Adaptive trajectory tracking anti-swing control strategy with gain self-tuning for 3D overhead cranes with payload hoisting and lowering

In practical applications, the transportation process of overhead cranes often involves the coordinated movement of trolleys, bridges, and cables. This makes it challenging for existing 3D anti-sway controllers for overhead cranes to achieve satisfactory performance. This paper proposes a gain self-tuning adaptive trajectory tracking anti-sway control strategy for 3D overhead cranes that hoist and lower payloads, which suffer from poor positioning accuracy, severe payload swing, excessive initial input force, low adaptability and robustness of existing control strategies, and dependence on manual selection of control gains. Specifically, to make the trolley, bridge, and cable move smoothly and suppress payload swing angles, an S-shaped transport trajectory is introduced. The tracking error is designed, and the trajectory tracking controller is designed based on the energy dissipation principle. The adaptive law is designed to evaluate uncertain system parameters and external environmental parameters, and then compensate for them in the trajectory tracking controller. Subsequently, to ensure the smooth start of the crane, a hyperbolic tangent function is added to the controller design to limit the initial driving force. To cope with the complexity and intricacy of actual crane operations, fuzzy control is utilized to modify controller gains online. This achieves intelligent self-tuning of the system’s gain, resulting in greater applicability and improved real-time performance and adjustment speed for system control. Finally, after strict mathematical analysis, the asymptotic stability of the system is proven. An extensive series of simulations and experimental trials substantiate the control effectiveness of the devised control strategy. The results show that the control strategy proposed in this paper exhibits excellent control performance and provides a new scheme for the automatic control of 3D overhead cranes.