학술논문
Receding Horizon Extended Linear Quadratic Regulator for RFS-Based Swarms With Target Planning and Automatic Cost Function Scaling
Document Type
Periodical
Author
Source
IEEE Transactions on Control of Network Systems IEEE Trans. Control Netw. Syst. Control of Network Systems, IEEE Transactions on. 8(2):566-575 Jun, 2021
Subject
Language
ISSN
2325-5870
2372-2533
2372-2533
Abstract
A cost function is constructed for the random finite-set-based swarm guidance problem mechanized by Gaussian mixtures. This cost function uses an automated problem-dependent scaling and introduces an activator function for quadratic convergence of far off Gaussians. The cost function also depends on a target planner to transform user-defined waypoints into “way-areas” by calculating a covariance matrix. These covariances are based upon the given distribution of the target/reference geometry. Then an extended linear-quadratic regulator (LQR) is defined for the swarm problem as an improvement to the iterative LQR (ILQR). The algorithm is tested in a software simulation, and it is found to have easier tuning than ILQR and generates smooth trajectories toward the targets.