Model-following Neuro-adaptive approach for robust control of high performance aircrafts

Based on dynamic inversion, a relatively straightforward approach is presented in this paper for nonlinear flight control design of high performance aircrafts, which does not require the normal and lateral acceleration commands to be first transferred to body rates before computing the required control inputs. This leads to substantial improvement of the tracking response. Promising results are obtained from six degree-offreedom simulation studies of F-16 aircraft, which are found to be superior as compared to an existing approach (which is also based on dynamic inversion). The new approach has two potential benefits, namely reduced oscillatory response (including elimination of non-minimum phase behavior) and reduced control magnitude. Next, a model-following neuron-adaptive design is augmented the nominal design in order to assure robust performance in the presence of parameter inaccuracies in the model. Note that in the approach the model update takes place adaptively online and hence it is philosophically similar to indirect adaptive control. However, unlike a typical indirect adaptive control approach, there is no need to update the individual parameters explicitly. Instead the inaccuracy in the system output dynamics is captured directly and then used in modifying the control. This leads to faster adaptation, which helps in stabilizing the unstable plant quicker. The robustness study from a large number of simulations shows that the adaptive design has good amount of robustness with respect to the expected parameter inaccuracies in the model.

Self Assessment-Based Decision Making for Multiagent Cooperative Search

This paper addresses a search problem with multiple limited capability search agents in a partially connected dynamical networked environment under different information structures. A self assessment-based decision-making scheme for multiple agents is proposed that uses a modified negotiation scheme with low communication overheads. The scheme has attractive features of fast decision-making and scalability to large number of agents without increasing the complexity of the algorithm. Two models of the self assessment schemes are developed to study the effect of increase in information exchange during decision-making. Some analytical results on the maximum number of self assessment cycles, effect of increasing communication range, completeness of the algorithm, lower bound and upper bound on the search time are also obtained. The performance of the various self assessment schemes in terms of total uncertainty reduction in the search region, using different information structures is studied. It is shown that the communication requirement for self assessment scheme is almost half of the negotiation schemes and its performance is close to the optimal solution. Comparisons with different sequential search schemes are also carried out. Note to Practitioners-In the futuristic military and civilian applications such as search and rescue, surveillance, patrol, oil spill, etc., a swarm of UAVs can be deployed to carry out the mission for information collection. These UAVs have limited sensor and communication ranges. In order to enhance the performance of the mission and to complete the mission quickly, cooperation between UAVs is important. Designing cooperative search strategies for multiple UAVs with these constraints is a difficult task. Apart from this, another requirement in the hostile territory is to minimize communication while making decisions. This adds further complexity to the decision-making algorithms. In this paper, a self-assessment-based decision-making scheme, for multiple UAVs performing a search mission, is proposed. The agents make their decisions based on the information acquired through their sensors and by cooperation with neighbors. The complexity of the decision-making scheme is very low. It can arrive at decisions fast with low communication overheads, while accommodating various information structures used for increasing the fidelity of the uncertainty maps. Theoretical results proving completeness of the algorithm and the lower and upper bounds on the search time are also provided.

Pseudo-time particle filtering for diffuse optical tomography

We recast the reconstruction problem of diffuse optical tomography (DOT) in a pseudo-dynamical framework and develop a method to recover the optical parameters using particle filters, i.e., stochastic filters based on Monte Carlo simulations. In particular, we have implemented two such filters, viz., the bootstrap (BS) filter and the Gaussian-sum (GS) filter and employed them to recover optical absorption coefficient distribution from both numerically simulated and experimentally generated photon fluence data. Using either indicator functions or compactly supported continuous kernels to represent the unknown property distribution within the inhomogeneous inclusions, we have drastically reduced the number of parameters to be recovered and thus brought the overall computation time to within reasonable limits. Even though the GS filter outperformed the BS filter in terms of accuracy of reconstruction, both gave fairly accurate recovery of the height, radius, and location of the inclusions. Since the present filtering algorithms do not use derivatives, we could demonstrate accurate contrast recovery even in the middle of the object where the usual deterministic algorithms perform poorly owing to the poor sensitivity of measurement of the parameters. Consistent with the fact that the DOT recovery, being ill posed, admits multiple solutions, both the filters gave solutions that were verified to be admissible by the closeness of the data computed through them to the data used in the filtering step (either numerically simulated or experimentally generated). (C) 2011 Optical Society of America

Experimental evidence for interplay of dynamic heterogeneity and finite-size effect in glassy polymers

Despite two decades of extensive research, direct experimental evidence of a dynamical length scale determining the glass transition of confined polymers has yet to emerge. Using a recently established experimental technique of interface micro-rheology we provide evidence of finite-size effect truncating the growth of a quantity proportional to a dynamical length scale in confined glassy polymers, on cooling towards the glass transition temperature. We show how the interplay of variation of polymer film thickness and this temperature-dependent growing dynamical length scale determines the glass transition temperature, which in our case of 2-3nm thick films, is reduced significantly as compared to their bulk values.

Molecular Dynamics of Thermotropic Liquid Crystals: Anomalous relaxation dynamics of calamitic and discotic liquid crystals

Recent optical kerr effect (OKE) studies have demonstrated that orientational relaxation of rod-like nematogens exhibits temporal power law decay at intermediate times not only near the isotropic–nematic (I–N) phase boundary but also in the nematic phase. Such behaviour has drawn an intriguing analogy with supercooled liquids. We have investigated both collective and single-particle orientational dynamics of a family of model system of thermotropic liquid crystals using extensive computer simulations. Several remarkable features of glassy dynamics are on display including non-exponential relaxation, dynamical heterogeneity, and non-Arrhenius temperature dependence of the orientational relaxation time. Over a temperature range near the I–N phase boundary, the system behaves remarkably like a fragile glass-forming liquid. Using proper scaling, we construct the usual relaxation time versus inverse temperature plot and explicitly demonstrate that one can successfully define a density dependent fragility of liquid crystals. The fragility of liquid crystals shows a temperature and density dependence which is remarkably similar to the fragility of glass forming supercooled liquids. Energy landscape analysis of inherent structures shows that the breakdown of the Arrhenius temperature dependence of relaxation rate occurs at a temperature that marks the onset of the growth of the depth of the potential energy minima explored by the system. A model liquid crystal, consisting of disk-like molecules, has also been investigated in molecular dynamics simulations for orientational relaxation along two isobars starting from the high temperature isotropic phase. The isobars have been so chosen that the phase sequence isotropic (I)–nematic (N)–columnar (C) appears upon cooling along one of them and the sequence isotropic (I)–columnar(C) along the other. While the orientational relaxation in the isotropic phase near the I–N phase transition shows a power law decay at short to intermediate times, such power law relaxation is not observed in the isotropic phase near the I–C phase boundary. The origin of the power law decay in the single-particle second-rank orientational time correlation function (OTCF) is traced to the growth of the orientational pair distribution functions near the I–N phase boundary. As the system settles into the nematic phase, the decay of the single-particle second-rank orientational OTCF follows a pattern that is similar to what is observed with calamitic liquid crystals and supercooled molecular liquids.

Ontheproof of the Poincare´ conjecture

In 2002, Perelman proved the Poincare conjecture, building on the work of Richard Hamilton on the Ricci flow. In this article, we sketch some of the arguments and attempt to place them in a broader dynamical context.

On the proof of the Poincare´ conjecture

In 2002, Perelman proved the Poincare conjecture, building on the work of Richard Hamilton on the Ricci flow. In this article, we sketch some of the arguments and attempt to place them in a broader dynamical context.

Minor mergers and their impact on the kinematics of old and young stellar populations in disk galaxies

By means of N-body simulations we investigate the impact of minor mergers on the angular momentum and dynamical properties of the merger remnant. Our simulations cover a range of initial orbital characteristics and gas-to-stellar mass fractions (from 0 to 20%), and include star formation and supernova feedback. We confirm and extend previous results by showing that the specific angular momentum of the stellar component always decreases independently of the orbital parameters or morphology of the satellite, and that the decrease in the rotation velocity of the primary galaxy is accompanied by a change in the anisotropy of the orbits. However, the decrease affects only the old stellar population, and not the new population formed from gas during the merging process. This means that the merging process induces an increasing difference in the rotational support of the old and young stellar components, with the old one lagging with respect to the new. Even if our models are not intended specifically to reproduce the Milky Way and its accretion history, we find that, under certain conditions, the modeled rotational lag found is compatible with that observed in the Milky Way disk, thus indicating that minor mergers can be a viable way to produce it. The lag can increase with the vertical distance from the disk midplane, but only if the satellite is accreted along a direct orbit, and in all cases the main contribution to the lag comes from stars originally in the primary disk rather than from stars in the satellite galaxy. We also discuss the possibility of creating counter-rotating stars in the remnant disk, their fraction as a function of the vertical distance from the galaxy midplane, and the cumulative effect of multiple mergers on their creation.

Network Reconstruction from dynamic data

Over the past decade, many powerful data mining techniques have been developed to analyze temporal and sequential data. The time is now fertile for addressing problems of larger scope under the purview of temporal data mining. The fourth SIGKDD workshop on temporal data mining focused on the question: What can we infer about the structure of a complex dynamical system from observed temporal data? The goals of the workshop were to critically evaluate the need in this area by bringing together leading researchers from industry and academia, and to identify promising technologies and methodologies for doing the same. We provide a brief summary of the workshop proceedings and ideas arising out of the discussions.

Growth Kinetics of Nanoclusters in Solution

We study the growth kinetics of nanoclusters in solution. There are two generic factors that drive growth: (a) reactions that produce the nanomaterial; and (b) diffusion of the nanomaterial due to chemical-potential gradients. We model the growth kinetics of ZnO nanoparticles via coupled dynamical equations for the relevant order parameters, We study this model both analytically and numerically. We find that there is a crossover in thenanocluster growth law: from L(t) similar to t(1/2) in the reaction-controlled regime to L(t) t(1/3) in the diffusion-controlled regime.

Amplitude death in complex networks induced by environment

We present a mechanism for amplitude death in coupled nonlinear dynamical systems on a complex network having interactions with a common environment like external system. We develop a general stability analysis that is valid for any network topology and obtain the threshold values of coupling constants for the onset of amplitude death. An important outcome of our study is a universal relation between the critical coupling strength and the largest nonzero eigenvalue of the coupling matrix. Our results are fully supported by the detailed numerical analysis for different network topologies.