Design of a hybrid controller for voice coil motors with simple self-learning fuzzy control

Abstract

The voice coil motor (VCM) has many excellent features such as high-starting thrust force, silence, low-cost and so on. In this paper, the dynamics of a VCM with the introduction of a lumped uncertainty is studied. It shows that the dynamic characteristics and motor parameters of the VCM are non-linear and time-varying. To resolve this problem, this paper proposes a hybrid control system, which comprised of a PD controller and simple self-learning fuzzy controller (SSFC), for the position tracking control of a VCM. The SSFC contains two sets of fuzzy inference system. One is the fuzzy controller and the other is the rule modifier. The modification value of each fuzzy rule is based on the fuzzy firing weight of each fuzzy rule to achieve satisfactory learning performance, thus it is suitable for on-line VCM control. Finally, the proposed hybrid control system is implemented on a 32-bit microcontroller for possible low-cost and high-performance industrial applications. The experimental results show that the proposed hybrid control system can achieve favorable tracking performance and is robust against payload variations of a VCM.

Research on high precision control algorithm

“…The core idea of fuzzy control algorithm is to apply fuzzy logic to control system. By fuzzifying input and output variables, it designs a set of fuzzy rules, and then makes reasoning and decisions according to these rules, and finally gets the output of the controller [2] . Fuzzy control algorithm usually includes the following steps, Fuzzification: the input variable is converted into a fuzzy set, and the membership degree of the input variable is described by membership function.…”

Overview of Position Servo Control Technology and Development of Voice Coil Motor

“…In the early research on fuzzy control, it is necessary to construct fuzzy rules first to achieve design performance through trial and error; however, this adjustment process of trial and error is undoubtedly very tedious and timeconsuming. Therefore, how to reasonably design self-learning or adaptive rules to adjust fuzzy rules online is a research point in the application of fuzzy control in VCM [20], [21].…”