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Adaptive Strategies in Compliant Motion Control

Adaptive Strategies in Compliant Motion Control
顺从运动控制中的自适应策略
批准号:
8803767
负责人:
Jean-Jacques Slotine
金额:
$0.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
1988
资助国家:
美国
项目状态:
已结题
起止时间:
1988-08-01 至 1990-01-31

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中文摘要
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英文摘要
There is a strong current research interest in adaptive control of robots, that is, control strategies which would allow a robot manipulator to automatically adapt to changes in its environment, loads, or to exceptional disturbances in order to successfully complete manipulation or assembly tasks. Some controller designs rely on approximations of system dynamics while others require measurements of joint accelerations and inversion of inertia matrices. This project further develops a new method which utilizes system kinetic energy rather than the fully expanded system dynamics. The method's strengths include guaranteed global convergence of the tracking, and computational simplicity. The project's focus is external control of unknown dynamic parameters, the problem of controlling the end effector of a passive mechanism along a desired trajectory using an active robot. This problem represents a large class of important practical applications involving motion control of complex mechanisms or mobile environments. Opening a door, turning a crank, as well as more complex problems in industrial settings belong to this class. The controlled mechanism has its own nonlinear dynamics which are generally unknown. The research will concentrate on transient performance of the control algorithms, and on trade-offs between joint space and Cartesian space formulations.
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Collaborative Research: Stability, security and emergency control for reconfigurable networked microgrids
  • 批准号:
    1809314
  • 项目类别:
    Standard Grant
  • 资助金额:
    $20.0万
  • 财政年份:
    2018
  • 负责人:
    Jean-Jacques Slotine
  • 依托单位:
Sliding Mode Observers
  • 批准号:
    8611640
  • 项目类别:
    Standard Grant
  • 资助金额:
    $0.0万
  • 财政年份:
    1986
  • 负责人:
    Jean-Jacques Slotine
  • 依托单位:
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海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis