Adaptive neural network control of multiple‐sectioned flexible riser with time‐varying output constraint and input nonlinearity

Abstract In this paper, an adaptive neural network controller is proposed for vibration suppression of a multisectional riser system with unknown boundary disturbance, time‐varying asymmetric output constraint, and input nonlinearity. The considered composed continuous connection several different pipes, its dynamic models are represented by set multiple continuously connected partial differential equations (PDEs) ordinary equation (ODE) at the top boundary. Considering nonlinearity, external uncertainty, radial basis function (RBF) networks adopted to eliminate effect these uncertain terms. Besides, barrier Lyapunov employed guarantee restrictions. With control, stability closed‐loop proved simulations given illustrate well performance control strategy.