Positioning in releasing manipulation by iterative learning control
In order to improve the positioning precision of the stop posture (position and orientation) of an object and decrease the trial numbers in our proposed releasing manipulation, two iterative learning control (ILC) schemes, learning control based on convergent condition (LCBCC), and learning control based on optimal principle (LCBOP) are designed in experimental-oriented way. These two methods are all based on a linearized system model. The experimental results show that these methods are effective. Having discussed the characteristics of these control methods, we conclude that in the case there is no enough system knowledge, LCBCC is the only choice to be used to learn the system knowledge; after the enough experience has been acquired, LCBOP is better than LCBCC, in the view of both of the convergent rate and the precision. In order to improve the positioning precision of the stop posture (position and orientation) of an object and decrease the trial numbers in our proposed releasing manipulation, two iterative learning control (ILC) schemes, learning control based on convergent condition (LCBCC), and learning control based on optimal principle (LCBOP) are designed in experimental-oriented way. These two methods are all based on a linearized system model. The experimental results show that these methods are effective. Having discussed the characteristics of these control methods, we conclude that in the case there is no enough system knowledge, LCBCC is the only choice to be used to learn the system knowledge; after the enough experience has been acquired, LCBOP is better than LCBCC, in the view of both of the convergent rate and the precision.
position iterative learning control dynamic manipulation impact slide