Adaptive IBVS and Force Control for Uncertain Robotic System with Unknown Dead-zone Inputs

This article introduces a novel control strategy for the uncertain eye-to-hand system, which is considered to work with unknown model of constraint surface and uncalibrated camera model. Besides, the uncertain dynamics and kinematics are also included in the system. In order to be closer to the real robot system, we also consider it with dead-zone inputs situation. So the parameter intervals and slopes of the dead-zone model is also unknown. Hence, a novel adaptive image-based visual servoing (IBVS) and force control approach is put forward. The control method of unknown force and uncalibrated camera model is achieved by adaptive control. The solution of unknown dead-zone inputs is completed by designing a inverse smooth model of dead-zone inputs to offset the nonlinear affect due to the actuator constraint, and the whole system is proved that the force tracking control and image position converge to zero asymptotically. Finally, the MATLAB simulation is set up and the experiment shows the validity of the proposed scheme.

Automated summary

This article introduces a novel adaptive image-based visual servoing and force control approach to deal with the uncertain eye-to-hand system, including unknown constraint surface model, uncalibrated camera model, uncertain dynamics, and kinematics. The unknown dead-zone inputs are handled by designing an inverse smooth model to offset the nonlinear affect due to the actuator constraint.