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Abstract
Rotary encoders are widely applied in a variety of industrial fields. However, as the exist of the installation, processing and demodulation circuits errors, the test result of the encoder is superimposed with periodic nonlinear errors and the encoder needs compensation to achieve high measurement accuracy. Traditional methods including the least square method (LSM) and back propagation artificial neural network (BP-ANN), are not capable of addressing nonlinear errors. Thus, a novel method based on improved particle swarm optimization (IPSO) and support vector machines (SVM) is proposed to provide better compensation. The proposed method incorporates the SVM method into the design of the compensation model, and the IPSO algorithm is applied to tune the SVM parameters. To validate the algorithm, four sets of data were obtained from encoders with different numbers of segments. The experimental results show that the IPSO-SVM algorithm has a better prediction precision and the nonlinear standard deviation of 180 petal-shaped numbers has dropped from 0.08° to 0.0005° after compensation over 0° to 360° measurement range. Based on the results, the proposed IPSO-SVM model provided more accurate compensation on the nonlinear errors to the capacitive angular encoders than other method.
Highlights
Rotary encoders applied to measure the angular position and speed have been broadly utilized in industrial automation control systems [1]–[4]
The nonlinear error of the capacitive encoder compensated by the back propagation artificial neural network (BP-artificial neural network (ANN)) has been obviously reduced from 0.08◦ to 0.005◦ over all the measurement range
The focus of this research was to develop a method to compensate the periodic nonlinear which effected by installation, processing, and demodulation circuit errors
Summary
Rotary encoders applied to measure the angular position and speed have been broadly utilized in industrial automation control systems [1]–[4]. An alternative approach to improve the measurement precision is compensate the periodic nonlinear error by way of a software algorithm. B. Hou et al.: Nonlinear Error Compensation of Capacitive Angular Encoders Based on IPSO SVM identifies a local optimum rather than the global optimum when fitting periodic errors [16], [19]. To overcome the shortcomings of the traditional PSO algorithm, such as slow convergence and the likelihood of falling into a local minimum, its performance was improved by reducing the speed and search range. The results of the analysis confirm the effectiveness of the IPSO algorithm in optimizing the selection of SVM learning parameters to provide more accurate and stable parametric predictions in nonlinear method.
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