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.

本文介绍了一种不确定的手控系统的新型控制策略，该策略可与未知的约束表面模型和未校准的相机模型一起使用。此外，系统还包括不确定的动力学和运动学。为了更接近真实的机器人系统，我们还考虑了死区输入情况。因此，死区模型的参数间隔和斜率也是未知的。因此，提出了一种新颖的基于自适应图像的视觉伺服（IBVS）和力控制方法。通过自适应控制实现了未知力和未标定相机模型的控制方法。通过设计死区输入的逆平滑模型以抵消由于执行器约束而产生的非线性影响，可以完成未知死区输入的解决方案，整个系统证明了力跟踪控制和图像位置渐近收敛到零。最后，建立了MATLAB仿真，实验表明了该方案的有效性。