摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-一项关于机器人的新研究现在开始了。根据NewsRx记者在秦皇岛市的新闻报道，研究了机器人在不确定动力学和时间误差约束下的自适应复合学习控制问题，现有的时间误差约束方法只能获得半全局结果或保证系统阶相关收敛速率。本研究经费来源于国家自然科学基金(NSFC)。本文将一个新的规定时间性能函数与一个基于跟踪误差的障碍函数相结合,提出了一种新的规定时间误差控制方法,该方法具有如下特点:1)约束方法是全局的;2)跟踪误差收敛到一个具有近似指数率的紧集,其可由用户预先分配，而不必考虑系统的顺序；3)可由用户预先分配沉降时间和紧集。为处理参数测量不准确引起的不确定动态，将新设计的非奇异定时积分终端滑模和基于Moore-Penrose伪逆的复合学习技术相结合,提出了一种新的定时复合学习机器人控制方法(FTCLRC),与现有的复合学习机器人控制方法只能保证指数收敛或有限时间收敛,这种控制方法依赖于不可预测的激励强度和初始系统状态。本文提出的D FTCLRC能够保证跟踪误差和参数估计误差在固定时间内收敛到零，且不存在奇异问题，特别是收敛时间仅取决于用户设计的参数，与系统初始状态无关，以及不可预测的励磁强度。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Robotics is now availab le. According to news reporting from Qinhuangdao, People's Republic of China, by NewsRx journalists, research stated, "This article investigates the adaptive co mposite learning control problem of robots subject to uncertain dynamics and pre scribed time error constraints. Existing prescribed time error constraint method s only achieve semiglobal results or guarantee system order-dependent convergenc e rate." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from Yanshan Universi ty, "In this article, by integrating a new prescribed time performance function into a tracking error-based barrier function, a novel prescribed time error cons traint method is proposed with the following appealing features: 1) the constrai nt method is global; 2) the tracking error converges to a compact set with a pro ximate exponential rate, which can be preassigned by the user regardless of syst em order; 3) both settling time and compact set can be preassigned by the user. To handle the uncertain dynamics caused by inaccurate measurement of parameters, a novel fixed-time composite learning robot control (FTCLRC) method is develope d by combining a newly designed nonsingular fixed-time integral terminal sliding mode and the Moore-Penrose pseudoinverse-based composite learning technique. In comparison with existing composite learning robot control methods that can only ensure exponential convergence, or finite-time convergence, which is dependent on the unpredictable excitation strengths and initial system states, the propose d FTCLRC can guarantee that both the tracking error and parameters estimation er ror converge to zero in fixed-time, under a weak IE without singularity issue. I n particular, the convergence time only depends on the user-designed parameters, independent of the system's initial states, and the unpredictable excitation st rengths."
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