基于扩展BP神经网络的一类非线性系统自适应控制设计

Adaptive control design for a class of nonlinear systems based on extended BP neural network

针对单输入单输出非线性系统的不确定性问题,提出了一种新型的基于扩展反向传播(BP)神经网络的自适应控制方法。首先,采用离线数据来训练BP神经网络的权值向量;然后,通过在线调节伸缩因子和逼近精度估计值的更新律,从而来达到控制整个系统的目的。在控制器的设计过程中,利用李亚普诺夫稳定性分析原理,保证了闭环系统的所有状态一致终极有界(UUB)。相比传统的BP神经网络自适应控制,所提方法能有效地减少在线调节的参数数目、减轻计算负担。仿真结果表明,该方法能够使闭环系统的所有状态都趋于零,即系统达到稳定状态。

Aiming at the uncertainty of Single-Input-Single-Output （SISO） nonlinear systems, a novel adaptive control design based on extended Back Propagation （BP） neural network was proposed. Firstly, the weight vectors of BP neural network were trained via the offline data. Then, the scaling factor and estimation parameter of approximate accuracy were adjusted online to control the whole system by update law. In the design process of controller, with the Lyapunov stability analysis, the adaptive control scheme was proposed to guarantee that all the states of the closed-loop system were Uniformly Ultimately Bounded （ UUB）. Compared with the traditional adaptive control method of BP neural network, the proposed method can effectively decrease the parameter number of online adjustment and reduce the burden of computation. The simulation results show that the proposed method can make all the states of the closed-loop system tend to be zero, which means the system reaches the steady state.