Explore the vast range of titles available.

PurposeIn typical model-based calibration, linearization errors are derived inevitably, and non-negligible negative impact will be induced on the identification results if the rotational kinematic errors are not small enough or the lengths of links are too long, which is common in the industrial cases. Thus, an accurate two-step kinematic calibration method minimizing the linearization errors is presented for a six-DoF serial robot to improve the calibration accuracy.Design/methodology/approachThe negative impact of linearization on identification accuracy is minimized by removing the responsible linearized kinematic errors from the complete kinematic error model. Accordingly, the identification results of the dimension-reduced new model are accurate but not complete, so the complete kinematic error model, which achieves high identification accuracy of the rest of the error parameters, is combined with this new model to create a two-step calibration procedure capable of highly accurate identification of all the kinematic errors.FindingsThe proportions of linearization errors in measured pose errors are quantified and found to be non-negligible with the increase of rotational kinematic errors. Thus, negative impacts of linearization errors are analyzed quantitatively in different cases, providing the basis for allowed kinematic errors in the new model. Much more accurate results were obtained by using the new two-step calibration method, according to a comparison with the typical methods.Originality/valueThis new method achieves high accuracy with no compromise on completeness, is easy to operate and is consistent with the typical method because the second step with the new model is conveniently combined without changing the sensors or measurement instrument setup.

The frequency domain analysis (FDA) offers greater computational efficiency than the method of characteristic (MOC) in simulating transient flow in pressurized pipes. However, its accuracy is hindered by linearisation errors. Violations of the assumptions for linearisation in friction term and valve equations during water distribution systems (WDSs) burst simulations make the FDA results meaningless. Linearisation procedures are modified as follows using the Heaviside property of pipeline bursts to address this problem: (1) the linearization of the friction term is adjusted, and (2) the valve equation is approximated using a three-step approach. The higher-order term dropped by the original FDA is linearly approximated to achieve better accuracy. The modified FDA is compared to the MOC in a real-life WDS by numerical experiment. Excellent precision can be observed even for a highly nonlinear case where the burst flow is 20% of the initial total demand. The simulation time is significantly shorter than when using the MOC. The proposed modification dramatically improves the applicability of the FDA for solving the nonlinear error issue during the simulation of the pipeline burst. This result implies the potential for its application in quick inverse analysis of pipeline bursts.

The feasibility of using wood-derived biochar (BC) to remove Pb, Mn, and Cu from landfill leachate was investigated and modeled in this study. BC was produced under the pyrolytic temperature of 740 °C. The effect of contact time, BC dosage and particle size on adsorption of the heavy metals onto BC was examined. BC was used in two forms i.e., pulverized (PWB) and crushed (CWB) to evaluate the effect of BC particle size on adsorption characteristics. The kinetics of Pb, Mn, and Cu adsorption onto PWB and CWB were assessed using the pseudo second-order and Elovich models, where both applied models could well describe the adsorption kinetics. Removal efficiencies of the heavy metals were increases by 1.2, 1.4, and 1.6 times, respectively, for Pb, Mn, and Cu, when PWB content of the leachate increased from 0.5 to 5 g L − 1 . Equilibrium adsorption capacity of the heavy metals onto BC in leachate system was evaluated using the Langmuir, non-linearized Freundlich, linearized Freundlich, and Temkin isotherms and found to have the following order for PWB: Non-linearized Freundlich > Temkin > Langmuir > Linearized Freundlich. The Langmuir and linearized Freundlich models could not adequately represent adsorption of the heavy metals onto BC, especially for CWB. The highest removal of 88% was obtained for Pb, while the greatest adsorption intensity was found to be 1.58 mg g − 1 for Mn. Using the non-linearized Freundlich isotherm significantly reduced adsorption prediction error. The adsorption affinity of PWB for Pb, Mn, and Cu was greater than that of CWB in all treatments. Wood-derived BC is suggested to be used for the removal of heavy metals from landfill leachate as an economical adsorbent.

In this paper, we focus on the event-triggered robust state estimation problems for nonlinear networked systems with constant measurement delays against denial-of-service (DoS) attacks. The computation of the extended Kalman filter (EKF) generates errors of linearization approximations, which can result in increased state estimation errors, and subsequently amplifies the linearization errors. DoS attacks interfere with the transmission of measurements sent to the remote robust state estimator by overloading the communication networks, while the communication rate of the communication channel is constrained. Therefore, an event-triggered robust state estimation algorithm based on sensitivity penalization with an explicit packet arrival parameter is derived to defend against DoS attacks and linearization errors. Meanwhile, the presence of measurement delays precludes the direct use of conventional state estimation algorithms, prompting us to devise an innovative state augmentation method. The results of the numerical simulations show that the proposed robust state estimator can appreciably improve the accuracy of state estimation.

This paper develops a general abstract framework for a posteriori estimates for immiscible incompressible two-phase flows in porous media. We measure the error by the dual norm of the residual and, for mathematical correctness, employ the concept of global and complementary pressures in the analysis. Our estimators allow to estimate separately the different error components, namely, the spatial discretization error, the temporal discretization error, the linearization error, the iterative coupling error, and the algebraic solver error. We propose an adaptive algorithm wherein the different iterative procedures (iterative linearization, iterative coupling, iterative solution of linear systems) are stopped when the corresponding errors do not affect significantly the overall error and wherein the spatial and temporal errors are equilibrated. Consequently, important computational savings can be achieved while guaranteeing a user-given precision. The developed framework covers fully implicit, implicit pressure–explicit saturation, or iterative coupling formulations; conforming spatial discretization schemes such as the vertex-centered finite volume method or the finite element method and nonconforming spatial discretization schemes such as the cell-centered finite volume method, the mixed finite element method, or the discontinuous Galerkin method; linearizations such as the Newton or the fixed-point one; and general linear solvers. Numerical experiments for a model problem are presented to illustrate the theoretical results. Only by stopping timely the linear and nonlinear solvers, speedups by a factor between 10 and 20 in terms of the number of total linear solver iterations are achieved.

The short-term hydrothermal scheduling (SHS), typically a complicated nonlinear, nonconvex and non-smooth optimization problem, is very important for the economic operation of power systems. Instead of heuristic algorithms popularly used in previous studies, this paper employs a mathematical approach, where a two-stage linear programming with special ordered sets (TLPSOS) is proposed to solve the SHS problem. The nonlinear thermal cost functions and hydropower output functions are approximated by using the special ordered sets. The TLPSOS involves two stages: solve the linearized model in the first stage, and eliminate the linearization errors in the second. Superior to heuristic algorithms, the TLPSOS does not rely on parameters, and can always give stable results. Applied to a widely used hydrothermal system which consists of four hydroplants and three thermal plants, the present method shows its efficiency and strength in obtaining results better than those of previous studies.

In this paper, we investigate the consistency of extended Kalman filter (EKF)-based cooperative localization (CL) from the perspective of observability. We analytically show that the error-state system model employed in the standard EKF-based CL always has an observable subspace of higher dimension than that of the actual nonlinear CL system. This results in unjustified reduction of the EKF covariance estimates in directions of the state space where no information is available, and thus leads to inconsistency. To address this problem, we adopt an observability-based methodology for designing consistent estimators in which the linearization points are selected to ensure a linearized system model with observable subspace of correct dimension. In particular, we propose two novel observability-constrained (OC)-EKF estimators that are instances of this paradigm. In the first, termed OC-EKF 1.0, the filter Jacobians are calculated using the prior state estimates as the linearization points. In the second, termed OC-EKF 2.0, the linearization points are selected so as to minimize their expected errors (i.e., the difference between the linearization point and the true state) under the observability constraints. The proposed OC-EKFs have been tested in simulation and experimentally, and have been shown to significantly outperform the standard EKF in terms of both accuracy and consistency.

The feasibility of using wood-derived biochar (BC) to adsorb cadmium from landfill leachate was investigated and modeled in the present study. The effect of contact time and biochar dosage on kinetics and isotherms of adsorption was investigated. Wood-derived biochar was produced under the pyrolytic temperature of about 740 °C. The kinetics of Cd adsorption on biochar was analyzed using the pseudo-first-order, pseudo-second-order and Elovich models. Results indicated that the pseudo-second-order model can best describe the adsorption kinetics. Equilibrium adsorption capacity of Cd in the leachate system was evaluated using the Langmuir, linearized and non-linearized Freundlich and Temkin isotherms, and was found to have the following order in describing the adsorption of Cd from landfill leachate onto BC: Non-linearized Freundlich>Temkin>Langmuir>Linearized Freundlich. Use of the non-linearized Freundlich isotherm significantly reduced adsorption prediction error. Results showed that wood-derived biochar could be promisingly used for the removal of Cd from landfill leachate. Application of biochar has a promising prospect in the context of landfill leachate treatment. Highlights Pseudo-second-order and Elovich expressions well described kinetics of Cd adsorption onto biochar Non-linear Freundlich followed by Tekmin models best represented the experimental equilibrium data Linearization of Freundlich model significantly affected the prediction error structure

In this paper, in order to reduce the linearization error of Kalman filters family, three new methods are proposed and their effectiveness and feasibility are evaluated by means of Simultaneous Localization and Mapping (SLAM) problem. In derivative based methods of Kalman filters family, linearization error is brought into estimation unavoidably because of using Taylor expansion to linearize nonlinear motion model and observation model. These three methods lessen the linearization error by replacing the Jacobian matrix of observation function with new formulas. Simulation results done with ‘Car Park Dataset’ indicate that all proposed methods have less linearization error than other mentioned methods and the method named Improved Weighted Mean Extended Kalman Filter (IWMEKF) performs much better than other mentioned Kalman filters in this paper on linearization error. In addition, simulation results confirm that our proposed approaches are computationally efficient. From estimation accuracy and computational complexity point of view, IWMEKF is the best solution for solving nonlinear SLAM problem among all Kalman filters mentioned in this paper.

This paper deals with the problem of transforming a single output nonlinear system with an auxiliary dynamics into an extended nonlinear observer canonical form (ENOCF). The proposed ENOCF depends on system output and auxiliary state, and admits a kind of high-gain observer. We provide two necessary conditions and an equivalent condition for the existence of such a transformation, and then apply the results to the Rössler system as a case study.