Trajectory Tracking Control for Subsea Mining Vehicles Based on Fuzzy PID Optimised by Genetic Algorithms

In deep-sea mining operations, the seabed sediments (mud and sand) are very soft and slippery. This often causes tracked vehicles to slip and veer off course when they are driving on the seafloor. To solve the path-tracking problem for deep-sea mining vehicles, this study suggests a path-tracking controller that can adapt to the seabed environment. Firstly, it is necessary to establish a kinematic and dynamic model of the mining vehicle's motion, analysing its seabed slippage and force application. The system has been developed on the basis of the Stanley algorithm and utilises a two-degree-of-freedom kinematic model, with lateral deviation and heading deviation acting as inputs. The establishment of fuzzy rules to adjust the gain parameter K enables the mining vehicle to adaptively modify its gain parameters according to the seabed environment and path. Secondly, a fuzzy PID controller is established and optimised to address the limitation that fuzzy PID control rules are constrained by the designer's experience. At the same time, a relationship was established between how fast the drive wheel accelerates and the slip rate based on the dynamic model. This stops the drive wheel from slipping by limiting how fast it can go. Finally, a mechanical model of the mining vehicle was created in Recurdyn and a system model was developed in MATLAB/Simulink for joint simulation analysis. The simulation results demonstrate the efficacy of the proposed control strategy, establishing it as a reliable method for tracking the path of subsea mining vehicles.