Distributed-integrated model predictive control for cooperative operation with multi-vessel systems

• Published in: Journal of marine science and technology Vol. 27; no. 4; pp. 1281 - 1301
• Main Authors: Tang, Ziying, Wang, Lei, Wang, Yiting, He, Huacheng, Li, Bo
• Format: Journal Article
• Language: English
• Published: Tokyo Springer Japan 01.12.2022; Springer; Springer Nature B.V
• Subjects: Actuators; Analysis; Automotive Engineering; Bivariate analysis; Communication; Comparative analysis; Computer applications; Control; Control equipment; Cooperative control; Dynamic models; Energy consumption; Engineering; Engineering Design; Engineering Fluid Dynamics; Fault tolerance; Iterative methods; Kalman filters; Mechanical Engineering; Motion control; Numerical analysis; Obstacle avoidance; Offshore Engineering; Original Article; Prediction models; Predictive control; Simulation methods; Thrust; Thrusters; Trajectory planning; Vessels; Bivariate thrust efficiency matrix; Distributed-integrated MPC; Cooperative control; Autonomous surface vessels
• ISSN: 0948-4280; 1437-8213
• DOI: 10.1007/s00773-022-00905-6

This paper proposes a novel Distributed-Integrated Model Predictive Control (DI-MPC) strategy for the multi-vessel cooperative path following, formation control and obstacle avoidance. Each vessel is designed with an individually distributed controller based on the MPC theory and communication graph. Subject to actuator limitations and formation constraints, the motion control and thrust allocation are integrated into a dynamic model to achieve direct control to the thrusters. A bivariate thrust efficiency matrix is embedded into the model to consider the hydrodynamic interaction effects between adjacent thrusters. The Nominal System is introduced to generate the linearized predictive model. To achieve consensus among various vessels, a real-time iterative negotiation framework is established. The Kalman Filter is utilized to estimate the low-frequency state variables from the external disturbances of environment loads and measurement noises. Numerical simulations based on the proposed distributed strategy and the centralized strategy are carried out under the scenario of cooperative operation in the Huangpu River (in Shanghai). Comparative analysis results demonstrate the high control performance of both the strategies. DI-MPC mainly contributes to the system flexibility, computational cost reduction (67.65%), energy consumption reduction (5.03%) and fault-tolerant capability. Furthermore, DI-MPC also shows strong applicability to large-scale cooperative control problems.