首页|New Robotics Findings from Yantai University Reported (Eventtriggered Practical Tracking Control for an Uncertain Free-flying Flexible-joint Space Robot With Dead-zone Input)

New Robotics Findings from Yantai University Reported (Eventtriggered Practical Tracking Control for an Uncertain Free-flying Flexible-joint Space Robot With Dead-zone Input)

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Data detailed on Robotics have been presented. According to news originating from Shandong, People’s Republic of China, by NewsRx correspondents, research stated, “This paper is devoted to the event-triggered practical tracking control of a class of uncertain free-flying flexible-joint space robots (FFSRs) under unknown dead-zone input. The remarkable characteristics of the paper are reflected by the coarse information on the reference signal since its time derivatives are not necessarily available for feedback, and moreover, by the serious uncertainties which contain unknown nonlinear dynamics, parameters without known nominal parts, and the external disturbance.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Shandong Province. Our news journalists obtained a quote from the research from Yantai University, “Then, the traditional control schemes on this topic become incapable. For this, a novel event-triggered control scheme is proposed by a skilful use of adaptive technique. Specifically, a dynamic gain with a smart choice of its updating law is incorporated into the vectorial backstepping framework, which not only overcomes the serious uncertainties contained in the system and dead-zone input but also handles the influence of sampling errors. Consequently, two adaptive event-triggered controllers are designed which ensure that all the states of the resulting closed-loop system are bounded while the system output practically tracks the reference signal, along with the exclusion of the Zeno phenomenon.”

ShandongPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRoboticsYantai University

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Feb.23)
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