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 riser system is composed of a continuous connection of several different pipes, and its dynamic models are represented by a set of multiple continuously connected partial differential equations (PDEs) and an ordinary differential equation (ODE) at the top boundary. Considering input nonlinearity, external disturbance, and system uncertainty, radial basis function (RBF) neural networks are adopted to eliminate the effect of these uncertain terms. Besides, a barrier Lyapunov function is employed to guarantee the restrictions. With the proposed boundary control, the stability of the closed‐loop system is proved and simulations are given to illustrate the well performance of the proposed control strategy.

An RBFNN‐based constraint control algorithm for electric vehicle AFS + DYC system with state delays and uncertain parameters

“…Additionally, the controller design is based on RBFNN and LKFs in order to handle the time delays in the system. A large number of research results [57][58][59][60] have confirmed the excellent ability of RBFNN to approximate the unknown term, so our proposed control scheme can also deal with the parameter uncertainty of the vehicle system, including vehicle mass, rotational inertia, and tire stiffness.…”