Online System Identification Algorithm without Persistent Excitation for Robotic Systems: Application to Reconfigurable Autonomous Vessels This paper investigates an online system identification problem of estimating unknown parameters in nonlinear system dynamics in the absence of persistently excitation. To estimate parameters, we develop an algorithm that updates parameter estimates using sensor data and a basis that is built on a finite number of recorded sensor data. Based on our proposed approach we show that the algorithm achieves exponential convergence in both state and parameter estimation errors without the persistent excitation condition. We demonstrate the effectiveness of the proposed approach using both simulations and experiments on a reconfiguration autonomous multivessel platform: Simulation results illustrate that the parameter estimated by the developed algorithm converge to their ground truths. Experiment results validate the performance of the developed algorithm in estimating platformxe2x80x99s system parameters across different multivessel configurations.

Learningbased Nonlinear Model Predictive Control of Reconfigurable Autonomous Robotic Boats: Roboats This paper presents a Learningbased Nonlinear Model Predictive Control (LBNMPC) algorithm for reconfigurable autonomous vessels to facilitate highaccurate path tracking. Each vessel is designed to latch to a predefined 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 realtime parameter estimator and a feedback control strategy, which is capable of ensuring highaccurate path tracking for any feasible configuration of vessels. Novelty of our method is in that the parameter is estimated online and adjusts control parameters (e.g., cost function and dynamic model) simultaneously to improve pathtracking performance. Through experiments on different configurations of connectedvessels, we demonstrate stability of our proposed approach and its effectiveness in highaccuracy in path tracking.