Lingzhi Yi

Work place: College of Information Engineering, Xiangtan University, Xiangtan, 411105, China



Research Interests: Computational Engineering, Engineering


LingZhi Yi was born on March 27, 1966 in Hunan in China. She graduated from the Xiangtan University in 1986. And after then she worked in Xiangtan University, act as the leader of the electricity-electronics committee of Xiangtan University. Her main research areas including AC speed control and power electronic devices, maximum power output control and position sensor technology of the Switched Reluctance wind power system, power generation control of the new high-efficiency solar, energy storage technology of the new energy electric car.

She is elected as a core young teacher of Hunan province in 1999, and as the person for 121 project of Hunan province in 2007. Up to today, she has published 100 papers in some famous magazines with 4 patents for invention and 18 software copyrights, 51papers cited by SCI and EI index included.

She is now an expert in National Energy Conservation Center, senior member of Chinese Association of Automation and executive director in Institute of Automation of Hunan.

Author Articles
A Novel and Improved Firefly Algorithm Based on Two-order Oscillation

By Yongbo Sui Lingzhi Yi Wenxin Yu

DOI:, Pub. Date: 8 May 2017

Firefly algorithm is a new and efficient intelligent algorithm proposed by Dr. Xin-She Yang at Cambridge University. In this paper, the conventional firefly algorithm will be introduced and improved using two-order oscillation. And an improved firefly algorithm based on two-order oscillation is proposed. And this method will be deduced and analyzed for astringency. Six typical functions will be tested to prove performances. Global Max/Min values in six multimodal functions could be found accurately with proposed scheme. The experiment results show our proposed method has excellent performances than conventional one.

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Research of Self-Tuning PID for PMSM Vector Control based on Improved KMTOA

By Lingzhi Yi Chengdong Zhang Genping Wang

DOI:, Pub. Date: 8 Mar. 2017

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.

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