scholarly journals Performance Improvement of Reluctance Synchronous Motor Using Brain Emotional Learning Based Intelligent Controller

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2595
Author(s):  
Mehrdad Ahmadi Kamarposhti ◽  
Hassan Shokouhandeh ◽  
Ilhami Colak ◽  
Kei Eguchi
Keyword(s):  
Intelligent Control ◽  
Control Function ◽  
Control Process ◽  
Synchronous Motor ◽  
Emotional Learning ◽  
Learning System ◽  
Control Functions ◽  
Control Scheme ◽  
Maximum Torque Per Ampere ◽  
Brain Emotional Learning

In this paper, intelligent control of a reluctance synchronous motor by an emotional controller, considering the effect of magnetic saturation on implementation, is analyzed; the maximum torque per ampere (MTPA) strategy is provided. According to the application of the proposed control scheme, the structure adequately performs the control of speed and magnetic flux of the reluctance synchronous motor drive. Additionally, the application of intelligent control based on an emotional learning system has provided adequate results to create a proper control process. The control function of a SynRM drive based on vector control in a rotor machine was compared with another based on emotional controllers and a PI controller regulated by genetic algorithms. The result of this comparison was the improvement of control functions by the controller based on the emotional controller. In addition, the MTPA based on search algorithms was well implemented in different situations. Due to its simplicity and independence from system parameters, the emotional controller can be considered as a potential operational method in the industry.

scholarly journals A Recurrent Wavelet based Brain Emotional Learning Network Controller for Nonlinear Systems

2021 ◽  
Author(s):  
Juncheng Zhang ◽  
Fei Chao ◽  
Hualin Zeng ◽  
Chih-Min Lin ◽  
Longzhi Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Control Systems ◽  
Nonlinear Problems ◽  
Emotional Learning ◽  
Learning System ◽  
Uncertain Environments ◽  
Learning Network ◽  
Brain Emotional Learning ◽  
The Brain

Abstract Conventional control systems often suffer from the co-existence of non-linearity and uncertainty. This paper proposes a novel brain emotional neural network to support addressing such challenges. The proposed network integrates a wavelet neural network into a conventional brain emotional learning network. This is further enhanced by the introduction of a recurrent structure to employ the two networks as the two channels of the brain emotional learning network. The proposed network therefore combines the advantages of the wavelet function, the recurrent mechanism, and the brain emotional learning system, for optimal performance on nonlinear problems under uncertain environments. The proposed network works with a bounding compensator to mimic an ideal controller, and the parameters are updated based on the laws derived from the Lyapunov stability analysis theory. The proposed system was applied to two uncertain nonlinear systems, including a Duffing-Homes chaotic system and a simulated 3-DOF spherical joint robot. The experiments demonstrated that the proposed system outperformed other popular neural-network-based control systems, indicating the superiority of the proposed system.

scholarly journals Self-Organizing Brain Emotional Learning Controller Network for Intelligent Control System of Mobile Robots

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 59096-59108 ◽  
Author(s):  
Qiuxia Wu ◽  
Chih-Min Lin ◽  
Wubing Fang ◽  
Fei Chao ◽  
Longzhi Yang ◽  
...  
Keyword(s):  
Control System ◽  
Mobile Robots ◽  
Intelligent Control ◽  
Emotional Learning ◽  
Intelligent Control System ◽  
Brain Emotional Learning ◽  
Self Organizing

Based on Multi-Estimator Technology to Control Speed of Induction Motor

2014 ◽  
Vol 577 ◽  
pp. 329-333
Author(s):  
Su Mei Feng ◽  
Zhi Ping Yan ◽  
Xue Mei Wu
Keyword(s):  
Induction Motor ◽  
Control Function ◽  
Control Process ◽  
Motor Speed ◽  
Speed Sensor ◽  
Control Scheme ◽  
Self Tuning ◽  
Rotor Flux ◽  
On Line ◽  
And Control

In order to improve the reliability of the control system, and to reduce the uncertainty of control process, this paper presents an excellent algorithm to control the induction motor speed using series estimator technology. The physical speed sensor is not used to detect speed of the motor, while applying the vector analysis method, through the stator current and the rotor flux are calculated to estimate motor speed. Corresponding to sensorless control drawbacks, the self-tuning control scheme is proposed through control scheme reformed. The double estimator technology is applied to reform scheme, the first order estimator is used to identify the system parameters, the second order estimator is used to calculate the parameters of the controller on-line, and control function is given real-time to control the speed of the motor, the simulation results show that the given control technology is advanced.

scholarly journals On optimal boundary and distributed control of partial integro–differential equations

2014 ◽  
Vol 24 (1) ◽  
pp. 5-25 ◽  
Author(s):  
Asatur Zh. Khurshudyan
Keyword(s):  
Differential Equations ◽  
Control Problem ◽  
Integral Transform ◽  
Control Function ◽  
Sufficient Conditions ◽  
Control Process ◽  
Problem Solution ◽  
Control Functions ◽  
Minimization Procedure ◽  
And Control

Abstract A method of optimal control problems investigation for linear partial integro-differential equations of convolution type is proposed, when control process is carried out by boundary functions and right hand side of equation. Using Fourier real generalized integral transform control problem solution is reduced to minimization procedure of chosen optimality criterion under constraints of equality type on desired control function. Optimality of control impacts is obtained for two criteria, evaluating their linear momentum and total energy. Necessary and sufficient conditions of control problem solvability are obtained for both criteria. Numerical calculations are done and control functions are plotted for both cases of control process realization.

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