Model-free robust adaptive super-twisting control of multi-lift overhead
cranes with finite-time convergence based on Iterative learning
Abstract
A model-free control (MFC) method based on iterative learning law
combined with adaptive super-twisting is proposed to realize the
synchronous coordination control of multi-lift overhead cranes system
for the problems of inaccurate modeling, system parameter variation and
disturbance uncertainty that exist in multi-lift overhead cranes system.
Firstly, a load coupling model of the double-container overhead crane
considering the deformation tangential force in the interlocking mode is
established. Secondly, a time-varying sliding mode surface designed by
using nonlinear functions effectively improves the convergence speed of
the system state. The method of iterative learning control (ILC) is
introduced to compensate the system dynamics to achieve model-free
control, and the dynamic learning rate is designed instead of the
constant learning rate to improve the convergence speed of the error of
the system and the steady-state performance. In order to suppress
uncertainty disturbances and avoid control gain overestimation, an
adaptive gain is added to the generalized super-twisting algorithm,
which has the advantages of both finite-time convergence and chattering
suppression, and improves the robustness and tracking performance of the
multi-lift overhead cranes system. The stability of the controlled
system is analyzed by using Lyapunov stability theory. The simulation
experiments illustrate the effectiveness of the proposed synchronization
control scheme.