This study investigates the influence of stator coil pitch on the electromagnetic performance of squirrel‐cage induction machine (IM) with emphasis on the flux‐weakening (FW) capability for electric vehicle applications. Three different winding topologies, obtained by changing the coil pitch, namely integer‐slot distributed winding (ISDW), integer‐slot concentrated winding, and fractional‐slot concentrated winding (FSCW), are studied. Their merits and demerits are revealed by comparing their performances characteristics, including winding harmonic index, total axial length, average torque, torque ripple, power losses, efficiency, and FW capability. It is revealed that due to the very high Magnetomotive force (MMF) harmonics induced in the bar current, the bar copper loss of the IM with FSCW is significant. It is also revealed that the parasitic effects of the double‐layer winding configurations are lower than those of the single‐layer (SL) winding configurations. It is also demonstrated that for the slot/pole/phase combinations lower than two )(q ≤ 2, the higher the coil pitch, the lower the parasitic effects and the higher the efficiency. Moreover, it is found that the combinations q ≥ 2, especially those having longer coil pitch and higher stator slot number, and the SL combinations offer better FW capability. Finally, the Finite‐element analysis (FEA) results of IM with long‐pitch ISDWs are validated by experimental measurements.
In this paper, a self-tuning algorithm for proportional integral derivative (PID) control based on the adaptive interaction (AI) approach theory efficiently used in artificial neural networks (ANNs) is proposed. In this approach, a system is decomposed into interconnected subsystems, and adaptation occurs in the interaction weights among these subsystems. The principle behind the adaptation algorithm is mathematically equivalent to a gradient descent algorithm. The same adaptation as the well-known backpropagation algorithm (BPA) can be achieved without the need of a feedback network, which would propagate the errors, by applying adaptive interaction. Thereby, the ANN controller can be adapted directly without wasting calculation time in order to increase the frequency response of the controller. The velocity control of a brushless DC motor (BLDCM) under slowly and rapidly changing load conditions is simulated to demonstrate the effectiveness of the algorithm. The AI tuning algorithm was used to tune up the PID gains, and the simulation results with PID adaptation process are presented by comparing the obtained results with the adaptive PID controller based on BPNN and a conventional PID controller.
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