Adaptive Neural Control for Strict-Feedback Nonlinear Systems Without Backstepping
Adaptive Neural Control for Strict-Feedback Nonlinear Systems Without Backstepping
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DOI：
10.1109/tnn.2009.2020982
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发表时间：
2009-07
影响因子：
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通讯作者：
Jang-Hyun Park;Seong-Hwan Kim;C. Moon
中科院分区：
文献类型：
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作者：
Jang-Hyun Park;Seong-Hwan Kim;C. Moon
针对单输入单输出（SISO）严格反馈非线性系统，提出了一种新的自适应神经网络控制算法。以往的严格反馈非线性系统的自适应神经网络控制算法大多是基于反推算法，这使得控制律和稳定性分析非常复杂。该方法的主要贡献是证明了严格反馈系统的状态反馈控制问题可以看作是系统的输出反馈控制问题的规范形式。因此，所提出的控制算法是相当简单的比以前的基于反推。该方法在很大程度上依赖于神经网络的普适逼近特性，仅用一个神经网络来逼近集中不确定系统的非线性。保证了神经网络权值和滤波跟踪误差的半线性李雅普诺夫稳定性。
In this brief, a new adaptive neurocontrol algorithm for a single-input-single-output (SISO) strict-feedback nonlinear system is proposed. Most of the previous adaptive neural control algorithms for strict-feedback nonlinear systems were based on the backstepping scheme, which makes the control law and stability analysis very complicated. The main contribution of the proposed method is that it demonstrates that the state-feedback control of the strict-feedback system can be viewed as the output-feedback control problem of the system in the normal form. As a result, the proposed control algorithm is considerably simpler than the previous ones based on backstepping. Depending heavily on the universal approximation property of the neural network (NN), only one NN is employed to approximate the lumped uncertain system nonlinearity. The Lyapunov stability of the NN weights and filtered tracking error is guaranteed in the semiglobal sense.