モデル不確かさ下の安全クリティカル系に対する学習ベース安全・安定性駆動制御
Learning-Based Safety-Stability-Driven Control for Safety-Critical Systems under Model Uncertainties
ガウス過程でモデル誤差を学習し、その不確かさ上限を用いた安全バリア制約と安定性制約を二次計画問題として解くことで、モデル不確かさ下でも安全と追従安定性を両立する制御手法を提案した論文。
著者: Lei Zheng, Jiesen Pan, Rui Yang, Hui Cheng, Haifeng Hu
分類: cs.RO
原文アブストラクト
Safety and tracking stability are crucial for safety-critical systems such as self-driving cars, autonomous mobile robots, industrial manipulators. To efficiently control safety-critical systems to ensure their safety and achieve tracking stability, accurate system dynamic models are usually required. However, accurate system models are not always available in practice. In this paper, a learning-based safety-stability-driven control (LBSC) algorithm is presented to guarantee the safety and tracking stability for nonlinear safety-critical systems subject to control input constraints under model uncertainties. Gaussian Processes (GPs) are employed to learn the model error between the nominal model and the actual system dynamics, and the estimated mean and variance of the model error are used to quantify a high-confidence uncertainty bound. Using this estimated uncertainty bound, a safety barrier constraint is devised to ensure safety, and a stability constraint is developed to achieve rapid and accurate tracking. Then the proposed LBSC method is formulated as a quadratic program incorporating the safety barrier, the stability constraint, and the control constraints. The effectiveness of the LBSC method is illustrated on the safety-critical connected cruise control (CCC) system simulator under model uncertainties.
