scholarly journals Intelligent control of induction motor without speed sensor

2021 ◽  
Vol 12 (2) ◽  
pp. 715
Author(s):  
M. Elgohary ◽  
E. Gouda ◽  
S. S. Eskander
Keyword(s):  
Induction Motor ◽  
Intelligent Control ◽  
Pso Algorithm ◽  
Pi Controller ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Speed Sensor ◽  
Artificial Neural ◽  
Speed Estimator ◽  
Artificial Neural Network Ann

<p>This paper presents a proposed sensorless algorithm for induction motor(IM)speed control based on artificial neural networks (ANNs).The Indirect rotor field oriented (IRFO) technique is applied to control the motor. It is designed based on the proportional-integral (PI) controller. The particle swarm optimization (PSO) algorithm is used as a good solution for the problems associated with the design of the proportional-integral (PI) controller gains.The PSO is compared with the conventional methods. The proposed controller (PSO-PI) is then integrated with the artificial neural network(ANN) speed estimator. The MATLAB/Simulink is used for the simulation of the system. The obtained simulation results for the proposed technique are very close to the actual ones.</p>

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.  

FPGA-realization of a speed control IC for induction motor drive

2016 ◽  
Vol 33 (6) ◽  
pp. 1835-1852 ◽  
Author(s):  
Ying-Shieh Kung ◽  
Seng-Chi Chen ◽  
Jin-Mu Lin ◽  
Tsung-Chun Tseng
Keyword(s):  
Induction Motor ◽  
Operation Time ◽  
Speed Control ◽  
Pi Controller ◽  
Modulation Scheme ◽  
Content Type ◽  
Current Vector ◽  
Speed Controller ◽  
Speed Estimator ◽  
Vhdl Code

Purpose – The purpose of this paper is to integrate the function of a speed controller for induction motor (IM) drive, such as the speed PI controller, the current vector controller, the slip speed estimator, the space vector pulse width modulation scheme, the quadrature encoder pulse, and analog to digital converter interface circuit, etc. into one field programmable gate array (FPGA). Design/methodology/approach – First, the mathematical modeling of an IM drive, the field-oriented control algorithm, and PI controller are derived. Second, the very high speed IC hardware description language (VHDL) is adopted to describe the behavior of the algorithms above. Third, based on electronic design automation simulator link, a co-simulation work constructed by ModelSim and Simulink is applied to verify the proposed VHDL code for the speed controller intellectual properties (IP). Finally, the developed VHDL code will be downloaded to the FPGA for further control the IM drive. Findings – In realization aspect, it only needs 5,590 LEs, 196,608 RAM bits, and 14 embedded 9-bit multipliers in FPGA to build up a speed control IP. In computational power aspect, the operation time to complete the computation of the PI controller, the slip speed estimator, the current vector controller are only 0.28 μs, 0.72 μs, and 0.96 μs, respectively. Practical implications – Fast computation in FPGA can speed up the speed response of IM drive system to increase the running performance. Originality/value – This is the first time to realize all the function of a speed controller for IM drive within one FPGA.

scholarly journals PMSM Speed Control Based on Particle Swarm Optimization and Deep Deterministic Policy Gradient under Load Disturbance

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 343
Author(s):  
Chiao-Sheng Wang ◽  
Chen-Wei Conan Guo ◽  
Der-Min Tsay ◽  
Jau-Woei Perng
Keyword(s):  
Particle Swarm Optimization ◽  
Permanent Magnet Synchronous Motor ◽  
Particle Swarm ◽  
Speed Control ◽  
Pso Algorithm ◽  
Computational Time ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Estimation Equations ◽  
Policy Gradient

Proportional integral-based particle swarm optimization (PSO) and deep deterministic policy gradient (DDPG) algorithms are applied to a permanent-magnet synchronous motor to track speed control. The proposed methods, based on notebooks, can deal with time delay challenges, imprecise mathematical models, and unknown disturbance loads. First, a system identification method is used to obtain an approximate model of the motor. The load and speed estimation equations can be determined using the model. By adding the estimation equations, the PSO algorithm can determine the sub-optimized parameters of the proportional-integral controller using the predicted speed response; however, the computational time and consistency challenges of the PSO algorithm are extremely dependent on the number of particles and iterations. Hence, an online-learning method, DDPG, combined with the PSO algorithm is proposed to improve the speed control performance. Finally, the proposed methods are implemented on a real platform, and the experimental results are presented and discussed.

Training Artificial Neural Networks With Improved Particle Swarm Optimization

2020 ◽  
pp. 63-77
Author(s):  
Kuruge Darshana Abeyrathna ◽  
Chawalit Jeenanunta
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Artificial Neural Networks ◽  
Local Minimum ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Extra Step ◽  
Artificial Neural

Particle Swarm Optimization (PSO) is popular for solving complex optimization problems. However, it easily traps in local minima. Authors modify the traditional PSO algorithm by adding an extra step called PSO-Shock. The PSO-Shock algorithm initiates similar to the PSO algorithm. Once it traps in a local minimum, it is detected by counting stall generations. When stall generation accumulates to a prespecified value, particles are perturbed. This helps particles to find better solutions than the current local minimum they found. The behavior of PSO-Shock algorithm is studied using a known: Schwefel's function. With promising performance on the Schwefel's function, PSO-Shock algorithm is utilized to optimize the weights and bias of Artificial Neural Networks (ANNs). The trained ANNs then forecast electricity consumption in Thailand. The proposed algorithm reduces the forecasting error compared to the traditional training algorithms. The percentage reduction of error is 23.81% compared to the Backpropagation algorithm and 16.50% compared to the traditional PSO algorithm.

Artificial Neural Network, Induction motor, scalar control, Speed Estimator

2014 ◽  
Vol 19 (1) ◽  
pp. 24-35
Author(s):  
Tiago Henrique dos Santos ◽  
Alessandro Goedtel ◽  
Sérgio Augusto Oliveira da Silva ◽  
Marcelo Suetake
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Scalar Control ◽  
Artificial Neural ◽  
Speed Estimator
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