Experimental results for output feedback adaptive robot control

This work examines three methods of adaptive output feedback control for robotic manipulators. Implementing output feedback for control, instead of full-state feedback, allows use of only the position information. The position can be measured quite accurately, while velocity and acceleration measurements tend to get corrupted by noise. As well, having only a position sensor reduces costs in producing the robot. The three methods examined each use some form of state estimation. The methods examined are: a method proposed by Lee and Khalil using a high-gain observer, Craig, Hsu, and Sastry’s method of adaptive robot control with the addition of a linear observer that we propose herein, and a method proposed by Gourdeau and Schwartz using an Extended Kalman Filter (EKF). The methods are all implemented in simulation for both noise-free and noise-contaminated cases, and experimentally on the Carleton University Direct-Drive Robot.