Generalization of Safe Optimal Control Actions on Networked Multiagent Systems

Lin Song, Neng Wan, Aditya Gahlawat, Chuyuan Tao, Naira Hovakimyan, Evangelos A. Theodorou

Research output: Contribution to journalArticlepeer-review


In this article, we propose a unified framework to instantly generate a safe optimal control action for a new task from existing controllers on multiagent systems. The control action composition is achieved by taking a weighted mixture of the existing controllers according to the contribution of each component task. Instead of sophisticatedly tuning the cost parameters and other hyperparameters for safe and reliable behavior in the optimal control framework, the safety of each single-task solution is guaranteed using the control barrier functions (CBFs) for high relative degree stochastic systems, which constrains the system state within a known safe operation region where it originates from. Linearity of CBF constraints in control ensures the feasibility of safe control action composition. The discussed framework can immediately provide reliable solutions to new tasks by taking a weighted mixture of solved component-task actions and satisfying some CBF constraints, instead of performing an extensive sampling to compute a new controller. Our results are verified and demonstrated on both a single unmanned aerial vehicle (UAV) and two cooperative UAV teams in an environment with obstacles.

Original languageEnglish (US)
Pages (from-to)491-502
Number of pages12
JournalIEEE Transactions on Control of Network Systems
Issue number1
StatePublished - Mar 1 2023


  • Control barrier functions (CBFs)
  • multiagent systems (MASs)
  • safe control
  • stochastic optimal control

ASJC Scopus subject areas

  • Control and Optimization
  • Signal Processing
  • Control and Systems Engineering
  • Computer Networks and Communications


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