A Novel Iterative Learning Model Predictive Control Method For Soft Bending Actuators

Soft robots attract research interests worldwide. However, its control remains challenging due to the difficulty in sensing and accurate modeling. In this paper, we propose a novel iterative learning model predictive control (ILMPC) method for soft bending actuators. The uniqueness of our approach is the ability to improve model accuracy gradually. In this method, a pseudo-rigid-body model is used to take an initial guess of the bending behavior of the actuator and the model accuracy is improved with iterative learning. Compared with conventional model free iterative learning control (ILC), the proposed method significantly reduces the learning curve. Compared with the model predictive control (MPC), the proposed method does not rely on an accurate model and it will output a satisfactory model after the learning process. A soft-elastic composite actuator (SECA) is used to validate the proposed method. Both simulation and experimental results show that the proposed method outperforms the conventional MPC and ILC.