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Model-based design is an important method of addressing problems associated with designing complex control systems. For complex dynamic systems in the presence of uncertainties, the modeling process from the first principles becomes extremely tedious and simplification in mechanism and parameter measurement may result in model inaccuracy. On the contrary, machine learning has the characteristic of fitting complicated equations, which makes it widely used in the research of model identification. However, it only brings a black-box model where the design schemes based on an analytical model cannot be applied. In this article, a simple and novel scheme for modeling and control of robotic manipulators is proposed; without prior knowledge, a dynamic model in an analytical form is obtained from artificially excited training data using the symbolic regression technique, and then, a controller is designed based on the dynamic model. Due to the ingenious experimental design, on one hand, the amount of training data is far less than the system identification method by machine learning. On the other hand, a decoupling feature is used in the model that greatly simplifies controller design. The experimental results on two-degree of freedom (DOF) and 6-DOF robotic manipulator simulators verify that the scheme is feasible and effective.

Unsupervised domain adaptation for person re-identification (Re-ID suffers severe domain discrepancies between source and target domains. To reduce the domain shift caused by the changes of context, camera style, or viewpoint, existing methods in this field fine-tune and adapt the Re-ID model with augmented samples, either through translating source samples to the target style or by assigning pseudo labels to the target. The former methods may lose identity details but keep redundant source background during translation, while the latter methods may assign noisy labels when the model meets the unseen background and person pose. To address the challenges, we mitigate the domain shift in the former translation direction by decoupling environment and identity-related features in a cyclic manner. We propose a novel individual-preserving and environmental-switching cyclic generation network (IPES-GAN . Our network has the following distinct features: 1 Decoupled features instead of fused features: the images are encoded into an individual part and an environmental part, which are proved beneficial to generation and adaptation; 2 Cyclic generation instead of one-step adaptive generation. The source and target environment features are swapped to generate cross-domain images with preserved identity-related features conditioned with source (target environment features, and then swapped again to generate back the input image, so that cyclic generation runs in a self-supervised way. Experiments carried out on two major benchmarks: Market-1501 and DukeMTMC-reID reveal state-of-the-art performance.

In the decoupling control process of the outer rotor coreless bearingless permanent magnet synchronous motor using least square support vector machine, the kernel width 2 and penalty factor of the least square support vector machine are difficult to tune, resulting in high kernel space complexity and low fitting accuracy. A new improved genetic algorithm is used to optimize the above parameters. Firstly, the generalized inverse system model of the original nonlinear multiple input output system is fitted online with the optimized least square support vector machine, and the generalized inverse system model is connected in series with the original system to form a pseudo-linear system. And then a linear closed-loop controller is used for control. Simulations and experiments show that the robustness and wide applicability of the original system are guaranteed. The dynamic and static performance of the system are improved, verifying the effectiveness of the scheme.

This article is concerned with the distributed Kalman filtering problem for interconnected dynamic systems, where the local estimator of each subsystem is designed only by its own information and neighboring information. A decoupling strategy is developed to minimize the impact of interconnected terms on the estimation performance, and then the recursive and distributed Kalman filter is derived in the minimum mean-squared error sense. Moreover, by using Lyapunov criterion for linear time-varying systems, stability conditions are presented such that the designed estimator is bounded. Finally, a heavy duty vehicle platoon system is employed to show the effectiveness and advantages of the proposed methods.

Few-shot learning, aiming to learn novel concepts from one or a few labeled examples, is an interesting and very challenging problem with many practical advantages. Existing few-shot methods usually utilize data of the same classes to train the feature embedding module and in a row, which is unable to learn adapting to new tasks. Besides, traditional few-shot models fail to take advantage of the valuable relations of the support-query pairs, leading to performance degradation. In this article, we propose a transductive relation-propagation graph neural network (GNN) with a decoupling training strategy (TRPN-D) to explicitly model and propagate such relations across support-query pairs, and empower the few-shot module the ability of transferring past knowledge to new tasks via the decoupling training. Our few-shot module, namely TRPN, treats the relation of each support-query pair as a graph node, named relational node, and resorts to the known relations between support samples, including both intraclass commonality and interclass uniqueness. Through relation propagation, the model could generate the discriminative relation embeddings for support-query pairs. To the best of our knowledge, this is the first work that decouples the training of the embedding network and the few-shot graph module with different tasks, which might offer a new way to solve the few-shot learning problem. Extensive experiments conducted on several benchmark datasets demonstrate that our method can significantly outperform a variety of state-of-the-art few-shot learning methods.

Recently, reconfigurable intelligent surface (RIS) has attracted continuous attention in wireless communications. Because the normal incidence condition of electromagnetic waves cannot always be satisfied in practical applications, the angular insensitivity in RISs has become an important issue. The angular sensitivity in RISs may lead to the failure of RIS-assisted wireless communication networks that rely on the reciprocity of wireless channels. In this work, an angle-insensitive 3-bit RIS meta-atom is proposed. By introducing metallic vias in the substrate of programmable meta-atoms, the interference induced by multiple reflections in the multilayered structure is restrained, and the decoupling between adjacent meta-atoms makes the metasurface insensitive to the incident angle. Hence the angular insensitivity can be achieved. An RIS is fabricated using the proposed angleinsensitive programmable meta-atom with metallic vias, which is capable of maintaining stable phase and amplitude responses at oblique incidences. Both simulated and measured results show stable angle performance. A maximum of 3150 phase range and eight digital coding states with 450 stable interval are obtained for wide incidence angles from 00 to 600. Finally, numerical simulations and experimental results of anomalous reflections are performed to verify the angular reciprocity of the proposed RIS, which is important in wireless communications.

Aided by a modified event-triggered communication policy (ETCP), this article addresses the dissipativity-based control synthesis problem for semi-Markovian switching systems (SMSSs) with simultaneous multiplicative probabilistic faults on sensors and actuators modules. The resulting model under consideration is more extensive, which covers semi-Markovian switching coefficients, transmission delays, and randomly occurring sensors and actuators faults in a unified systematic analytical framework instead of investigating separately in some existing works. More specifically, the probabilistic faults are assumed to happen on both the sensors and actuators modules simultaneously, and the distortion probability for each sensor and actuator is irrelevant, which can be characterized by multiplicate mutually independent stochastic variables that obeys certain statistical features and probabilistic distribution delineate on the interval [ 0 , ✠ ] ( ✠ ≥ 1 ) . To reduce the bandwidth usage, a novel event-triggered strategy is designed. Additionally, in the light of this newly developed ETCP, and considering the effects of the signal transmission delays and multitudinous probabilistic failures, a generalized and more realistic faulty pattern for SMSSs is presented, which is more fit for real applications. Hereby, the principal superiority of the established new type faulty pattern lies in its practicality and generality, which contains some previous faulty models as special scenarios. By constructing an appropriate semi-Markovian Lyapunov functional (SMLF) together with mathematical analysis technique and matrix inequality decoupling operation, sojourn-time-dependent sufficient conditions for determining both the control gain matrices and triggered configuration coefficients are developed and formulated in terms of a group of feasible linear matrix inequalities (LMIs). Eventually, several practical examples are exploited to substantiate the validity and practicability of the developed control design methodology.

This article is concerned with the development of a new event-triggered parity space fault detection (FD) scheme. A linear discrete-time system model with varying sampling periods is presented for handling the problem of event-triggered FD and a new parity relation is established. Based on this, an event-triggered residual generator is constructed and the generated residual is completely decoupled from event-triggered transmission error. The design of the parity matrix is formulated into an optimization problem and an optimal solution of the parity matrix is obtained by using singular value decomposition. The issue of residual evaluation is also considered in the event-triggering implementation. The novelties of this article are twofold. First, a new event-triggered parity relation is obtained and the parity space-based residual signal achieves complete decoupling with the event-triggered transmission error. Second, the calculation of the parity matrix is independent of event parameters. So the design of the parity space-based residual generator and event generator can be carried out independently. Finally, a simulation example is considered to demonstrate the effectiveness of the proposed method.

Relations between US and Chinese corporations are increasingly strained. We examine how differences in the key domains of corporate governance, underlying corporate philosophy, innovation, and dispute resolution contribute to this strained relationship. We illustrate these differences by examining the relations between Skyworks Solutions (US) and ZTE (China). We discuss the three broad strategies available to US managers in engaging with Chinese corporations, depending on the extent of their involvement and comfort with discord: playing ball, straddling the middle ground, or decoupling.

The existing studies for tracking consensus of multiagent systems (MASs) are all restricted to networks with only cooperative relationships among agents. Tracking consensus, however, requires beyond these traditional models due to the ubiquitous competition in many real-world MASs, such as biological systems and social systems. Taking into account this fact, this article aims to extend the dynamics of tracking consensus to signed networks containing both cooperative and competitive relationships among agents. A group of agents with general linear dynamics is considered. The cases of the fixed network as well as switching networks are analyzed, respectively. In the end, some algebraic conditions related to the network structure and the positive/negative edge weight are established to ensure the implementation of tracking consensus. Moreover, the single decoupling system is allowed to be strictly unstable in theory, and the upper bound of the eigenvalue modulus of the system matrix related to the system instability is given.

The analysis and design problems of formation-containment control for high-order linear time-invariant (LTI) multiagent systems (MASs) on directed graphs with observer-based output-feedback protocols are given in this work. To expand the feasibility of state formation configuration, two well-structured compensation signals are introduced for the leaders and followers in the protocols, respectively. Benefitting from the compensation signal of followers, the decoupling between formation control of leaders and containment control of followers is achieved. Thus, a necessary and sufficient condition is first established such that the formation-containment control for high-order LTI MASs can be achieved. Moreover, a heuristic iterative algorithm is developed to compute the controller gains, observer gains, as well as the compensation signals. Finally, two numerical examples are implemented to illustrate the time-varying formation-containment control of high-order MASs, which shows the validity and practicability of the theoretical results and algorithm.

In this article, we consider a known charged isotropic Durgapal IV solution and extend it to its anisotropic version using gravitational decoupling by means of extended geometric deformation (EGD) approach, which provides a more generic way for the decoupling of gravitational sources. Following this approach, we employ linear transformation on both metric potentials and split the original set of field equations into two subsystems. The first subsystem corresponds to charged isotropic fluid while the second one is related to the additional source, crucial for anisotropy. The isotropic one is specified through metric functions of known charged isotropic solution, while the solution of second sector is determined by considering two different constraints on additional source Θ a b , i.e., mimicking of isotropic pressure and a linear equation of state which relating Θ 0 0 and Θ 1 1 . The matching conditions at the stellar surface are discussed in detail, where outer geometry is represented by Reissner–Nordstrom solution. We discuss the physical properties of the stellar model in comparison with its uncharged anisotropic analogue, which is provided in Appendix. Physical analysis is carried out using different physical indicators including energy conditions, equilibrium equation, casualty condition, Herrera’s cracking concept, adiabatic index, compactness and surface redshift for compact star PSR J 1416-2230. We find that anisotropic stellar model is stable for larger range of γ (coupling constant) in the presence of electric charge and the effects of anisotropy are diminished near stellar surface.

In this article, a novel control algorithm is developed for a class of nonlinear stochastic systems subject to multiple disturbances, including exogenous dynamic disturbance and general non-Gaussian noise. An observer is designed to estimate the exogenous disturbance, and then the disturbance compensation is incorporated into a feedback control strategy for the non-Gaussian system. Considering the ability of entropy in randomness quantification, a performance index is established based on the generalized entropy optimization principle. Furthermore, it is adjusted to be available for the controller solution, which also solves the coupling between two kinds of disturbances. On this basis, the optimal controller is provided in a recursive way, with which the closed-loop stability and good antidisturbance ability can be guaranteed simultaneously. Compared with the existing studies on the non-Gaussian stochastic systems, the proposed control algorithm has merits in multiple disturbances decoupling and enhanced antidisturbance performance. Finally, a simulation example is given to demonstrate the effectiveness of theoretical results.

This paper investigates the state estimation problem for a class of complex networks, in which the dynamics of each node is subject to Gaussian noise, system uncertainties and nonlinearities. Based on a regularized least-squares approach, the estimation problem is reformulated as an optimization problem, solving for a solution in a distributed way by utilizing a decoupling technique. Then, based on this solution, a class of estimators is designed to handle the system dynamics and constraints. A novel feature of this design lies in the unified modeling of uncertainties and nonlinearities, the decoupling of nodes, and the construction of recursive approximate covariance matrices for the optimization problem. Furthermore, the feasibility of the proposed estimators and the boundedness of the mean-square errors are ensured under a developed criterion, which is easier to check than some typical estimation strategies including the linear matrix inequalities-based and the variance-constrained ones. Finally, the effectiveness of the theoretical results is verified by a numerical simulation.

In order to explore the potential of profit margin improvement, a novel three-scale integrated optimization model of furnace simulation, cyclic scheduling, and supply chain of ethylene plants is proposed and evaluated. A decoupling strategy is proposed for the solution of the three-scale model, which uses our previously proposed reactor scale model for operation optimization and then transfers the obtained results as a parameter table in the joint MILP optimization of plant-supply chain scale for cyclic scheduling. This optimization framework simplifies the fundamental MINLP into several sub-models, and improves the interpretability and extendibility. In the evaluation of an industrial case, a profit increase at a percentage of 3.25% is attained in optimization compared to the practical operations. Further sensitivity analysis is carried out for strategy evolving study when price policy, supply chain, and production requirement parameters are varied. These results could provide useful suggestions for petrochemical enterprises on thermal cracking production.

A demagnetization fault not only degrades the performance of a PMSM drive system, but also may even cause the irreversible demagnetization. This paper presents an improved-sliding-mode-observer (ISMO)-based equivalent-input-disturbance (EID) method to reconstruct a demagnetization fault and to perform fault-tolerant control for the demagnetization fault in a PMSM drive system. The models of healthy and demagnetized PMSM are first established. Next, an ISMO is constructed by inserting a decoupling term. It completely removes the influence of the motor speed on the reconstruction of a demagnetization fault. Then, the ISMO-based EID approach is developed to reconstruct the fault with high precision and maintain system control performance even for a demagnetization fault. The stability of the whole system is proved by using the concept of globally uniformly ultimately bounded (GUUB). Finally, the validity of the method is demonstrated using hardware-in-the-loop simulation (HILS).

This article studies the robust adaptive asymptotic tracking control problem for uncertain nonlinear systems with event-triggered inputs. Contrary to the existing results, a novel nonaffine nonlinear control input separated design scheme is developed based on the Taylor decoupling technique instead of the previous approximation ways. Correspondingly, the augmented dimension parameter updated laws and the unknown bound estimation of the compounded disturbance with a positive continuous integrable function are skillfully introduced. Also, by introducing a suitable decreasing function of tracking error variables, an improved adaptive event-triggered mechanism is constructed to achieve the asymptotic tracking and the communication load can be effectively alleviated. Moreover, it is shown that all the signals in the closed-loop system are uniformly bounded and the tracking errors converge to zero in a preset compact set. Finally, the validity of the proposed method is illustrated by simulation on an inverted pendulum model.

Dominant frequency attenuation is a significant concern for frequency-based criteria of blasting vibration control. It is necessary to develop a concise and practical prediction equation describing dominant frequency attenuation. In this paper, a prediction equation of dominant frequency that accounts for primary parameters influencing the dominant frequency was proposed based on theoretical and dimensional analyses. Three blasting experiments were carried out in the Chiwan parking lot for collecting blasting vibration data used to conduct regression analysis of the proposed prediction equation. The fitting equations were further adopted to compare the reliability of three different types of dominant frequencies in the proposed equation and to explore the effects of different charge structures on the dominant frequency attenuation. The apparent frequency proved to be more reliable to express the attenuation law of the dominant frequency. The reliability and superiority of the proposed equation employing the apparent frequency were verified by comparison with the other five prediction equations. The smaller blasthole diameter or decoupling ratio leads to the higher initial value and corresponding faster attenuation of the dominant frequency. The blasthole diameter has a greater influence on the dominant frequency attenuation than the decoupling ratio does. Among the charge structures applied in the experiments, the charge structure with decoupling ratio of 1.5 and blasthole diameter of 48 mm results in the greatest initial value and corresponding fastest attenuation of the dominant frequency.

In this paper, the state estimation problem for singularly perturbed switched complex dynamic networks (CDNs) is addressed, in which the persistent dwell-time (DT) switching mechanism is employed to depict the switchings among parameters of each node. In the aforementioned switching mechanism, the concept of stage consisting of the persistent portion and the DT portion is introduced. Based on the singular perturbation theory, a two-time-scaling variables containing fast and slow states are taken into account on the CDNs simultaneously, which make the constructed networks more realistic. Furthermore, the main aim is to design a mode-dependent estimator to track the state information that cannot be directly obtained and further ensures the global uniform exponential stability of the investigated systems with a generalized dissipativity property. Some sufficient criteria on the existence of the desired mode-dependent state estimator are established by utilizing a modified matrix decoupling method. Finally, the availability of the estimator design procedures is verified by a simulation example.

In this paper, for a class of continuous-time multivariable linear systems with strong coupling property, a new optimal tracking controller is proposed, which can simultaneously decouple the closed-loop system in its dynamic, and provide optimal performance. Firstly, a new quadratic performance index considering decoupling design is introduced, and then the optimal tracking controller is derived by minimizing the new index according to the minimum principle. Finally, the weighting matrices are provided, by using which decoupling and tracking can both be realized. Experiments on a Coupled-Tank are conducted, whose results show the superiority of the proposed method comparing with the conventional linear quadratic tracking (LQT) controller.

In this paper, a fixed-time controller under the mechanism of event-trigger is designed for a class of nonlinear pure-feedback systems with non-differentiable non-affine functions. By properly modeling non-affine terms, the limitation of the partial derivatives of non-affine functions is eliminated. In our design process, we first develop a fixed-time adaptive controller using decoupling method. Then, a relative threshold event-trigger mechanism (ETM) is introduced in Section 3.1. The proposed controller can not only stabilize the system within a fixed-time, but also save communication resources more effectively. Lastly, the feasibility of the proposed control scheme is verified by two simulation examples.

In this study, the real-time (R-T) reachable set control problem for neutral singular Markov jump system (NSMJS) with mixed delays and bounded disturbance is studied under non-zero initial conditions. Firstly, with the help of the free-weighting matrix method and Lyapunov stability theory, sufficient conditions for R-T reachable set control of NSMJS are given by employing linear matrix inequality technique. Secondly, the controller gains of the closed-loop system are obtained by means of the matrix decoupling method. Finally, the effectiveness of the proposed method is verified by a direct current (DC) motor drive system with practical application background.

This paper designs a class of low-profile compact MIMO antennas on a single radiating patch using simple and systematic characteristic modes (CMs) method. Rather than using conventional method of radiation pattern and polarization diversities, this work demonstrates that by taking advantage of a proper superposition of some even/odd modes, it is able to generate similar radiation pattern and polarization no matter which port is excited. It is also found that port isolation and impedance matching are dependent on not only operating modes but also input couplings. The proposed CMs method consists of three main steps. First, adjust desired even and odd modes to be equal in resonant frequency (ResF). Second, suppress unwanted modes without affecting desired ones. Lastly, determine feeding positions to obtain proper input couplings. In this way, satisfactory port isolation and impedance matching could be achieved simultaneously and quickly, getting rid of complicated decoupling structures and time-consuming optimization. To exemplify how the proposed CMs method works, two MIMO antennas are successfully designed by just using simple slots and shorting pins. A prototype for the proposed four-port MIMO antenna is fabricated and measured for validation.

Liberalized gas markets in Europe are organized as entry-exit regimes so that gas trade and transport are decoupled. The decoupling is achieved via the announcement of technical capacities by the transmission system operator (TSO) at all entry and exit points of the network. These capacities can be booked by gas suppliers and customers in long-term contracts. Only traders who have booked capacities up-front can “nominate” quantities for injection or withdrawal of gas via a day-ahead market. To ensure feasibility of the nominations for the physical network, the TSO must only announce technical capacities for which all possibly nominated quantities are transportable. In this paper, we use a four-level model of the entry-exit gas market to analyze possible welfare losses associated with the decoupling of gas trade and transport. In addition to the multilevel structure, the model contains robust aspects to cover the conservative nature of the European entry-exit system. We provide several reformulations to obtain a single-level mixed-integer quadratic problem. The overall model of the considered market regime is extremely challenging and we thus have to make the main assumption that gas flows are modeled as potential-based linear flows. Using the derived single-level reformulation of the problem, we show that the feasibility requirements for technical capacities imply significant welfare losses due to unused network capacity. Furthermore, we find that the specific structure of the network has a considerable influence on the optimal choice of technical capacities. Our results thus show that trade and transport are not decoupled in the long term. As a further source of welfare losses and discrimination against individual actors, we identify the minimum prices for booking capacity at the individual nodes.

Zero-shot Learning (ZSL) is a highly non-trivial task to generalize from seen to unseen classes. In this paper, we propose spherical zero-shot learning (SZSL) to address the major challenges in ZSL. By decoupling the similarity metric in the spherical embedding space into radius and angle, our SZSL can map classes to hyperspherical surfaces of different radiuses, which greatly increases its flexibility. Specifically, we introduce the spherical alignment on angles to spread classes as uniformly as possible to alleviate the hubness problem and simultaneously preserve the inter-class semantic structure to make the alignment more reasonable. We also introduce the spherical calibration with a minimum entropy based regularizer by adopting a larger radius for unseen classes than seen classes to reduce the prediction bias. Extensive experiments on five middle-scale benchmarks and large-scale ImageNet dataset demonstrate that the proposed approach consistently achieves superior performance for the traditional and generalized settings of ZSL.

This paper presents a robust cascade vision/force approach to control industrial robots interacting with unknown workpieces considering model uncertainties. This cascade structure, consisting of an inner vision loop and an outer force loop, avoids the conflict between the force and vision control in traditional hybrid methods without decoupling force and vision systems. To apply an advanced image-based visual servoing (IBVS) compensator, some newly modified image features are used which render an invertible image interaction matrix. A practical task-based method is proposed to extract the features corresponding to the desired path in 3D space. An adaptive robust Kalman filter (ARKF) is adopted for filtering the noise in the force feedback signal. A robust continuous integral sliding mode control method (CISMC) is developed for both IBVS and force compensators. CISMC exploits advantages of the modified super twisting algorithm (MSTA) to reduce the chattering. The stability of the proposed cascade controller is proved. Additionally, a contact detector algorithm (CDA) is developed to manage the robot's free motion and its interaction with the workpiece. To evaluate the performance of the proposed method, several experimental tests are performed and compared with other well-known methods. The results show the superiority of the proposed approach.

In this brief, the resilient ${H_{\infty}}$ filter design problem for continuous-time nonlinear systems is addressed. A Takagi-Sugeno (T-S) fuzzy model with norm-bounded uncertainties is used to represent the nonlinear plant. Meanwhile, the fuzzy filter to be designed is assumed to have gain variations. A useful matrix inequality decoupling approach is proposed to separate the product terms with different types of uncertainties. Then, the resulting design condition of the resilient ${H_{\infty}}$ filter is described by strict linear matrix inequalities (LMIs). Compared with the existing fuzzy resilient filtering results, the proposed design method shows a strong advantage of reducing design conservatism. Finally, a simulation example is provided to demonstrate the feasibility and the advantage of the proposed design method.

Lightly damping linear dynamic characteristics of a flexure-based stage mainly limit control bandwidth when its piezo-actuating hysteresis is well compensated. In this paper, a novel damped decoupled XY nanopositioning stage embedding graded local resonators (GLRs) is developed and optimized based on the maximization of motion range and first-order natural frequency. The modal damping strengthening mechanism of local resonant metastructures is analyzed based on the built dynamic model. The GLRs embedded in the working platform has different dynamic parameters, which are optimized based on $H_2$ norm. The motion decoupling is guaranteed by the designed hybrid flexure-guided mechanism while strengthening the Z-direction supporting stiffness. The static/ scanning-frequency dynamic experiment and trajectory tracking control with a PID controller is implemented. The experimental results indicate that maximum motion crosstalk is 0.69\%, and validate the effectiveness of improving the linear dynamics of the stage and expanding control bandwidth.

This paper presents a current control methodology equipped with a state and disturbance observer (SDO) to address the uncertain disturbance problem existing in an LCL-type single-phase grid-tied inverter system. In this methodology, disturbance feedforward compensation and full-state feedback of the combined proportional-integral (PI) regulator are utilized to provide more degrees of freedom for a robust design of the controller. In order to realize this technology without additional sensors, a reduced-order SDO is designed by decoupling the real inverter plant into a nominal model with lumped uncertainty and disturbance (LUD), making the estimation results more robust adaption for system uncertainty. Furthermore, the upper bound of the LUD is concerned and innovatively used to evaluate the size of the uncertainty region of the LUD estimated by the observer, that may be important to anti-disturbance control. Other system performances, such as grid current distortion suppression and robust stability, are also analyzed. Finally, the effectiveness of the proposed control strategy is fully supported by simulation and experimental results.

This article investigates the design methods and performance optimization of segmented-double-stator switched reluctance machine (SDS-SRM). First, two segmented U-shaped single-stator SRMs are introduced, including inner-rotor and outer-rotor constructions. In order to enhance the torque and power density, these two kinds of single-stator SRMs are combined together to constitute an SDS-SRM. Then, the relationships of torque ripple coefficient with the mechanical offset angles of the inner and outer rotor teeth are investigated to obtain the optimal rotor structure, and the corresponding conduction phase sequences for double-stator excitation mode are also analyzed. Furthermore, the flux distributions of the inner and outer stators with different coil magnetic polarities configurations are researched, and it reveals that the phase windings both on inner and outer stators with NSSNNSSN polarities have better magnetic field decoupling characteristics. To evaluate the final topology of the proposed SDS-SRM, the static magnetic characteristics and dynamic performances are presented. Finally, the SDS-SRM prototype is manufactured and tested to validate the theory and simulation analyses.

The plants of nano air vehicles (NAVs) are generally unstable, adversely coupled, and uncertain. Besides, the autopilot hardware of a NAV has limited sensing and computational capabilities. Hence, these vehicles need a single controller referred to as robust simultaneously stabilizing decoupling (RSSD) output feedback controller that achieves simultaneous stabilization (SS), desired decoupling, robustness, and performance for a finite set of unstable multi-input-multioutput adversely coupled uncertain plants. To synthesize an RSSD output feedback controller, a new method that is based on a central plant is proposed in this article. Given a finite set of plants for SS, we considered a plant in this set that has the smallest maximum v-gap metric as the central plant. Following this, the sufficient condition for the existence of a simultaneous stabilizing controller associated with such a plant is described. The decoupling feature is then appended to this controller using the properties of the eigenstructure assignment method. Afterward, the sufficient conditions for the existence of an RSSD output feedback controller are obtained. Using these sufficient conditions, a new optimization problem for the synthesis of an RSSD output feedback controller is formulated. To solve this optimization problem, a new genetic algorithm-based offline iterative algorithm is developed. The effectiveness of this iterative algorithm is then demonstrated by generating an RSSD controller for a fixed-wing NAV. The performance of this controller is validated through numerical and hardware-in-the-loop simulations.

This note provides the first nonlinear analysis of the industry standard "partial decoupling plus nested PI loops" control of voltage sourced inverters. In spite of its enormous popularity, to date only linearization-based tools are available to carry out the analysis, which are unable to deal with large-signal stability and fail to provide estimates of the domain of attraction of the desired equilibrium. Instrumental to establish our result is the representation of the closed-loop dynamics in a suitable Lure-like representation, that is, a forward system in closed-loop with a static nonlinearity. The stability analysis is then done by generating an adequate Popov multiplier. Comparison with respect to linearization is discussed together with numerical results demonstrating non-conservativeness of the proposed conditions.

The paper presents a wall-climbing robot featuring self-compliance for variable curvature facades. The high payload and maneuverability make it highly potential in heavy-duty operation of industrial applications. The robot consists of two traction modules and one link module. Each traction module is equipped with magnetic adhesion and crawler traction submodules. The two traction modules are connected by the link module with 4 degrees of freedom (DoF) of passive compliance. Variable curvature facade self-compliance is achieved by the passive compliant link module, which results in the attitude change decoupling of the two traction modules. The attitude can be adjusted under the effect of magnetic adhesion force to comply with the curvature variations. High payloads are achieved by the large contact area between the crawler and the surface. Omni-directional high maneuverability is enabled by the speed difference between the motors of two traction modules. The robot can maneuver in any direction on the surface with a minimum radius of 1 m and carry 36 kg payload on vertical surfaces.

China's natural gas market is not only growing rapidly but is also in the process of liberalization, indicating the potential of oil-gas price decoupling. In this situation, this study re-examines whether oil-gas price decoupling is happening in China, in order to analyze the achievements of market reform. First, a multi-scale decomposition approach is used to find oil-gas price patterns in the time-frequency domain. Then, a quantile-on-quantile analysis is used to acquire the entire dependence structure between oil and gas prices. The findings indicate that the association between oil and gas prices increases over scales but varies for quantiles. Specifically, clear evidence is provided that China's liquefied natural gas (LNG) price is independent of the oil prices selected for this study in the short term, whereas no additional evidence exists that proves oil and gas prices have already been decoupled. This finding indicates that the oil-indexation pricing mechanism still remains dominant. Based on the empirical results, the authors suggest that the establishment of trading hubs, the removal of restrictive clauses in contracts, and the diversification of pricing mechanisms should be simultaneously promoted in China.

Effective application of intelligent diagnosis methods requires that training and testing data to follow a consistent probability distribution. However, changes in working condition lead to inevitable changes in probability distribution of collected signals, which makes the intelligent models unable to accurately classify unlabeled data obtained from different working conditions. To solve this problem, a triplet metric driven multi-head graph neural network (GNN) augmented with decoupling adversarial learning is proposed. First, we creatively use square L2 distance and triplet loss to train and measure similarity between vibration signals, and convert multiple one-dimensional signals into a sample in graph structures. This change adds correlation information between samples to dataset, thereby improving training effect of model. Then, to comprehensively analyze information contained in the vertices and edges of graphs, a graph neural network augmented with multi-head attention mechanism is constructed. Finally, in response to the complex situation of the target domain containing multiple unknown conditions, a multi-domain decoupling adversarial learning strategy is proposed to achieve the fine-grained adaptation of distinguishable structures in target domain. Three experiments are conducted to compare with six well-established models, and average accuracy of 96.67 %, 86.85 %, 100 % are achieved by proposed method, which shows significant superiority of the proposed framework.

The main purpose of this article is to investigate the consensus of linear multiagent networks with time-varying characteristics under sampled-data communications, where the time-varying characteristics include both time-varying topologies and the node's linear time-varying dynamics. By using the decoupling method, we prove that the sampled-data consensus problem of multiagent networks is equal to the stability problem of sampled-data systems. Then, the globally asymptotical consensus is investigated for multiagent networks with time-varying characteristics by virtue of the Lyapunov function method. It should be noted that when the Lyapunov function method is utilized to investigate the stability problem of control systems, it is always assumed that the derivative of the constructed Lyapunov function is not more than zero. This assumption is removed here and as a replacement, the average value of the derivative of the Lyapunov function in a period to be negative is needed.

Ground roll noise is a common type of seismic noise arising from land acquisition. Suppressing ground roll noise has long been a challenging problem due to the strong coupling between ground rolls and reflection signals. The easiest way to suppress ground roll noise is by a bandpass filter, which removes the low-frequency ground roll and retains the high-frequency signals. Due to the coupling issue, the bandpass filter, however, is prone to cause the removal of low-frequency signals and the existence of high-frequency noise. To combat this problem, we propose a novel dictionary learning (DL) method for decoupling the ground rolls and reflection signals in the low-frequency band. We first filter the seismic shot gather using a strong low-pass filter, by which we remove all ground rolls. Then, we apply the DL method to retrieve the leaked low-frequency reflection signals. The dictionary atoms are obtained from the high-frequency reflection signals and then used to code the low-frequency mixture between ground rolls and signals. As a result, the reflection signals in the low-frequency part are easily separated by the sparse coding process and are added back to the high-frequency signals. The final output of the proposed algorithm is the summation between the high-frequency reflections and the retrieved low-frequency reflections. We apply the proposed method to both synthetic and real shot gathers containing ground roll noise and demonstrate its promising results.

Simulation and emulation are popular approaches for experimentation in Computer Networks. However, due to their respective inherent drawbacks, existing solutions cannot perform both fast and realistic control plane experiments. To close this gap, we introduce Horse. Horse is a hybrid solution with an emulated control plane, for realism, and simulated data plane, for speed. Our decoupling of the control and data plane allows us to speed up the experiments without sacrificing control plane realism.

A fast and precise interpretation of SAR images is an important and challenging research topic. Some progress has been made in optical image interpretation through decoupling analysis method, while research on decoupling components of SAR images is still in a blank stage. To make an initial exploration on the component interpretation of SAR target images, we propose a new network based on a deep generative model and a new decoupling method. Due to the lack of real training samples that meet the required condition, we use electromagnetic simulation software FEKO to construct the training data sets. In our proposed method, we use the tag information of training samples to constrain the hidden variable layer and improve the structure and loss function of the residual variation autoencoder (Res-VAE) network. By optimizing the newly defined loss function, the network can get the decipherable component features and achieve component interpretation of SAR images. Our experiments verify the feasibility and practicability of the proposed network through the simulation data sets and MSTAR data sets. The results show that the proposed method is effective in interpreting the target components of SAR images.

Base isolation systems are efficient in decoupling the building from the ground during earthquakes, leading to a reduction in seismic demand to be experienced by the isolated building. Although Steel Reinforced Elastomeric Isolators (SREIs) have been proven to perform well under earthquakes, they are mostly applied to special buildings and are not a cost-effective solution for low-rise buildings in developing countries like India. Although there are existing studies on Unbonded Fibre Reinforced Elastomeric Isolator (UFREIs), effectiveness of alternative low-cost fibres was not studied. Hence, this paper focuses on parametric studies of UFREI systems with various locally available fibres. Initial dimensions of UFREI required for seismic isolation of a two storeyed framed building were approximated. For these specific dimensions, performance of UFREI numerical models with various low cost locally available fibres were studied and compared with high strength fibres by conducting parametric studies using ABAQUS. Interesting results obtained by varying the strength, thickness and number of fibre layers are discussed in detail. A new parameter, peak stress ratio ( S n ) was proposed which helps in deciding the type of fibre to be used in UFREI systems. Based on the observations from these studies, a suitable configuration of UFREI was proposed. Significant improvement in the seismic performance of the base isolated building was observed from the linear static and dynamic analyses.

In-wheel motors (IWMs) as a crucial component of an electric vehicle need to be tested at the end of the production line, at service intervals, and during motor design validation. This article presents an improved synthetic loading (SL) method for the rapid characterization of the IWMs. The proposed method emulates mechanical loading at a specific operating point by current harmonic injection. We introduce a novel algorithm for the current reference setting that is based on the manufacturer’s motor design data and accounts for the motor’s nonlinearities. Also, the method features a trapezoidal reference instead of a sinusoidal, which lowers the instantaneous peak current of the converter and, thereby, increases the attainable torque–speed range. The performance of the proposed SL method is comparable to the classic back-to-back (BB) testing method, which is confirmed by the theoretical analysis, simulations, and experimental results. The difference in motor efficiency measured with the BB and the improved SL method is smaller than 1% in most operating points. The proposed method offers considerable time and cost savings for the manufacturer—eliminating the mechanical coupling/decoupling operation—and can be applied to surface-mounted permanent magnet synchronous machines in general.

A three-dimensional (3D) model in the STL (StereoLithography) format is the most widely used 3D file format for 3D printing. At present, the definition and the method of calculating the complexity of 3D models itself is a relatively difficult problem. There is no accurate algorithm to calculate and characterize the complexity of such models, and they can be expressed only qualitatively. Thus, with the aim of defining and calculating the complexity of a 3D model, this study uses the voxel method to map all the attributes of the triangular facets of the model. The coupling method that calculates the interference ratio using the original mutual calculation factors is decoupled and uses the mapping and intersection of the triangular facets to the voxels to realize a fast calculation of the overall interference ratio. Its computational complexity is expressed by $$O\left(N^\ast M'\right),M'\ll M$$ . The quantitatively expressed model interference ratio, i.e., the degree of the model complexity, is calculated to facilitate optimization of the design structure and the model configuration of the 3D model. The accuracy and computational efficiency of the algorithm have been verified by performing calculations on different types of STL models, which proves the correctness and credibility of the method for rapidly calculating the interference ratio proposed in this work.

The mapping relationship between the driving space and the workspace is essential for the precise control of a cable-driven hyper-redundant robot. For a hyper-redundant robot driven by cables, the relationships between the driving space and the joint space and between the joint space and the workspace were studied. A joint-decoupling kinematics analysis method was proposed and a kinematic analysis was presented. Based on the analysis of the coupling effect between the cable-driving space and the joint space, a decoupling analysis of the whole cable-driving space and joint space was conducted to eliminate the coupling effect between the joints, and the mapping relationship between the driving cables and the joint angles was obtained. Given the initial and target orientations of the hyper-redundant robot, the variation law for each joint angle was obtained using quintic polynomial trajectory planning and the pseudo-inverse Jacobian matrix, and then the driving cable variation law could be solved. Based on the results, the joint angle changes and the workspace trajectories were solved in turn. By comparing with the initial trajectory, the simulation results verified the appropriateness of the decoupling analysis.

Hydraulic servo shaking table is an essential testing facility to simulate the actual vibration situation in real time. As a parallel mechanism, multiaxis hydraulic servo shaking table shows strong coupling characteristic among different degrees of freedom. When the multiaxis hydraulic shaking table moves to one direction, some unnecessary related motions will appear in other directions, which seriously affect the control performance. An effective approach to decouple motions in command direction and in unnecessary related directions is an urgent need for a higher precision control performance. In this work, the coupling phenomena and reasons of the multiaxis hydraulic servo table are analyzed based on dynamic model of a multiaxis hydraulic servo shaking table. In this regard, multiaxis hydraulic servo shaking table with strong coupling within the physical space is transformed into a set of single-input single-output systems that are independent of each other in the modal space. A decoupling control strategy is proposed in modal space to restrain the coupling motions. Simulation and experimental results show that the proposed control strategy can effectively improve the control performance and the decoupling effect.

This paper proposed a fiber Bragg grating (FBG)-based three-axis vibration sensor by using only five sensing elements. A composite structure of the cross-beam-type elastomer with multiplexed FBGs has been designed for three-axis vibration sensing. Because of the mechanical decoupling function of the structure and the reasonable arrangement of FBG sensing elements, the designed sensor possesses great advantages of low cross coupling and self temperature compensation capability. Theoretical model of the composite structure has been established, strain distribution and dynamic characteristics of the composite structure have been studied by the finite element analysis method. Amplitude-frequency test result shows that resonant frequencies of the sensor in X, Y, and Z directions were 980Hz, 960Hz, and 640Hz, respectively. Vibration sensitivities in X, Y, and Z directions were 9.7pm/g, 10.13pm/g, and 7.45pm/g, respectively. Vibration calibration test have validated that the designed sensor possess the capability to measure three-axis vibration.

The paper studies the progressive decoupling algorithm (PDA) of Rockafellar and focuses on the elicited version of the algorithm. Based on a generalized Yosida-regularization of Spingarn’s partial inverse of an elicitable operator, it is shown that the elicited progressive decoupling algorithm (EPDA), in a particular nonmonotone setting, linearly converges at a rate that could be viewed as the rate of a rescaled PDA, which may provide certain guidance to the selection of the parameters in computational practice. A preliminary numerical experiment shows that the choice of the elicitation constant has an impact on the efficiency of the EPDA. It is also observed that the influence of the elicitation constant is generally weaker than the proximal constant in the algorithm.

In order to solve the serious heating problem of high-efficiency high-voltage motor due to high power density and large electromagnetic load, this study carried out a fluid network to investigate the global fluid–solid coupling to predict temperature and analyze motor energy efficiency. Taking a 6 kV, 4-pole, 2500 kW compact high-efficiency high-voltage motor as an example, according to cooling and ventilated structure and fluid flow characteristics in the motor, the fluid flow and heat transfer characteristics can be obtained by solving the global fluid network model which is set up to divide the whole fluid field into several local decoupling zones (sub-models) including external fan, cooler and internal ventilated path. The 3D fluid–solid coupling local models have been established respectively which are effectively coupled to the motor global fluid network by fluid pressure. The model is then used to find the optimized structure to enhance the cooling by changing the height of the wind deflector and adjust the number of wind deflectors and the temperature is lower than when it was not optimized. The maximum temperature of the stator and rotor of the internal ventilated path and the outlet temperature of the internal ventilated path are reduced, which the motor power level can be further improved and the purpose of energy saving can be achieved.

The methods and performance of a 3D pin-by-pin neutronics code based on the 2D/1D decoupling method are presented. The code was newly developed as an effort to achieve enhanced accuracy and high calculation performance that are sufficient for the use in practical nuclear design analyses. From the 3D diffusion-based finite difference method (FDM) formulation, decoupled planar formulations are established by treating pre-determined axial leakage as a source term. The decoupled axial problems are formulated with the radial leakage source term. To accelerate the pin-by-pin calculation, the two-level coarse mesh finite difference (CMFD) formulation, which consists of the multigroup node-wise CMFD and the two-group assembly-wise CMFD is implemented. To enhance the accuracy, both the discontinuity factor method and the super-homogenization (SPH) factor method are examined for pin-wise cross-section homogenization. The parallelization is achieved with the OpenMP package. The accuracy and performance of the pin-by-pin calculations are assessed with the VERA and APR1400 benchmark problems. It is demonstrated that pin-by-pin 2D/1D alternating calculations within the two-level 3D CMFD framework yield accurate solutions in about 30 s for the typical commercial core problems, on a parallel platform employing 32 threads.

In this paper, a five-degree-of-freedom (5-DOF) DC hybrid magnetic bearing (HMB) for supporting the high-speed motor is proposed. The rotor in the novel HMB is easy to achieve 5-DOF stable levitation, of which the decoupling and levitation performance are enhanced. The equivalent magnetic circuit method (EMCM) and finite element analysis (FEA) are employed to verify its reliability and rationality. The mathematical formulas of levitation forces are obtained, and the basic performances of key electromagnetic parameters are simulated and confirmed. It can be concluded that the proposed 5-DOF DC HMB has reasonable magnetic circuits, and can produce the 5-DOF levitation forces in one unit. Moreover, the mathematical models are correct.

In this paper, a time-dependent robust optimization model is proposed by introducing a concept of expected interval quality loss as the robustness metric. The purpose is to provide a potential analysis tool for power semiconductor devices involving stochastic processes. Unlike conventional robustness indicators, the metric is formulated by calculating the expectation and variation of the maximal instantaneous quality loss and capturing the auto-correlation of the quality loss over the time interval. In terms of model solving, the time-dependent robust optimization involves a nested optimization, namely the outer design optimization requires to call the inner robustness analysis frequently. It will lead to extremely low computational efficiency. For reducing robustness evaluations, a decoupling framework is proposed to convert the nested optimization into a sequential iterative process of design optimization and time-dependent robustness analysis. For further improving the efficiency of each robustness analysis, a semi-second-order approximation of quality characteristics is created based on the information at the previous iteration. Two engineering applications of power semiconductor devices verify the validity and feasibility of the proposed approach.