Improving the Accuracy of Eccentric Magnetic Absolute Encoders Using Radial Basis Function Neural Network and Third-Order PLL

In this study, in order to improve the accuracy of eccentric magnetic absolute encoders (EMAE), an angle calculation method that combines radial basis function neural networks (RBFNN) and third-order phase-locked loops (TOPLL) is proposed. EMAE consists of a simple multipole magnet, but the eccentricity causes periodic distortion in the signals, which degrades accuracy. In addition, the accuracy of magnetic encoders is degraded by nonideal components, such as amplitude mismatches, phase shifts, low- and high-order harmonics, random noise, and DC offsets. The proposed method uses amplitude normalization and RBFNN to reduce these disturbances. Moreover, the steady-state error that occurs when the ramp frequency is input is eliminated by using TO-PLL. The effectiveness of the proposed method was demonstrated through a simulation.