Neural Network-Based Fixed-Time Tracking Control for Input-Quantized Nonlinear Systems With Actuator Faults

This study reports a fixed-time tracking control problem for strict-feedback nonlinear systems with quantized inputs and actuator faults where the total number of faults is allowed to be infinite. By taking advantage of radial basis function neural networks (RBFNNs), unknown nonlinear function terms...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2024-03, Vol.35 (3), p.3978-3988
Main Authors: Sun, Wei, Wu, Jing, Su, Shun-Feng, Zhao, Xudong
Format: Article
Language:English
Subjects:
Actuator faults
Actuators
Adaptive systems
Algorithms
Closed loops
Control algorithms
Control systems
Control theory
Convergence
Dynamic models
Faults
Feedback control
fixed-time tracking control
Hysteresis
neural network (NN)
Neural networks
Nonlinear control
Nonlinear systems
Nonlinearity
quantized inputs
Radial basis function
Theoretical analysis
Tracking
Tracking control
Tracking errors
Upper bound
Upper bounds
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Summary:This study reports a fixed-time tracking control problem for strict-feedback nonlinear systems with quantized inputs and actuator faults where the total number of faults is allowed to be infinite. By taking advantage of radial basis function neural networks (RBFNNs), unknown nonlinear function terms in the system dynamic model can be effectively approached. In addition, based on the sector property of quantization nonlinearities and the structure of the actuator fault model, novel adaptive estimations and innovative auxiliary design signals are constructed to compensate for the influence caused by actuator faults and quantized inputs properly in the fixed-time convergence settings. Then, rigorous theoretical analysis manifests that the proposed control scheme can make the output tracking error converge to a small neighborhood of the origin within a fixed time, and the upper bound of the setting time not only does not depend on initial states of the system but also can be preassigned by selecting parameters appropriately. Meanwhile, all the signals in the closed-loop system remain bounded. Finally, a numerical example and a practical example of a single-link manipulator are presented to demonstrate the effectiveness of the proposed control algorithm.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2022.3201504