A Voice Coil Motor-Driven Precision Positioning System Based on Self-Learning Nonlinear PID
Cheng Miaomiao1, Zhai Penghui1, Zhang Yingjie2, Li Jian2, Feng Kai2
1. School of Electrical and Information Engineering Hunan University Changsha 410082 China; 2. School of Mechanical and Vehicle Engineering Hunan University Changsha 410082 China
Abstract：As a typical precise-positioning and long-stroke motion system, the precision macro-micro air-floating motion table has been taking more and more research interest due to its free-of-contact friction characteristics. Voice coil motor (VCM) has been proposed as the main driving element for the micro motion table due to its fast response and non-contact feed drives. However, due to the mover suspending to the stator, VCM is susceptible to interference such as load disturbance, vibration, and noise. Some solutions have been proposed in the existing research to eliminate the disturbances and improve positioning accuracy. However, either limited effect or complex implementation problems exist. Therefore, this paper proposes a novel self-learning nonlinear PID control method to improve the positioning accuracy and robustness of the VCM precision positioning system. The main idea of the proposed control method is to construct a nonlinear PID control law based on the arcsine function, and apply the neural network algorithm to adaptively adjust the weight parameters of the proposed nonlinear function with the time-varying system errors. Accordingly, the pre-designed PID control law assures the PID control parameters change reasonably by following the proposed arcsine function. The BP neural network can provide control flexibility, thus improving the model robustness and control accuracy. Therefore, the proposed self-learning nonlinear PID control is the potential for nonlinear, multivariable, and interference-susceptible systems, such as the precision macro-micro air-floating motion system. The unit step response experiment is first performed on the micromotion table. The experimental results show that the overshoot of the traditional PID controller is about 8 %, and the steady-state positioning accuracy is 1 μm. The steady-state positioning accuracy of the self-learning nonlinear PID controller is 0.5 μm, and there is no overshoot. The proposed method provides better positioning accuracy and transient response. Some experiments are further carried out with varying load conditions. According to the experimental results, the traditional PID controller presents a greatly increased overshoot and settling time, while the proposed self-learning nonlinear PID presents the same control performance as the no-load case. The proposed method improves the robustness of the VCM-driven micromotion table. Finally, the long-stroke positioning experiment is performed on the macro-micro motion stable. The micro-motion stable adopts the proposed self-learning nonlinear PID control, while the macro-motion stable adopts the traditional linear PID control. The results show that the macro-motion stable follows the micro motion stable with a fast response. Besides, the positioning accuracy is within 0.5 μm, consistent with the short-stroke positioning experimental results. That proves the decoupled control characteristics of the macro/micro-motion stable. The positioning accuracy of the system is determined by the control performance of the micro-motion stable. Concluded above, a novel self-learning nonlinear PID controller is proposed for the VCM-driven micro motion stable. It provides with better positioning accuracy, fast transient response and enhanced robustness according to the experimental results. Both short-stroke and long-stroke positioning experiments are carried out. The results verify that the micro motion table is mechanically decoupled from the macro motion table. On the basis of this, the proposed self-learning nonlinear PID and the traditional PID are proposed to be used for the micro/macro motion table respectively. Finally, a reduced control complexity and improved control performance could be achieved for the precision macro-micro air-floating motion table.
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2023, Vol. 38 
