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.

[1]  George A. Bekey,et al.  System identification- an introduction and a survey , 1970 .

[2]  Z. Hou,et al.  Dual-stage Optimal Iterative Learning Control for Nonlinear Non-affine Discrete-time Systems , 2007 .

[3]  Fionnuala Connolly,et al.  Automatic design of fiber-reinforced soft actuators for trajectory matching , 2016, Proceedings of the National Academy of Sciences.

[4]  Cecilia Laschi,et al.  Control Strategies for Soft Robotic Manipulators: A Survey. , 2018, Soft robotics.

[5]  Zheng Li,et al.  Robotic Glove with Soft-Elastic Composite Actuators for Assisting Activities of Daily Living. , 2019, Soft robotics.

[6]  Jay H. Lee,et al.  Model predictive control technique combined with iterative learning for batch processes , 1999 .

[7]  Claire J. Tomlin,et al.  Learning-based model predictive control on a quadrotor: Onboard implementation and experimental results , 2012, 2012 IEEE International Conference on Robotics and Automation.

[8]  Petros A. Ioannou,et al.  Robust Adaptive Control , 2012 .

[9]  Xiangjie Liu,et al.  Feedback-Assisted Iterative Learning Model Predictive Control with Nonlinear Fuzzy Model , 2014 .

[10]  Kevin L. Moore,et al.  Iterative Learning Control: Brief Survey and Categorization , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[11]  Shinji Doki,et al.  Sensorless control of permanent-magnet synchronous motors using online parameter identification based on system identification theory , 2006, IEEE Transactions on Industrial Electronics.

[12]  Blake Hannaford,et al.  Measurement and modeling of McKibben pneumatic artificial muscles , 1996, IEEE Trans. Robotics Autom..

[13]  J. R. Cueli,et al.  Iterative nonlinear model predictive control. Stability, robustness and applications , 2008 .

[14]  Bryan A. Jones,et al.  Three dimensional statics for continuum robotics , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Gregory S. Chirikjian,et al.  A modal approach to hyper-redundant manipulator kinematics , 1994, IEEE Trans. Robotics Autom..

[16]  F. Miyazaki,et al.  Bettering operation of dynamic systems by learning: A new control theory for servomechanism or mechatronics systems , 1984, The 23rd IEEE Conference on Decision and Control.

[17]  Maarten Steinbuch,et al.  Learning-based identification and iterative learning control of direct-drive robots , 2005, IEEE Transactions on Control Systems Technology.

[18]  John Kenneth Salisbury,et al.  Configuration Tracking for Continuum Manipulators With Coupled Tendon Drive , 2009, IEEE Transactions on Robotics.