Adaptive finite-time extended state observer-based model predictive control with Flatness motivated trajectory planning for 5-DOF tower cranes

IF 2.5 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control Pub Date : 2024-11-26 DOI:10.1016/j.ejcon.2024.101149

Hue Luu Thi , Van Chung Nguyen , Tung Lam Nguyen

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Abstract

This paper introduces a new method to control a 5-DOF tower crane (3DTC). By considering the 3DTC as a flat system, a time-optimal trajectory is proposed for the payload. System states and control signal references can be calculated based on the flatness theory. In addition, the 3DTC works in an environment containing many factors impacting control performance and the system states are hard to measure. An adaptive finite-time extended state observer (AFT-ESO) is introduced to solve these problems. With AFT-ESO, system states and lumped disturbances can be estimated accurately, facilitating the prediction for Lyapunov-based model predictive control (LMPC) when an accurate model is required. The LMPC takes advance of the second-order sliding mode control stability conditions as a strict constraint to guarantee the global stabilization of the closed-loop system. Finally, simulations based on the quasi-physical model are proposed to show the effectiveness and robustness of the proposed strategy.

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来源期刊

European Journal of Control 工程技术-自动化与控制系统

CiteScore

5.80

自引率

5.90%

发文量

131

审稿时长

1 months

期刊介绍： The European Control Association (EUCA) has among its objectives to promote the development of the discipline. Apart from the European Control Conferences, the European Journal of Control is the Association''s main channel for the dissemination of important contributions in the field. The aim of the Journal is to publish high quality papers on the theory and practice of control and systems engineering. The scope of the Journal will be wide and cover all aspects of the discipline including methodologies, techniques and applications. Research in control and systems engineering is necessary to develop new concepts and tools which enhance our understanding and improve our ability to design and implement high performance control systems. Submitted papers should stress the practical motivations and relevance of their results. The design and implementation of a successful control system requires the use of a range of techniques: Modelling Robustness Analysis Identification Optimization Control Law Design Numerical analysis Fault Detection, and so on.