欠驱动起重机的高效轨迹规划与跟踪控制

IF 0.4 4区 计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS
Xinyu Long, Mingwei Sun, Zengqiang Chen
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引用次数: 0

摘要

起重机是典型的欠驱动装置,这给轨迹规划和控制带来了挑战。为解决这一问题,进行了参数化降维轨迹规划和跟踪控制。通过使用微分平坦度(DF)方法对对象进行降维,其中状态和控制变量直接通过平坦输出(FO)表示。结合考虑多约束条件的伪谱法(PSM),利用多项式函数对FO进行参数化,将FO空间中的轨迹规划问题转化为参数规划问题。通过这样做,可以减少待优化的自由度。对于系统中的高阶项和各种扰动,采用扩展状态观测器进行估计和补偿。通过数值仿真和硬件实验验证了该方法的可行性和有效性。DOI: 10.61416 / ceai.v25i2.8307
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Trajectory Planning and Tracking Control for Underactuated Crane
The crane is a typical underactuated plant, which makes the trajectory planning and control to be challenging. To solve this problem, parameterized reduced-dimension trajectory planning and tracking control are carried out. The plant is reduced in dimension by using the differential flatness (DF) method, wherein the state and control variables are represented directly through the flat output (FO). Combined with the pseudospectral method (PSM) wherein multiple constraints are taken into account, the FO is parameterized by using the polynomial functions, and the trajectory planning in the FO space is transformed into a parameter planning problem. By doing so, it can reduce the degree of freedoms to be optimized. For the high-order terms and various disturbances in the FO, the extended state observer is used for estimation and compensation. Both numerical simulation and hardware experiments are performed to demonstrate the feasibility and efficiency of the proposed method. DOI: 10.61416/ceai.v25i2.8307
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来源期刊
CiteScore
1.50
自引率
22.20%
发文量
0
审稿时长
6 months
期刊介绍: The Journal is promoting theoretical and practical results in a large research field of Control Engineering and Technical Informatics. It has been published since 1999 under the Romanian Society of Control Engineering and Technical Informatics coordination, in its quality of IFAC Romanian National Member Organization and it appears quarterly. Each issue has up to 12 papers from various areas such as control theory, computer engineering, and applied informatics. Basic topics included in our Journal since 1999 have been time-invariant control systems, including robustness, stability, time delay aspects; advanced control strategies, including adaptive, predictive, nonlinear, intelligent, multi-model techniques; intelligent control techniques such as fuzzy, neural, genetic algorithms, and expert systems; and discrete event and hybrid systems, networks and embedded systems. Application areas covered have been environmental engineering, power systems, biomedical engineering, industrial and mobile robotics, and manufacturing.
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