Model Predictive Control for Finger Joint Trajectory of TU Biomimetic Hand

A long standing goal in rehabilitation robotics is to emulate the human-like stable grasping operations by upper limb prosthesis. For stable grasping and manipulation by a prosthetic hand, the finger joints should follow the trajectories of the natural counterparts. Although a number of commercial prosthetic hands and research prototypes have been developed, mimicking the human-like movement is still a challenge. One of the main reasons for this is the lack of fast on-line error minimizing control approach during trajectory tracking. In this paper, we propose a model predictive control (MPC) architecture that can generate desired motions through online error minimization. Focus is on emulating the human-like finger joint trajectories by Tezpur University (TU) Biomimetic Hand. The MPC perform the online error minimization and control in a unified way by tracking reference trajectories. The reference trajectories were generated by recording the finger joint trajectories of human hand. The proposed MPC was implemented on the TU Biomimetic Hand index finger; wherein finger joint trajectories of human finger was used to actuate the control architecture. The simulation results show that the finger joint trajectories of TU Biomimetic Hand are in close conformity to that of the human finger. The experimental results for the TU Biomimetic Hand index finger satisfies the dynamic constraints of human hand and thus ensures stable grasping by the prosthetic hand.