Adaptive neural network finite time control for quadrotor UAV with unknown input saturation

This paper investigates finite-time control of uncertain robotic manipulators with external disturbances by means of neural network control and backstepping technique. To solve the “explosion of terms” in traditional backstepping control, a second-order command filter is designed, and the virtual input and its first-order derivative can be obtained accurately in a finite time. The parameters of the neural network are updated by using the tracking error signals. The proposed controller can guarantee that the tracking error converges to a small region of the origin in some finite time. Finally, we give a simulation study to show the effectiveness of the proposed method.

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Robust-Adaptive Neural Network Finite-Time Control and Vibration Suppression for Flexible-Base Flexible-Joint Space Manipulator

2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) ◽

10.1109/ihmsc.2018.00059 ◽

2018 ◽

Author(s):

Zhiyong Chen ◽

Tingting Zhang ◽

Zhenhan Li

Keyword(s):

Neural Network ◽

Finite Time ◽

Vibration Suppression ◽

Joint Space ◽

Adaptive Neural Network ◽

Space Manipulator ◽

Flexible Joint ◽

Time Control ◽

Flexible Base ◽

Robust Adaptive

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Finite-time predictor line-of-sight–based adaptive neural network path following for unmanned surface vessels with unknown dynamics and input saturation

International Journal of Advanced Robotic Systems ◽

10.1177/1729881418814699 ◽

2018 ◽

Vol 15 (6) ◽

pp. 172988141881469 ◽

Cited By ~ 8

Author(s):

Yalei Yu ◽

Chen Guo ◽

Haomiao Yu

Keyword(s):

Neural Network ◽

Finite Time ◽

Input Saturation ◽

Path Following ◽

Lyapunov Stability Theory ◽

Line Of Sight ◽

Adaptive Neural Network ◽

Minimal Learning Parameter ◽

Surface Vessels ◽

Learning Parameter

In the presence of unknown dynamics and input saturation, a finite-time predictor line-of-sight–based adaptive neural network scheme is presented for the path following of unmanned surface vessels. The proposed scheme merges with the guidance and the control subsystem of unmanned surface vessels together. A finite-time predictor–based line-of-sight guidance law is developed to ensure unmanned surface vessels effectively converging to and following the referenced path. Then, the path-following control laws are designed by combining neural network-based minimal learning parameter technique with backstepping method, where minimal learning parameter is applied to account for system nonparametric uncertainties. The key features of this scheme, first, the finite-time predictor errors are guaranteed; second, designed controllers are independent of the system model; and third, only required two parameters update online for each control law. The rigorous theory analysis verifies that all signals in the path-following guidance-control system are semi-globally uniformly ultimately bounded via Lyapunov stability theory. Simulation results illustrate the effectiveness and performance of the presented scheme.

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