Constrained Manipulator Force Position Hybrid Control Based on Fuzzy Neural Network

Guogang Wang; Qilong Sun

doi:10.54097/y5cxes54

Authors

Guogang Wang
Qilong Sun

DOI:

https://doi.org/10.54097/y5cxes54

Keywords:

Constrained Robotic Arm, Fuzzy Neural Network, Sliding Mode Control Algorithm, Model Reduction

Abstract

Aiming at the problem that the end motion of a robot arm is constrained and cannot be accurately modeled when it is in contact with the working environment, a reduced order fuzzy neural network sliding mode control method is designed to realize the high-precision tracking of the Angle, angular velocity and contact force of each joint. Firstly, the model of constrained double-joint manipulator is reduced to simplify the dynamic model and reduce the computational difficulty. Then, fuzzy neural network is used to approximate the friction and nonlinear control quantity generated during the movement of constrained manipulator. Finally, sliding mode control method is used to improve the robustness of the controller and the stability of the system is proved by Lyapunov function. The simulation results show that the tracking error of each joint of the constrained manipulator is controlled within 0.003.

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