Neural network-based adaptive sliding mode control for underactuated dual overhead cranes suffering from matched and unmatched disturbances

Abstract

To improve transportation capacity, dual overhead crane systems (DOCSs) are playing an increasingly important role in the transportation of large/heavy cargos and containers. Unfortunately, when trying to deal with the control problem, current methods fail to fully consider such factors as external disturbances, input dead zones, parameter uncertainties, and other unmodeled dynamics that DOCSs usually suffer from. As a result, dramatic degradation is caused in the control performance, which badly hinders the practical applications of DOCSs. Motivated by this fact, this paper designs a neural network-based adaptive sliding mode control (SMC) method for DOCS to solve the aforementioned issues, which achieves satisfactory control performance for both actuated and underactuated state variables, even in the presence of matched and mismatched disturbances. The asymptotic stability of the desired equilibrium point is proved with rigorous Lyapunov-based analysis. Finally, extensive hardware experimental results are collected to verify the efficiency and robustness of the proposed method.

Modeling and control of overhead cranes: A tutorial overview and perspectives

“…More clearly, in such applications, the trolley can rotate around the vertical axis to handle the steel bars in the warehouse. Moreover, the overhead cranes with two independently controlled carts have also been studied in the literature [270]. Another specific structure is the spider crane where the cart can also move vertically [207].…”

Adaptive fuzzy sliding-mode control of under-actuated systems with unknown input gain function

“…Theorem 1.Consider the behavior of the under-actuated dynamic system described by (6). With the feedback control law (31), where the adaptive variables are adjusted by ( 51) to (53), the control objective is achieved as lim t!'…”

“…1 It must be pointed out that most of the mechanical systems are in the category of under-actuated dynamic systems, such as marine vessels, 2 surface vessels, 3 autonomous underwater vehicles, 4 flexible spacecrafts, 5 and dual overhead cranes. 6 Furthermore, in some cases, the system’s internal dynamics are unknown and the presence of disturbances causes serious challenges. 7 Consequently, the design of robust controllers for under-actuated nonlinear systems has become necessary.…”

Forecasting the yield of wafer by using improved genetic algorithm, high dimensional alternating feature selection and SVM with uneven distribution and high-dimensional data

“…With the development of artificial intelligence [3,[11][12][13], the WYP can be tackled as a binary classification problem using machine learning technologies with the class labels annotated by experts [4]. The WYP has received considerable attention, and the proposed methods can be divided into three types: expert experience methods, deep learning methods, and shallow structured machine learning methods.…”