Multi-phase motors have recently replaced three-phase induction motors in a variety of applications due to the numerous benefits they provide, and the absence of speed sensors promotes induction motors with variable speed drives. Sensorless speed control minimizes unnecessary speed encoder cost, reduces maintenance, and improves the motor drive’s reliability. The performance comparison of the fuzzy sliding mode controller (FSMC) with adaptive fuzzy proportional integral derivative (AFPID) control methods for sensorless speed control of six-phase induction motors was analyzed in this study, and the proposed control system has an advantage for multiphase machines, specifically six-phase induction motors (IMs) in this study, as they are the current active research area for electric vehicles, hybrid electric vehicles, aerospace, ship propulsion, and high-power applications. The speed control of a six-phase induction motor was performed by using an AFPID controller and FSMC. The comparative performance analysis was based on sensorless speed control of the six-phase induction motor. A proportional integral derivative (PID) controller is commonly employed as it is used to eliminate oscillations, but it has several drawbacks, such as taking a long time to decrease the error and stabilize the system at constant speed. The fuzzy type-2 and PID controllers were hybridized so as to obtain the advantages of both to enhance the system performance. Finally, the comparison result revealed that the FSMC preforms significantly better by achieving good tracking performance. The control technique maintains the sliding mode approach’s robustness while providing reduced overshoots with a smooth control action, and the FSMC revealed good dynamic response under load variations when compared to the AFPID controller.

The super twisting sliding-mode observer (ST-SMO) has been proposed to achieve an effective method for alleviating low-order harmonics of measured quantities, issues related to DC drift, and suppression of chattering due to low-frequency sampling. The conventional ST-SMO, on the other hand, suffers from control delay in the convergence trajectory as a result of the system disturbance, resulting in decreased anti-disturbance capability and impacting the estimation accuracy and energy consumption. This paper proposed an ST-SMO with convergence improvement to address the issue related to the sliding mode controller along the sliding surface. To begin, a nonlinear sliding mode manifold is created to achieve the optimal ST-SMO convergence trajectory along the sliding surface. Then, a disturbance compensation term is added to the control law to eliminate the system control delay. In comparison to the conventional ST-SMO, the investigated method can effectively improve the anti-disturbance capability of the induction motor (IM) Observer resulting in improved speed estimation (rotor flux control under applied load torque disturbances, speed reversal, and zero speed operation), good performance, and stability. The simulation and experimental studies are carried out for an induction motor with a 5.5kW rating. Both simulation and experimental results prove good robustness against the applied load torque disturbances and convergence of rotor speed to its real value.

Recent research reveals that multi-phase motors in electric propulsion systems are highly recommended due to their improved reliability and efficiency over traditional three phase motors. This research presented a comparison of optimal model design of a six phase squirrel cage induction motor (IM) for electric propulsion by using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A six phase squirrel cage induction motor is designed and simulated by ANSYS Motor-CAD. In order to find the best fit method, simulation results are compared and applied to the motors for electric propulsion, considering the influence of design upon the motor performance. The six-phase squirrel cage induction motor is more energy efficient, reliable and cost effective for the electric propulsion compared to the conventional three phase motor. In this study, first the initial parameters of the six phase squirrel cage induction motor have been determined and then these parameters have been compared with optimized values by Genetic Algorithm (GA) and PSO optimization. The motor designed is optimized using efficiency and power losses as the fitness function. The six phase squirrel cage induction motor is designed using ANSYS Motor-CAD and the simulation results were also presented along with two-dimensional and three-dimensional geometry. The result shows that the weight and power loss are reduced to 161 kg and 0.9359 Kw respectively, while the efficiency and power factor are increased to 0.95 and 0.87 respectively when PSO is used. This shows that the result is promising.

This article proposes the speed estimation principles dedicated to the observer structures based on the machine mathematical model. The rotor speed is reconstructed based on the mathematical model of a machine by using both adaptive and nonadaptive schemes. The presented principle is generalized to the classical nonlinear system in the vector form and can be applied to induction machines. The proposed rotor speed reconstruction approach is based on an algebraic relationship, and the observer system mathematical model has the same rank as the induction machine. The speed observer structure can be unstable due to the challenge of stabilizing the sensorless control of the induction machine at low-speed, near zero speed, or in the low-speed regenerating mode of operation. As a result, the new stabilizing functions based on Lagrange identity are proposed in this work to improve the range of observer stability. The proposed approach includes newly developed stabilization mechanisms that ensure observer stability under both motoring and regenerating modes of operations at the low rotor speed and improve the observer range of stability. The Lyapunov theorem is used during the design procedure for stability purposes. The simulation and experimental studies are carried out for an induction machine adaptive and nonadaptive full-order observer. The experimental results show that stable operation of the system is obtained, and the range of observer stability is improved, especially at low-speeds and in a regenerating mode of operation, concluding that the proposed solution is suitable for use in industrial applications.

This study proposes a reactive torque flux control (RTFC) and modified speed estimation of a speed observer based on multiscalar control of an induction motor (IM) traction drive for electric vehicles (EVs). It employs an extended-speed adaptive and nonadaptive speed estimation with a stabilizing function to improve the low-speed stability of sensorless IMs. An approach for rotor speed estimation using machine models of multiscalar control plus the stabilizing function is used to control the IM. The proposed RTFC controls the flux of IM drives, in which the reactive torque component replaces the flux control target used in the Direct field-oriented control (DFOC). As a result, it resolves one PI tuning issue. The modified speed estimation design strategy for adaptive full-order observers to address the challenge is proposed to eliminate the unstable zone during a low-speed regenerative mode of operation. The pole placement method chooses the stabilizing function gains to minimize anticipated rotor speed errors and increase resilience against parameter uncertainties at regenerating mode and low-speed operation. The proposed speed estimator performance is compared to motor speed throughout several driving profiles of EVs, including motoring, constant speed, low and zero speed, and regenerating modes of operation. The proposed RTFC approach requires only one PI controller for the reactive torque flux control. Though the scheme has a comparable performance to one with an additional flux controller, as in DFOC, it significantly reduces the computational burden. The results show that the proposed speed observer performs well across various driving profiles, and using an adaptive full-order observer (AFO) for modified speed estimation of sensorless control in EV is effective and stable. Experimental studies are carried out on a three-phase, 5.5 kW induction motor to validate the effectiveness and performance of the proposed control scheme.

Induction motors (IMs) experience power losses when a portion of the input power is converted to heat instead of driving the load. The combined effect of copper losses, core losses, and mechanical losses results in IM power losses. Unfortunately, the core losses in the motor, which have a considerable impact on its energy efficiency, are not taken into account by the generally employed dynamic model in the majority of the studies. Due to this, the motor rating often corresponds to the worst-case load in applications, but the motor frequently operates below rated conditions. A hybridized model reference adaptive system (MRAS) with sliding mode control (SMC) is used in this study for sensorless speed control of an induction motor with core loss, allowing the motor to operate under a variety of load conditions. As a result, the machine can run at maximum efficiency while carrying its rated load. By adjusting the 𝛼-axis current in the 𝛼-𝛽 reference frame in vector-controlled drives, the system's performance is enhanced by running the motor at its optimum flux. Regarding the torque and speed of both induction motors with and without core loss, the Adaptive Observer Sliding Mode Control (AOSMC) has been constructed and simulated in this case. The AOSMC with core loss produced good performance when the proposed controller was tested.