A Novel Separation Principle-Based Stabilization for 6-Dof Overhead Crane Under Fault Injection Data Onslaught

This paper introduces a new approach to stabilize the 6-DoF Overhead Crane by controlling its movements without requiring state measurements, using the separation principle. The movements of the system studied in this paper are described more closely than the existing crane model when considering six behaviors simultaneously: trolley and bridge movement, hoisting drum rotation, two swing angles along the $x$ -axis and $y$ -axis of the payload, and axial payload oscillation. The mission of the proposal is to make all the above behaviors of the 6-DOF crane system stable accurately enough at the desired positions and minimize both the horizontal swings of the payload and the axial oscillation of the cable by utilizing information about the position of the trolley, the bridge, and the length of cable. To guarantee these objectives, a cooperation regime controller comprising a new asymptotic state observer, which is elaborately constructed via a neural network, and the output feedback backstepping hierarchical sliding mode vehicle is integrated into the controller to improve the closed-loop system's adaption. This cooperation controller has also evolved to prop up the fault injection data onslaught by eliminating all the information about the input system into the procedure. Furthermore, to enhance the flexibility of the closed scheme, an updating law for the observer's parameter is developed based on the data-driven principle. All of them are developed and designed not only to handle these above tasks but also to avoid the undesired finite-escape-time (FET) phenomenon. All the results are proven systematically by three theorems and validated their performance through two scenarios and simulated by the Matlab/Simulink platform.