Control design for intelligent and connected vehicle swarm systems: A generalized Udwadia–Kalaba approach
TL;DR
The adaptive robust controller (ARC) is compared with an LQR-based controller under the same constraint-following framework and results show that ARC produces smaller constraint errors and stronger disturbance attenuation while maintaining safe cooperative motion.
Abstract
This paper investigates safe formation control of intelligent and connected vehicle swarm (ICVS) systems in obstacle-rich environments with nonlinear dynamics, bounded safety constraints, and model uncertainties. First, a constraint-oriented modeling framework is developed for ICVS systems, in which artificial potential field (APF) functions describe obstacle evasion and inter-agent collision avoidance, while formation deformation and recovery are integrated into a unified constrained-motion objective. Second, a generalized Udwadia–Kalaba (GUK) approach with diffeomorphism-based transformed constraints is applied to the constrained vehicle swarm system. The diffeomorphic transformation maps bounded inter-vehicle distance constraints into unconstrained variables, thereby supporting spacing regulation for both fixed and time-varying reference distances. Third, an adaptive robust controller is designed to achieve obstacle avoidance, collision avoidance, formation adjustment, and trajectory following in the presence of model uncertainties and external disturbances. The adaptive robust controller (ARC) is further compared with an LQR-based controller under the same constraint-following framework. Simulation results show that ARC produces smaller constraint errors and stronger disturbance attenuation while maintaining safe cooperative motion.