This paper experimentally implements a surface following algorithm for unknown surfaces under a hybrid position and force scheme. The mechanization uses a low friction roller at the end of a two-link manipulator so that primarily the normal force is measured. With the normal force information and joint angle information, the surface normal direction is calculated and a position reference is calculated along the surface tangent. Experimental evaluation shows the algorithm works for a line, a concave arc, and a convex arc. Also, when different force levels are commanded or different controllers are used, the algorithm still performs well. Thus, the algorithm allows a constant force to be applied while following a surface whose shape is not known a priori.

This paper compares the experimental evaluation of robust reduced-order hybrid position and force controllers to standard full-order and reduced-order linear-quadratic Gaussian (LQG) controllers. A robust reduced-order position controller and a reduced-order force controller are designed using the SANDY direct optimization program. Full-order LQG position and force controllers are designed and then reduced through balanced truncation. Experimental results show that the reduced-order controllers provide comparable performance to the full-order controllers. Thus, by use of direct-optimization, the benefits of smaller controllers (smaller memory requirements and faster sampling rates) can be achieved while accounting for varying plant conditions (robustness).