Neural network state observer-based robust adaptive fault-tolerant quantized iterative learning control for the rigid-flexible coupled robotic systems with unknown time delays
作者：
Highlights：
• Confronting with the unmodelled dynamics and time-delay influence of the reduced-order rigid-flexible coupled robotic systems (RFCRSs), the RBFNN-based state observer for the RFCRSs is first developed, only based on the joint angular positions of the controlled robotic systems.
• In contrast to previous full state-feedback control schemes, a novel state observer-based fault-tolerant robust adaptive quantized iterative learning output feedback control (RAQILOFC) law is proposed for trajectory tracking and vibration suppression of RFCRSs in face of actuator faults and hysteresis quantization.
• By virtue of the filtered tracking errors, it is proved by barrier composite energy function (CEF) that tracking errors and the elastic vibrations in presence of unmodelled dynamics and unknown external disturbances will eventually converge to zero with a small neighborhood along with the iteration axis.
摘要
•Confronting with the unmodelled dynamics and time-delay influence of the reduced-order rigid-flexible coupled robotic systems (RFCRSs), the RBFNN-based state observer for the RFCRSs is first developed, only based on the joint angular positions of the controlled robotic systems.•In contrast to previous full state-feedback control schemes, a novel state observer-based fault-tolerant robust adaptive quantized iterative learning output feedback control (RAQILOFC) law is proposed for trajectory tracking and vibration suppression of RFCRSs in face of actuator faults and hysteresis quantization.•By virtue of the filtered tracking errors, it is proved by barrier composite energy function (CEF) that tracking errors and the elastic vibrations in presence of unmodelled dynamics and unknown external disturbances will eventually converge to zero with a small neighborhood along with the iteration axis.
论文关键词：Iterative learning control,Fault-tolerant,Rigid-flexible coupled robotic systems,Neural networks state observer,Hysteresis quantization,Time-delay
论文评审过程：Received 22 January 2022, Revised 21 April 2022, Accepted 23 May 2022, Available online 5 June 2022, Version of Record 5 June 2022.
论文官网地址：https://doi.org/10.1016/j.amc.2022.127286
