Quadratic Programming for Continuous Control of Safety-Critical Multiagent Systems Under Uncertainty

This article studies the control problem for safety-critical multiagent systems based on quadratic programming (QP). Each controlled agent is modeled as a cascade connection of an integrator and an uncertain nonlinear actuation system. In particular, the integrator represents the position–velocity relation, and the actuation system describes the dynamic response of the actual velocity to the velocity reference signal. The notion of input-to-output stability is employed to characterize the essential velocity-tracking capability of the actuation system. The standard QP algorithms for collision avoidance may be infeasible due to uncertain actuator dynamics. Even if feasible, the solutions may be non-Lipschitz because of possible violation of the full rank condition of the active constraints. Also, the interaction between the controlled integrator and the uncertain actuator dynamics may lead to significant robustness issues. Based on the current development of nonlinear control theory and numerical optimization methods, this article first contributes a new feasible-set reshaping technique and a refined QP algorithm for feasibility, robustness, and local Lipschitz continuity. Then, we present a nonlinear small-gain analysis to handle the inherent interaction for guaranteed safety of the closed-loop multiagent system. The proposed method is illustrated by numerical simulation and a physical experiment.

I. Introduction

Attaining primary objectives while satisfying motion constraints is an essential yet challenging task for vehicles and robotic systems. This has been one of the most attractive topics in the interdisciplinary literature of controls and robotics in the past decades [1], [2], [3], [4], [5], [6], [7], [8].