Trajectory Tracking Control for Autonomous Underwater Vehicle based on Canopy Beetle Swarm Optimized Model Predictive Control

Autonomous Underwater Vehicle (AUV) can effectively complete a variety of underwater operations, including ocean exploration, pipeline inspection, hull cleaning and maintenance, instead of manual work in the highly uncertain Marine environment, and has great potential in the development of complex and changeable underwater environment. AUV trajectory tracking control technology is one of the key directions of underwater missions. A good optimization control algorithm can make AUV work efficiently in underwater, save energy and make it last longer. This paper combined Canopy Beetle Swarm Optimization Algorithm with Model Predictive Control, which can combine the dynamic optimization capability of MPC and the advantages of high-speed convergence of CBSO. CBSO-MPC is not only more stable and efficient than fmincon and other conventional optimization algorithms but also can achieve the same optimization efficiency as BSO-MPC. Using Canopy fast clustering can achieve dynamic screening of beetle subpopulations, and it has improved the search speed of BSO by nearly 30%.