Adaptive Neural Network Output-feedback Stabilization for a Class of Stochastic Nonlinear Strict-feedback Systems
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摘要: 针对具有严格反馈形式的随机非线性系统, 首次引入神经网络控制技术, 设计了适当形式的随机控制 Lyapunov函数, 并运用反推(Backstepping)技术和非线性观测器设计技术, 构造出一类自适应神经网络输出反馈控制器. 在一定条件下, 证明了闭环系统平衡点依概率稳定. 仿真算例验证了所给控制方案的有效性.
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关键词:
- 输出反馈镇定 /
- 随机非线性严格反馈系统 /
- 非线性观测器 /
- 神经网络 /
- 自适应反推
Abstract: Neural network (NN) control scheme is first introduced into a class of stochastic nonlinear strict-feedback systems. Based on the well known backstepping method and the technique of nonlinear observer design, a suitable stochastic control Lyapunov function is then proposed to construct an adaptive neural network output-feedback controller. Under some conditions, it is shown that the equilibrium of the closed-loop system is stable in probability. A simulation example is given to illustrate the effectiveness of the proposed control scheme. -
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