Soft grippers with good passive compliance can effectively adapt to the shape of a target object and have better safe grasping performance than rigid grippers. However, for soft or fragile objects, passive compliance is insufficient to prevent grippers from crushing the target. Thus, to complete nondestructive grasping tasks, precision force sensing and control are immensely important for soft grippers. In this article, we proposed an online learning self-tuning nonlinearity impedance controller for a tactile self-sensing two-finger soft gripper so that its grasping force can be controlled accurately. For the soft gripper, its grasping force is sensed by a liquid lens-based optical tactile sensing unit that contains a self-sensing fingertip and a liquid lens module and has many advantages of a rapid response time (about 0.04 s), stable output, good sensitivity (>0.4985 V/N), resolution (0.03 N), linearity (R2 > 0.96), and low cost (power consumption: 5 mW, preparation cost <CNY 100). The proposed force controller for the soft gripper was designed based on the Hammerstein nonlinear model, and due to adaptive laws designed by an adaptive theory and the full online sequential extreme learning machine, respectively, its parameters can be adjusted online. The simulation and experiment results demonstrate that the proposed force controller exhibits good force control performance and robustness in a nonlinear contact environment. Moreover, because of its simple control structure and good online learning ability, the proposed controller has the advantages of being real time and easy to realize, which shows its potential applications in many grasping tasks, such as collecting biological samples and sorting industrial products.
Keywords: force control; full online sequential extreme learning machine; liquid lens-based optical tactile sensing unit; soft gripper.