Adaptive Inversion Research Articles

Metaphysics Of Controlled Evolution (Anthropic Principle, Evolutionary Epistemology And Ethics Of Nano-Bio Technologies)

With the entry of anthropogenic civilization into the stage of the information society, the post-academic phase of the historical evolution of scientific rationality began, the attributes of which are the specific methodology of scientific knowledge, scientific ethos and ontology. Natural philosophy becomes the meta-theoretical core of modern theory of anthropogenesis and the conceptual basis of biotechnology. As the initial postulate of the metaphysical structure of modern technologies and the transdisciplinary theory of their implementation (social verification), the most promising is the anthropic principle of participation. It is used as a starting postulate, on the basis of which the concept of a stable adaptive human strategy is formulated. It is represented by three systems of generation, coding, implementation and replication of adaptively significant information: respectively, biological (genetic), sociocultural and technorationalistic adaptive modules. A necessary and sufficient condition for the genesis of this cycle was the formation and complication of the system jf information communication between individuals through the symbolic sign system not directly related to the genetic code. As a result, the spontaneous coevolutionary process of interaction between the "subject" (living organisms) and the "object" (material world), is the teleological trend of the movement towards the complete rationalization of the World as It Is, its merger with the World of Due. The trajectory of evolution is determined by three parameters that can potentially be estimated empirically: descriptive-spontaneous (evolutionary efficiency), value-teleological (evolutionary correctness) and integral (quality of life). This metasystem transition can be called adaptive inversion, which, therefore, is the most important attribute of anthropogenesis, in general, and the formation of civilization, in particular. Adaptive inversion was established, in our opinion, as a supporting structure of human evolutionary strategy, embodied and maintained as an irresistible cultural and psychological intention to transform reality. This intention, although in different ideological and cultural forms in different types of civilization, was most pronounced precisely in the transatlantic ("Western") version of the technogenic civilization. Renunciation of it and replacing of the desire to slow down the transformation of our habitat and to bring it into the framework of acceptable risk by something less radical seems incompatible with the "human nature." A human (Homo sapiens), as the carrier of the rational beginning of reality, acts as a condition for observance of the anthropic principle . The integrity of reality has a time-bound dimension and assumes the constant emergence, formation and overcoming of the subject-object (epistemological) antinomy and its ontological (essential - obliging) equivalent. Thus, the stratification of the global evolutionary process into selective and semantic (teleological) coevolutionary and therefore ontologically inseparable components follows from the anthropic principle.

Robust Adaptive Nonlinear Dynamic Inversion Control for an Air-breathing Hypersonic Vehicle

This paper presents a robust adaptive nonlinear dynamic inversion control approach for the longitudinal dynamics of an air-breathing hypersonic vehicle. The proposed approach adopts a fast adaptation law using high-gain learning rate, while a low-pass filter is synthesized with the modified adaptive scheme to filter out the high-frequency content of the estimates. This modified high-gain adaptive scheme achieves a good transient process and a nice robust property with respect to parameter uncertainties, without exciting high-frequency oscillations. Based on input-output linearization, the nonlinear hypersonic dynamics are transformed into equivalent linear systems. Therefore, the pole placement technique is applied to design the baseline nonlinear dynamic inversion controller. Finally, the simulation results of the modified adaptive nonlinear dynamic inversion control law demonstrate the proposed control approach provides robust tracking of reference trajectories.

Adaptive mixed-norm seismic inversion for non-Gaussian errors

The discrepancies between geophysical measurements and forward-modelled data are commonly modelled as Gaussian errors, thereby necessitating the use of least-squares optimisation methods. However, given the many inevitable difficulties and ambiguities in data acquisition, processing, and interpretation, subsurface-property estimation from remote geophysical measurements is subject to non-Gaussian errors. We propose to minimise the misfit with a robust error measure, which is based on a generalised Gaussian distribution. A suboptimal solution is proposed through a mixed-norm functional combination of the l1 norm and l2 norm. A mixed-norm parameter is introduced to determine the relative importance between the l1 norm and l2 norm functional, which is a function of the kurtosis of the errors. The novelty of the proposed mixed-norm algorithm is that no knowledge of the seismic errors is required. The relative contributions of the l1 norm and l2 norm are adjusted based on the partially inverted elastic properties. Both synthetic and field data demonstrate the effectiveness of the proposed algorithm.

A multiple model adaptive inverse method for nonlinear heat transfer system with temperature-dependent thermophysical properties

A multiple model adaptive inverse (MMAI) method is proposed to estimate the boundary heat flux distribution of nonlinear heat transfer system with temperature-dependent thermophysical properties. In the temperature space of system, the nonlinear heat transfer system is divided into several linear subspaces, and for each subspace, the linear prediction submodel of the temperature at the measurement point is established. Furthermore, according to the instantaneous matching degree between each prediction submodel and the actual heat transfer system, the prediction submodels then are weighted and synthesized to gain the global prediction model of the nonlinear heat transfer system. Finally, based on the global prediction model, the boundary heat fluxes are simultaneously estimated through rolling optimization. Numerical experiments are performed to study the effects of system nonlinearity and measurement errors on the inversion results. Comparisons with the existing dynamic matrix control inverse method and the adaptive sequential function specification method are also conducted, and they all show the validity of the inverse method established in this paper.

Adaptive multinary inversion of gravity and gravity gradiometry data

We have developed a novel approach for inversion of gravity and gravity gradiometry data based on multinary transformation of the model parameters. This concept is a generalization of binary density inversion to the models described by any number of discrete model parameters. The multinary inversion makes it possible to explicitly exploit the sharp contrasts of the density between the host media and anomalous targets in the inversion of gravity and gravity gradiometry data. In the framework of the multinary inversion method, we use the given values of density and error functions to transform the density distribution into the desired step-function distribution. To accommodate a possible deviation of the densities from the fixed discrete values, we develop an adaptive technique for selecting the corresponding standard deviations, guided by the inversion process. The novel adaptive multinary inversion algorithm is demonstrated to be effective in determining the shape, location, and densities of the anomalous targets. We find that this method can be effectively applied for the inversion of the full tensor gravity gradiometry (FTG) data computer simulated for the SEG salt density model and for the field FTG data collected in the Nordkapp Basin, Barents Sea.

Adaptive multi-step Full Waveform Inversion based on Waveform Mode Decomposition

Full Waveform Inversion (FWI) can be used to build high resolution velocity models, but there are still many challenges in seismic field data processing. The most difficult problem is about how to recover long-wavelength components of subsurface velocity models when seismic data is lacking of low frequency information and without long-offsets. To solve this problem, we propose to use Waveform Mode Decomposition (WMD) method to reconstruct low frequency information for FWI to obtain a smooth model, so that the initial model dependence of FWI can be reduced. In this paper, we use adjoint-state method to calculate the gradient for Waveform Mode Decomposition Full Waveform Inversion (WMDFWI). Through the illustrative numerical examples, we proved that the low frequency which is reconstructed by WMD method is very reliable. WMDFWI in combination with the adaptive multi-step inversion strategy can obtain more faithful and accurate final inversion results. Numerical examples show that even if the initial velocity model is far from the true model and lacking of low frequency information, we still can obtain good inversion results with WMD method. From numerical examples of anti-noise test, we see that the adaptive multi-step inversion strategy for WMDFWI has strong ability to resist Gaussian noise. WMD method is promising to be able to implement for the land seismic FWI, because it can reconstruct the low frequency information, lower the dominant frequency in the adjoint source, and has a strong ability to resist noise.

Barrier Lyapunov function-based adaptive control for hypersonic flight vehicles

This paper presents an adaptive control strategy for hypersonic flight vehicles (HFVs) subject to parametric uncertainties, external disturbances and faulty actuators. Besides the guaranteed tracking performance, the main novelty of this study is to strictly constrain the flight states, which are crucial to hypersonic flights, such as the bounded angle of attack required by the scramjet. To achieve this, the disturbed control-oriented model of HFVs with uncertain parameters and faulty actuators is first rewritten into a parameterized form, based on which novel adaptive controllers are then designed for velocity and altitude subsystems. By introducing barrier Lyapunov functions to design procedures, specific tracking performances of velocity and altitude are guaranteed, while the other flight states of angle of attack, flight path angle, pitch angle and pitch angle rate can be kept within the prescribed ranges. Command filters are employed in the back-stepping design for altitude subsystem to limit the magnitudes of virtual controls and to avoid the complicated analytical calculations. In addition, an adaptive inverse is integrated into the adaptive control scheme to sufficiently compensate for the unknown dead-zone nonlinearity in elevator. It is shown that the developed control system regulates the tracking errors within some neighborhoods of zero, which become sufficiently small by proper adjustments on design parameters. Finally, the numerical simulation is provided for comparison and verification purposes.

Adaptive eigenspace method for inverse scattering problems in the frequency domain

A nonlinear optimization method is proposed for the solution of inverse scattering problems in the frequency domain, when the scattered field is governed by the Helmholtz equation. The time-harmonic inverse medium problem is formulated as a PDE-constrained optimization problem and solved by an inexact truncated Newton-type iteration. Instead of a grid-based discrete representation, the unknown wave speed is projected to a particular finite-dimensional basis of eigenfunctions, which is iteratively adapted during the optimization. Truncating the adaptive eigenspace (AE) basis at a (small and slowly increasing) finite number of eigenfunctions effectively introduces regularization into the inversion and thus avoids the need for standard Tikhonov-type regularization. Both analytical and numerical evidence underpins the accuracy of the AE representation. Numerical experiments demonstrate the efficiency and robustness to missing or noisy data of the resulting adaptive eigenspace inversion method.

Time Varying Back Propagating Algorithm for MIMO Adaptive Inverse Controller

In the field of automatic control system design, adaptive inverse is a powerful control technique. It identifies the system model and controls automatically without having prior knowledge about the dynamics of plant. In this paper neural network based adaptive inverse controller is proposed to control a MIMO system. Multi layer perception and back propagation are combinedly used in this investigation to design the NN learning algorithm. The developed structure represents the ability to identify and control the MIMO system. Mathematical derivation and simulation results for both plant identification and control are shown in this paper. Further, to prove the superiority of the proposed technique, performances are compared with recursive least square (RLS) method for the same MIMO system. RLS based adaptive inverse scheme is discussed in this paper for plant identification and control. Also the obtained simulated results are compared for both plant parameter estimation and tracking trajectory performance.

Stability and Convergence Analysis of Direct Adaptive Inverse Control

In adaptive inverse control (AIC), adaptive inverse of the plant is used as a feed-forward controller. Majority of AIC schemes estimate controller parameters using the indirect method. Direct adaptive inverse control (DAIC) alleviates the adhocism in adaptive loop. In this paper, we discuss the stability and convergence of DAIC algorithm. The computer simulation results are presented to demonstrate the performance of the DAIC. Laboratory scale experimental results are included in the paper to study the efficiency of DAIC for physical plants.

Design of the Nonlinear Structure Adaptive Model Inversion Flight Control System

[abstFig src='/00280006/17.jpg' width='300' text='Design of the nonlinear SAMI flight control system' ] The advanced aircraft has those technological characteristics: stealth, supersonic cruise, super maneuverability, multi-target attack, multi-role, high load, long range cruise, integrated avionics, short takeoffs and vertical landings. These characteristics need multi-subsystem integration in the advanced aircraft. In order to meet the need of advance aircraft, the nonlinear control system is necessary. In this paper, the nonlinear structure adaptive model inversion system was first used for the nonlinear control problem of super-maneuver aircraft. The sufficient and necessary condition of the control law was analyzed. Finally the results of the height-angle maneuver simulation show that the designed control system has good performance.

Optimization-based model reference adaptive control for dynamic positioning of a fully actuated underwater vehicle

This paper presents a predictive optimization-based model reference adaptive control (MRAC) approach for dynamic positioning (DP) of a fully actuated underwater vehicle subject to dynamic uncertainties and actuator saturation. Compared with conventional linear reference model-based approaches, this proposed MRAC controller utilizes an optimized reference model composed of the closed-loop approximate vehicle model under a nonlinear model predictive controller, in which both the state and input constraints are considered. An adaptive dynamic inversion controller is designed to track the reference trajectory in the presence of dynamic uncertainties, and a single hidden layer neural network is incorporated to compensate for the mismatch of the actual and approximate models and ensure the convergence of tracking errors. The effectiveness of the proposed DP approach is validated by comparative simulations performed with a remotely operated vehicle.

Pooled ecotype sequencing reveals candidate genetic mechanisms for adaptive differentiation and reproductive isolation.

The early stages of speciation are often characterized by the formation of partially reproductively isolated ecotypes, which evolve as a by-product of divergent selective forces that are endemic to different habitats. Identifying the genomic regions, genes and ultimately functional polymorphisms that are involved in the processes of ecotype formation is inherently challenging, as there are likely to be many different loci involved in the process. To localize candidate regions of the genome contributing to ecotype formation, we conducted whole-genome pooled sequencing (pool-seq) with 47 coastal perennial and 50 inland annual populations of the yellow monkeyflower, Mimulus guttatus. Coastal perennial and inland annual ecotypes of M.guttatus have previously been shown to be ecologically reproductively isolated and highly locally adapted to their respective habitats. Our pool-seq results found allelic differentiation between the ecotypes for two chromosomal inversions, suggesting that frequencies of inversion heterokaryotypes are strongly differentiated between the ecotypes. Further, there were elevated levels of nonsynonymous change across chromosomal inversions. Across the genome, we identified multiple strong candidate genes potentially driving the morphological, life history and salt tolerance differences between the ecotypes. Several candidate genes coincide with previously identified quantitative trait locus regions and also show a signature of recent natural selection. Overall, the results of our study add to growing support for a major role of chromosomal inversions in adaptation and speciation and provide new insights into the genetic mechanisms underlying classic plant ecotype adaptations to wet and dry habitats.

Adaptive waveform inversion: Theory

Conventional full-waveform seismic inversion attempts to find a model of the subsurface that is able to predict observed seismic waveforms exactly; it proceeds by minimizing the difference between the observed and predicted data directly, iterating in a series of linearized steps from an assumed starting model. If this starting model is too far removed from the true model, then this approach leads to a spurious model in which the predicted data are cycle skipped with respect to the observed data. Adaptive waveform inversion (AWI) provides a new form of full-waveform inversion (FWI) that appears to be immune to the problems otherwise generated by cycle skipping. In this method, least-squares convolutional filters are designed that transform the predicted data into the observed data. The inversion problem is formulated such that the subsurface model is iteratively updated to force these Wiener filters toward zero-lag delta functions. As that is achieved, the predicted data evolve toward the observed data and the assumed model evolves toward the true model. This new method is able to invert synthetic data successfully, beginning from starting models and under conditions for which conventional FWI fails entirely. AWI has a similar computational cost to conventional FWI per iteration, and it appears to converge at a similar rate. The principal advantages of this new method are that it allows waveform inversion to begin from less-accurate starting models, does not require the presence of low frequencies in the field data, and appears to provide a better balance between the influence of refracted and reflected arrivals upon the final-velocity model. The AWI is also able to invert successfully when the assumed source wavelet is severely in error.

Acceleration measurement-based incremental nonlinear flight control for air-breathing hypersonic vehicles

In this paper, nonlinear flight control problem for Air-breathing Hypersonic Vehicles (AHVs) is addressed. Designing a high quality flight controller for AHVs is highly challenging due to model uncertainties caused by inherent structural/aerodynamic couplings, high nonlinearities within the large envelope. A nonlinear flight control approach named Sensor-Based Backstepping (SBB) is employed to design a highly robust controller for AHVs, and ensure that the controller does not suffer from large parameter variations and model uncertainties. The SBB approach is essentially an incremental type of approximate dynamic inversion (ADI) control scheme developed based on singular perturbation theory and multi-loop backstepping design methodology. The SBB uses angular accelerometers to capture dynamic changes in time derivatives of body angular rates, and requires nearly no accurate model information in its implementation. In this way, unlike model based adaptive nonlinear control laws such as adaptive nonlinear dynamic inversion (ANDI), dependency on accurate full model information and the associated identification workload is removed. Several key issues associated with inherent characteristics of AHVs before the SBB based AHV controller is developed. To validate the SBB flight controller, numerical simulations are performed on a nonlinear AHV model with sudden model parameter uncertainties introduced, and the controller is compared with the linear controller augmented Linear Quadratic Regulator (LQR) and the real-time model based ANDI controller. The results have demonstrated the superior tracking performance of the SBB approach, and highlighted its strong robustness to model uncertainties.

An adaptive stochastic inverse solver for multiscale characterization of composite materials

SummaryWe present an adaptive variant of the measure‐theoretic approach for stochastic characterization of micromechanical properties based on the observations of quantities of interest at the coarse (macro) scale. The salient features of the proposed nonintrusive stochastic inverse solver are identification of a nearly optimal sampling domain using enhanced ant colony optimization algorithm for multiscale problems, incremental Latin‐hypercube sampling method, adaptive discretization of the parameter and observation spaces, and adaptive selection of number of samples. A complete test data of the TORAY T700GC‐12K‐31E and epoxy #2510 material system from the National Institute for Aviation Research report is employed to characterize and validate the proposed adaptive nonintrusive stochastic inverse algorithm for various unnotched and open‐hole laminates. Copyright © 2016 John Wiley & Sons, Ltd.

Dimensionally adaptive hp-finite element simulation and inversion of 2D magnetotelluric measurements

Magnetotelluric (MT) problems often contain different subdomains where the conductivity of the media depends upon one, two, or three spatial variables. Traditionally, when a MT problem incorporates a three-dimensional (3D) subdomain, the numerical method employed for simulation and inversion was 3D over then entire domain. In here, we propose to take advantage of the possibly lower dimensionality of certain subdomains during the inversion process. By doing so, we obtain significant computational savings (up to 75% in some scenarios) and increased accuracy on the results. We numerically illustrate this method by employing two dimensional (2D) computations based on a multi-goal oriented hp-adaptive Finite Element Method (FEM) that exhibits superior convergence properties. Additionally, we provide a formulation for implementing an efficient adjoint based method for the computation of the derivatives of the impedance, and we show the importance of the (a) proper selection of the inversion variable, and (b) the advantages of using both the Transverse Electric (TE) and Transverse Magnetic (TM) measurements for the proper inversion of MT data.

Prediction and Recovery of Aircraft Unstable Nonlinear Phenomena Using Bifurcation Analysis and Backstepping Method

To protect the aircraft flight safety across the envelope of angle of attack, bifurcation analysis and backstepping method are investigated to predict and suppress the unstable nonlinear flight phenomena. By applying bifurcation analysis and continuation method to the flight motion, the onsets of both the chaotic phenomenon called falling leaf motion and the catastrophe phenomenon named coupled jump behavior are derived. To stabilize these unstable motions, a backstepping and disturbance observer based flight controller is designed. According to the main function of the control surface, we divide the flight controller into the airspeed subsystem and the flight path subsystem, where the airspeed subsystem is regulated by an adaptive dynamic inversion controller while the flight path subsystem is stabilized by a third-order compound controller. Considering the parametric uncertainties of aerodynamics, three sliding mode disturbance observers are presented as compensators to approximate the compound uncertainties. Simulations demonstrate that the proposed controller can recover the aircraft from falling leaf motion or coupled jump behavior to straight level flying.

Adaptive Digital Signal Predistortion for Nonlinear Communication Systems Using Successive Methods

Highly-efficient operation of communication systems requires effective compensation of nonlinear distortion with memory. The main contributor to nonlinearity is the high-power amplifier (HPA) when operated close to saturation. This results in two major detrimental effects: spectral regrowth causing interference in adjacent frequency bands and in-band distortion in the form of constellation warping and clustering. This paper introduces a novel family of adaptive digital signal predistortion schemes that successively modifies the HPA input to drive nonlinear distortion with memory toward zero. This family of schemes is capable of suppressing the spectral regrowth and in-band distortion simultaneously, while keeping the HPA operating efficiently close to saturation. In addition, the proposed solution offers the system designer a beneficial tunability feature to select the levels of suppression. Reduced-complexity Volterra model is adopted and is implemented on-the-fly to cope with systems with high degree of nonlinearity and large memory span. Furthermore, the proposed schemes are made adaptive by applying stochastic gradient method offline during training phase to effectively deal with nonlinear systems whose characteristics are unknown a priori. Extensive computer simulations demonstrate that the proposed adaptive family of predistortion schemes approaches the performance of the perfectly predistorted solution, and can considerably outperform techniques based on adaptive inverse, commonly adopted in the literature.

Tracking changes in chromosomal arrangements and their genetic content during adaptation.

There is considerable evidence for an adaptive role of inversions, but how their genetic content evolves and affects the subsequent evolution of chromosomal polymorphism remains controversial. Here, we track how life-history traits, chromosomal arrangements and 22 microsatellites, within and outside inversions, change in three replicated populations of Drosophila subobscura for 30 generations of laboratory evolution since founding from the wild. The dynamics of fitness-related traits indicated adaptation to the new environment concomitant with directional evolution of chromosomal polymorphism. Evidence of selective changes in frequency of inversions was obtained for seven of 23 chromosomal arrangements, corroborating a role for inversions in adaptation. The evolution of linkage disequilibrium between some microsatellites and chromosomes suggested that adaptive changes in arrangements involved changes in their genetic content. Several microsatellite alleles increased in frequency more than expected by drift in targeted inversions in all replicate populations. In particular, there were signs of selection in the O3+4 arrangement favouring a combination of alleles in two loci linked to the inversion and changing along with it, although the lack of linkage disequilibrium between these loci precludes epistatic selection. Seven other alleles increased in frequency within inversions more than expected by drift, but were not in linkage disequilibrium with them. Possibly these alleles were hitchhiking along with alleles under selection that were not specific to those inversions. Overall, the selection detected on the genetic content of inversions, despite limited coverage of the genome, suggests that genetic changes within inversions play an important role in adaptation.