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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.

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.

To show the benefits of proposed controller, a comparative simulation results between conventional PI controller, sliding mode controller and suggested controller were carried out.

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.

This work proposes a method to improve the estimation performance at standstill and low speed operations of an adaptive fuzzy logic speed‐sensorless field‐oriented control of an induction motor.

First, the speed estimation algorithm presented in Tursini et al., which it has been designed to consider constant speed operation, is modified in an attempt to reduce the estimation error. Second, the speed regulation by fuzzy logic controller (FLC) with fuzzy adapted gains (FAG) is proposed for speed regulation. The main features of the proposed algorithm are investigated and compared with those of the algorithm of (Tursini) considering different dynamic operating conditions.

Simulation results clearly show the performance of the proposed algorithm.

The proposed scheme is recommended for applications requiring robust speed control and field‐orientation even in the presence of some key parameter deviations.

The great value of offline identification of machine parameters is when the machine manufacturer does not provide its parameters. Most machine control strategies require parameter values, and some circumstances in the industrial sector only require offline identification. This paper aims to present a new offline method for estimating induction motor parameters based on least squares and a salp swarm algorithm (SSA).

The central concept is to use the classic least squares (LS) method to acquire the majority of induction machine (IM) constant parameters, followed by the SSA method to obtain all parameters and minimize errors.

The obtained results showed that the LS method gives good results in simulation based on the assumption that the measurements are noise-free. However, unlike in simulations, the LS method is unable to accurately identify the machine’s parameters during the experimental test. On the contrary, the SSA method proves higher efficiency and more precision for IM parameter estimation in both simulations and experimental tests.

After performing a primary identification using the technique of least squares, the initial intention of this study was to apply the SSA for the purpose of identifying all of the machine’s parameters and minimizing errors. These two approaches use the same measurement from a simple running test of an IM, and they offer a quick processing time. Therefore, this combined offline strategy provides a reliable model based on the identified parameters.