, dengan karakteristik maksimal 10% overshoot, 1% error steady state dan rise time kurang dari 2 milidetik. Sementara dalam pengujian real time dengan MyRIO hasilnya tanpa overshoot, 5.5% error steady state maksimal dan rise time maksimal 5 detik.Kata kunci: Kendali skalar, PID, Particle Swarm Optimization, LabView ABSTRACTInduction motor is designed at nominal speed as default, we have to change its stucture to obtain dessired speed. Many researchers developt method how to control induction motor as simple as DC motor, one of the methods is scalar control. Scalar control has several benefits, such as simply, low cost, easily designed and implemented, and the main banefit is no necessary motor parameters. Using scalar control with PID controller that optimized Partical Swarm Optimization (PSO) algoritm, will ease to control 3 phase induction motor variant speed. Simulation auto tunning using PSO has done on LabView, it has some characteristic, they are 10% overshoot, 1% steady state error, and rise time within 2ms. In other hand, real time test using MyRIO got no overshoot, 5.5% steady state error maximal, and rise time maximal 5 s characteristic.Keywords: Scalar control, PID, Particle Swarm Optimization, LabView

A Novel Control Scheme for PWM Boost Converter based on Sliding Mode Control using Particle Swarm Optimization

2020 ◽  
Vol 14 ◽  
Author(s):  
Gang Liu ◽  
Dong Qiu ◽  
Xiuru Wang ◽  
Ke Zhang ◽  
Huafeng Huang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Sliding Mode ◽  
Particle Swarm ◽  
Mathematic Model ◽  
Absolute Error ◽  
Boost Converter ◽  
Sliding Mode Controller ◽  
Swarm Optimization ◽  
Steady State Performance

Background: The PWM Boost converter is a strongly nonlinear discrete system, especially when the input voltage or load varies widely, therefore, tuning the control parameters of which is a challenge work. Objective: In order to overcome the issues, particle swarm optimization (PSO) is employed for tuning the parameters of a sliding mode controller of a boost converter. Methods: Based on the analysis of the Boost converter model and its non-linear characteristics, a mathematic model of a boost converter with a sliding mode controller is built firstly. Then, the parameters of the Boost controller are adjusted based on the integrated time and absolute error (ITAE), integral square error (ISE) and integrated absolute error (IAE) indexes by PSO. Results: Simulation verification was performed, and the results show that the controllers tuned by the three indexes all have excellent robust stability. Conclusion: The controllers tuned by ITAE and ISE indexes have excellent steady-state performance, but the overshoot is large during the startup. The controller tuned by IAE index has better startup performance and slightly worse steady-state performance.

Performance Analysis for a Gantry Crane System (GCS) Using Priority-Based Fitness Scheme in Binary Particle Swarm Optimization

2014 ◽  
Vol 903 ◽  
pp. 285-290 ◽  
Author(s):  
Hazriq Izzuan Jaafar ◽  
Zaharuddin Mohamed ◽  
Amar Faiz Zainal Abidin ◽  
Zamani Md Sani ◽  
Jasrul Jamani Jamian ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Control Structure ◽  
Lagrange Equation ◽  
Particle Swarm ◽  
New Method ◽  
Gantry Crane ◽  
Binary Particle Swarm Optimization ◽  
Optimal Parameters ◽  
Swarm Optimization

This paper presents development of an optimal PID and PD controllers for controlling the nonlinear Gantry Crane System (GCS). A new method of Binary Particle Swarm Optimization (BPSO) algorithm that uses Priority-based Fitness Scheme is developed to obtain optimal PID and PD parameters. The optimal parameters are tested on the control structure to examine system responses including trolley displacement and payload oscillation. The dynamic model of GCS is derived using Lagrange equation. Simulation is conducted within Matlab environment to verify the performance of the system in terms of settling time, steady state error and overshoot. The result not only confirmed the successes of using new method for GCS, but also shows the new method performs more efficiently compared to the continuous PSO. This proposed technique demonstrates that implementation of Priority-based Fitness Scheme in BPSO is effective and able to move the trolley as fast as possible to the desired position with low payload oscillation.

Application of an Improved Internal Model Controller Based on the Particle Swarm Optimization in Network Control System

2013 ◽  
Vol 397-400 ◽  
pp. 1137-1144
Author(s):  
Wei Chen ◽  
Wen Bin Wang ◽  
Zhi Kai Zhao ◽  
Zhi Yuan Yan
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Internal Model ◽  
Particle Swarm ◽  
Internal Model Control ◽  
Network Control ◽  
Step Response ◽  
Network Control System ◽  
Swarm Optimization ◽  
Internal Model Controller

Internal Model Control (IMC) is widely used in Network Control System (NCS) with its strong robustness and simple parameter adjustment. But the accurate dynamic inversion of the IMC model is not easy to find out. To solve this problem, an improved Internal Model Controller is designed with a PID controller and feedback loop, then the Particle Swarm Optimization (PSO) is used to optimize all the parameters of the improved controller. At last, simulation results show that the improved Internal Model Controller can maintain the system stability and the performance of the step response is extremely great in terms of rapidity and anti-interference ability, compared with the classic internal model controller, which enables NCS to achieve a better control effect.

scholarly journals Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Hamid Reza Mohammadi ◽  
Ali Akhavan
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Steady State ◽  
Equivalent Circuit ◽  
Induction Motor ◽  
Particle Swarm ◽  
Circuit Parameter ◽  
Swarm Optimization ◽  
Full Load

A cost effective off-line method for equivalent circuit parameter estimation of an induction motor using hybrid of genetic algorithm and particle swarm optimization (HGAPSO) is proposed. The HGAPSO inherits the advantages of both genetic algorithm (GA) and particle swarm optimization (PSO). The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the nameplate data or experimental tests. In this paper, the problem formulation uses the starting torque, the full load torque, the maximum torque, and the full load power factor which are normally available from the manufacturer data. The proposed method is used to estimate the stator and rotor resistances, the stator and rotor leakage reactances, and the magnetizing reactance in the steady-state equivalent circuit. The optimization problem is formulated to minimize an objective function containing the error between the estimated and the manufacturer data. The validity of the proposed method is demonstrated for a preset model of induction motor in MATLAB/Simulink. Also, the performance evaluation of the proposed method is carried out by comparison between the results of the HGAPSO, GA, and PSO.

Auto PID Tuning of Speed Control of DC Motor Using Particle Swarm Optimization Based on FPGA

2015 ◽  
Vol 776 ◽  
pp. 390-395 ◽  
Author(s):  
Hilal Tayara ◽  
Deok Jin Lee ◽  
Kil To Chong
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Rise Time ◽  
Particle Swarm ◽  
Speed Control ◽  
Dc Motor ◽  
Pid Controllers ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Pid Tuning

This paper introduces auto tuning of proportional-integral-derivative (PID) controllers of DC motor using particle swarm optimization (PSO) method. The DC motor was modeled in Simulink and PSO was implanted on FPGA “cyclone IV E” using the soft processor NIOS II. The results were efficient in reducing the steady state error, settling time, rise time and maximum overshoot in speed control of a DC motor.

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