Model-free robust adaptive control of overhead cranes with finite-time convergence based on time-delay control

Abstract

In this paper, a model-free robust adaptive control scheme with finite-time convergence based on time-delay control is proposed for anti-sway and positioning control of two-dimensional underactuated overhead cranes. First, the whole overhead cranes system is simplified to an ultra-local model for time delay estimation (TDE). TDE brings a direct and effective model-free property but also an estimation error. Second, a sliding mode disturbance observer is designed to estimate and compensate for the TDE error. Third, sliding mode control (SMC) is used to enhance the robustness of the controller. An adaptive integral sliding surface is then designed to accelerate the sliding surface convergence rate and shorten the convergence time. To further optimize the selection of parameters, the parameter estimation is integrated to enhance the performance of model-free control. In the final analysis of the simulation, data yield that the introduction of parameter estimation increases the control performance by more than 20% on average, and the above facts verify the effectiveness of the scheme. Finally, the stability of the closed-loop control system is analyzed by using Lyapunov stability theory, and the effectiveness and robustness of the control scheme are verified through computer simulation results.

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

“…The closed-loop scheme studied in [39] ensured the stability, as well as the adaption of the Rubber Tired Gantry Crane. Model-free robust adaptive control based on integral sliding mode control and sliding mode disturbances observer was proposed in [42] to steer the overhead crane system. This paper also introduced a TDE technique to solve the timedelay issue and estimate the lumped uncertainty.…”

Prescribed-time stabilization control for p-norm stochastic nonlinear systems based on homogeneous dominant technique

“…Notably, the results of Lin et al (2023), Chen and Jiao (2010), Yin et al (2011), Yu et al (2019), Khoo et al (2013), Zhai and Song (2016), Zha et al (2014), Cui and Xie (2022), and Zhai (2013) achieve stochastic finite-time stabilization within some stochastic settling time, which is usually an unknown value depending on the initial conditions. However, the uncertainty and randomness of settling time make these results difficult to be applied in practice (Holloway and Krstić, 2019; Liu and Xu, 2023; Song et al, 2017; Wang et al, 2022; Wang and Zhu, 2015), promoting research on prescribed-time control.…”