A pure signal-based stiffness estimation for VSA devices

The capability of controlling both the position/torque and the stiffness of the joints is the main feature of the next generation of robots based on Variable Stiffness Actuators (VSA). For the purpose of accurate control, recent works have pointed out that is not possible to rely completely on analytical models of the stiffness characteristics of the transmissions/joints and that an on-line estimation of stiffness is often mandatory. Building on our previous results, we present a new method to estimate the stiffness based only on input-output signals, without any knowledge of motor parameters nor the need of joint torque sensing. In addition, a Recursive Least Squares method based on a QR decomposition (QR-RLS) is used, which is very robust to poor excitation conditions. In order to deal more efficiently with noisy signals, a combination of two filtering actions is also considered, with a causal Kinematic Kalman Filter (KKF) and a non-causal Savitzky-Golay (SG) filter. Simulation results and comparison with two other state-of-the-art stiffness estimators are presented.