Adaptive Decoupling Research Articles

Multivariable adaptive decoupling control based on stochastic configuration networks with serial-parallel switching distribution

A multivariable adaptive decoupling control scheme is proposed based on stochastic configuration networks with serial-parallel switching distribution (SPSCN). Firstly, a linear controller is designed by combining a PID controller, feedback decoupling, and one-step optimal control. Secondly, a nonlinear controller is presented to deal with higher-order nonlinear terms and unknown external perturbations. SPSCN is used to improve the prediction accuracy of unmodeled dynamics. It combines uniform and normal search strategies in a serial-parallel fashion, aiming at improving the node quality and reducing the model complexity. The approximation performance of the SPSCN algorithm is demonstrated by performing approximation experiments with two functions and four benchmark datasets. Compared with the generalized minimum variance control (GMVC) algorithm in controlling the process of cement raw material decomposition, our proposed control scheme outperforms.

Shape Evolution in Two Acts: Morphological Diversity of Larval and Adult Neoaustraranan Frogs.

Phenotypic traits can evolve independently at different stages of ontogeny, optimizing adaptation to distinct ecological contexts and increasing morphological diversity in species with complex life cycles. Given the relative independence resulting from the profound changes induced by metamorphosis, niche occupation and resource utilization in tadpoles may prompt evolutionary responses that do not necessarily affect the adults. Consequently, diversity patterns observed in the larval shape may not necessarily correspond to those found in the adult shape for the same species, a premise that can be tested through the Adaptive Decoupling Hypothesis (ADH). Herein, we investigate the ADH for larval and adult shape differentiation in Neoaustrarana frogs. Neoaustrarana frogs, particularly within the Cycloramphidae family, exhibit remarkable diversity in tadpole morphology, making them an ideal model for studying adaptive decoupling. By analyzing 83 representative species across four families (Alsodidae, Batrachylidae, Cycloramphidae, and Hylodidae), we generate a morphological dataset for both larval and adult forms. We found a low correlation between larval and adult shapes, species with a highly distinct larval shape having relatively similar shape when adults. Larval morphological disparity is not a good predictor for adult morphological disparity within the group, with distinct patterns observed among families. Differences between families are notable in other aspects as well, such as the role of allometric components influencing shape and morphospace occupancy. The larval shape has higher phylogenetic structure than the adult. Evolutionary convergence emerges as a mechanism of diversification for both larval and adult shapes in the early evolution of neoaustraranans, with shape disparity of tadpoles reaching stable levels since the Oligocene. The widest occupation in morphospace involves families associated with dynamically changing environments over geological time. Our findings support the ADH driving phenotypic diversity in Neoaustrarana, underscoring the importance of considering ontogenetic stages in evolutionary studies.

High-Order Neural-Network-Based Multi-Model Nonlinear Adaptive Decoupling Control for Microclimate Environment of Plant Factory.

Plant factory is an important field of practice in smart agriculture which uses highly sophisticated equipment for precision regulation of the environment to ensure crop growth and development efficiently. Environmental factors, such as temperature and humidity, significantly impact crop production in a plant factory. Given the inherent complexities of dynamic models associated with plant factory environments, including strong coupling, strong nonlinearity and multi-disturbances, a nonlinear adaptive decoupling control approach utilizing a high-order neural network is proposed which consists of a linear decoupling controller, a nonlinear decoupling controller and a switching function. In this paper, the parameters of the controller depend on the generalized minimum variance control rate, and an adaptive algorithm is presented to deal with uncertainties in the system. In addition, a high-order neural network is utilized to estimate the unmolded nonlinear terms, consequently mitigating the impact of nonlinearity on the system. The simulation results show that the mean error and standard error of the traditional controller for temperature control are 0.3615 and 0.8425, respectively. In contrast, the proposed control strategy has made significant improvements in both indicators, with results of 0.1655 and 0.6665, respectively. For humidity control, the mean error and standard error of the traditional controller are 0.1475 and 0.441, respectively. In comparison, the proposed control strategy has greatly improved on both indicators, with results of 0.0221 and 0.1541, respectively. The above results indicate that even under complex conditions, the proposed control strategy is capable of enabling the system to quickly track set values and enhance control performance. Overall, precise temperature and humidity control in plant factories and smart agriculture can enhance production efficiency, product quality and resource utilization.

A Block-Based Adaptive Decoupling Framework for Graph Neural Networks.

Graph neural networks (GNNs) with feature propagation have demonstrated their power in handling unstructured data. However, feature propagation is also a smooth process that tends to make all node representations similar as the number of propagation increases. To address this problem, we propose a novel Block-Based Adaptive Decoupling (BBAD) Framework to produce effective deep GNNs by utilizing backbone networks. In this framework, each block contains a shallow GNN with feature propagation and transformation decoupled. We also introduce layer regularizations and flexible receptive fields to automatically adjust the propagation depth and to provide different aggregation hops for each node, respectively. We prove that the traditional coupled GNNs are more likely to suffer from over-smoothing when they become deep. We also demonstrate the diversity of outputs from different blocks of our framework. In the experiments, we conduct semi-supervised and fully supervised node classifications on benchmark datasets, and the results verify that our method can not only improve the performance of various backbone networks, but also is superior to existing deep graph neural networks with less parameters.

Adaptive decoupling planning method for the product crowdsourcing design tasks based on knowledge reuse

Crowdsourcing platforms are an effective way for enterprises to improve innovation abilities, reduce design costs and shorten design cycles during the boom of mass personalized customization. However, the absence of intelligent decoupling planning methods is a decisive reason for the poor intelligent decomposition of design tasks on crowdsourcing platforms. Additionally, their accumulated vast design experience and data, which should have been sufficiently reutilized to bridge that gap, have been neglected. Therefore, an adaptive intelligent decoupling planning method for relatively complex product crowdsourcing design tasks is proposed in this study, simultaneously considering knowledge reuse and bilateral feature matching. First, the requirement-knowledge-resource (RKR) characterization model is established according to the extraction of historical design experience to lay a foundation for the knowledge reuse. Second, based on the knowledge reuse, a hierarchical decoupling solution framework merged with the improved HITS-NetClus hybrid algorithm is built to perform retrieval reuse of knowledge and decoupling planning of tasks. Third, the resource competitiveness feedback strategy is taken to refine the decoupling planning scheme to meet resource competitive conditions. Finally, we demonstrate the intelligence of incorporating the hierarchical decoupling solution framework to achieve automatic decomposition using a real crowdsourcing design task for the personalized sewing machine. Our method also improves the resource adaptation capability of task decomposition schemes in practice.

A multi-module generative adversarial network augmented with adaptive decoupling strategy for intelligent fault diagnosis of machines with small sample

In actual industrial environment, intelligent diagnosis method requires a sufficient number of samples to ensure application effect. However, once industrial system fails, it usually stops immediately, resulting in extremely limited fault signals collected by monitoring system. Lack of fault samples makes the model difficult to fully train and tends to over-fitting, which makes the effect of intelligent diagnosis method poor. To solve this problem, a multi-module generative adversarial network augmented with adaptive decoupling strategy is proposed. Firstly, we creatively use an adaptive learning method to update the latent vector instead of sampling from specific distribution. The digits of the latent vector obtained in this way can come from different distributions, and a better combination effect can be obtained. Then, in order to solve mode collapse, conditional controller obtained through category label is used to directly adjust each intermediate output of generator to prevent influence of conditional control from weakening as the transmission path extends. Finally, in order to provide stronger constraints for the generator, reconstruction module is added to the network to force final generated samples to cover each subdomain of the target dataset. Two experiments are conducted, and average accuracy of 95.32%, 96.86% are achieved by proposed method.

Adaptive Decoupling Between Receivers of Multireceiver Wireless Power Transfer System Using Variable Switched Capacitor

Simultaneously charging multiple devices is a unique advantage of wireless power transfer (WPT). However, it is challenging to analyze and optimize the performance of a multireceiver (multi-Rx) WPT system due to the intricate coupling among coils. In this article, a method for adaptive decoupling between Rxs is proposed for electronic device charging, which can achieve independent regulation of each output in a multi-Rx WPT system. A simple feedback control avoids tedious system structure and position parameters’ measurement and calculation. With a fixed driving frequency, the number of Rx is not limited. It is analyzed that the quadrature phenomenon of the transmitter (Tx) and Rx currents can be used as a sign that the cross-coupling is fully compensated. A phase synchronization channel is established that shares power transfer coils. An microprogrammed control unit-(MCU) free feedback loop is constructed in Rx to control the variable switched capacitor and finally realize adaptive cross-coupling compensation. A two-load experimental prototype was built to verify the theoretical analysis. Compared with the system without adaptive decoupling between Rxs, the measured system efficiency and output power of the proposed system were improved by 69% and 14.3 times, respectively.

Juvenile ecology drives adult morphology in two insect orders.

Most animals undergo ecological niche shifts between distinct life phases, but such shifts can result in adaptive conflicts of phenotypic traits. Metamorphosis can reduce these conflicts by breaking up trait correlations, allowing each life phase to independently adapt to its ecological niche. This process is called adaptive decoupling. It is, however, yet unknown to what extent adaptive decoupling is realized on a macroevolutionary scale in hemimetabolous insects and if the degree of adaptive decoupling is correlated with the strength of ontogenetic niche shifts. It is also unclear whether the degree of adaptive decoupling is correlated with phenotypic disparity. Here, we quantify nymphal and adult trait correlations in 219 species across the whole phylogeny of earwigs and stoneflies to test whether juvenile and adult traits are decoupled from each other. We demonstrate that adult head morphology is largely driven by nymphal ecology, and that adult head shape disparity has increased with stronger ontogenetic niche shifts in some stonefly lineages. Our findings implicate that the hemimetabolan metamorphosis in earwigs and stoneflies does not allow for high degrees of adaptive decoupling, and that high phenotypic disparity can even be realized when the evolution of distinct life phases is coupled.

Adaptive Decoupling Control Using Radial Basis Function Neural Network for Permanent Magnet Synchronous Motor Considering Uncertain and Time-Varying Parameters

In this paper, a novel control scheme with respect to the adaptive decoupling controller based on radial basis function neural network (ADEC-RBFNN) is developed. On one hand, in order to improve the system performance of the torque closed-loop control system (TCLCS) of the permanent magnet synchronous motor (PMSM) with the effects of the dynamic coupling and back electromotive force (EMF), we present a novel ADEC with which the TCLCS is asymptotically stable under Lyapunov stability theory. On the other hand, considering the uncertainty and time variant of both the PMSM and ADEC parameters, the RBFNN is utilized to optimize the ADEC parameters to achieve optimal system performance. Ultimately, experimental results demonstrate that the torque and current with the proposed control scheme have the good performance of small fluctuation and fast response in the whole ranges of the speed and torque, that is to say, the system with the proposed control scheme is with the good decoupling performance.

On the evolution of carry‐over effects

The environment experienced early in life often affects the traits that are developed after an individual has transitioned into new life stages and environments. Because the phenotypes induced by earlier environments are then screened by later ones, these 'carry-over effects' influence fitness outcomes across the entire life cycle. While the last two decades have witnessed an explosion of studies documenting the occurrence of carry-over effects, little attention has been given to how they adapt and diversify. To aid future research in this area, we present a framework for the evolution of carry-over effects. Carry-over effects can evolve in two ways. First, the expression of traits later in life may become more or less dependent on the developmental processes of earlier stages (e.g., 'adaptive decoupling'). Genetic correlations between life stages then either strengthen or weaken. Alternatively, those influential developmental processes that begin early in life may become more or less sensitive to that earlier environment. Here, plasticity changes in all the traits that share those developmental pathways across the whole life cycle. Adaptive evolution of a carry-over effect is governed by selection on the induced phenotypes in the later stage, and also by selection on any developmentally linked traits in the earlier life stage. When these selective pressures conflict, the evolution of the carry-over effect will be biased towards maximizing performance in the life stage with stronger selection. Because life stages often contribute unequally to total fitness, the strength of selection in any one stage depends on: (a) the relationship between the traits and the stage-specific fitness components (e.g., juvenile survival, adult mating success), and (b) the reproductive value of the life stage. Considering the evolution of carry-over effects reveals several intriguing features of the evolution of life histories and phenotypic plasticity more generally. For instance, carry-over effects that manifest as maladaptive plasticity in one life stage may represent an adaptive strategy for maximizing fitness in stages with stronger selection. Additionally, adaptation to novel environments encountered early in the life cycle may be faster in the presence of carry-over effects that influence sexually selected traits.

Dual-Rate Adaptive Decoupling Controller and Its Application to a Dual-Tank Water System

For a class of nonlinear multivariable dual-rate systems with strong couplings and uncertainties, a dual-rate adaptive decoupling controller with nonlinear feedforward compensation is proposed. First, a dual-rate model consisting of a linear model and a nonlinear term is derived by using the polynomial transformation technique. The unknown parameter matrices in the linear model are identified by using a proposed recursive least square algorithm and available dual-rate data, while the unknown nonlinear term is compensated by using the identification error. Based on the dual-rate model, the adaptive decoupling controller is then designed. The stability of the closed-loop system is presented. Finally, experiments are conducted on a dual-tank flow-level cascade control system. The results illustrate the effectiveness of the proposed method.

Online Multiparameter Estimation for Robust Adaptive Decoupling PI Controllers of an IPMSM Drive: Variable Regularized APAs

This paper proposes the variable regularized affine projection algorithms (VR-APAs) for the online multiparameter estimation of the interior permanent magnet synchronous motors (IPMSMs). Unlike the conventional APAs with a fixed regularization factor, the normalized gradient of the mean-square error is introduced in the proposed VR-APA to update the variable regularization which ensures a fast convergence rate, accurate estimation, and low steady-state error. Moreover, the proposed VR-APA does not require any accurate priori information of the motor parameters, making it highly feasible for the IPMSM. In order to accurately estimate the stator d–q axis inductances, stator resistance, flux linkage, and load torque, the two-time scale approach in the proposed VR-APAs is used due to the difference in the IPMSM dynamics. Of various applications of the proposed VR-APAs, such as condition monitoring, fault analysis, and controller design, these estimated multiparameters are updated online to the adaptive decoupling PI controllers to achieve the robustness against the parameter variations due to the temperature increase and load disturbances under various operating conditions (i.e., speed change, load change, and speed reversal). Finally, the comparative experimental verifications via a prototype IPMSM with TMS320F28335 DSP programmed by Code-Composer-Studio are conducted to confirm the effectiveness of the proposed VR-APAs.

Adaptive multi-loop IMC-based PID controller tuning using bat optimisation algorithm for two interacting conical tank process

In this paper, multi-loop adaptive internal model controller (IMC)-based PID is designed for the two interacting conical tank level process (TICTLP). The nonlinear TICTLP is decomposed into linear transfer function matrix around the operating points and the effective open loop transfer function (EOTF) is developed using simplified decoupler. The IMC-based PID controller parameters are obtained for EOTF model using bat optimisation algorithm (BOA). A weighted sum of integral time absolute error is used as control design objective function for multi-loop IMC-PID design which yields faster settling time with minimum over shoot. The fuzzy-based adaptive gain scheduling is used to provide complete control to TICTLP and fuzzy-based adaptive decoupler is implemented to eliminate the dynamic interaction between control loops. The simulation results of proposed controller are compared with conventional ZN-PID, IMC controllers to show the superiority of proposed controller. The simulation response of proposed controller indicates the performance improvement control schemes interns of time domain performance indices, servo tracking, regulatory response and faster settling time.

Adaptive Reduced Dimension Fuzzy Decoupling Control Method with Its Application to a Deployable Antenna Panel

This paper addresses the multiple-input multiple-output (MIMO) control problem with an active deformation adjustment mechanism on a 5-metre deployable antenna panel. An adaptive reduced-dimension fuzzy decoupling (ARDFD) control strategy based on fusion functions is proposed to eliminate the effects uncertainties due to coupled subsystems by designing and incorporating a fuzzy decoupling disturbance observer. Moreover, the MIMO system can be decomposed into a number of single-input single-output (SISO) systems using the unit diagonal matrix decoupling theory. Based on Lyapunov stability theory, it was shown that the error of fuzzy interference observer and the error between the fuzzy rule post parameter matrix and optimal post parameter matrix converge to a small region. Results of the simulation also show that the ARDFD control strategy can significantly eliminate adverse effects of the coupled subsystems.

Adaptive Multi loop IMC Based PID controller tuning using Bat Optimization algorithm for Two Interacting Conical Tank Process

In this paper, multi-loop adaptive internal model controller (IMC)-based PID is designed for the two interacting conical tank level process (TICTLP). The nonlinear TICTLP is decomposed into linear transfer function matrix around the operating points and the effective open loop transfer function (EOTF) is developed using simplified decoupler. The IMC-based PID controller parameters are obtained for EOTF model using bat optimisation algorithm (BOA). A weighted sum of integral time absolute error is used as control design objective function for multi-loop IMC-PID design which yields faster settling time with minimum over shoot. The fuzzy-based adaptive gain scheduling is used to provide complete control to TICTLP and fuzzy-based adaptive decoupler is implemented to eliminate the dynamic interaction between control loops. The simulation results of proposed controller are compared with conventional ZN-PID, IMC controllers to show the superiority of proposed controller. The simulation response of proposed controller indicates the performance improvement control schemes interns of time domain performance indices, servo tracking, regulatory response and faster settling time.

Human Evolutionary Carcinogenesis and Effects of Demographic and Epidemiologic Transitions in Low- and Middle-Income Countries.

ABSTRACT INTRODUCTION In many countries cancer is or threatens to become the leading cause of death, although incidence and mortality rates differ between high-income and low- and middle-income countries. Developments in evolutionary biology have revealed that carcinogenesis is even more complex than previously thought. Several theories attempt to integrate the various existing points of view about what is known to date. OBJECTIVE Analyze and explain the main current theories of carcinogenesis and explore their possible application to understanding the demographic and epidemiologic transitions’ effects on cancer population dynamics in low- and middle-income countries. EVIDENCE ACQUISITION A systematic literature review was carried out in MEDLINE (via PubMed), SCOPUS (via Science Direct) and SciELO. Consistency and quality of evidence in articles reviewed were taken into account; we excluded studies with consistency levels of IV and V, and those with limited or insufficient quality of evidence. DEVELOPMENT Human evolution has led to a type of life history characterized by numerous tradeoffs with oncogenic implications. Cultural coevolution and socioeconomic development have affected cancer population dynamics. Several theories explain carcinogenesis from an ecological and evolutionary perspective, among them somatic mutation, adaptive oncogenesis, life history theories, and the Noble and Hochberg model. The human environmental effect on cancer risk is manifested in the influence of demographic and epidemiologic transitions in low- and middle-income countries, where cancer represents a high disease burden due to the effects of recently introduced environmental factors in native environments, accentuation of adaptive decoupling, and diversification of genetic polymorphisms for cancer susceptibility. CONCLUSIONS The Noble and Hochberg model best explains the population dynamics of cancer in low- and middle-income countries, especially regarding the effects of recently introduced environmental factors on native environments, adaptive decoupling and genetic diversity (manifest in differences in clinical and biological tumor expression by level of economic development), in response to demographic and epidemiologic transitions.

Performance Tradeoffs, Ontogenetic Conflict, and Multisport Athletes: How is an Ironman Triathlete Like a Frog?

Life-history theory is a cornerstone of modern evolutionary biology that addresses myriad phenomena ranging from demography and population structure to the evolution of aging and senescence. Trade-offs may arise in a number of contexts, from allocation-based (e.g., egg size vs. egg number) to genomic conflicts (e.g., intralocus sexual conflict in which genes that perform well in males perform poorly in females). Here we test for performance tradeoffs in human athletes. We show that in Ironman triathletes, swimming performance trades off with cycling and running performance. The tradeoff appears to be plastic, in that only highly trained athletes experience the tradeoff. We then investigate whether wood frogs (Rana sylvatica) experience similar locomotor performance tradeoffs, to ask whether the divergent environments experienced by tadpoles and frogs leads to ontogenetic conflict (tradeoffs over development). We show that although swimming and jumping performance are positively correlated, antagonistic natural selection may still favor alternative adaptive optima in the two life history stages. However, "adaptive decoupling" of the life stages during metamorphosis may resolve ontogenetic conflict and facilitate independent adaptation to both environments. Thus, whereas performance tradeoffs are general in both systems, the unique selective environment of amphibians has favored the evolution of mechanisms to alleviate the costs of those tradeoffs.

Anopheles gambiae larvae mount stronger immune responses against bacterial infection than adults: evidence of adaptive decoupling in mosquitoes

BackgroundThe immune system of adult mosquitoes has received significant attention because of the ability of females to vector disease-causing pathogens while ingesting blood meals. However, few studies have focused on the immune system of larvae, which, we hypothesize, is highly robust due to the high density and diversity of microorganisms that larvae encounter in their aquatic environments and the strong selection pressures at work in the larval stage to ensure survival to reproductive maturity. Here, we surveyed a broad range of cellular and humoral immune parameters in larvae of the malaria mosquito, Anopheles gambiae, and compared their potency to that of newly-emerged adults and older adults.ResultsWe found that larvae kill bacteria in their hemocoel with equal or greater efficiency compared to newly-emerged adults, and that antibacterial ability declines further with adult age, indicative of senescence. This phenotype correlates with more circulating hemocytes and a differing spatial arrangement of sessile hemocytes in larvae relative to adults, as well as with the individual hemocytes of adults carrying a greater phagocytic burden. The hemolymph of larvae also possesses markedly stronger antibacterial lytic and melanization activity than the hemolymph of adults. Finally, infection induces a stronger transcriptional upregulation of immunity genes in larvae than in adults, including differences in the immunity genes that are regulated.ConclusionsThese results demonstrate that immunity is strongest in larvae and declines after metamorphosis and with adult age, and suggest that adaptive decoupling, or the independent evolution of larval and adult traits made possible by metamorphosis, has occurred in the mosquito lineage.

Adaptive Decoupling of Nonideal Machine and Sensor Properties for Extraction of Fine Details When Using the Motor Drive as a Diagnostic Sensor

This paper presents methods to enhance the quality of spatial information that can be extracted when using the motor drive as a diagnostic sensor. When using the state-of-the-art motor drive-based techniques for extracting spatial information related to the state of wear in a gear train driven by an ac machine, position sensor-induced periodic position measurement errors and machine-induced spatial harmonic torque ripple can degrade the quality and the detail of the information that can be extracted. In this work, this degradation of information is displayed experimentally, and it is demonstrated that the quality of the spatial information extracted can be significantly improved through implementation of existing model reference adaptive system (MRAS)-based nonideal sensor property decoupling techniques and newly developed MRAS-based spatial harmonic torque ripple decoupling techniques. Furthermore, it is identified and demonstrated in this work that by removing the gear mesh harmonic component from the gear wear information extracted using the state-of-the-art techniques, and then calculating the spatial rate of change of this new information, a new signal is generated that strongly correlates to local gear tooth defects in a gear train driven by an ac machine.

Adaptive De-Coupling and Multi-BS Association in Heterogeneous Networks

This paper proposes a novel method for maximizing the spatial frequency reuse in cellular networks by exploiting the possibility to simultaneously associate each user to multiple base-stations (BSs). To this end, the proposed technique presents a criterion for determining when a user should be associated with one or more BSs and whether the uplink and downlink should be decoupled, based on minimizing the average interference power across the network. To minimize the effect of interference that may occur due to increasing the spatial frequency reuse, the proposed association methods are integrated with an overlap checking scheme to insure that the resources are orthogonalized between the interfering BSs and user equipments. The results show that significant performance improvements can be attained by the proposed scheme in comparison with other benchmark techniques.