Automatic speed control of an asymmetrical six-phase induction motor using emotional controller (BELBIC)

Abstract

An induction motor (IM) with two sets of three-phase windings in stator is called a six-phase IM (SPIM). SPIM has lower torque pulsation compared to a three-phase IM and using SPIM, the power rating of inverter legs can be reduced. This paper offers a high performance intelligent controller for speed automatic control of a SPIM. A brain emotional learning based intelligent controller (BELBIC) is proposed as the speed controller. This emotional controller has simple structure with high auto learning feature and it is independent of parameters variations. A direct vector control in rotor flux frame based on space vector pulse width modulation (SVPWM) is used to decouple flux and torque control of the SPIM like a conventional three-phase machine. In this work, the d-axis current and speed reference signals are accurately tracked. An overview of the BELBIC is provided. A comparison with the conventional PI controller and Sliding Mode Control (SLMC) points out the superiority of the proposed BELBIC in speed response and torque ripple. Experimental results indicate that the BELBIC achieves a good speed regulation with a less control effort and torque ripple.

Adaptive control of a class of uncertain nonlinear systems using brain emotional learning and Legendre polynomials

“…It is based on the computational model of a limbic system in the human brain. Many applications of this controller have been presented in recent decades, such as speed control of permanent magnet synchronous motors (Mohammdi-Milasi et al, 2004) robotics (Sharbafi et al, 2010), speed control of induction motor (Daryabeigi et al, 2014), power systems (Sheikholeslami et al, 2006), and process control (El-Garhy and El-Shimy, 2015). Recently, some stability analyses based on Lyapunov theorem have been presented for BELBIC (Jafari et al, 2013; Klecker et al, 2017).…”

Artificial emotional reinforcement learning for automatic generation control of large‐scale interconnected power grids

“…A promising approach is to introduce emotional learning to RL, which could deal with continuous variables and generate the control policies by not only a logical but also a humanistic intelligent part. In recent years, emotional learning-based intelligent (ELI) controller has been presented to handle the above issue, which showed the superior control performance in various real-world applications, including non-linear control of an interconnected power system [19], doubly-fed induction generator [20], power-flow control [21], sensorless speed control of switched reluctance motor [22], dynamic voltage regulator [23], interline power-flow controller [24], asymmetrical six-phase induction motor [25], real-time position control of a servo-hydraulic rotary actuator [26], unmanned ground vehicle navigation [27] and so on. Moreover, emotional learning was developed with combining Qlearning for traffic flow forecasting of multi-agent systems [28,29].…”