A Novel Parameter Estimation Method for Pneumatic Soft Hand Control Applying Logarithmic Decrement for Pseudo‐Rigid Body Modeling

Abstract

Controlling soft robots, especially soft hand grasping, is complex due to their ubiquitous deformation, prompting the use of reduced model‐based controllers to provide sufficient state information for high dynamic response control performance. However, most modeling techniques face computational efficiency and complexity of parameter identification issues. To alleviate this, a paradigm coupling an analytical modeling approach based on pseudo‐rigid body modeling and the logarithmic decrement method (PRBM + LDM) for parameter estimation is proposed. Using a soft robot hand test bed, the PRBM + LDM model for a closed‐loop position controller is applied and is compared with a simple proportional–integral–derivative controller (PID controller) static shape control of soft continuum robots using deep visual inverse kinematic models. Furthermore, the PRBM + LDM model‐based force controller is compared with simple constant pressure grasping control by pinching tasks on low‐weight, small objects—a screwdriver, a potato chip, and a brass coin. The PRBM + LDM‐based position controller outperforms the simple PID position controller, and the PRBM + LDM‐based force controller achieves a higher success rate than the constant pressure grasping control in the pinching tasks. In conclusion, the PRBM + LDM modeling technique proves to be a convenient and efficient way to model the dynamic behavior of soft actuators closely and can be applied to build high‐precision position and force controllers.