制御リアプノフ関数を用いたロボットの非線形モデル予測制御
Nonlinear Model Predictive Control of Robotic Systems with Control Lyapunov Functions
NMPCと制御リアプノフ関数(CLF)を統合した制御器を、計算資源が限られたロボット上で初めてリアルタイム実装し、セグウェイでシミュレーションと実機の両方で評価した論文。
著者: Ruben Grandia, Andrew J. Taylor, Andrew Singletary, Marco Hutter, Aaron D. Ames
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
The theoretical unification of Nonlinear Model Predictive Control (NMPC) with Control Lyapunov Functions (CLFs) provides a framework for achieving optimal control performance while ensuring stability guarantees. In this paper we present the first real-time realization of a unified NMPC and CLF controller on a robotic system with limited computational resources. These limitations motivate a set of approaches for efficiently incorporating CLF stability constraints into a general NMPC formulation. We evaluate the performance of the proposed methods compared to baseline CLF and NMPC controllers with a robotic Segway platform both in simulation and on hardware. The addition of a prediction horizon provides a performance advantage over CLF based controllers, which operate optimally point-wise in time. Moreover, the explicitly imposed stability constraints remove the need for difficult cost function and parameter tuning required by NMPC. Therefore the unified controller improves the performance of each isolated controller and simplifies the overall design process.
