微分平坦性を活用した制約付き多入力制御アフィン系の効率的な学習ベースモデル予測制御
Exploiting Differential Flatness for Efficient Learning-based Model Predictive Control of Constrained Multi-Input Control Affine Systems
微分平坦性を利用し、入力制約と半空間平坦状態制約を満たす多入力非線形アフィン系の学習ベースMPCを、2つの逐次凸最適化のみで実現し、計算効率を大幅に向上させた。
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著者: Tobias A. Farger, Adam W. Hall, Angela P. Schoellig
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
Learning-based control techniques use data from past trajectories to control systems with uncertain dynamics. However, learning-based controllers are often computationally inefficient, limiting their practicality. To address this limitation, we propose a learning-based controller that exploits differential flatness, a property of many robotic systems. Recent research on using flatness for learning-based control either is limited in that it (i) ignores input constraints, (ii) applies only to single-input systems, or (iii) is tailored to specific platforms. In contrast, our approach uses a system extension and block-diagonal cost formulation to control general multi-input, nonlinear, affine systems. Furthermore, it satisfies input and half-space flat state constraints and guarantees probabilistic Lyapunov decrease using only two sequential convex optimizations. We show that our approach performs similarly to, but is multiple times more efficient than, a Gaussian process model predictive controller in simulation, and achieves competitive tracking in real hardware experiments.
