Abstract
An ideal haptic device should transmit a wide range of stable virtual model impedances (Z-width) with high transparency. Magneto-rheological fluid (MR) brakes are advantageous in haptic devices since they are passive actuators. However, they cannot provide high transparency and smooth interaction due to high viscous friction, residual torque, slow response, sticking and hysteresis effects. On the other hand, active actuators cannot simulate high virtual impedances stably, but provide high transparency with a closed loop control algorithm. In the proposed hybrid actuation a task divider control (TDC) algorithm was developed for torque sharing between two actuators to provide a large Z-width and improve both transparency and smoothness. The algorithm employs two parameters which were estimated experimentally and extended to entire achievable impedance range by artificial neural network (ANN) and curve fitting techniques. A 1-DOF device having an excitation motor at the user side and brushless DC motor and MR-brake in the haptic side was used in the experiments. The excitation motor is used to generate a white noise torque input to simulate a user for frequency domain transparency tests. Results of the proposed and conventional closed loop impedance control (CLIC) algorithms were compared. The proposed algorithm improves the transparency of MR-brake by eliminating its drawbacks and presents a larger Z-width than the active actuator alone.
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