BLDC Motor Control System Based on Quadratic Single Neuron Adaptive PID Algorithm

BLDC Motor Control System Based on Quadratic Single Neuron Adaptive PID Algorithm

Xiaoyuan Wang, Tao Fu, Xiaoguang Wang
DOI: 10.4018/ijapuc.2014010102
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Brushless DC (BLDC) motors are widely used for many industrial applications because of their high efficiency, high torque and low volume. In view of the problem that the current control method of speed regulation system of BLDC motor has poor control effect caused by fixed parameters of PID controller, an adaptive PID algorithm with quadratic single neuron (QSN) was designed. Quadratic performance index was introduced in adjustment of weight coefficients; expected optimization effect was gotten by calculating control law. QSN adaptive PID controller can change its parameters online when operating conditions are changed, it can also change its control characteristic automatically. Matlab simulations and experiment results showed that the proposed approach has less overshoot, faster response, stronger ability of anti-disturbance, the results also showed more effectiveness and efficiency than the conventional PID model in motor speed control.
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Brushless DC(BLDC) motor has a series of advantages such as simple structure, high reliability, convenient maintenance, high efficiency, no excitation loss and so on. So far, BLDC motor has been widely used in aerospace, CNC machine tools, electrical equipment, mining and other fields. For BLDC motor the controller plays a very vital role in influencing its performance for applications concerning load variations (Krishnan, R. 2010; Chau, K. T., Chan, C. C., & Liu, C.2008; Liao, L. Y., Wang, Z. F., Deng, Y. D., & Hu, X. L. 2003).

The proportional-integral-derivative (PID) controller is widely used in many control applications because of its simplicity and effectiveness, but there are some limitations in conventional PID algorithm because PID controller tuning parameter is fixed, however BLDC motor control system is a multi-variable, nonlinear, strong coupling system, therefore the PID control parameters need to be adjusted according to actual situation. Due to changes in the external disturbance by the system parameters, it is difficult to achieve the best control effect. Therefore, control strategy of high performance electrical drives must be adaptive and robust. As a result, interest in emerging intelligent control systems for electrical drives has increased significantly and numerous intelligent control schemes for BLDC motors are projected.

Some intelligent control algorithm has been studied and applied to the control of BLDC motor. The PID self-tuning methods based on the relay feedback technique were presented for a class of systems (Wang, Q. G., Zou, B., Lee, T. H., & Bi, Q. 1997; Chen, Y., Hu, C., & Moore, K. L.; Al-Mashakbeh, A. S. O. 2009). A genetic algorithm was used to find the optimum tuning parameters of the PID controller by taking integral absolute error as fitting function (Altinten, A., Erdogan, S., Alioglu, F., Hapoglu, H., & Alpbaz, M. 2004; Ansari, U., Alam, S., & Minhaj un Nabi Jafri, S. 2011). Wang, C. H., Liu, H. L., and Lin, T. C. (2002) employed an observer based direct adaptive fuzzy-neural network controller with supervisory controller for a class of high order unknown nonlinear systems. Lin, F. J., and Wai, R. J. (2001) applied the recurrent fuzzy neural network to the hybrid control system for a linear induction motor servo drive. An adaptive fuzzy sliding-mode control system, which combines the merits of sliding mode control, the fuzzy inference mechanism and the adaptive algorithm, is proposed by Lin, F. J., & Chiu, S. L (1998). An adaptive PID control tuning was proposed to cope with the control problem for a class of uncertain chaotic systems with external disturbance (Chang, W. D., & Yan, J. J. 2005). The effectiveness of fuzzy controllers applied to speed control of motor drives experiencing parameter deviations has been presented (Nounou, H. N., & Rehman, H. U. 2007; Shen, J. X., Zhu, Z. Q., Howe, D., & Buckley, J. M. 2005; Syed, F. U., Kuang, M. L., Smith, M., Okubo, S., & Ying, H. 2009). The papers (Yen, J. Y., Chen, Y. L., & Tomizuka, M. 2002; Kovudhikulrungsri, L., & Koseki, T. 2006) have derived the control laws using variable sampling models. Although the previous researches presented robust control in multi-rate or variable sampling, their controllers were complex and not easy to implement.

This paper presents a quadratic single neuron (QSN) adaptive PID control algorithm, quadratic performance index is introduced in adjustment of the weight coefficients of the single neuron controller design, it inherits the advantages of conventional PID controller such as simple, practical, easy to debug, it refers to the practical experience. The QSN adaptive PID algorithm is introduced in the BLDC motor control system and the experimental system and experimental results were given.

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