首页|Researcher’s Work from Northeastern University Focuses on Robotics (Observer-bas ed adaptive tracking control of robotic manipulators with predefined time-guaran teed performance: Theory and experiment)

Researcher’s Work from Northeastern University Focuses on Robotics (Observer-bas ed adaptive tracking control of robotic manipulators with predefined time-guaran teed performance: Theory and experiment)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in robotic s. According to news originating from Shenyang, People’s Republic of China, by N ewsRx editors, the research stated, “This paper investigates the challenging pro blem of fixed time trajectory tracking for robotic manipulators under the presen ce of unavailable model perturbation, external disturbance and from different in itial states.” Funders for this research include National Natural Science Foundation of China; Applied Basic Research Program of Liaoning Province. The news editors obtained a quote from the research from Northeastern University : “Firstly, a novel fixed-time extended state observer (FESO) is designed to est imate and compensate the lumped disturbance, which is analyzed and proved to be stable in the sense of fixed time bounded stability. Secondly, a new type of fix ed-time prescribed performance control (FPPC) is constructed to guarantee the sy stem convergences to stable state within a predefined time and enhance transient performance. Furthermore, a novel continuous fixed time nonsingular fast termin al sliding mode variable is established, which addresses singularity obstacle in terminal sliding mode. Together with FESO and FPPC, a new fixed-time adaptive n onsingular fast terminal sliding mode controller (FANFTSMC) is developed. Meanwh ile, an adaptive terminal sliding mode reaching law adopted in FANFTSMC promotes the robustness and decreases the chattering phenomenon.”

Northeastern University, Shenyang, Peopl e’s Republic of China, Asia, Emerging Technologies, Machine Learning, Robotics, Robots

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(MAY.9)