Nonlinear Model Predictive Control of Shipboard Boom Cranes Based on Moving Horizon State Estimation

As important equipment in offshore engineering and freight transportation, shipboard cranes, working in non-inertial coordination systems, are complicated nonlinear systems with strong couplings and typical underactuation. To tackle the challenges in the controller design for shipboard boom cranes, which is a representative type of shipboard cranes, a comprehensive framework embedding moving horizon estimation (MHE) in model predictive control (MPC) is constructed in this paper while considering disturbances and noise. By utilizing MHE, velocity information can be estimated with high precision even though this is influenced by disturbances and measurement noises. This expected superiority can greatly ease the difficulties in directly measuring all states of shipboard boom cranes. Then, the estimated information can be passed to MPC to derive the optimal control law by solving a constrained optimal problem. During this process, the physical limits of shipboard boom cranes are fully considered. Therefore, the practicability of the proposed framework is highly suitable for the actual requirements of shipboard boom cranes. Finally, the framework is verified by designing three typical scenarios with different disturbances and/or noises. Comparisons with other control approaches are also performed to demonstrate the effectiveness.