An Improved Fourier Five-Sensor (IF5S) method for separating straightness and yawing errors of a linear slide based on multiple sensor parameter sets and least square regression technique

In this paper, an Improved Fourier Five-Sensor (IF5S) measurement method is proposed for separating the straightness and yawing motion errors as well as determining the profile of a linear slide. The previous F5S method [3] used the constant parameters initially to estimate the profile function based on three sensor equations for different angle ranges. The profile estimation and error separation are implemented via an iterative method which can only yield acceptably accurate results with tremendous computational efforts. Here, the improved F5S method applies the least square regression technique instead of the iterative method to estimate the profile functions by using three distinct sets of parameters and different fused sensor data according to the travel of the linear slide. Various errors can then be separated based on the calculated profile function. Simulation results confirm that the IF5S method provides better performance and effectiveness as compared to the previous F5S method.