Novel design of an optimized synergetic control for dual stator induction motor

Abstract

Purpose This paper aims to present a contribution to improve the performance of vector control scheme of double star induction motor drive (DSIM) by using an optimized synergetic control approach. The main advantage of synergetic control is that it supports all parametric and nonparametric uncertainties, which is not the case in several control strategies. Design/methodology/approach The suggested controller is developed based on the synergistic control theory and the particle swarm optimization (PSO) algorithm which allow to obtain the optimal parameter of suggested controller to improve the performance of control system. Findings To show the benefits of proposed controller, a comparative simulation results between conventional PI controller, sliding mode controller and suggested controller were carried out. Originality/value The obtained simulation results illustrate clearly that synergetic controller ensures a rapid response, asymptotic stability of the closed-loop system in the all range operating condition and system robustness in presence of parameter variation in all range of operating conditions.

Investigation of predictive direct torque control of Double Star permanent magnet synchronous machine (DSPMSM)

“…These benefits include high reliability, the capability to function in one or two phases in the event of a fault, reduced capacity, and increased frequency [5] [12] [13]. Additionally, these motors, by mitigating pulsing torque and reducing current per phase, find extensive use in numerous high-power applications, notably in the realms of high-power hybrid electric vehicles, aviation, as well as the propulsion systems of electric locomotives and ships [4] [7].…”

Optimal Fractional Order Proportional Integral Controller for Dual Star Induction Motor Based on Particle Swarm Optimization Algorithm

“…The simpler and effective solutions that exist nowadays in the majority of engineering control applications employ proportional integral derivative (PID) in the regulation loop [13]. On the other hand, lots of nonlinear control laws have been proposed including sliding mode control (SMC) [14,15], synergetic control [16][17][18], predictive control [19], backstepping control [20,21], and high-order SMC techniques [5]. In addition, the response and the speed dynamic of a system can be improved using intelligent techniques, such as fuzzy logic and neural networks [22][23][24].…”