Abstract-The Permanent Magnet Synchronous Motor has been applying widely due to it's high efficiency, high reliability, relatively low cost and low moment of inertia. However, the PMSM drives are easily affected by the uncertain factors such as the variation of motor parameters and load disturbance. In order to realize the control of the PMSM accurately, a novel adaptive chaotic kinetic molecular theory optimization algorithm was implemented for seeking the best parameters of PID controller. In the PMSM vector control system, the speed loop will be adjusted by a CKMTOA PID controller. In modified kinetic molecular theory optimization algorithm, the adaptive inertia weight factors are used to accelerate the convergence speed, and chaotic searching is conducted within the neighbor set of the solutions to avoid the local minima. The model of PMSM and its` space vector control system are set up in the software of MATLAB/Simulink. The effectiveness of the self-tuning CKMTOA PID controller is verified by comparing with the conventional PID and particle swarm optimization algorithm. The extensive simulations and analysis also show the effectiveness of the proposed approach.
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.
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