Finite-Time Neural Network Fault-Tolerant Control for Robotic Manipulators under Multiple Constraints

Electronics

Lingxi Peng¹  Zhao Zhang¹  Jianing Zhang¹  Xiaowei Wang¹ 

[1] School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China;

关键词: actuator faults;    input saturation;    dead zone;    output constraints;    finite time;   

DOI  :  10.3390/electronics11091343

来源: DOAJ

【 摘 要 】

In this study, a backstepping-based fault-tolerant controller for a robotic manipulator system with input and output constraints was developed. First, a barrier Lyapunov function was adopted to ensure that the system output satisfied time-varying constraints. Subsequently, the actuator input saturation and asymmetric dead-zone characteristics were also considered, and the actuator characteristics were described using a continuous function. The impacts of actuator failures and unknown dynamical parameters of the system were eliminated by employing Gaussian radial basis function neural networks. The external disturbances were compensated for, using a disturbance observer. Meanwhile, a finite-time dynamic surface technique was adopted to accelerate the convergence of the system errors. Finally, simulation of a 2-degrees-of-freedom robotic manipulator system showed the effectiveness of the proposed controller.

【 授权许可】

Unknown