Self-Adaptive Fuzzy Control Approach for Jack-up Rig Jacking System Based on Particle Swarm Optimization

Many offshore projects, such as offshore oil and gas exploration and offshore wind farm development, have required controlling the position of elevating the hull in a stable and balanced manner in recent years. The Jack-up Rig (JuR) jacking control systems are a revolutionary innovation that is already being employed in offshore drilling and other maritime structures. The system is utilized automatically to control and stabilize the position of the JuR during sea state disturbances to keep platforms from being displaced. As a result, developing an improved control theory for the Jacking system (JS) of a JuR is very important. In this paper, first, we investigate how to adapt to the effects of external forces and hydrodynamic amplification using a particle swarm optimization strategy based on a fuzzy controller. Then, using the fuzzy controller as the foundation, we proposed the PSO - Self Adaptive Fuzzy Controller (PSO-SAFC) guaranteed by Lyapunov criteria and compares it with other fuzzy-based algorithms to show the benefit of adaptively and stability of the system. To verify the proposed algorithm, embedded STM32F746VG central processor used to build the algorithm and run Simulink models on Matlab, the collected data on the positions of the legs and the tilt compared it to the setting command values to move the Rig body and provided control signals to the Drive system. Eventually, the advantages of the proposed approach are demonstrated by the simulation and experiment outcomes.