Dynamic Error Model Research Articles

Emergence of global value chains as key driver behind industrial development: evidence from participating African countries

Purpose The purpose of this paper is to examine the role of global value chains (GVC) in industrial development of emerging economies, with particular focus on participating African countries. The findings of this study are expected to provide insight on the need for more developing countries to participate in GVC. Design/methodology/approach This study is built upon the neoclassical and endogenous growth theories, which postulate that savings, physical capital and human capital are the fundamental drivers of development in productive sectors of the economy. The investigation, covering the period 1980–2021, is carried out by using the unrestricted error correction model and dynamic ordinary least squares model. Findings The results of this study reveal that GVC stands as the dominant factor driving industrial development, compared to savings, physical capital and human capital. The findings, therefore, seem to contradict the postulation of conventional theories. The policy implications of the findings are not far-fetched. First, industrial development in the participating African countries has benefited largely from GVC; hence, it is necessary to encourage more participation. Second, industrial development also benefited from the control variables (savings, physical capital and human capital), hence the need to sustain their complementary role. Thirdly, only three African countries are actively participating in GVC, which suggests that more countries need to join, to facilitate industrial development. Originality/value Previous studies have not given adequate attention to African countries that participate in GVC, thus creating a void that needs to be filled. This study, therefore, produced results that are relevant to policy-making on industrial development in African countries.

Adaptive Path-Tracking Control Algorithm for Autonomous Mobility Based on Recursive Least Squares with External Condition and Covariance Self-Tuning

This paper introduces an adaptive path-tracking control algorithm for autonomous mobility based on recursive least squares (RLS) with external conditions and covariance self-tuning. The advancement and commercialization of autonomous driving necessitate universal path-tracking control technologies. In this study, we propose a path-tracking control algorithm that does not rely on vehicle parameters and leverages RLS with self-tuning mechanisms for external conditions and covariance. We designed an integrated error for effective path tracking that combines the lateral preview distance and yaw angle errors. The controller design employs a first-order derivative error dynamics model with the coefficients of the error dynamics estimated through the RLS using a forgetting factor. To ensure stability, we applied the Lyapunov direct method with injection terms and finite convergence conditions. Each regression process incorporates external conditions, and the self-tuning of the injection terms utilizes residuals. The performance of the proposed control algorithm was evaluated using MATLAB®/Simulink® and CarMaker under various path-tracking scenarios. The evaluation demonstrated that the algorithm effectively controlled the front steering angle for autonomous path tracking without vehicle-specific parameters. This controller is expected to provide a versatile and robust path-tracking solution in diverse autonomous driving applications.

Geometric Attitude Tracking Control for Rigid Body Based on a Novel Attitude Error Dynamic Model on SO(3)

Geometric Attitude Tracking Control for Rigid Body Based on a Novel Attitude Error Dynamic Model on SO(3)

Predefined-time sliding mode position and attitude coupling control for in-cabin robots on space stations

As space science advances, the significance of space robotics research escalates, particularly in control methodologies. Addressing the work efficiency requirements during the mission of the in-cabin robot, it is necessary to develop a fast and robust position and attitude coupling controller for in-cabin robots. This paper presents a predefined-time sliding mode position and attitude coupling control method, employing the SE(3) frame description. The first, the dynamic model of the in-cabin robot is developed within the SE(3) framework. Then, the predefined-time position and attitude coupling sliding mode controller is designed with a sliding surface based on the dynamic error model, and a hyperbolic tangent function is used to suppress system chattering. In addition, the stability of the proposed control method is rigorously confirmed using the Lyapunov theorem, ensuring that the convergence time is accurately predefined. Finally, simulation results are provided to demonstrate the effectiveness of this novel control strategy. A comparative analysis with fixed-time sliding mode control highlights its superior performance.

Application of improved appointed time control in helicopter mode of a tilt-rotor eVTOL aircraft

In this paper, an appointed time sliding mode control scheme is proposed for trajectory tracking control of a tilt-rotor electric vertical takeoff and landing (eVTOL) aircraft. First, the error dynamic model of helicopter mode of the tilt-rotor eVTOL aircraft is presented. Second, an appointed time prescribed performance function (ATPPF) is designed as the expected value of the error, which guides the error to converge to zero at the appointed time. Then, a predefined time controller ensures that the actual error tracks the expected error before the settling time set by the ATPPF. By setting the time parameters of the ATPPF and the predefined time controller to the same value, the actual error will converge to zero vicinity at the appointed time. Finally, the predefined time convergence of the closed-loop system is proved via Lyapunov analysis. The effectiveness of the appointed time control strategy is demonstrated in the simulation results in the presence of external disturbances, dynamic uncertainties and control input constraints.

Adaptive sliding mode anti-swing control of 4-DOF tower crane based on a nonlinear disturbance observer

Tower cranes are widely applied in outdoor environments with inevitable external disturbances, which can reduce transportation efficiency and safety. To improve the transient control performance of the tower crane when transporting goods and to guarantee good robustness, this paper designs an adaptive sliding mode Anti-swing control method based on a nonlinear disturbance observer. Firstly, a 4-DOF tower crane error dynamics model considering external disturbances and air friction is established, and then, a nonlinear disturbance observer is designed to estimate the aggregate disturbance. Further, a disturbance effect indicator (DEI) is set to judge the advantages and disadvantages of the disturbance effect on the tower crane system from a new perspective. Finally, beneficial disturbance effects are organically combined with a sliding mode control method possessing an adaptive mechanism to eliminate payload swing by introducing favorable interference. Using Lyapunov stability analysis in conjunction with the LaSalle invariance principle, the closed-loop system is shown to be asymptotically stable. Simulation results show that the controller proposed in this paper achieves accurate positioning by driving the trolley and the jib, and at the same time, can keep the payload swing angle slight during the working process and eliminate the payload swing angle after accurate positioning. Moreover, it is also robust in the face of external disturbances and system parameter variations.

Model predictive path tracking control of intelligent vehicles based on dual-stage disturbance observer under multi-channel disturbances

Abstract Parameter fluctuations, unmodeled dynamics, speed variation, steering actuator faults, and other multi-channel uncertain disturbances are the key challenges faced by the path tracking control of intelligent vehicles, which will affect the accuracy and stability of the path tracking. Therefore, a model predictive control (MPC) method based on a dual-stage disturbance observer (DDOB) is proposed in this paper. First, a tracking error dynamics model considering multi-channel uncertain disturbances is constructed, based on which a model predictive controller is designed to obtain the nominal front wheel steering angle by the Karush–Kuhn–Tucker (KKT) condition. Furthermore, the DDOB is designed to enable real-time estimation of the system disturbances, and then the estimated disturbances are used as the compensation for the nominal front wheel steering angle, which establishes the MPC control law with parallel compensation of the DDOB. Finally, the error boundedness of the DDOB and the global stability of the model predictive controller are analyzed. The effectiveness and superiority of the proposed algorithm are verified through Carsim–Simulink simulation and hardware-in-the-loop experiments.

A Method for Simulating the Positioning Errors of a Robot Gripper

The research is aimed at creating a methodology for increasing the positioning accuracy of an industrial robot and minimizing the vibration of the robot gripper by applying machine learning based on the developed mathematical model for estimating the positioning error. Two components of positioning accuracy are considered: geometric and kinematic errors and elastic static deformations. The dynamic error in the partial system of motion of the robot manipulator links is analyzed. The equation of partial motions is obtained from Lagrange’s differential equation of motion of the II kind. The system of differential equations for the positioning error was solved analytically by Euler’s method. An example of modeling the position and orientation error of the gripper due to temperature deformations of the third link for the manipulator scheme is given. An example of the modeling of static deformations and errors of the manipulator with elastic pliability of the robot links is given. An example of dynamic error modeling in a partial system of motion of the robot links is given. The proposed method of modeling robot gripper positioning errors makes it possible to increase the positioning accuracy of the industrial robot and minimize the vibration of the gripper. Having a mathematical model of positioning errors, it is possible to compensate for the positioning error by changing the speed of movement of the gripper reference point before determining the direct kinematic task.

Design and analysis of tracking differentiator based on SO(3)

Motivated by the issue of insufficient dynamic performance and tracking accuracy in SO(3)-based attitude tracking differentiators during large-angle maneuvers and complex trajectory tracking, a novel design approach for a three-degree-of-freedom attitude tracking differentiator within the SO(3) framework is proposed by incorporating second-order system theory and Lie group theory and improving the classical tracking differentiator. The kinematics model and error dynamics model of a rigid body on SO(3) are derived, and a reasonable virtual control input on SO(3) is constructed subsequently in order to achieve better dynamic response and tracking performance. Simulation and experimental results validate that the designed tracking differentiator could realize rapid and smooth convergence during large-angle maneuvers, and the initial large tracking error rapidly drops to near zero in a short period of time; additionally, it can also track expected time-varying curves well in complex trajectory tracking, with initial errors rapidly decreasing and maintaining at normal levels, demonstrating excellent tracking and control capabilities. There are strong application prospects for this new approach in addition to its theoretical significance.

Mediated learning: A computational rendering of ketamine-induced symptoms.

This article explores the contribution of the double error dynamic asymptote computational associative learning model to understanding the role of mediated learning mechanisms in the generation of spurious associations, as those postulated to characterize schizophrenia. Three sets of simulations for mediated conditioning, mediated extinction, and a mediated enhancement of latent inhibition, a unique model prediction, are presented. For each set of simulations, a parameter that modulates the impact of associative memory retrieval and the dissipation of nonperceptual activated representations through the network was manipulated. The effect of this operation is analyzed and compared to ketamine-induced effects on associative memories and mediated learning. The model's potential to predict these effects and present a plausible error-correction associative mechanism is discussed in the context of animal models of schizophrenia. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Development of a Universal Adaptive Control Algorithm for an Unknown MIMO System Using Recursive Least Squares and Parameter Self-Tuning

Development of a Universal Adaptive Control Algorithm for an Unknown MIMO System Using Recursive Least Squares and Parameter Self-Tuning

Error Model for the Assimilation of All-Sky FY-4A/AGRI Infrared Radiance Observations.

The Advanced Geostationary Radiation Imager (AGRI) carried by the FengYun-4A (FY-4A) satellite enables the continuous observation of local weather. However, FY-4A/AGRI infrared satellite observations are strongly influenced by clouds, which complicates their use in all-sky data assimilation. The presence of clouds leads to increased uncertainty, and the observation-minus-background (O-B) differences can significantly deviate from the Gaussian distribution assumed in the variational data assimilation theory. In this study, we introduce two cloud-affected (Ca) indices to quantify the impact of cloud amount and establish dynamic observation error models to address biases between O-B and Gaussian distributions when assimilating all-sky data from FY-4A/AGRI observations. For each Ca index, we evaluate two dynamic observation error models: a two-segment and a three-segment linear model. Our findings indicate that the three-segment linear model we propose better conforms to the statistical characteristics of FY-4A/AGRI observations and improves the Gaussianity of the O-B probability density function. Dynamic observation error models developed in this study are capable of handling cloud-free or cloud-affected FY-4A/AGRI observations in a uniform manner without cloud detection.

Dynamic error modeling and analysis of articulated arm coordinate measuring machine with integrated joint module

The self-driven articulated arm coordinate measuring machine (AACMM) is a new non-orthogonal flexible coordinate measuring equipment providing automatic positioning and measurement with integrated joint modules introduced to its rotary joints. The dynamic measuring accuracy is the crucial indicator of the self-driven AACMM performance. However, the part manufacturing assembly error and structural dynamic deformation error will seriously reduce the dynamic measuring accuracy. Therefore, a modeling compensation approach based on error propagation and accumulation mechanism analysis of the above two aspects is necessarily required to preserve the automatic measuring accuracy of the self-driven AACMM. In this paper, the authors propose an innovative modeling method for evaluating and compensating for the dynamic measuring errors of the self-driven AACMM. The ideal measuring model is constructed assisted with on the classical Denavit–Hartenberg parameter methodology and the complex structure of the self-driven AACMM. The source and its propagation mechanism of the dynamic error from the self-driven AACMM were analyzed systematically to decouple the superposed error impact on measuring precision. The dynamic measurement error compensation mechanism model is presented considering the influence of instantaneous spatial attitude on the dynamic structural parameters of self-driven AACMM. Experimental results indicate that the single-point repeatability measurement error fluctuation of the self-driven AACMM is 0.046 mm. The radius measuring error of the gauge ball reduces from −0.26 mm to −0.19 mm after the error model compensation. Additionally, the measuring accuracy is demonstrated to be effectively improved by the dynamic error compensation mechanism model.

Three‐dimensional path following control of underactuated autonomous underwater vehicles with nonzero roll dynamics: A novel line‐of‐sight‐guided approach

AbstractMost existing path following control approaches for underactuated Autonomous underwater vehicles (AUVs) assumed that the roll angle can be passively stabilized at zero, and simplified the problem to the 5‐degree‐of‐freedom model. However, in practice, the roll motion suffers from undesirable oscillations induced by environmental disturbances, unbalanced propeller torque, etc. To preserve the stability of the system under nonzero roll dynamics, this paper proposes a novel line‐of‐sight‐ (LOS) guided path following control approach. Firstly, the spatial path following error dynamics model is derived to comprehensively reveal the negative effect of roll dynamics. Secondly, an improved LOS guidance law is designed to compensate for the nonzero roll angle. A salient feature is that the proposed guidance law can be applied to the underwater vehicle no matter it is underactuated in the roll motion or not. Thirdly, by assigning the guidance angles to the desired attitude angles, a robust attitude tracking controller is developed, in which the adaptive estimation technique is employed to handle the lumped uncertainties. The tanh‐based saturation function is incorporated such that the resultant control signals are inherently bounded. The closed‐loop path following and attitude tracking errors are proved to be globally asymptotically stable at the origin. Comparative simulation results are provided to substantiate the effectiveness and superiority of the proposed method.

Dynamic error modeling and compensation of a scanning probe on CMM

Dynamic error modeling and compensation of a scanning probe on CMM

Nonlinear Guidance Laws for Anti-tank Guided Missile to Intercept Maneuvering Tank Targets Using Optimal Error Dynamics and Relative Virtual Model

Nonlinear Guidance Laws for Anti-tank Guided Missile to Intercept Maneuvering Tank Targets Using Optimal Error Dynamics and Relative Virtual Model

Noncooperative Target Finite-Time Surrounding Control of Spacecraft Formation

This paper proposes a novel distributed control method for surrounding a noncooperative target that has maneuverability by spacecraft formation. A relative orbit error dynamic model between the target and the formation is established dependent on a reference spacecraft under the 2-body assumption. To estimate and compensate for the target’s control input rapidly, a novel finite-time extended state observer is developed. It is stable in the sense of fast finite-time uniformly ultimately bounded stability. A fast terminal sliding mode controller is proposed for finite-time convergence of the system. Simulation examples are implemented to show the effectiveness of proposed algorithm.

Online learning-based active disturbance rejection control of autonomous surface vehicles with HIL simulations

Modeling and control of autonomous surface vehicles (ASVs) is a challenging task due to the requirement of real-time adaptation to internal hydrodynamic parameter variations and external ocean disturbances induced by wind, waves, and ocean currents. In this paper, an online learning-based active disturbance rejection control method is proposed by combining real-time model learning and anti-disturbance control. Specifically, a filtering learning extended state observer (FLESO) is proposed for online identifying the unknown hydrodynamic parameters, the unknown control input gain, and estimating system uncertainties composed of the unknown external disturbances and dynamic modeling errors, with guaranteed convergence. Then, a self-learning anti-disturbance control law is developed based on the FLESO, enabling self-learning anti-disturbance speed tracking without prior knowledge of model parameters. A salient feature of the proposed method is that the unknown hydrodynamic parameters, the unknown control input gain, and the unknown system uncertainties can be estimated online simultaneously. Lyapunov stability analysis is employed to verify the stability of the closed-loop system. The effectiveness of the proposed online learning-based active disturbance rejection control method is demonstrated through HIL simulations.

Dynamic error modeling, analysis and compensation of planar flexible multilink mechanism for high-speed precision presses considering the thermal expansion effect of sliding and angular contact ball bearings

Dynamic error modeling, analysis and compensation of planar flexible multilink mechanism for high-speed precision presses considering the thermal expansion effect of sliding and angular contact ball bearings

Disturbance observer–based fixed-time sliding mode trajectory tracking control for marine surface vehicles with uncertain dynamics

In this paper, a fixed-time sliding mode control scheme is developed to fulfill the trajectory tracking task of a marine surface vehicle with unknown dynamics. To restrain the adverse effects of the unknown dynamics including the parameter inaccuracy and exogenous disturbances, a fixed-time disturbance observer is designed to estimate the lumped uncertainties using the bi-limit homogeneous theory without requiring any knowledge of the model uncertainties. Then, a nominal tracking controller is proposed to stabilize the error dynamic model in the sense of fixed-time Lyapunov stability, based on which a novel integral-type sliding mode manifold with bi-limit homogeneity is constructed to drive tracking error convergence in fixed time. To enhance the robustness of the vessel control system, a disturbance observer–based fixed-time integral sliding mode tracking controller is finally proposed, and the chattering phenomenon is effectively alleviated by direct estimation compensations. The analysis of Lyapunov stability indicates that the closed-loop system is fixed-time stable. Numerical simulations on a model vessel are carried out to validate theoretical results of the proposed control scheme.