反推
控制理论(社会学)
非线性系统
人工神经网络
观察员(物理)
控制器(灌溉)
函数逼近
计算机科学
自适应控制
先验与后验
近似误差
控制工程
人工智能
工程类
算法
控制(管理)
物理
哲学
认识论
生物
量子力学
农学
作者
Jin Young Choi,Jay A. Farrell
出处
期刊:IEEE Transactions on Neural Networks
[Institute of Electrical and Electronics Engineers]
日期:2001-01-01
卷期号:12 (5): 1103-1112
被引量:132
摘要
This paper extends the application of neurocontrol approaches to a new class of nonlinear systems diffeomorphic to output feedback nonlinear systems with unmeasured states. A neural-based adaptive observer is introduced for state estimation as well as system identification using only output measurements during online operation. System identification is achieved via the online approximation of a priori unknown functions. The controller is designed using the backstepping control design procedure. Leakage terms in the adaptive laws and nonlinear damping terms in the backstepping controller are introduced to prevent instability from arising due to the inherent approximation error. A primary benefit of the online function approximation is the reduction of approximation errors, which allows reduction of both the observer and controller gains. A semi-global stability analysis for the proposed approach is provided and the feasibility is investigated by an illustrative simulation example.
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