Spacecraft formation flying in the port-Hamiltonian framework

The problem of controlling the relative position and velocity in multi-spacecraft formation flying in the planetary orbits is an enabling technology for current and future research. This paper proposes a family of tracking controllers for different dynamics of Spacecraft Formation Flying (SFF) in the framework of port-Hamiltonian (pH) systems through application of timed Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC). The leader–multi-follower architecture is used to address this problem. In this regard, first we model the spacecraft motion in the pH framework in the Earth Centered Inertial frame and then transform it to the Hill frame which is a special local coordinate system. By this technique, we may present a unified structure which encompasses linear/nonlinear dynamics, with/without perturbation. Then, using the timed IDA-PBC method and the contraction analysis, a new method for controlling a family of SFF dynamics is developed. The numerical simulations show the efficiency of the approach in two different cases of missions.

     

Synthèse du mèthyl-1 diformyl-2,3 pyrrole,de la méthyl-1 pyrrolo[2,3-d] pyridazine et de la méthyl-1 oxo-6 (6h)cycloheptatrieno[b] pyrrole

This communication describes the synthesis of l-methyl-2,3-diformylpyrrole. This new compound is used to prepare a new heterocycle, l-methylcyclohepta[b]pyrrol-6-one and thus allows a new synthesis of l-methylpyrrolo[2,3-d]pyridazine.     

Predictive control of uncertain nonlinear parabolic PDE systems using a Galerkin/neural-network-based model

In this paper, a model predictive control (MPC) scheme for a class of parabolic partial differential equation (PDE) systems with unknown nonlinearities, arising in the context of transport-reaction processes, is proposed. A spatial operator of a parabolic PDE system is characterized by a spectrum that can be partitioned into a finite slow and an infinite fast complement. In this view, first, Galerkin method is used to derive a set of finite dimensional slow ordinary differential equation (ODE) system that captures the dominant dynamics of the initial PDE system. Then, a Multilayer Neural Network (MNN) is employed to parameterize the unknown nonlinearities in the resulting finite dimensional ODE model. Finally, a Galerkin/neural-network-based ODE model is used to predict future states in the MPC algorithm. The proposed controller is applied to stabilize an unstable steady-state of the temperature profile of a catalytic rod subject to input and state constraints.     

Pool-Frenkel effect and high frequency dielectric constant determination of semiconducting P2O5-Li2MoO4-Li2O and P2O5-Na2MoO4-Na2O bulk glasses

The ternary 70P₂O₅-10Li₂MoO₄-20Li₂O and 70P₂O₅-10Na₂MoO₄-20Na₂O glasses, prepared by the press-melt quenching technique, were studied at temperatures between 298 and 418 K for their high dc electric field properties. For the above purpose, the effect of a strong electric field on the dc conduction of these amorphous bulk samples was investigated using the gap-type electrode configuration. At low electric fields, the current-voltage (I — V) characteristics have a linear shape, while at high electric fields (> 10³ V/cm), bulk samples show nonlinear effects (nonohmic conduction). Current-voltage curves show increasing departure from Ohm’s law with increasing current density, leading to critical phenomena at a maximum voltage (threshold voltage), known as switching (switch from a low-conduction state to a higher-conduction state at threshold voltage). The Pool-Frenkel high-field effect was observed at electrical fields of about 10³–10⁴ V/cm; then the lowering factor of the potential barrier, the high frequency dielectric constant, and the refractive index of these glasses were determined.      

A new approach to calculate the gluon polarization

We derive the Leading-Order (LO) master equation to extract the polarized gluon distribution G(x,Q ²)=xδg(x,Q ²) from polarized proton structure function, g^p₁(x,Q²)g^{p}_{1}(x,Q^{2}). By using a Laplace-transform technique, we solve the master equation and derive the polarized gluon distribution inside the proton. The test of accuracy which is based in our calculations on two different methods, confirms that we achieve to the correct solution for the polarized gluon distribution. To determine the polarized gluon distribution xδg(x,Q ²) more accurately, we only need to have more experimental data on the polarized structure functions, g₁^p(x,Q²)g_{1}^{p}(x,Q^{2}). Our result for polarized gluon distribution is in good agreement with some phenomenological models.