クロスエントロピー法に基づく安全な学習型勾配不要モデル予測制御
Safe Learning-based Gradient-free Model Predictive Control Based on Cross-entropy Method
不確実な外乱下で非微分目的関数を持つ非線形系を制御するため、ガウス過程で外乱を学習するMPCとクロスエントロピー法によるサンプリング最適化、制御Lyapunov関数と制御バリア関数による最小介入補助制御を統合し、確率的な安全性を確保する枠組みを提案した。
著者: Lei Zheng, Rui Yang, Zhixuan Wu, Jiesen Pan, Hui Cheng
分類: cs.RO
原文アブストラクト
In this paper, a safe and learning-based control framework for model predictive control (MPC) is proposed to optimize nonlinear systems with a non-differentiable objective function under uncertain environmental disturbances. The control framework integrates a learning-based MPC with an auxiliary controller in a way of minimal intervention. The learning-based MPC augments the prior nominal model with incremental Gaussian Processes to learn the uncertain disturbances. The cross-entropy method (CEM) is utilized as the sampling-based optimizer for the MPC with a non-differentiable objective function. A minimal intervention controller is devised with a control Lyapunov function and a control barrier function to guide the sampling process and endow the system with high probabilistic safety. The proposed algorithm shows a safe and adaptive control performance on a simulated quadrotor in the tasks of trajectory tracking and obstacle avoidance under uncertain wind disturbances.
