Adaptive Planning and Tracking of Trajectories for the Simulation of Maneuvers with Multibody Models

We study the simulation of maneuvers with multibody models. We propose a framework based on a hierarchical decomposition of the problem inspired from the work on autonomous vehicles. The result is a system that not only is capable of piloting a vehicle model of arbitrary complexity along a given track, but it is also able to compute the track itself based on given criteria. The ability to generate feasible tracks, i.e. tracks that are compatible with the dynamic characteristics of the vehicle and other maneuver-defining constraints, is of crucial importance when the vehicle is operating close to its performance boundaries. A maneuver is here mathematically defined as an optimal control problem. This definition feeds into a planning layer, that determines the vehicle trajectory by solving the optimization problem for a reduced vehicle model. The resulting trajectory feeds into a control layer, that tracks the computed optimal path using a non-linear model predictive formulation and steers the multibody model accordingly. The reduced models used at the planning and tracking levels are adapted, either in a batch mode or online, to optimize the predictive capabilities of planner and tracker. The proposed framework is demonstrated on problems related to maneuvering rotorcraft systems.