首页|Investigators at Yanshan University Detail Findings in Robotics (Fixed-time Comp osite Learning Control of Robots With Prescribed Time Error Constraints)
Investigators at Yanshan University Detail Findings in Robotics (Fixed-time Comp osite Learning Control of Robots With Prescribed Time Error Constraints)
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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."
QinhuangdaoPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRobotRoboticsYan shan University
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