Neural network state observer-based robust adaptive fault-tolerant quantized iterative learning control for the rigid-flexible coupled robotic systems with unknown time delays

DOI10.1016/J.AMC.2022.127286OpenAlexW4281635273WikidataQ114953175 ScholiaQ114953175MaRDI QIDQ2148053

Haoping Wang, Yang Tian, Xing-Yu Zhou

Publication date: 21 June 2022

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2022.127286

zbMATH Keywords

time-delayiterative learning controlfault-toleranthysteresis quantizationneural networks state observerrigid-flexible coupled robotic systems

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Model systems in control theory (93Cxx) Controllability, observability, and system structure (93Bxx)

Related Items (5)

Fixed-time neural network trajectory tracking control for the rigid-flexible coupled robotic mechanisms with large beam-deflections ⋮ Nonlinear disturbance observer-based robust predefined time tracking and vibration suppression control for the rigid-flexible coupled robotic mechanisms with large beam-deformations ⋮ Dynamic modeling and neural network compensation for dual-flexible servo system with an underactuated hand ⋮ Dynamic learning-based fault tolerant control for robotic manipulators with actuator faults ⋮ Event-triggered iterative learning control for asynchronously switched systems

Cites Work

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• Adaptive learning tracking for robot manipulators with varying trial lengths
• Boundary adaptive fault-tolerant control for a flexible Timoshenko arm with backlash-like hysteresis
• Boundary control for a constrained two-link rigid–flexible manipulator with prescribed performance
• Iterative learning control for output‐constrained nonlinear systems with input quantization and actuator faults
• Disturbance observer‐based adaptive boundary iterative learning control for a rigid‐flexible manipulator with input backlash and endpoint constraint

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