Active attitude fault-tolerant tracking control of flexible spacecraft via the Chebyshev neural network

2018 ◽  
Vol 41 (4) ◽  
pp. 925-933 ◽  
Author(s):  
Kunfeng Lu ◽  
Tianya Li ◽  
Lijun Zhang

This paper describes a novel finite-time attitude tracking control approach for flexible spacecraft. This is achieved by integrating sliding-mode control and the active real-time fault-tolerant reconfiguration method. In this approach, the attitude error dynamics and the kinematics of the flexible spacecraft are first established. Then, a nonsingular terminal sliding-mode surface is designed, based on finite-time control theory. Applying the Chebyshev neural network, the uncertain dynamics induced by external disturbances and uncertain inertia parameters are approximated and estimated. The nominal control law and the compensation control law to obtain the active reconfiguration fault-tolerant controller are finally developed in normal and fault conditions, respectively. The closed-loop tracking system is proved to be uniformly ultimately bounded stable after a finite time. Numerical simulations are presented for a flexible spacecraft to illustrate the efficiency of the proposed controller.

Author(s):  
Qun Zong ◽  
Xiuyun Zhang ◽  
Shikai Shao ◽  
Bailing Tian ◽  
Wenjing Liu

In this paper, finite-time fault-tolerant attitude tracking control is investigated for rigid spacecraft system with external disturbances, inertia uncertainties and actuator faults. A novel finite-time disturbance observer combined with a nonsingular terminal sliding mode controller is developed. Using an equivalent output error injection approach, a finite-time disturbance observer with simple structure is firstly designed to estimate lumped uncertainty. Then, to remove the requirement of prior knowledge about lumped uncertainty and reduce chattering, an adaptive finite-time disturbance observer is further proposed, and the estimations converge to the neighborhood of the true values. Based on the designed observer, a unified finite-time attitude controller is obtained automatically. Finally, both additive and multiplicative faults are considered for simulations and the results illustrate the great fault-tolerant capability of the proposed scheme.


Author(s):  
Bing Huang ◽  
Ai-jun Li ◽  
Yong Guo ◽  
Chang-qing Wang ◽  
Jin-hua Guo

This paper investigates the finite-time attitude tracking control problem for spacecraft in the presence of external disturbances and actuator faults. Two anti-unwinding attitude tracking control schemes have been proposed based on the rotation matrix and sliding mode control technology. Utilizing a fast terminal sliding mode surface, the first controller can fulfill the finite-time attitude tracking control task with disturbance rejection ability. The second controller can improve the system reliability when the actuator fault occurs. Rigorous mathematical analysis and proof concludes that the proposed controllers can make a spacecraft track the desired attitude command in finite time. Numerical simulation results are presented to demonstrate the effectiveness of the proposed controllers.


Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Yanchao Yin ◽  
Hongwei Niu ◽  
Xiaobao Liu

A novel neural network sliding mode control based on multicommunity bidirectional drive collaborative search algorithm (M-CBDCS) is proposed to design a flight controller for performing the attitude tracking control of a quad tilt rotors aircraft (QTRA). Firstly, the attitude dynamic model of the QTRA concerning propeller tension, channel arm, and moment of inertia is formulated, and the equivalent sliding mode control law is stated. Secondly, an adaptive control algorithm is presented to eliminate the approximation error, where a radial basis function (RBF) neural network is used to online regulate the equivalent sliding mode control law, and the novel M-CBDCS algorithm is developed to uniformly update the unknown neural network weights and essential model parameters adaptively. The nonlinear approximation error is obtained and serves as a novel leakage term in the adaptations to guarantee the sliding surface convergence and eliminate the chattering phenomenon, which benefit the overall attitude control performance for QTRA. Finally, the appropriate comparisons among the novel adaptive neural network sliding mode control, the classical neural network sliding mode control, and the dynamic inverse PID control are examined, and comparative simulations are included to verify the efficacy of the proposed control method.


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