EigenMPC：固有多様体に着想を得た機械系の高効率振動を励起するモデル予測制御フレームワーク
EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems
固有多様体理論に基づき、制御なしで持続する省エネな周期的振動を発見・収束させる非線形モデル予測制御手法を提案し、シミュレーションで検証した。
著者: Andre Coelho, Alin Albu-Schaeffer, Arne Sachtler, Hrishik Mishra, Davide Bicego, Christian Ott, Antonio Franchi
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
