Robust adaptive backstepping INTSM control for robotic manipulators based on ELM
- Resource Type
- Authors
- Miao-Miao Gao; Lijian Ding; Xiao-Zheng Jin
- Source
- Neural Computing and Applications. 34:5029-5039
- Subject
- 0209 industrial biotechnology
Computer science
Robot manipulator
Stability (learning theory)
02 engineering and technology
Residual
Compensation (engineering)
020901 industrial engineering & automation
Artificial Intelligence
Control theory
Approximation error
Backstepping
Control system
0202 electrical engineering, electronic engineering, information engineering
Trajectory
020201 artificial intelligence & image processing
Software
Extreme learning machine
- Language
- ISSN
- 1433-3058
0941-0643
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.