Increasing the accuracy and the repeatability of position control for micromanipulations using Heteroscedastic Gaussian Processes

Abstract

Abstract-Many recent studies describe micromanipulation systems by using complex Analytic Forward Models (AFM), but such models are difficult to build and incapable of describing unmodelable factors, such as manufacturing defects. In this work, we propose the Enhanced Analytic Forward Model (EAFM), an integrated model of the AFM and the Heteroscedastic Gaussian Processes (HGP). The EAFM can compensate the shortfalls of the AFM by training the HGP on the residual of the AFM. This also allows the HGP to learn the repeatability of the micromanipulation system. Based on the EAFM, we further contribute an optimal position controller for improving the accuracy and the repeatability. This optimal EAFM controller is implemented and tested on a three degreeof-freedom micromanipulator based micromanipulation system. Two sets of real-world experiments are carried out to verify our method. The results demonstrate that the controller using EAFM can statistically achieve higher accuracy and repeatability than solely using the AFM.

Optimal Design and Tracking Control of a Superelastic Flexure Hinge Based 3-PRR Compliant Parallel Manipulator

“…A nominal inverse kinematic model of the CPM can be formulated according to the geometric relationships of the CPM. However, it cannot be used to control the CPM directly, since there are many unmodeled factors in the 3-PRR CPM are not considered in that model, such as the rotation error of the AEP superelastic flexures, the material nonlinearity, the manufacture uncertainties, and the assembling errors [19]. To deal with the model mismatches and external disturbances of CPMs, many close-loop control strategies are proposed, such as the PID controller, sliding mode controller, robust controller and the adaptive neural network controller [20]- [22].…”