一类大系统的分散智能控制

Decentralized Intelligent Control for a Class of Large-scale Systems

该文针对一类具有未知函数增益的非线性大系统,基于滑模控制和Lyapunov稳定性理论,运用多层神经网络非线性映射逼近能力,提出一种分散的具有连续特性的自适应神经控制器的设计策略。通过分析,从理论上证明了闭环大系统是全局稳定的,跟踪误差有界且均方可积。仿真结果表明控制方案是有效的。

Based on the principle of sliding mode control and Lyapunov stability theory,and the nonlinear map approximation capability of multilayer neural networks,an intelligent decentralized neural controller for a class of nonlinear large-scale systems with unknown function gains is studied.Theoretical analysis shows that the designed scheme can guarantee the global stability of the systems with tracking errors holding bounds and its mean square performance.The simulations verify the effectiveness and robustness of the designed algorithm.