Theoretical Insights on Contraction-Type Iterative Learning Control for Biorobotic Systems with Preisach Hysteresis

Abstract

This article offers new insights on the learning control approach developed by [Hu et al. IEEE/ASME Trans. Mechatronics, 19(1): 191-200, 2014]. Theoretical insights are further proposed to unveil why the contraction-type iterative learning control (ILC) schemes are suitable and effective in compensating for hysteresis, widely existing in biorobotic locomotion. Under such circumstances, iteration-based second-order dynamics is adopted to describe the biorobotic systems acted upon by one unknown Preisach hysteresis term. The memory clearing operator is mathematically proven to enable feasibility of contractiontype ILC methods, regardless of whether the initial state is accurately set or not. The simulation examples confirm that the developed iteration-based controller combined with a preceded operator effectively reduce tracking errors caused by the hysteresis nonlinearity. Furthermore, the new insights on theoretical feasibility are definitively corroborated in accordance with the previously published experimental results.

Visual Inspection and Batting Decision of Table Tennis Robot

“…Although the vision system was used to track and predict the movement of table tennis at that time, in the vision system at that time, the development of PC hardware Behind, the image processing speed cannot keep up, so that the calculated table tennis coordinates and the accuracy of the predicted table tennis trajectory cannot be guaranteed well [24][25]. According to Japanese video recordings of table tennis robots, the robot simply cannot react when the ball speed is relatively fast, so the visual system at that time was not suitable for detecting high-speed moving objects [26][27].…”