Multilayer neurocontrol of high-order uncertain nonlinear systems with active disturbance rejection

Multilayer neural networks can approximate endogenous disturbances with relatively high accuracy. However, for multilayer-neural-network-based control methods of high-order uncertain nonlinear systems, hard to handle large exogenous disturbances especially for mismatched types, complex controller scheme and so on, make them difficult to be practical. Therefore, a novel high-performance multilayer neurocontroller which can simultaneously reject matched and mismatched disturbances will be proposed in this paper. Specially, strong endogenous and exogenous disturbances will be feedforwardly compensated. Additionally, the proposed controller not only protects from "explosion of complexity," but also owns a simple scheme.