基于正交函数网络的不确定混沌系统的控制

提出了基于正交函数网络的不确定混沌系统的自适应控制方法．通过利用计算简单、收敛速度快的单隐层正交函数神经网络，构建了一类不确定混沌系统的控制器．利用李雅普诺夫稳定性定理得到了该网络控制器的权值更新规则并保证了权值误差和跟踪误差的有界性．该控制器不仅能够保证混沌系统以有界误差对指定轨迹进行精确跟踪，也能够使有外部扰动的混沌系统快速跟踪一个指定的轨迹．最后，利用陈氏混沌系统和Lorenz系统进行了系统仿真，结果表明了该控制器在混沌控制中的有效性。

Control of uncertain chaos system based on orthogonal function network

A direct adaptive control method is developed for a class of uncertain chaos system. A class of controller is constructed using a single hidden layer Legendre orthogonal neural network which has the advantages of simple calculation and fast convergence. The network weight adaptation rule is derived from Lyapunov stability analysis, and guarantees that the adapted weight errors and the tracking error are bounded. Uncertain chaos system can track the desired smooth reference trajectory with bounded errors using the adaptive feedback controller, even if the chaos system suffers some outer perturbations. At last, numerical simulations of the controlled Chen system and Lorenz system are used to illustrate the practical potential of the theoretical results.