Neural-Network Augmentation of Existing Linear Controllers

Abstract

A method to augment existing linear controllers with a multilayer neural network is presented. The neural network is adapted online to ensure desired closed-loop response in the face of parametric plant uncertainty; no off-line training is required. The benefit of this scheme is that the neural-network output is simply added to the nominal control signal, thereby preserving the existing control architecture. Furthermore, the nominal control signal is only modified if the desired closed-loop response is not met. This method applies to a large class of modern and classical linear controllers. Stability guarantees are provided via Lyapunov-like analysis, and the efficacy of this scheme is illustrated through two numerical examples.