A Direct Torque Control of Permanent Magnet Synchronous Motor for Electric Vehicles Based on ADRC Optimized by CKMTOA-KELM

Lingzhi Yi, Chengdong Zhang, Yanfang Jia, Yangbo Ou

Abstract


Electric vehicles are considered as a new generation of transport to solve the energy crisis. Permanent magnet synchronous motor (PMSM) has been widely used in electric vehicle drive system. A new direct torque control (DTC) for PMSM based on active-disturbance rejection control (ADRC) optimized by improved kernel extreme learning machine (KELM) method is proposed in this paper, which aims to overcome the defects of traditional PI controller. The CKMTOA-KELM optimal regression model is obtained by using chaotic kinetic molecular theory optimization algorithm (CKMTOA) to optimize the kernel parameters and penalty coefficients of KELM regression model. CKMTOA uses chaos search to prevent the algorithm from falling into local optimum and improves the convergence rate by employing adaptive inertia weighting factor. Finally, the ADRC controller embedded the CKMTOA-KELM optimal regression model is analyzed and optimized to improve the dynamic response speed and anti-jamming capability of the system and enhance the robustness of the system. The simulation and experiment results have verified the feasibility and effectiveness of this method.

Keywords


electric vehicles; permanent magnet synchronous motor (PMSM); direct torque control (DTC); ADRC; chaotic kinetic molecular theory optimization algorithm (CKMTOA); KELM.


DOI
10.12783/dteees/appeec2018/23524

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