Releasing manipulation with learning control

The properties of releasing manipulation are given. To improve the precision of object posture and decrease trial numbers, two iterative learning control schemes, learning control based on convergent condition (LCBCC), and learning control based on optimal principle (LCBOP) are designed in an experiment-oriented way. These two methods are based on a linearized model. The experimental results show that these methods are effective. After discussing the characteristics of these control methods, we postulate that in the case of where the system does not have enough knowledge, LCBCC is the only choice and to learn system knowledge, after enough experience has been acquired, LCBOP is better than LCBCC, form the view point of convergence rate and precision.