Neural Network-Based Adaptive Boundary Control of a Flexible Riser With Input Deadzone and Output Constraint

IEEE Trans Cybern. 2022 Dec;52(12):13120-13128. doi: 10.1109/TCYB.2021.3102160. Epub 2022 Nov 18.

Abstract

In this article, vibration abatement problems of a riser system with system uncertainty, input deadzone, and output constraint are considered. For obtaining better control precision, a boundary control law is constructed by employing the backstepping method and Lyapunov's theory. The output constraint is guaranteed by utilizing a barrier Lyapunov function. Adaptive neural networks are designed to cope with the uncertainty of the riser and compensate for the effect caused by the asymmetric deadzone nonlinearity. With the designed controller, the output constraint is satisfied, and the system stability is guaranteed through Lyapunov synthesis. In the end, numerical simulation results are provided to display the performance of the developed adaptive neural network boundary control law.

MeSH terms

  • Algorithms*
  • Computer Simulation
  • Feedback
  • Neural Networks, Computer
  • Nonlinear Dynamics*