Trajectory Tracking Control for Seafloor Tracked Vehicle by Adaptive Neural-Fuzzy Inference System Algorithm

Trajectory tracking control strategy and algorithm for the tracked vehicle moving on the seafloor has aroused much concerns due to the commonly occurred serious slip and trajectory deviation caused by the seafloor extremely soft and cohesive sediment. An improved multi-body dynamic model of a seafloor tracked vehicle (STV) has been established in a simulation code RecurDyn/Track. A particular terramechanics model with a dynamic shear displacement expression for the vehicle-sediment interaction has been built and integrated into the multi-body dynamic model. The collaborative simulation between the mechanical multi-body dynamic model in Recur Dyn/Track and the control model in MATLAB/Simulink has been achieved. Different control algorithms performances including a PID control, a fuzzy control and a neural control, have been compared and proved the traditional or individual intelligent controls are not particularly suitable for the tracked vehicle on the seafloor. Consequently, an adaptive neural-fuzzy inference system (ANFIS) control algorithm with hybrid learning method for parameter learning which is an integrated control method combined with the fuzzy and neural control, has been adopted and designed. A series of collaborative simulations have been performed and proved the ANFIS algorithm can achieve a better trajectory tracking control performance for the STV as its trajectory deviation can be maintained within a permissible range.