ラグランジュ系の高精度トラッキング制御のための安定性保証付き学習ベース手法
Provably Robust Learning-Based Approach for High-Accuracy Tracking Control of Lagrangian Systems
ガウス過程で加速度誤差を近似し、その不確かさ上限をロバスト外ループ制御に組み込むことで、閉ループ系の安定性を保証しつつ高精度な追従を実現する学習ベース制御法を提案した論文。
著者: Mohamed K. Helwa, Adam Heins, Angela P. Schoellig
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Lagrangian systems represent a wide range of robotic systems, including manipulators, wheeled and legged robots, and quadrotors. Inverse dynamics control and feedforward linearization techniques are typically used to convert the complex nonlinear dynamics of Lagrangian systems to a set of decoupled double integrators, and then a standard, outer-loop controller can be used to calculate the commanded acceleration for the linearized system. However, these methods typically depend on having a very accurate system model, which is often not available in practice. While this challenge has been addressed in the literature using different learning approaches, most of these approaches do not provide safety guarantees in terms of stability of the learning-based control system. In this paper, we provide a novel, learning-based control approach based on Gaussian processes (GPs) that ensures both stability of the closed-loop system and high-accuracy tracking. We use GPs to approximate the error between the commanded acceleration and the actual acceleration of the system, and then use the predicted mean and variance of the GP to calculate an upper bound on the uncertainty of the linearized model. This uncertainty bound is then used in a robust, outer-loop controller to ensure stability of the overall system. Moreover, we show that the tracking error converges to a ball with a radius that can be made arbitrarily small. Furthermore, we verify the effectiveness of our approach via simulations on a 2 degree-of-freedom (DOF) planar manipulator and experimentally on a 6 DOF industrial manipulator.
