Robust adaptive backstepping INTSM control for robotic manipulators based on ELM

Authors：Gao, Miao-Miao; Jin, Xiao-Zheng^*; Ding, Li-Jian

Source：NEURAL COMPUTING & APPLICATIONS, 2022, 34(7): 5029-5039.

DOI：10.1007/s00521-021-05824-y

• Robotic manipulators
• Integral nonsingular terminal sliding mode control (INTSMC)
• Backstepping control
• Extreme learning machine (ELM)

This paper investigates a novel control algorithm to deal with trajectory tracking control problems of robotic manipulators based on adaptive backstepping integral nonsingular terminal sliding mode control (BINTSMC). The proposed approach is developed based on an integration between the integral nonsingular terminal sliding mode control (INTSMC) and stability analysis procedure of backstepping control technique. In addition, the extreme learning machine (ELM) learning algorithm is introduced to approximate lumped uncertain component of the dynamics model. The approximated lumped uncertain terms are transmitted to rebuild the dynamics and provide a compensation feedback for control systems. Moreover, the robust term is utilized to counteract the approximation residual error, wherein the nonconstant ELM approximation error is analyzed. The superior performance of the BINTSMC is validated by comparing conventional NTSMC in the simulations.