Optimization of Interplanetary Rendezvous Trajectories for Solar Sailcraft Using a Neurocontroller

As for all low-thrust spacecraft, finding optimal solar sailcraft trajectories is a difficult and time-consuming task that involves a lot of experience and expert knowledge, since the convergence behavior of optimizers that are based on numerical optimal control methods depends strongly on an adequate initial guess, which is often hard to find. Even if the op-timizer converges to an ”optimal trajectory”, this trajectory is typically close to the initial guess that is rarely close to the global optimum. This paper demonstrates, that artificial neural networks in combination with evolutionary algorithms can be applied successfully for optimal solar sailcraft steering. Since these evolutionary neurocontrollers explore the trajectory search space more exhaustively than a human expert can do by using tradi-tional optimal control methods, they are able to find steering strategies that generate better trajectories, which are closer to the global optimum. Results are presented for a Near Earth Asteroid rendezvous mission and for a Mercury rendezvous mission

Adaptive Neuro-Genetic Control of Chaos applied to the Attitude Control Problem

Conventional adaptive control techniques have, for the most part, been based on methods for linear or weakly non-linear systems. More recently, neural network and genetic algorithm controllers have started to be applied to complex, non-linear dynamic systems. The control of chaotic dynamic systems poses a series of especially challenging problems. In this paper, an adaptive control architecture using neural networks and genetic algorithms is applied to a complex, highly nonlinear, chaotic dynamic system: the adaptive attitude control problem (for a satellite), in the presence of large, external forces (which left to themselves led the system into a chaotic motion). In contrast to the OGY method, which uses small control adjustments to stabilize a chaotic system in an otherwise unstable but natural periodic orbit of the system, the neuro-genetic controller may use large control adjustments and proves capable of effectively attaining any specified system state, with no a prioriknowledge of the dynamics, even in the presence of significant noise

PLGA, chitosan or chitosan-coated PLGA microparticles for alveolar delivery?. A comparative study of particle stability during nebulization

Various types of rifampicin (RIF)-loaded microparticles were compared for their stability during nebulization. Poly(lactide-co-glycolide) (PLGA), chitosan (CHT) and PLGA/CHT microparticles (MPs) were prepared by emulsion or precipitation techniques. MPs ability to be nebulized (NE%) as well as stability during freeze-drying or/and nebulization (NEED%), were evaluated after RIF extraction from MPs and determination by light spectroscopy. MP mean diameters and -potential values were measured by dynamic light scattering, morphology was assessed by SEM, cytotoxicity by MTT method and mucoadhesive properties by mucin association. In all cases, freeze-drying prior to nebulization did not affect EE%, NE or NEED%. In CHT, MPs RIF encapsulation efficiency (EE%) decreased with increasing CHT concentration (viscosity) and CHT-MP NEED% was higher when the polymer was crosslinked by glutaraldehyde. PLGA MPs, exhibited both higher RIF EE% and also higher nebulization ability and NEED%, compared to CHT ones, but also higher cytotoxicity. However, when the two polymers were combined in the PLGA/CHT MPs, EE%, NE% and NEED% increased with increasing MP CHT-content. PLGA/CHT MPs with 0.50% or 0.75% CHT exhibited highest EE% for RIF and also best nebulization ability and stability, compared to all other MP formulations studied. Additionally they had good mucoadhesive properties and comparably low cytotoxicity. © 2007 Elsevier B.V. All rights reserved

Genetic Programming for Prediction and Control

The relatively ‘new’ field of genetic programming has received a lot of attention during the last few years. This is because of its potential for generating functions which are able to solve specific problems. This paper begins with an extensive overview of the field, highlighting its power and limitations and providing practical tips and techniques for the successful application of genetic programming in general domains. Following this, emphasis is placed on the application of genetic programming to prediction and control. These two domains are of extreme importance in many disciplines. Results are presented for an oral cancer prediction task and a satellite attitude control problem. Finally, the paper discusses how the convergence of genetic programming can be significantly speeded up through bulk synchronous model parallelisation

Bulk synchronous parallelization of industrial electromagentic software

The parallelization of existing/industrial electromagnetic software using the bulk synchronous parallel (BSP) computation model is presented. The software employs the finite element method with a preconditioned conjugate gradient-type solution for the resulting linear systems of equations. A geometric mesh-partitioning approach is applied within the BSP framework for the assembly and solution phases of the finite element computation. This is combined with a nongeometric, data-driven parallel quadrature procedure for the evaluation of right-hand-side terms in applications involving coil fields. A similar parallel decomposition is applied to the parallel calculation of electron beam trajectories required for the design of tube devices. The BSP parallelization approach adopted is fully portable, conceptually simple, and cost-effective, and it can be applied to a wide range of finite element applications not necessarily related to electromagnetics