Adaptive Fuzzy Control for Nonlinear 5-DOF Tower Crane Systems With State Constraints

In practical applications, tower cranes often operate in outdoor environments and are subject to a variety of disturbances. Additionally, the control performance is significantly affected by uncertain dynamics, such as frictional forces and payload mass. To address these challenges, this paper presents a novel adaptive fuzzy control mechanism for nonlinear 5-degree of freedom (5-DOF) tower cranes with variable cable length. The proposed fuzzy adaptive mechanism leverages fuzzy logic to compensate for dynamic uncertainties and external disturbances thereby enhancing the overall robustness of the controlled system. Moreover, to ensure safe operation and avoid collisions, auxiliary constraint terms are introduced to restrict the actuated state variables within predefined bounds throughout the transportation process. In particular, an adaptive law is developed to accurately estimate the payload mass， which is critical for precise cable positioning. To the best of our knowledge, this work proposes a novel adaptive fuzzy closed-loop control framework for 5-DOF tower crane systems with varying cable lengths. The framework systematically integrates online payload mass estimation, state variable constraints, and fuzzy adaptive compensation to improve system stability and robustness. Based on the Lyapunov technique and Barbalat’s lemma, the system stability is theoretically analyzed and proven. Finally, the effectiveness of the proposed controller is validated by hardware experiments.