基于动态神经网络的一类非线性组合系统自适应控制

针对一类非线性组合大系统,提出一种用动态神经网络逼近组合大系统的新型设计方法·首先由动态神经网络辨识非线性组合大系统,也就是利用动态神经网络逼近系统的未知项和互联项,其次设计控制器使实际系统的状态来跟踪参考模型的轨迹·利用Lyapunov稳定性理论保证跟踪误差和其他信号是最终一致有界的·通过一个非线性系统例子的仿真证明这种设计方法的可行性·这种设计方法能够解决大系统中最为复杂的互联项问题,得出基于神经网络的自适应控制律·

Adaptive Control of a Class of Nonlinear Composite Systems Using Dynamic Neural Networks

?A new design process is presented for a class of nonlinear composite systems to which the dynamic neural network is used to approximate. Nonlinear composite systems are identified by means of dynamic neural network, i.e., use dynamic neural network to approximate to the unknown and interconnected terms of a system. Then design a controller to enable state of the actual system to track the locus of reference model. Based on Lyapunov's stability theory, the tracking error and other signals are both proved uniform and bounded eventually. The practicability of such a design process is verified by a simulative example of nonlinear system. A conclusion is thus drawn that the design process can solve most complicated problem of interconnected terms in a composite system. A law of adaptive control is therefore suggested on neural network basis.