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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Robust Adaptive Neural Tracking Control for a Class of Stochastic Nonlinear Interconnected Systems
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Robust Adaptive Neural Tracking Control for a Class of Stochastic Nonlinear Interconnected Systems

机译：一类随机非线性互联系统的鲁棒自适应神经跟踪控制

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In this paper, an adaptive neural decentralized control approach is proposed for a class of multiple input and multiple output uncertain stochastic nonlinear strong interconnected systems. Radial basis function neural networks are used to approximate the packaged unknown nonlinearities, and backstepping technique is utilized to construct an adaptive neural decentralized controller. The proposed control scheme can guarantee that all signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded in the sense of fourth moment, and the tracking errors eventually converge to a small neighborhood around the origin. The main feature of this paper is that the proposed approach is capable of controlling the stochastic systems with strong interconnected nonlinearities both in the drift and diffusion terms that are the functions of all states of the overall system. Simulation results are used to illustrate the effectiveness of the suggested approach.
机译：针对一类多输入多输出不确定的随机非线性强互联系统，提出了一种自适应神经分散控制方法。径向基函数神经网络用于近似封装的未知非线性，而后推技术则用于构建自适应神经分散控制器。所提出的控制方案可以保证最终的闭环系统的所有信号在第四矩的意义上半全局一致地最终有界，并且跟踪误差最终收敛到原点周围的一个小邻域。本文的主要特征是，所提出的方法能够控制随机系统，这些随机系统在漂移和扩散方面均具有很强的互连非线性，而漂移和扩散是整个系统所有状态的函数。仿真结果用于说明所建议方法的有效性。

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