A Neural Network Based Dynamic Control Method for Soft Pneumatic Actuator with Symmetrical Chambers

Dynamic modeling and control of the soft pneumatic actuators are challenging research. In this paper, a neural network based dynamic control method used for a soft pneumatic actuator with symmetrical chambers is proposed. The neural network is introduced to create the dynamic model for predicting the state of the actuator. In this dynamic model, the effect of the uninflated rubber block on bending deformation is considered. Both pressures of the actuator are used for predicting the state of the actuator during the bending motion. The controller is designed based on this dynamic model for trajectory tracking control. Three types of trajectory tracking control experiments are performed to validate the proposed method. The results show that the proposed control method can control the motion of the actuator and track the trajectory effectively.

[1]  Jamie Paik,et al.  Modeling, Design, and Development of Soft Pneumatic Actuators with Finite Element Method   , 2016 .

[2]  Matteo Cianchetti,et al.  Dynamic Model of a Multibending Soft Robot Arm Driven by Cables , 2014, IEEE Transactions on Robotics.

[3]  Cecilia Laschi,et al.  Learning dynamic models for open loop predictive control of soft robotic manipulators. , 2017, Bioinspiration & biomimetics.

[4]  Yiqing Li,et al.  Modeling and Analysis of Soft Pneumatic Actuator with Symmetrical Chambers Used for Bionic Robotic Fish. , 2020, Soft robotics.

[5]  Jochen J. Steil,et al.  Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control † , 2017, Sensors.

[6]  Sanghyun Yoon,et al.  Soft Robotics: A Review of Recent Developments of Pneumatic Soft Actuators , 2020, Actuators.

[7]  Daniela Rus,et al.  Design, kinematics, and control of a soft spatial fluidic elastomer manipulator , 2016, Int. J. Robotics Res..

[8]  Tao Wang,et al.  A computationally efficient dynamical model of fluidic soft actuators and its experimental verification , 2019, Mechatronics.

[9]  Daniela Rus,et al.  Dynamics and trajectory optimization for a soft spatial fluidic elastomer manipulator , 2016, Int. J. Robotics Res..

[10]  LuoMing,et al.  Theoretical Modeling and Experimental Analysis of a Pressure-Operated Soft Robotic Snake , 2014 .

[11]  David Wingate,et al.  Learning nonlinear dynamic models of soft robots for model predictive control with neural networks , 2018, 2018 IEEE International Conference on Soft Robotics (RoboSoft).

[12]  WuPang,et al.  The Structure, Design, and Closed-Loop Motion Control of a Differential Drive Soft Robot , 2017 .

[13]  CianchettiMatteo,et al.  A Bioinspired Soft Robotic Gripper for Adaptable and Effective Grasping , 2015 .

[14]  Weihua Li,et al.  A Structural Optimisation Method for a Soft Pneumatic Actuator , 2018, Robotics.

[15]  Ian D. Walker,et al.  A Neural Network Controller for Continuum Robots , 2007, IEEE Transactions on Robotics.

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

[17]  ShapiroYoel,et al.  Modeling a Hyperflexible Planar Bending Actuator as an Inextensible Euler–Bernoulli Beam for Use in Flexible Robots , 2015 .