より良い制御バリア関数を学習する手法
Learning a Better Control Barrier Function
保守的な手作りCBFを出発点に、MPCで安全を保ちながら軌道データを集め、ニューラルネットで真の安全集合に近づくCBFを学習する。
著者: Bolun Dai, Prashanth Krishnamurthy, Farshad Khorrami
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
Control barrier functions (CBFs) are widely used in safety-critical controllers. However, constructing a valid CBF is challenging, especially under nonlinear or non-convex constraints and for high relative degree systems. Meanwhile, finding a conservative CBF that only recovers a portion of the true safe set is usually possible. In this work, starting from a "conservative" handcrafted CBF (HCBF), we develop a method to find a CBF that recovers a reasonably larger portion of the safe set. Since the learned CBF controller is not guaranteed to be safe during training iterations, we use a model predictive controller (MPC) to ensure safety during training. Using the collected trajectory data containing safe and unsafe interactions, we train a neural network to estimate the difference between the HCBF and a CBF that recovers a closer solution to the true safe set. With our proposed approach, we can generate safe controllers that are less conservative and computationally more efficient. We validate our approach on two systems: a second-order integrator and a ball-on-beam.
