離散時間高次制御バリア関数を用いたモデル予測制御のための反復凸最適化
Iterative Convex Optimization for Model Predictive Control with Discrete-Time High-Order Control Barrier Functions
任意の相対次数の制御バリア関数に対応するため、非線形ダイナミクスと離散時間高次制御バリア関数を各時刻で線形化し、安全性を保証しつつ高速計算を実現する反復凸最適化フレームワークを提案した。
著者: Shuo Liu, Jun Zeng, Koushil Sreenath, Calin A. Belta
分類: math.OC, cs.RO, cs.SY, eess.SY, math.DS
原文アブストラクト
Safety is one of the fundamental challenges in control theory. Recently, multi-step optimal control problems for discrete-time dynamical systems were formulated to enforce stability, while subject to input constraints as well as safety-critical requirements using discrete-time control barrier functions within a model predictive control (MPC) framework. Existing work usually focus on the feasibility or the safety for the optimization problem, and the majority of the existing work restrict the discussions to relative-degree one control barrier functions. Additionally, the real-time computation is challenging when a large horizon is considered in the MPC problem for relative-degree one or high-order control barrier functions. In this paper, we propose a framework that solves the safety-critical MPC problem in an iterative optimization, which is applicable for any relative-degree control barrier functions. In the proposed formulation, the nonlinear system dynamics as well as the safety constraints modeled as discrete-time high-order control barrier functions (DHOCBF) are linearized at each time step. Our formulation is generally valid for any control barrier function with an arbitrary relative-degree. The advantages of fast computational performance with safety guarantee are analyzed and validated with numerical results.
