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This paper presents a control strategy based on time-varying fixed-time convergent higher order control barrier functions for a class of leader-follower multi-agent systems under signal temporal logic (STL) tasks. Each agent is assigned a local STL task which may be dependent on the behavior of agents involved in other tasks. In each local task, one agent called the leader has knowledge on the associated tasks and controls the performance of the subgroup involved agents. Our approach finds a robust solution to guarantee the fixed-time satisfaction of STL tasks in a least violating way and independent of the agents initial conditions. In particular, the robust performance of the task satisfaction is based on the knowledge of the leader from the followers and can be adjusted in a user-specified way.
This paper presents a control strategy based on a new notion of time-varying fixed-time convergent control barrier functions (TFCBFs) for a class of coupled multi-agent systems under signal temporal logic (STL) tasks. In this framework, each agent is
We study the problem of controlling multi-agent systems under a set of signal temporal logic tasks. Signal temporal logic is a formalism that is used to express time and space constraints for dynamical systems. Recent methods to solve the control syn
In this paper, we introduce the notion of periodic safety, which requires that the system trajectories periodically visit a subset of a forward-invariant safe set, and utilize it in a multi-rate framework where a high-level planner generates a refere
This paper deals with data-driven output synchronization for heterogeneous leader-follower linear multi-agent systems. Given a multi-agent system that consists of one autonomous leader and a number of heterogeneous followers with external disturbance
We present a robust control framework for time-critical systems in which satisfying real-time constraints is of utmost importance for the safety of the system. Signal Temporal Logic (STL) provides a formal means to express a variety of real-time cons