基于变结构神经网络的L1自适应控制

针对传统模型参考自适应控制存在的鲁棒性问题和神经网络结构庞大因而计算量膨胀的问题,提出了一种变结构神经网络L1自适应控制方法,其中变结构神经网络用于在线辨识系统存在的未知非线性函数,该网络通过对节点进行唤醒与催眠以动态调节结构,以最少的节点数进行有效的逼近,降低计算复杂度;L1自适应控制用于网络权值学习与系统非线性补偿,反馈回路中设有一个低通滤波器,只要满足L1增益条件,就能确保系统的输入输出信号的瞬态响应和稳态跟踪性能与一个期望的线性时不变系统的响应保持一致。通过对四旋翼飞行器进行仿真,验证了该方法的有效性。

L1 Adaptive Control Method Based on Variable Structure Neural Network

L1 adaptive control method based on variable structure neural network is proposed to solve the problems that the robustness exists in the traditional model reference adaptive control and the neural network structure is so bulky that the computation expands. The neural network is used as an identification generator for unknown nonlinear functions in the system, and the structure of the network is adjusted dynamically by activating and hypnotizing the nodes to approximate the functions with minimum nodes and reduce the computation procedures. The L1 adaptive control is used to obtain the weights of the network and compensate the nonlinearity of the system. A low pass filter is adopted in the feedback loop, so long as the L1 gain requirement is fulfilled, the transient response of input/output signals and the steady-state tracking performance of the system will be coincident with the specifications of the desired linear time-invariant system. The simulation results of quadrotor show the efficiency of the novel method.