Neural Network-Based Adaptive Boundary Control of a Flexible Riser With Input Deadzone and Output Constraint
针对带有系统不确定性、输入死区和输出约束的立管系统，提出一种基于神经网络和反步法的边界控制律，保证输出约束和系统稳定性，并通过数值仿真验证效果。
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.