A Unified Safety Protection and Extension Governor

被引:0
作者
Li, Nan [1 ]
Li, Yutong [2 ]
Kolmanovsky, Ilya [3 ]
机构
[1] Tongji Univ, Sch Automot Studies, Shanghai 201804, Peoples R China
[2] Ford Motor Co, Dearborn, MI 48126 USA
[3] Univ Michigan, Dept Aerosp Engn, Ann Arbor, MI 48109 USA
关键词
Protection; Optimization; Filters; Linear systems; Automotive engineering; Supervisory control; Systems operation; Motors; Companies; Additives; Algorithms; constraints; predictive control; safety-critical systems; MODEL-PREDICTIVE CONTROL; CONTROL INVARIANT-SETS; COMPUTATION; SYSTEMS;
D O I
10.1109/TAC.2024.3489723
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this note, we propose a supervisory control scheme that unifies the abilities of safety protection and safety extension. It produces a control that keeps the system safe indefinitely when such a control exists. When such a control does not exist, it optimizes the control to maximize the time before any safety violation, which translates into more time to seek recovery and/or mitigate any harm. We describe the scheme and develop an approach that integrates the two abilities into a single constrained optimization problem with only continuous variables. For linear systems with convex constraints, the problem reduces to a convex quadratic program and is easy to solve. We illustrate the proposed safety supervisor with an automotive example.
引用
收藏
页码:2599 / 2606
页数:8
相关论文
共 26 条
  • [11] Theory and computation of disturbance invariant sets for discrete-time linear systems
    Kolmanovsky, I
    Gilbert, EG
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 1998, 4 (04) : 317 - 367
  • [12] Discrete-time drift counteraction stochastic optimal control: Theory and application-motivated examples
    Kolmanovsky, I. V.
    Lezhnev, L.
    Maizenberg, T. L.
    [J]. AUTOMATICA, 2008, 44 (01) : 177 - 184
  • [13] Li N, 2025, Arxiv, DOI arXiv:2211.12628
  • [14] Li Y., 2021, Proc. Learn. Dyn. Control, P1093
  • [15] Robust Action Governor for Discrete-Time Piecewise Affine Systems With Additive Disturbances
    Li, Yutong
    Li, Nan
    Tseng, H. Eric
    Girard, Anouck
    Filev, Dimitar
    Kolmanovsky, Ilya
    [J]. IEEE CONTROL SYSTEMS LETTERS, 2022, 6 : 950 - 955
  • [16] Model predictive control: Recent developments and future promise
    Mayne, David Q.
    [J]. AUTOMATICA, 2014, 50 (12) : 2967 - 2986
  • [17] Constrained model predictive control: Stability and optimality
    Mayne, DQ
    Rawlings, JB
    Rao, CV
    Scokaert, POM
    [J]. AUTOMATICA, 2000, 36 (06) : 789 - 814
  • [18] An interior point method for nonlinear programming with infeasibility detection capabilities
    Nocedal, Jorge
    Oeztoprak, Figen
    Waltz, Richard A.
    [J]. OPTIMIZATION METHODS & SOFTWARE, 2014, 29 (04) : 837 - 854
  • [19] Computing Robust Controlled Invariant Sets of Linear Systems
    Rungger, Matthias
    Tabuada, Paulo
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2017, 62 (07) : 3665 - 3670
  • [20] Scalable Computation of Robust Control Invariant Sets of Nonlinear Systems
    Schaefer, Lukas
    Gruber, Felix
    Althoff, Matthias
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2024, 69 (02) : 755 - 770