期刊
2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC)
卷 -, 期 -, 页码 2437-2442出版社
IEEE
DOI: 10.1109/CDC51059.2022.9992915
关键词
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This paper proposes a Nonlinear Model-Predictive Control method that can find and converge to energy-efficient regular oscillations without any control action. The approach is based on the Eigenmanifold theory, which defines the oscillations of a robot as an invariant submanifold of its state space. By formulating the control problem as a nonlinear program, the controller can handle constraints in the state and control variables and achieve energy efficiency during the convergence phase.
This paper proposes a Nonlinear Model-Predictive Control (NMPC) method capable of finding and converging to energy-efficient regular oscillations, which require no control action to be sustained. The approach builds up on the recently developed Eigenmanifold theory, which defines the sets of line-shaped oscillations of a robot as an invariant two-dimensional submanifold of its state space. By defining the control problem as a nonlinear program (NLP), the controller is able to deal with constraints in the state and control variables and be energy-efficient not only in its final trajectory but also during the convergence phase. An initial implementation of this approach is proposed, analyzed, and tested in simulation.
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