Tendon-Driven Robotic Arm Control Method Based on Radial Basis Function Adaptive Tracking Algorithm

Abstract

With the rapid development of intelligent technology, robotic arms are widely used in different fields. The study combines the tendon drive theory and radial basis function neural network to construct the robotic arm model, and then combines the back-stepping method and non-singular fast terminal sliding mode to improve the controller and system optimization of the tendon drive robotic arm model. Simulation tests on commercial mathematical software platforms yielded that joint 2 achieves stable overlap of position trajectory and velocity trajectory after 0.2s and 0.5s with errors of 1° and 1°/s, respectively. Radial basis function neural network approximation of robotic arm error converged to the true value at 14s. The optimized joint achieved the accuracy of trajectory tracking after 0.2s. Also the control torque of joint 2 changes at 1.5s, 4.5s and 8s and its change is small. The tendon tension curve was smoother and more stable in the range of -0.05N~0.0.5N to show that the robotic arm model has superiority after the optimization of the controller, and the interference observer had accurate estimation of the tracking trajectory of the tendon-driven robotic arm. Therefore, the radial basis function-based adaptive tracking algorithm had higher accuracy for the tendon-driven robotic arm model and provided technical reference for the control system of the intelligent robotic arm.