Event-Triggered Control for Safety-Critical Systems With Unknown Dynamics

This article addresses the problem of safety-critical control for multiagent systems with unknown dynamics in unknown environments. It has been shown that stabilizing affine control systems to desired (sets of) states while optimizing quadratic costs subject to state and control constraints can be reduced to a sequence of quadratic programs (QPs) by using control barrier functions (CBFs) and control Lyapunov functions (CLFs). One of the main challenges in this approach is obtaining accurate system dynamics of all components in the system, which is especially difficult when online model identification is required given limited computational resources and system data. We address this problem by proposing a robust framework (to unknown dynamics including uncertainties) through defining adaptive affine control dynamics that are updated based on the error states obtained by real-time sensor measurements. We define a CBF for a safety requirement on the unmodeled agents based on the adaptive dynamics and error states, and reformulate the safety-critical control problem as the abovementioned sequence of QPs. Then, we determine a set of events that trigger the QPs and ensure safety when solving them. We also derive a condition that guarantees the satisfaction of a CBF constraint between events. The proposed framework can also be used for state convergence guarantees for systems with unknown dynamics based on CLFs. We illustrate the effectiveness of the proposed framework on a robot control problem, an adaptive cruise control problem and a traffic merging problem using autonomous vehicles. We also compare the proposed event-driven method with the classical time-driven approach.

Automated summary

This article addresses the problem of safety-critical control for multiagent systems with unknown dynamics in unknown environments. It demonstrates the reduction of stabilizing affine control systems to quadratic programs using control barrier functions (CBFs) and control Lyapunov functions (CLFs). The article proposes a robust framework that updates adaptive affine control dynamics based on real-time sensor measurements and error states, and reformulates the safety-critical control problem as a sequence of quadratic programs.