Title: Force/position control of constrained reconfigurable manipulators with sliding mode control based on adaptive neural network

Authors: Ruchika; Naveen Kumar

Addresses: Department of Mathematics, National Institute of Technology Kurukshetra, Haryana-136119, India; K.M. Government (P.G) College Narwana, Jind, Haryana-136119, India ' Department of Mathematics, National Institute of Technology Kurukshetra, Haryana-136119, India

Abstract: A reconfigurable manipulator can achieve proficient end effector and elongate workspace. However, deformable link causes frequent changes in shape and therefore bring difficulties to model and control the manipulator. In view of distinctive behaviour because of bending operation, a sliding mode based mechanism with no prior dynamic information is introduced for validated control operation. The nonlinear term included in the sliding mode is to improve the convergence rate. Moreover, we show that fast terminal sliders reinforce parametric uncertainty as compared to conventional sliders. The neural network system is adopted for the estimation of nonlinear components whereas the friction term and constraint force of each joint are compensated with the help of adaptive control. The Lyapunov theory proves the stability of a closed-loop system. Finally, simulations are performed in a comparative manner with two different configuration controls that will provide the benefit of the design method.

Keywords: finite time convergence; RBF neural network; adaptive bound; reconstruction error; terminal sliding mode control.

DOI: 10.1504/IJMIC.2023.130124

International Journal of Modelling, Identification and Control, 2023 Vol.42 No.3, pp.259 - 269

Received: 25 Feb 2022
Received in revised form: 26 May 2022
Accepted: 30 May 2022

Published online: 05 Apr 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article