Mechanical Design and Data-Enabled Predictive Control of a Planar Soft Robot

作     者：Wang, Huanqing Zhang, Kaixiang Lee, Kyungjoon Mei, Yu Zhu, Keyi Srivastava, Vaibhav Sheng, Jun Li, Zhaojian 

作者机构：Michigan State Univ Dept Mech Engn E Lansing MI 48824 USA Univ Calif Riverside Dept Mech Engn Riverside CA 92521 USA Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA 

出 版 物：《IEEE ROBOTICS AND AUTOMATION LETTERS》 (IEEE Robot. Autom.)

年 卷 期：2024年第9卷第9期

页      面：7923-7930页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 

基　　金：U.S. National Science Foundation Project [ECCS-2024649  CMMI-2320698] 

主　　题：Soft robotics Robots Fabrics Computational modeling Aerospace electronics Pneumatic systems Predictive control Modeling control and learning for soft robots soft sensors and actuators data-driven control predictive control 

摘      要：Soft robots offer a unique combination of flexibility, adaptability, and safety, making them well-suited for a diverse range of applications. However, the inherent complexity of soft robots poses great challenges in their modeling and control. In this letter, we present the mechanical design and data-driven control of a pneumatic-driven soft planar robot. Specifically, we employ a data-enabled predictive control (DeePC) strategy that directly utilizes system input/output data to achieve safe and optimal control, eliminating the need for tedious system identification or modeling. In addition, a dimension reduction technique is introduced into the DeePC framework, resulting in significantly enhanced computational efficiency with minimal to no degradation in control performance. Comparative experiments are conducted to validate the efficacy of DeePC in the control of the fabricated soft robot.