Modeling and Adaptive Neural Network Control for a Soft Robotic Arm With Prescribed Motion Constraints
Yan Yang, Jiangtao Han, Zhijie Liu, Zhijia Zhao, Keum‐Shik Hong
Abstract
This paper presents a dynamic model and performance constraint control of a line-driven soft robotic arm. The dynamics model of the soft robotic arm is established by combining the screw theory and the Cosserat theory. The unmodeled dynamics of the system are considered, and an adaptive neural network controller is designed using the backstepping method and radial basis function neural network. The stability of the closed-loop system and the boundedness of the tracking error are verified using Lyapunov theory. The simulation results show that our approach is a good solution to the motion constraint problem of the line-driven soft robotic arm.
Topics & Concepts
Control theory (sociology)BacksteppingConstraint (computer-aided design)Controller (irrigation)Artificial neural networkRobotic armComputer scienceLyapunov functionLyapunov stabilityAdaptive controlTracking (education)Control engineeringMathematicsControl (management)Nonlinear systemArtificial intelligenceEngineeringPhysicsAgronomyBiologyPsychologyGeometryQuantum mechanicsPedagogySoft Robotics and ApplicationsAdaptive Control of Nonlinear SystemsRobot Manipulation and Learning