Data-Driven Model-Free Adaptive Positioning and Anti-Swing Control for Bridge Cranes

Abstract

In the positioning and anti-swing control of bridge crane, a model-free adaptive control (MFAC) based on data-driven is proposed in order to eliminate the dependence of controller design on the model and the influence of unmodeled dynamics and uncertain disturbances on the controller performance. Only using the input and output data of the bridge crane system, the virtual full format dynamic linearized data model of the bridge crane nonlinear system is obtained through the data-driven modeling method. On the basis of this virtual data model, a model-free adaptive control law and a pseudo-Jacobian matrix estimation algorithm are designed according to the optimization theory under the constraint conditions. The stability of the closed loop system and the convergence of the system error are analyzed and proved by Lipschitz condition and inequality theory. The effectiveness of the control strategy for positioning and anti-swing control of bridge cranes is verified on simulated simulation and experimental platform of bridge crane. The results show that the proposed method is feasible and has good anti-disturbance performance and robustness.

Hybrid input shaping and fuzzy logic‐based position and oscillation control of tower crane system

“…Thus, there is a need to provide suitable operating conditions, ensure quick delivery of goods, and minimize the cost of maintenance for effective automation of the cranes. Interestingly, many scholars (such as in (Rigatos, 2023;Shao et al, 2023;Xu et al, 2023)) have engaged in analysing the dynamic characteristics of crane systems and proposed numerous control strategies to optimize their performance. Nonetheless, most existing studies for the analysis and payload vibration suppression focused on gantry cranes (Lei et al, 2023;Stein & Singh, 2023).…”