TITLE:
Hamiltonian Servo: Control and Estimation of a Large Team of Autonomous Robotic Vehicles
AUTHORS:
Vladimir Ivancevic, Peyam Pourbeik
KEYWORDS:
Team of UGVs, Kalman Servo, Hamiltonian Control, Bayesian Estimation
JOURNAL NAME:
Intelligent Control and Automation,
Vol.8 No.4,
November
30,
2017
ABSTRACT: This paper proposes a novel Hamiltonian servo system, a combined modeling framework for control and estimation of a large team/fleet of autonomous robotic vehicles. The Hamiltonian servo framework represents high-dimensional, nonlinear and non-Gaussian generalization of the classical Kalman servo system. After defining the Kalman servo as a motivation, we define the affine Hamiltonian neural network for adaptive nonlinear control of a team of UGVs in continuous time. We then define a high-dimensional Bayesian particle filter for estimation of a team of UGVs in discrete time. Finally, we formulate a hybrid Hamiltonian servo system by combining the continuous-time control and the discrete-time estimation into a coherent framework that works like a predictor-corrector system.