Cao Xibin, Shi Peng, Li Zhuoshi, Liu Ming
IEEE Trans Neural Netw Learn Syst. 2018 Sep;29(9):4303-4313. doi: 10.1109/TNNLS.2017.2756993. Epub 2017 Nov 1.
This paper investigates the neural-network-based adaptive control problem for a class of continuous-time nonlinear systems with actuator faults and external disturbances. The model uncertainties in the system are not required to satisfy the norm-bounded assumption, and the exact information for components faults and external disturbance is totally unknown, which represents more general cases in practical systems. An indirect adaptive backstepping control strategy is proposed to cope with the stabilization problem, where the unknown nonlinearity is approximated by the adaptive neural-network scheme, and the loss of effectiveness of actuators faults and the norm bounds of exogenous disturbances are estimated via designed online adaptive updating laws. The developed adaptive backstepping control law can ensure the asymptotic stability of the fault closed-loop system despite of unknown nonlinear function, actuator faults, and disturbances. Finally, an application example based on spacecraft attitude regulation is provided to demonstrate the effectiveness and the potential of the developed new neural adaptive control approach.
本文研究了一类具有执行器故障和外部干扰的连续时间非线性系统基于神经网络的自适应控制问题。系统中的模型不确定性无需满足范数有界假设，且部件故障和外部干扰的精确信息完全未知，这代表了实际系统中更一般的情况。提出了一种间接自适应反步控制策略来解决稳定问题，其中未知非线性通过自适应神经网络方案进行逼近，执行器故障的有效性损失和外部干扰的范数界通过设计的在线自适应更新律进行估计。所提出的自适应反步控制律能够确保故障闭环系统的渐近稳定性，尽管存在未知非线性函数、执行器故障和干扰。最后，给出了一个基于航天器姿态调节的应用实例，以证明所提出的新型神经自适应控制方法的有效性和潜力。
