Hierarchical least-square optimization is often used in robotics to inverse a direct function when multiple incompatible objectives are involved. Typical examples are inverse kinematics or dynamics. ...The objectives can be given as equalities to be satisfied (e.g. point-to-point task) or as areas of satisfaction (e.g. the joint range). This paper proposes a complete solution to solve multiple least-square quadratic problems of both equality and inequality constraints ordered into a strict hierarchy. Our method is able to solve a hierarchy of only equalities 10 times faster than the iterative-projection hierarchical solvers and can consider inequalities at any level while running at the typical control frequency on whole-body size problems. This generic solver is used to resolve the redundancy of humanoid robots while generating complex movements in constrained environments.
This paper presents an interpolating element-free Galerkin (IEFG) method for solving the two-dimensional (2D) elastic large deformation problems. By using the improved interpolating moving ...least-squares method to form shape function, and using the Galerkin weak form of 2D elastic large deformation problems to obtain the discrete equations, we obtain the formulae of the IEFG method for 2D elastic large deformation problems. As the displacement boundary conditions can be applied directly, the IEFG method can acquire higher computational efficiency and accuracy than the traditional element-free Galerkin (EFG) method, which is based on the moving least-squares approximation and can not apply the displacement boundary conditions directly. To analyze the influences of node distribution, scale parameter of influence domain and the loading step on the numerical solutions of the IEFG method, three numerical examples are proposed. The IEFG method has almost the same high accuracy as the EFG method, and for some 2D elastic large deformation problems the IEFG method even has higher computational accuracy.
The performance of a sensor-based rotor flux-oriented vector control induction machine drive is highly influenced by the estimated value of the rotor time constant/rotor resistance which varies ...continuously with temperature when it is driven with the variable loads. Therefore, to achieve accurate torque control, online tunning of the rotor time constant is necessary. This article proposes a novel and simple adaption technique to estimate the rotor time constant. In this proposed method, the reference model of the rotor flux vector is derived using the voltage model of the induction machine, which is independent of the rotor time constant. The current controller uses the model of the rotor flux vector that is derived using the current model of the induction machine. This current model is dependent on the rotor time constant. The error between the rotor flux vectors derived using the current model and the voltage model is minimized using the least-squares method. In this process, the rotor time constant is always tuned to its actual value. The article also presents a simple approach to estimate the parameters of induction machine like the stator resistance, the net leakage inductance, and the initial value of the rotor time constant in an offline mode at a standstill condition. To validate the proposed algorithm, two experimental prototypes are developed for two different power ratings of 3.7 and 67 kW. Experimental results from these prototypes confirm the effectiveness, reliability, and stability of the proposed algorithm both in the motoring and regenerative modes of operation. This method of the adaptation of the rotor time constant is simple and, therefore, useful for practicing engineers.
The robust Kalman filter design problem for two-dimensional uncertain linear discrete time-varying systems with stochastic noises is investigated in this study. First, we prove that the solution to a ...certain deterministic regularized least squares problem constrained by the nominal two-dimensional system model is equivalent to the generalized two-dimensional Kalman filter. Then, based on this relationship, the robust state estimation problem for two-dimensional uncertain systems with stochastic noises is interpreted as a deterministic robust regularized least squares problem subject to two-dimensional dynamic constraint. Finally, by solving the robust regularized least squares problem and using a simple approximation, a recursive robust two-dimensional Kalman filter is determined. A heat transfer process serves as an example to show the properties and efficacy of the proposed filter.
An effective method to estimate the state of health (SOH) of lithium ion batteries is illustrated in this work. This method uses an adaptive transformation of charging curves at different stages of ...life to quantify the extent of capacity fade and derive a time-based parameter to enable an accurate SOH estimation. This approach is easy for practical implementation and universal to chemistry or cell geometry, with minimal demand of learning. With a typical constant current-constant voltage (CC-CV) charging method for a lithium ion battery, this approach uses an equivalent circuit model to characterize the CC portion of the charging curve and derive a transformation function and a time-based parameter to estimate SOH at any stage of life via a nonlinear least squares method to identify model parameters. The SOH estimation errors (discrepancy between estimated and experimental values, denoted as Delta SOH) are under 2% before the end of life in cases shown at 25 degree C and 60 degree C and a range of typical discharging rates up to 3C. With different sizes and chemistries, the Delta SOHs are all less than 3%.
We analyze the Basis Pursuit recovery of signals with general perturbations. Previous studies have only considered partially perturbed observations Ax + e. Here, x is a signal which we wish to ...recover, A is a full-rank matrix with more columns than rows, and e is simple additive noise. Our model also incorporates perturbations E to the matrix A which result in multiplicative noise. This completely perturbed framework extends the prior work of Candes, Romberg, and Tao on stable signal recovery from incomplete and inaccurate measurements. Our results show that, under suitable conditions, the stability of the recovered signal is limited by the noise level in the observation. Moreover, this accuracy is within a constant multiple of the best-case reconstruction using the technique of least squares. In the absence of additive noise, numerical simulations essentially confirm that this error is a linear function of the relative perturbation.
We consider least squares (LS) approaches for locating a radiating source from range measurements (which we call R-LS) or from range-difference measurements (RD-LS) collected using an array of ...passive sensors. We also consider LS approaches based on squared range observations (SR-LS) and based on squared range-difference measurements (SRD-LS). Despite the fact that the resulting optimization problems are nonconvex, we provide exact solution procedures for efficiently computing the SR-LS and SRD-LS estimates. Numerical simulations suggest that the exact SR-LS and SRD-LS estimates outperform existing approximations of the SR-LS and SRD-LS solutions as well as approximations of the R-LS and RD-LS solutions which are based on a semidefinite relaxation.
The author discusses common method bias in the context of structural equation modeling employing the partial least squares method (PLS-SEM). Two datasets were created through a Monte Carlo simulation ...to illustrate the discussion: one contaminated by common method bias, and the other not contaminated. A practical approach is presented for the identification of common method bias based on variance inflation factors generated via a full collinearity test. The author's discussion builds on an illustrative model in the field of e-collaboration, with outputs generated by the software WarpPLS. They demonstrate that the full collinearity test is successful in the identification of common method bias with a model that nevertheless passes standard convergent and discriminant validity assessment criteria based on a confirmation factor analysis.
The conditioning theory of the ML-weighted least squares and ML-weighted pseudoinverse problems is explored in this article. We begin by introducing three types of condition numbers for the ...ML-weighted pseudoinverse problem: normwise, mixed, and componentwise, along with their explicit expressions. Utilizing the derivative of the ML-weighted pseudoinverse problem, we then provide explicit condition number expressions for the solution of the ML-weighted least squares problem. To ensure reliable estimation of these condition numbers, we employ the small-sample statistical condition estimation method for all three algorithms. The article concludes with numerical examples that highlight the results obtained.
•How BDA-AI technologies can improve green supply chain collaboration and environmental process integration.•Impact of suppliers’ cross-functional integration and treatment capacities on hospital ...green performance.•Green digital learning as a moderating role in the process of green supply chain collaboration.•The use of BDA-AI by decision-makers in the achievement of a proactive environmental strategy.
Big data analytics and artificial intelligence (BDA-AI) technologies have attracted increasing interest in recent years from academics and practitioners. However, few empirical studies have investigated the benefits of BDA-AI in the supply chain integration process and its impact on environmental performance. To fill this gap, we extended the organizational information processing theory by integrating BDA-AI and positioning digital learning as a moderator of the green supply chain process. We developed a conceptual model to test a sample of data from 168 French hospitals using a partial least squares regression-based structural equation modeling method. The findings showed that the use of BDA-AI technologies has a significant effect on environmental process integration and green supply chain collaboration. The study also underlined that both environmental process integration and green supply chain collaboration have a significant impact on environmental performance. The results highlight the moderating role of green digital learning in the relationships between BDA-AI and green supply chain collaboration, a major finding that has not been highlighted in the extant literature. This article provides valuable insight for logistics/supply chain managers, helping them in mobilizing BDA-AI technologies for supporting green supply processes and enhancing environmental performance.
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