Li Jing, Chen Weisheng, Li Junmin, Fang Yiqi
Department of Applied Mathematics, Xidian University, Xi'an, PR China.
ISA Trans. 2009 Oct;48(4):468-75. doi: 10.1016/j.isatra.2009.05.004. Epub 2009 Jun 26.
In this paper, the adaptive neural network output-feedback stabilization problem is investigated for a class of stochastic nonlinear strict-feedback systems. The nonlinear terms, which only depend on the system output, are assumed to be completely unknown, and only an NN is employed to compensate for all unknown upper bounding functions, so that the designed controller is more simple than the existing results. It is shown that, based on the backstepping method and the technique of nonlinear observer design, the closed-loop system can be proved to be asymptotically stable in probability. The simulation results demonstrate the effectiveness of the proposed control scheme.
本文研究了一类随机非线性严格反馈系统的自适应神经网络输出反馈镇定问题。假设仅依赖于系统输出的非线性项完全未知，仅采用一个神经网络来补偿所有未知的上界函数，使得所设计的控制器比现有结果更简单。结果表明，基于反步法和非线性观测器设计技术，可以证明闭环系统在概率意义下渐近稳定。仿真结果验证了所提控制方案的有效性。
