Neural-Network-Based Adaptive Feedback Linearization Control for 6-DOF Wave Compensation Platform

Abstract

Ocean resource exploration expands into deep and ultra-deep waters, which has posed great challenges to the 6-DOF parallel platform that requires to finish the long-span and high-velocity wave compensation task with high precision and anti-interference ability. The control strategy employed in the asymmetric hydraulic system of large aspect ratio requires more careful considerations when operating in the harsh and severe environment. An adaptive feedback linearization control strategy was proposed by employing the radial basis function neural network (RBFNN) for identification. First, a nonlinear model of the asymmetric hydraulic system was established. Then, an adaptive controller was designed based on RBFNN and feedback linearization. Finally, simulations were performed by using MATLAB/Simulink under the five-stage wave environment at a 90° wave encounter angle and under the external interference condition. The result shows that this method has a good traceability and robustness compared to classic PID and sliding mode control methods, which is more suitable in control of the wave compensation platform in complex sea conditions. The new controller can significantly increase the compensation accuracy and anti-interference ability, and provide a workbench for the 6-DOF parallel platform operation in deep waters.

Keywords

• wave compensation platform
• radial basis function neural network
• hydraulic system
• feedback linearization
• adaptive control