用于斯图尔特并行机构任务空间跟踪的具有动态规定性能的快速有限时间复合控制装置

IF 6.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Automation Science and Engineering Pub Date : 2024-10-25 DOI:10.1109/TASE.2024.3483268
Yu Tang;Guoqin Gao
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引用次数: 0

摘要

为了提高钢箱梁除锈喷砂机器人在建模误差和外界干扰不确定情况下斯图尔特并联机构(SPM)任务空间的跟踪控制性能,提出了一种具有动态规定性能(DPP)的快速有限时间复合控制方法。首先,通过在Lyapunov微分包含中只引入一个变指数系数(VEC)的指数幂调节项,给出了多输入多输出非线性系统的VEC有限时间稳定性定理。其次,基于所提出的理论,分别设计了VEC有限时间扰动观测器和VEC有限时间超扭转控制器,并将其组合成快速有限时间复合控制,增强了系统的鲁棒性,实现了SPM在任务空间中的快速平滑收敛。最后,通过设计动态非对称规定性能函数和改进的基于障碍函数的约束转换函数,将其整合到快速有限时间复合控制中,有效地将时变不确定性下的跟踪误差约束在小范围内,实现了非超调瞬态性能和较高的稳态跟踪精度。提供连续平稳的控制输入转矩,降低输入能耗。仿真和样机实验验证了该方法的有效性。为了提高具有建模误差和外部干扰不确定性的Stewart并联机构(SPM)的控制性能,首先提出了变指数系数有限时间稳定性定理。在此基础上,分别设计了VEC有限时间扰动观测器和VEC有限时间超扭转控制算法,并将其组合成一种快速有限时间复合控制方法,以实现SPM在任务空间跟踪的快速、平滑的有界时间收敛性能。在此基础上,设计了动态非对称规定性能函数和改进的基于障碍函数的约束转换函数,并将其引入快速有限时间复合控制中,进一步实现非超调瞬态性能,同时提供平滑、不过量的控制输入,降低输入能量消耗。
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A Fast Finite-Time Composite Control With Dynamic Prescribed Performance for Tracking in Task Space of Stewart Parallel Mechanism
To improve tracking control performance in the task space of the Stewart parallel mechanism (SPM) with uncertainties of modeling errors and external disturbances in our developed rust removal sandblasting robot for steel box girder, a fast finite-time composite control method with dynamic prescribed performance (DPP) is proposed. Firstly, by introducing only one exponential power regulation term with a variable exponent coefficient (VEC) into Lyapunov differential inclusion, a VEC finite-time stability theorem for the multi-input multi-output nonlinear system is proposed. Secondly, based on the proposed theorem, a VEC finite-time disturbance observer and a VEC finite-time super-twisting control are respectively designed and combined into a fast finite-time composite control, to enhance system robustness and achieve fast and smooth finite-time convergence for SPM tracking in task space. Finally, the fast finite-time composite control with DPP is formed by devising a dynamic asymmetric prescribed performance function and a modified barrier-function-based constraint transformation function and integrating them into the fast finite-time composite control, to effectively constrain the tracking error into small ranges under time-varying uncertainties, achieve non-overshoot transient performance and high steady-state tracking accuracy, provide continuous and smooth control input torque, and reduce input energy consumption. The effectiveness is validated by simulation and prototype experiments. Note to Practitioners—To improve the control performance of the Stewart parallel mechanism (SPM) with uncertainties of modeling error and external disturbances, a variable exponent coefficient (VEC) finite-time stability theorem is firstly proposed. On this basis, a VEC finite-time disturbance observer and a VEC finite-time super-twisting control algorithm are respectively designed and combined into a fast finite-time composite control method, in order to achieve fast and smooth bounded-time convergence performance for the SPM tracking in task space. Furthermore, a dynamic asymmetric prescribed performance function and a modified barrier-function-based constraint transformation function are respectively devised and introduced into the fast finite-time composite control, aiming to make it further realize non-overshoot transient performance while providing smooth and non-excessive control inputs and reducing input energy consumption.
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来源期刊
IEEE Transactions on Automation Science and Engineering
IEEE Transactions on Automation Science and Engineering 工程技术-自动化与控制系统
CiteScore
12.50
自引率
14.30%
发文量
404
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Automation Science and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. T-ASE welcomes results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, security, service, supply chains, and transportation. T-ASE addresses a research community willing to integrate knowledge across disciplines and industries. For this purpose, each paper includes a Note to Practitioners that summarizes how its results can be applied or how they might be extended to apply in practice.
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