U-neural network-enhanced control of nonlinear dynamic systems

Neural networks have been widely used as an approximation for nonlinear dynamic plants in control system design, but they are almost never used as a proper dynamic inverter, particularly with dynamic models available in advance. This study presents a universally feasible U-neural network (U-NN) structure to facilitate the designing control of all dynamic systems modelled with linear/nonlinear polynomial/state space equations. With the presented U-NN, this study proposes a procedure for a model independent control system design, U-control framework/platform. The procedure, against a traditional model based on control system design and a model free/data driven-based design (such as PID control, iterative learning control, and model free control), removes the boundaries of the linear/nonlinear and polynomial/state space model sets, where the model structures are universally treated within the new framework. Furthermore, this study analyses the U-control properties and gives a step-by-step implementation procedure. Several bench test examples demonstrate the effectiveness of the design procedure, as well as the routines in applications. (C) 2019 Elsevier B.V. All rights reserved.