This paper presents the dynamic parameter identification of the 7-DOF WAM™Arm using a novel physically consistent regression technique. Due to model and data errors, physically impossible parameters can arise with classical estimations methods. Such infeasible estimations cannot be used in robot control or simulation. This paper proposes a semidefinite programming (SDP) reformulation of the classical ordinary least squares method. This enables the inclusion of constraints guaranteeing physically feasible solutions only. The SDP method efficiently finds the feasible solution with the lowest regression error. Regression data processing issues related to the WAM robot are also addressed.

In beating-heart surgeries, two natural disturbances affect surgeon's skills: respiration and heartbeats. Heart motions are too dynamic to be manually compensated, therefore robotic-assisted surgery has the potential to improve surgical tasks, especially those requiring high precision such as needle insertion or suturing. In our approach only force data are used to autonomously compensate physiological motions. Two force control architectures are confronted and experimentally compared, using a lightweight 7 DOF WAM arm. One is based on linear predictive control, which uses a mathematical model to predict the system behavior. The other one is based on model reference adaptive control with stochastic active observers.