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.
Highlights
Today’s military forces face an increasingly complex operating environment, whether dealing with humanitarian operations or in the theater of war
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
In the recent paper [23], we have proposed a dynamics and control model for a joint autonomous land-air operation, consisting of a swarm of unmanned ground vehicles (UGVs) and swarm of unmanned aerial vehicles (UAVs)
Summary
Today’s military forces face an increasingly complex operating environment, whether dealing with humanitarian operations or in the theater of war. This paper proposes a Hamiltonian servo system which is a combined modeling framework for the control and estimation of such autonomous teams of UV’s. Its modeling framework was linear dynamics and linear control, that is, linearized mechanics of multi-body systems (derived using Newton-Euler, Lagrangian, Gibs-Appel or kinetostatics equations of motion) and controlled by Kalman’s linear quadratic controllers (for a comprehensive review, see [5]-[11]) The pinnacle of this approach to robotics in the last decade has been the famous Honda robot ASIMO (see [12]), with a related Hamiltonian biomechanical simulator [13]. From a mathematical point-of-view, in this paper we attempt to provide nonlinear, non-Gaussian and high-dimensional generalization of the classical Kalman servo system, outlined
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