Variable-Weight NMPC-Based Trajectory Tracking and Anti-Sway Control for Hydraulic Luffing Cranes
In driver-operated hydraulic luffing crane tasks, achieving precise motion control and effective load stabilization is essential for safe and efficient operation. This study develops a variable-weight nonlinear model predictive control (VW-NMPC) approach for driver-in-the-loop trajectory tracking and anti-sway regulation. A coupled nonlinear dynamic model integrating hydraulic, luffing, and payload dynamics is established. A virtual reference feedback mechanism is introduced to reduce feedback delays and suppress instability arising from strong nonlinearities and structural coupling. A state-dependent weighting mechanism dynamically adjusts control priorities according to driver commands and operating conditions, enabling balanced optimization in human–machine cooperation. Hardware-in-the-loop experiments show a velocity deviation of less than $2.53 \times 10 ^{-2}$ m/s and payload swing angles within $2.95 \times 10 ^{-2}$ rad under typical working scenarios. The proposed method significantly reduces operator dependence and enhances robustness, safety, and intelligence in crane operations across varying environments. These results provide a solid theoretical and practical foundation for next-generation intelligent crane control systems. Note to Practitioners—Operators of hydraulic luffing cranes often struggle to keep the boom moving smoothly while controlling payload sway. These challenges become more pronounced when the hydraulic system shows strong interaction between the structure, the actuators, and changing loads. This work is motivated by the need for a control approach that assists operators directly, improves motion stability, and reduces dependence on manual skill. The proposed method introduces two ideas that can be applied in practice. The first is a virtual feedback signal that helps the crane respond more quickly to operator input. It reduces the delay and instability that often appear in coupled hydraulic and mechanical systems and helps prevent oscillations that are difficult to manage by hand. The second idea is a weighting logic that adjusts control priorities based on the operator’s commands. As working conditions change, the controller automatically balances between accurate motion tracking and effective sway suppression. This improves responsiveness, limits overshoot, and helps maintain stable operation during demanding tasks. Hardware-in-the-loop tests show that the approach can keep speed variations small and reduce payload sway. This lowers operator workload and improves overall safety. The method, however, relies on both a reasonably accurate system model and real-time computation. Future work may simplify the algorithm or add online adaptation to broaden its use. The same principles may also benefit other hydraulic machines that require coordinated motion and stabilization, such as excavators, aerial platforms, or telescopic handlers.