不確かなダイナミクスと制約下での安全制御合成
Safe Control Synthesis with Uncertain Dynamics and Constraints
ダイナミクスモデルと安全制約の不確かさを確率的・最悪ケースの両方で考慮したCLF/CBF制約を定式化し、SOCPとして効率的に安全安定制御を合成する手法を提案した。
著者: Kehan Long, Vikas Dhiman, Melvin Leok, Jorge Cortés, Nikolay Atanasov
分類: cs.RO, math.OC
原文アブストラクト
This paper considers safe control synthesis for dynamical systems with either probabilistic or worst-case uncertainty in both the dynamics model and the safety constraints. We formulate novel probabilistic and robust (worst-case) control Lyapunov function (CLF) and control barrier function (CBF) constraints that take into account the effect of uncertainty in either case. We show that either the probabilistic or the robust (worst-case) formulation leads to a second-order cone program (SOCP), which enables efficient safe and stable control synthesis. We evaluate our approach in PyBullet simulations of an autonomous robot navigating in unknown environments and compare the performance with a baseline CLF-CBF quadratic programming approach.
