An Adaptive Linear-Neuron-Based Third-Order PLL to Improve the Accuracy of Absolute Magnetic Encoders

Abstract

Absolute magnetic encoders (AMEs) use two magnets: a ring multipolar magnet (MPM) generating high-resolution and improving the accuracy for the encoder, and a bipolar magnet in the center calculating the number cycle of MPM signals. The phase outputs of these AMEs are tracked from the sinusoidal signals of the MPM. However, these sine/cosine signals are disturbed by amplitude differences, offsets, phase-shift, harmonic components, and random noise. In order to solve this problem, this paper presents an adaptive linear neuron based on a third-order phase-locked loop (ALN-PLL) to improve the accuracy of AMEs. The proposed approach consists of two main parts: The first part is an ALN algorithm that uses the phase feedback of the third-order PLL in order to build the mathematical model of input signals, and then reject the disturbances. The second part is a third-order PLL that is designed based on a dominant pole approximation algorithm. The proposed PLL can reduce noise and eliminate dc-error during the phase step, frequency step, and frequency ramp. The simulation and experimental results demonstrate the effectiveness of the proposed approach.