Adaptive Inversion Research Articles

Adaptive Spectral Inversion for inverse medium problems

A nonlinear optimization method is proposed for the solution of inverse medium problems with spatially varying properties. To avoid the prohibitively large number of unknown control variables resulting from standard grid-based representations, the misfit is instead minimized in a small subspace spanned by the first few eigenfunctions of a judicious elliptic operator, which itself depends on the previous iteration. By repeatedly adapting both the dimension and the basis of the search space, regularization is inherently incorporated at each iteration without the need for extra Tikhonov penalization. Convergence is proved under an angle condition, which is included into the resulting Adaptive Spectral Inversion (ASI) algorithm. The ASI approach compares favorably to standard grid-based inversion using L 2-Tikhonov regularization when applied to an elliptic inverse problem. The improved accuracy resulting from the newly included angle condition is further demonstrated via numerical experiments from time-dependent inverse scattering problems.

Generalized inverse matrix-graphic deep learning algorithm for multispectral pyrometer temperature inversion.

The multispectral radiometric temperature measurement technique is affected by the unknown emissivity, and there is no multispectral radiometric temperature inversion algorithm applicable to any scene or target. To address the above problems, this paper converts the multispectral radiometric temperature inversion problem into an image recognition problem containing the temperature information to be measured, and proposes a graphical multispectral radiometric temperature adaptive inversion algorithm. In this paper, we use the difference between spectral channels to convert the one-dimensional radiation data into a two-dimensional radiation map; use the generalized inverse to obtain the spectral emissivity distribution features, fuse them with the two-dimensional radiation map, and use an improved deep learning network to achieve adaptive temperature inversion. It is experimentally verified that the algorithm proposed in this paper can achieve simultaneous inversion of temperature and emissivity for any scene or target with sufficient data set.

Adaptive neural approximated inverse control for photovoltaic power generation servo systems with all states constrained

For PV power generation servo systems with motor hysteresis, an all-state constrained decentralized adaptive approximation inversion control strategy is suggested in order to further increase the tracking precision of photovoltaic (PV) panels to the sun and the efficiency of solar energy utilization. Firstly, the hysteresis phenomenon in the servo motors of photovoltaic panels is considered, and the hysteresis-approximate inverse compensator is first studied in the servo motors of photovoltaic panels. Secondly, to deal with the constrained states such as rotor angle, angular velocity, and stator current in practical servo motors, and to ensure all states are strictly confined within each set of constraints with pre-set tracking performance, the asymmetric barrier Lyapunov functions and the error transformed functions are designed. Finally, to test the effectiveness of the proposed control strategy, a hardware-in-the-loop simulation platform and a photovoltaic servo system for PV power generation are built.

Improved adaptive thin-layer inversion for semi-airborne transient electromagnetic method

ABSTRACT To improve the resolution of electromagnetic inversion for thin layers, electromagnetic one-dimensional inversion was studied. The smooth conductivity model produced by Occam's inversion cannot accurately represent the information of the subterranean thin resistive layers, leading to erroneous inversion findings. The existing thin resistive layers’ inversion method sets the model constraint term at the thin resistive layers to 0, resulting in abrupt changes in resistivity values. Given these problems, we proposed an adaptive roughness matrix calculation method to improve the thin, low-resistive-layer resolution. The resistivity difference between neighboring layers of the updated inversion model determines the roughness matrix, allowing for the realization of adaptive inversion of the thin layer. It achieves semi-airborne transient electromagnetic enhanced adaptive thin-layer inversion and automatically manages the model constraint term. The calculation of the synthetic model demonstrates that the improved adaptive thin-layer inversion method does not need to know the thin, low-resistive layers information in advance. The model can produce appropriate inversion results regardless of the presence of thin, low-resistive layers. Finally, the drilling results are consistent with the inversed appearance of the semi-airborne transient electromagnetic field data. Other geophysical adaptive thin resistive layers inversion can also benefit from this paper's findings.

Improved Adaptive NDI Flight Control Law Design Based on Real-Time Aerodynamic Identification in Frequency Domain

The traditional aircraft controller design is usually based on the off-line aerodynamic model. Due to the deviation of the off-line aerodynamic model, the flight quality is difficult to meet the requirements when the aircraft is flying in the real atmosphere. To solve this problem, this paper proposes a frequency domain identification-based improved adaptive nonlinear dynamic inversion (NDI) control method (FDI-ANDI). In this paper, an online recursive aerodynamic parameter identification method in the frequency domain is first designed, and then an adaptive dynamic inversion control method based on the online aerodynamic parameter identification results is established. Finally, aiming at the problem of the slow response speed of the NDI controller, an improved adaptive dynamic inversion control law is designed by using the method of series lead correction. Compared with the traditional control method, the adaptive dynamic inversion method based on online aerodynamic identification has stronger robustness and a faster response speed in the face of model uncertainty. The final simulation analysis shows that the method has a better control effect than the traditional control method.

Adaptive Neural Piecewise Implicit Inverse Controller Design for a Class of Nonlinear Systems Considering Butterfly Hysteresis

In this article, an adaptive neural piecewise implicit inverse control strategy is proposed to effectively compensate for butterfly hysteresis effectively. First, a new butterfly Krasnoselskii–Pokrovskii (BKP) model is developed for the double-loop butterfly hysteresis characteristics. Second, an adaptive neural piecewise implicit inverse control strategy is designed to mitigate the butterfly-like hysteresis without constructing its analytical inverse model. Finally, experimental results on the dielectric elastomer actuator (DEA) motion control platform demonstrate the effectiveness of the adaptive neural piecewise implicit inverse control strategy.

Localized adaptive waveform inversion: regularizations for Gabor deconvolution and 3-D field data application

SUMMARYRecently, we have developed a localized adaptive waveform inversion (LAWI) method to tackle the cycle-skipping issue in velocity reconstruction through seismic waveform inversion. The LAWI method employs a local matching filter, computed using Gabor deconvolution, to measure the instantaneous time-shift between observed and calculated data. Unlike the adaptive waveform inversion (AWI) approach, the LAWI method can take the non-stationarity between observed and calculated data into account. In this work, we investigate two types of regularization based on prior information about the expected filter, which could be a minimum-norm filter or a delta-shape filter, with regard to their effects on the robustness and resolution of inversion. We demonstrate on synthetic data the advantages and disadvantages of these two types of prior information, where the delta-type LAWI may handle multiple observed phases not initially predicted by the starting velocity model. Therefore, we apply the delta-type LAWI to a high-quality 3-D field data set in the North Sea, eliminating the need for data-windowing tuning, which can be tedious and time-consuming for 3-D data. Under different workflows with varying reliable initial models and frequency bands of the pressure data considered, we show that the LAWI approach is robust, effective and efficient for reconstructing the P-wave velocity, while other approaches such as AWI and graph-space optimal-transport method may require meticulous data-tuning strategies to converge to the correct model. Well logs and data fits, primarily from early arrivals, give us confidence that this LAWI approach could be applied to various acquisitions and subsurface targets, thanks to its phase-driven principle.

Adaptive Neural Inverse Optimal Tracking Control for Uncertain Nonlinear System With Actuator Faults

Most optimal control method aim at finding a control law to minimize the corresponding cost function, and the key problem is to solve the Hamilton–Jacobi–Bellman equation (HJBE). However, the solution of HJBE is hard and even unavailable to obtain for nonlinear system and, thus, the optimal control method can not be developed. To address this problem, an adaptive neural inverse optimal control method is proposed for nonlinear systems with external disturbances and actuator failures in this article. The cost function in this article is defined based on the controller and, therefore, once the controller is obtained, the corresponding cost function is minimized. Accordingly, the directly solution of HJBE is avoided. In more detail, the auxiliary system is constructed to develop the auxiliary controller, based on which the actual optimal fault-tolerant controller can be further obtained. The uncertainties in system dynamics are compensated by neural networks, and the unknown actuator failures are handled by adaptive control gain. It is proved that nonlinear system is input-to-state stabilizable (ISS) and the proposed controller can minimize the cost function that puts penalty on tracking error and system input. Two simulation examples are provided to demonstrate the effectiveness of the proposed control method.

Observer-Based adaptive neural inverse optimal consensus control of nonlinear multiagent systems

The objective of this article is to present an adaptive neural inverse optimal consensus tracking control for nonlinear multi-agent systems (MASs) with unmeasurable states. In the control process, firstly, to approximate the unknown state, a new observer is created which includes the outputs of other agents and their estimated information. The neural network is used to reckon the uncertain nonlinear dynamic systems. Based on a new inverse optimal method and the construction of tuning functions, an adaptive neural inverse optimal consensus tracking controller is proposed, which does not depend on the auxiliary system, thus greatly reducing the computational load. The developed scheme not only insures that all signals of the system are cooperatively semiglobally uniformly ultimately bounded (CSUUB), but also realizes optimal control of all signals. Eventually, two simulations provide the effectiveness of the proposed scheme.

Adaptive trans-dimensional inversion of multimode dispersion curve based on slime mold algorithm

Adaptive trans-dimensional inversion of multimode dispersion curve based on slime mold algorithm

An Adaptive Matrix Method for the Solution of a Nonlinear Inverse Heat Transfer Problem and Its Experimental Verification

An adaptive matrix inverse (AMI) method is presented to identify the temperature and unknown boundary heat flux in a domain of a regular or irregular shape with temperature-dependent properties. The nonlinear problem is broken down into a number of linear submodels, and for each submodel, the temperature is obtained in measuring points. Next, based on the matching degree between the temperatures measured and calculated by each prediction submodel, the submodels are weighted and combined to create the full model for the solution of an inverse nonlinear heat transfer problem. Comparisons are also made with the existing multiple model adaptive inverse (MMAI) algorithm and method based on the Levenberg–Marquardt algorithm (LMA). The results of the presented numerical tests for undisturbed and disturbed “measuring” data indicate that the heat fluxes identified by the AMI method are close to the exact values. The application of the presented method for bodies with an irregular shape is also demonstrated. The AMI method has been experimentally verified during the thick-walled cylinder cooling process. The proposed method can be applied in online diagnostic systems for thermal state monitoring.

Automated salt model building: From compaction trend to final velocity model using waveform inversion

Conventional seismic velocity model building in complicated salt-affected areas requires the explicit identification of salt boundaries in migrated images and typically involves testing of possible subsurface scenarios through multiple generations. The resulting velocity models are slow to generate and may contain interpreter-driven features that are difficult to verify. We show that it is possible to build a full final velocity model using advanced forms of full-waveform inversion applied directly to raw field data, starting from a model that contains only a simple 1D compaction trend. This approach rapidly generates the final velocity model and migrates processed reflection data at least as accurately as conventionally generated models. We demonstrate this methodology using an ocean-bottom-node data set acquired in deep water over Walker Ridge in the Gulf of Mexico. Our approach does not require exceptionally long offsets or the deployment of special low-frequency sources. We restrict the inversion so it does not use significant energy below 3 Hz or offsets longer than 14 km. We use three advanced forms of waveform inversion to recover the final model. The first is adaptive waveform inversion to proceed from models that begin far from the true model. The second is nonlinear reflection waveform inversion to recover subsalt velocity structure from reflections and their long-period multiples. The third is constrained waveform inversion to produce salt- and sediment-like velocity floods without explicitly identifying salt boundaries or velocities. In combination, these three algorithms successively improve the velocity model so it fully predicts the raw field data and accurately migrates primary reflections, though explicit migration forms no part of the workflow. Thus, model building via waveform inversion is able to proceed from field data to the final model in just a few weeks. It entirely avoids the many cycles of model rebuilding that may otherwise be required.

Improved model reference‐based adaptive nonlinear dynamic inversion for fault‐tolerant flight control

AbstractDue to lack of robustness, Nonlinear Dynamic Inversion (NDI) control cannot resist the influence of disturbances. In this paper, an adaptive fault‐tolerant control method, called Improved Model Reference‐based Adaptive NDI (IMR‐ANDI), is proposed and applied to develop a fault‐tolerant flight controller to improve robustness to faults and disturbances. To improve the estimation accuracy, this paper also modified the piecewise‐constant adaptation, which improves the accuracy of adaptive estimation without increasing the sampling frequency. Moreover, a low‐pass filter is introduced to the control law creatively to weaken the disturbance of high‐frequency dynamics to the adaptation, which balances the dynamic performance and robustness as well as prevents the system oscillation. Finally, a fault‐tolerant flight controller is developed based on the IMR‐ANDI and verified under the actuator fault and center of gravity variations. All simulation and hardware‐in‐the‐loop experiment results conform to the theoretical analysis, indicating that the developed IMR‐ANDI fault‐tolerant controller is robust to faults and disturbances and can achieve expected control performance.

Prediction of reservoir physical parameters based on adaptive step size stochastic inversion

Reservoir physical parameter prediction is the ultimate goal of the quantitative seismic interpretation. Rock physics inversion is a method to obtain reservoir physical parameters from seismic elastic properties and the accuracy of predicted physical parameters depends largely on the inversion algorithm. We proposed an adaptive step size stochastic algorithm. In the algorithm, the step size of the parameters to be inverted are not fixed, but adaptive. Firstly, the parameters to be inverted are sampled according to their prior probability density distribution, and the error of each sampling point is calculated. Then select the sampling points with small errors and calculate their probability density distribution, and then encrypt the sampling point according to the new probability density distribution. Repeat the sampling several times, and finally get the optimal solution to the parameters to be inverted. Compared with the conventional given step size algorithm, the adaptive step size algorithm is more accurate. The prediction of reservoir physical parameters based on adaptive step size stochastic inversion is applied to sandstone reservoir, obtained reservoir physical parameters, such as porosity, water saturation, clay content, and their uncertainty distribution.

Long-Term and Seasonal Dynamics of Inversion Polymorphism of the Malaria Mosquito <i>Anopheles messeae</i> Fall. (Diptera: Culicidae) in the Population of the Central Part of the Species Range (Western Siberia, Tomsk Region)

The assessment of the level of genetic variability from 1974 to 2020 in the population of the malaria mosquito Anopheles messeae Fall. in the village of Kolarovo (Tomsk region) was carried out on the basis of cytogenetic analysis of polytene chromosomes. Analysis of the dynamics of inversion polymorphism of the malaria mosquito Anopheles messeae Fall. in the period from 1974 to 2020, according to the frequencies of inversion variants of chromosomes and gametes in the population village Kolarovo showed the replacement of “northern” variants in the period of the 90s of the last century with “southern” variants and the stabilization of these changes until 2020. The correlation of the frequency distribution of chromosomal inversions from 1972 to 2020 was revealed with an annual temperature including a semi-annual average April–September and semi-annual average October–March temperature. The results obtained made it possible to reveal the nature of dynamic processes associated with the modification of a specific ecological niche during a long period due to climate warming. The spatiotemporal stationarity (dynamic stability) of adaptive inversion polymorphism is evidence of high population-genetic (or rather species-specific) homeostasis and the absence of tendencies to speciation.

Observer-Based Adaptive Inverse Optimal Output Regulation for a Class of Uncertain Nonlinear Systems

This paper addresses the adaptive inverse optimal output regulation problem for a class of uncertain nonlinear systems driven by an exosystem. The unknown parameters, internal disturbances, and unmeasured states are contained in the nonlinear system. Firstly, the output regulation problem is decomposed into a feedforward control design problem which can be solved by the internal model based on the output regulation theory, and an adaptive inverse optimal stabilization problem. Then an auxiliary system is designed, and a new state observer related to the auxiliary system is given. By combining adaptive control technology and inverse optimal control method, a novel adaptive output feedback inverse optimal controller is developed to make the output of the system track the reference signal fast. With this control strategy, all the signals of the closed-loop system are uniformly ultimately bounded (UUB), and the newly well-defined cost functional which is connected with the auxiliary system and the controller can be minimized. Finally, a simulation case is put forward to verify the feasibility of the newly raised controller and the state observer.

Localized adaptive waveform inversion: theory and numerical verification

SUMMARYCorrectly interpreting phase events thanks to data processing techniques based on correlation or deconvolution has been the focus of numerous studies in the field of high-resolution seismic imaging using full-waveform inversion. To mitigate the non-convexity of the misfit function and the risk to converge towards non-informative local minima, correlation and deconvolution techniques make it possible to focus on phase information instead of amplitude information and to design more convex misfit function, alleviating the dependency of the full-waveform inversion process on the accuracy of initial models. Such techniques however rely on the assumption that phase events can be compared one by one, or that all the phase events are shifted in time in a similar way. This assumption is not satisfied in practice, which limits the effectiveness of these correlation/deconvolution-based methods. To overcome this issue, we propose to account for the non-stationary relation between observed and predicted data through a local in-time deconvolution technique, based on time–frequency analysis of the signal using a Gabor transform. This makes it possible to estimate instantaneous time-shift between locally coherent phase events. This strategy generalizes the conventional normalized deconvolution technique, which has been popularized under the name of adaptive waveform inversion. To support the introduction of our novel method, we compare it with four misfit functions based respectively on classical cross-correlation, penalized cross-correlation, penalized deconvolution, and adaptive waveform inversion. We analyse the behaviour of these methods on specific scenarios, and then propose a comparison on 2-D synthetic benchmarks. We show how our ‘localized’ adaptive waveform inversion applies in these realistic tests and overcomes some of the limitations of the aforementioned techniques.

Modified adaptive discrete-time incremental nonlinear dynamic inversion control for quad-rotors in the presence of motor faults

Unmanned air vehicles are intrinsically non-linear, unstable, uncertain, and prone to a variety of faults, most commonly the motor faults. The main objective of this paper is to develop a fault-tolerant control algorithm for the quadrotors with the motor faults. Accordingly, a novel adaptive modified incremental nonlinear dynamic inversion (MINDI) control is proposed to stabilize and control the quad-rotor with partial motor faults. The controller consists of a MINDI controller augmented with a discrete-time nonlinear adaptive algorithm. Since the incremental nonlinear dynamic inversion (INDI) algorithm is essentially based on the sensor measurements, it necessitates the angular rates differentiation and therefore amplifies the high-frequency noises produced by the gyroscopes. The application of derivative filters causes unavoidable internal state delays in the INDI structure. Henceforth, the performance of the controller developed for the unstable and uncertain quadrotors degrades considerably. To address this drawback, this paper proposes the MINDI controller which basically derives the angular accelerations from the angular moment estimations. Furthermore, to increase the robustness of the MINDI against motor faults, a discrete-time adaptive controller has been incorporated. The performance of the proposed controllers is verified both through the nonlinear simulations and testbed experiments. The results are compared with a recent efficient algorithm, which had been implemented on a quad-rotor model.

Sliding Mode Disturbance Observer-Based Adaptive Dynamic Inversion Fault-Tolerant Control for Fixed-Wing UAV

Unmanned aerial vehicles (UAVs) have been widely applied over the past decades, especially in the military field. Due to the unpredictability of the flight environment and failures, higher requirements are placed on the design of the control system of the fixed-wing UAV. In this study, a sliding mode disturbance observer-based (SMDO) adaptive dynamic inversion fault-tolerant controller was designed, which includes an outer-loop sliding mode observer-based disturbance suppression dynamic inversion controller and an inner-loop real-time aerodynamic identification-based adaptive fault-tolerant dynamic inversion controller. The sliding mode disturbance observer in the outer-loop controller was designed based on the second-order super-twisting algorithm to alleviate chattering. The aerodynamic identification in the inner-loop controller adopts the recursive least squares algorithm to update the aerodynamic model of the UAV online, thereby realizing the fault-tolerant control for the control surface damage. The effectiveness of the proposed SMDO enhanced adaptive fault-tolerant control method was validated by mathematical simulation.

Temperature-associated selection linked to putative chromosomal inversions in king scallop (Pecten maximus).

The genomic landscape of divergence—the distribution of differences among populations or species across the genome—is increasingly characterized to understand the role that microevolutionary forces such as natural selection and recombination play in causing and maintaining genetic divergence. This line of inquiry has also revealed chromosome structure variation to be an important factor shaping the landscape of adaptive genetic variation. Owing to a high prevalence of chromosome structure variation and the strong pressure for local adaptation necessitated by their sessile nature, bivalve molluscs are an ideal taxon for exploring the relationship between chromosome structure variation and local adaptation. Here, we report a population genomic survey of king scallop (Pecten maximus) across its natural range in the northeastern Atlantic Ocean, using a recent chromosome-level genome assembly. We report the presence of at least three large (12–22 Mb), putative chromosomal inversions associated with sea surface temperature and whose frequencies are in contrast to neutral population structure. These results highlight a potentially large role for recombination-suppressing chromosomal inversions in local adaptation and suggest a hypothesis to explain the maintenance of differences in reproductive timing found at relatively small spatial scales across king scallop populations.