This paper presents a Learning-based Nonlinear Model Predictive Control (LB-NMPC) algorithm for reconfigurable autonomous vessels to facilitate high-accurate path tracking. Each vessel is designed to latch to a pre-defined point of another vessel that allows the vessels to form a rigid body. The number of possible configurations of such vessels exponentially grows as the total number of vessels increases, which imposes a technical challenge in modeling and identification. In this work, we propose a framework consisting of a real-time parameter estimator and a feedback control strategy, which is capable of ensuring high-accurate path tracking for any feasible configuration of vessels. Novelty of our method is in that the parameter is estimated on-line and adjusts control parameters (e.g., cost function and dynamic model) simultaneously to improve path-tracking performance. Through experiments on different configurations of connected-vessels, we demonstrate stability of our proposed approach and its effectiveness in high-accuracy in path tracking.
|Original language||English (US)|
|Title of host publication||IEEE International Conference on Intelligent Robots and Systems|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|State||Published - Nov 1 2019|