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This paper seeks to explore the sensitivity of these parameters and their impact on fiscal policy outcomes. We use the existing literature to establish possible ranges for each parameter, and we examine how changes within these ranges can alter the outcomes of fiscal policy. In this way, we aim to highlight the importance of these parameters in the formulation and evaluation of fiscal policy.

The role of fiscal policy, its effects and multipliers continues to be a subject of intense debate in macroeconomics. Despite adopting a New Keynesian approach within a macroeconomic model, the reactions of macroeconomic variables to fiscal shocks can vary across different contexts and theoretical frameworks. This paper aims to investigate these diverse reactions by conducting a sensitivity analysis of parameters. Specifically, the study examines how key variables respond to fiscal shocks under different parameter settings. By analyzing the behavioral dynamics of these variables, this research contributes to the ongoing discussion on fiscal policy. The findings offer valuable insights to enrich the understanding of the complex relationship between fiscal shocks and macroeconomic outcomes, thus facilitating informed policy debates.

This paper aims to investigate key elements of New Keynesian Dynamic Stochastic General Equilibrium (DSGE) models. The focus is on the calibration of parameters and their impact on macroeconomic variables, such as output and inflation. The study also examines how different parameter settings affect the response of monetary policy to fiscal measures. In conclusion, this study has relied on theoretical exploration and a comprehensive review of existing literature. The parameters and their relationships have been analyzed within a robust theoretical framework, offering valuable insights for further research on how these factors influence model forecasts and inform policy recommendations derived from New Keynesian DSGE models. Moving forward, it is recommended that future work includes empirical analyses to test the reliability and effectiveness of parameter calibrations in real-world conditions. This will contribute to enhancing the accuracy and relevance of DSGE models for economic policy decision-making.

This study is motivated by the aim to provide a deeper understanding of the roles macroeconomic model parameters play concerning responses to expansionary fiscal policies and the subsequent reactions of monetary authorities. Comprehensive reviews that encompass this breadth of relationships within a single text are rare in the literature, making this work a valuable contribution to stimulating discussions on macroeconomic policies.

This study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control.

Firstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory.

Extensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking.

This research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

Autonomous flight of unmanned aerial vehicles (UAVs) in global position system (GPS)-denied environments has become an increasing research hotspot. This paper aims to realize the indoor fixed-point hovering control and autonomous flight for UAVs based on visual inertial simultaneous localization and mapping (SLAM) and sensor fusion algorithm based on extended Kalman filter.

The fundamental of the proposed method is using visual inertial SLAM to estimate the position information of the UAV and position-speed double-loop controller to control the UAV. The motion and observation models of the UAV and the fusion algorithm are given. Finally, experiments are performed to test the proposed algorithms.

A position-speed double-loop controller is proposed, by fusing the position information obtained by visual inertial SLAM with the data of airborne sensors. The experiment results of the indoor fixed-points hovering show that UAV flight control can be realized based on visual inertial SLAM in the absence of GPS.

A position-speed double-loop controller for UAV is designed and tested, which provides a more stable position estimation and enabled UAV to fly autonomously and hover in GPS-denied environment.

This study aims to improve the control accuracy and antidisturbance performance of the manipulator with the flexible link, a combined controller, which combines the novel backstepping sliding mode controller based on the extended state observer (ESO) and super-twisting sliding mode controller.

First, the dynamic of the system is constructed by Lagrange method and assumed mode method, and then the dynamic is decoupled by the singular perturbation theory to obtain the slow-varying subsystem and fast-varying subsystem. For the slow-varying subsystem, the novel backstepping sliding mode controller based on ESO is used to achieve joint tracking. For the fast-varying subsystem, the super-twisting sliding mode controller is used for vibration suppression. At the same time, to suppress chattering, the tanh function is used to replace the sign function in the reaching law.

The simulation results show that the combined control has better trajectory tracking performance, antiinterference performance and vibration suppression performance than traditional sliding mode control (SMC).

A novel backstepping sliding mode controller based on ESO is designed to guarantee the performance of the tracking trajectory. The new controller improves the converge rate. A super-twisting sliding mode controller, which can stabilize the fast-varying subsystem, is used to suppress the vibration of flexible link.

Collaborative robots (cobots) are widely used in various manipulation tasks within complex industrial environments. However, the manipulation capabilities of cobot manipulation planning are reduced by task, environment and joint physical constraints, especially in terms of force performance. Existing motion planning methods need to be more effective in addressing these issues. To overcome these challenges, the authors propose a novel method named force manipulability-oriented manipulation planning (FMMP) for cobots.

This method integrates force manipulability into a bidirectional sampling algorithm, thus planning a series of paths with high force manipulability while satisfying constraints. In this paper, the authors use the geometric properties of the force manipulability ellipsoid (FME) to determine appropriate manipulation configurations. First, the authors match the principal axes of FME with the task constraints at the robot’s end effector to determine manipulation poses, ensuring enhanced force generation in the desired direction. Next, the authors use the volume of FME as the cost function for the sampling algorithm, increasing force manipulability and avoiding kinematic singularities.

Through experimental comparisons with existing algorithms, the authors validate the effectiveness and superiority of the proposed method. The results demonstrate that the FMMP significantly improves the force performance of cobots under task, environmental and joint physical constraints.

To improve the force performance of manipulation planning, the FMMP introduces the FME into sampling-based path planning and comprehensively considers task, environment and joint physical constraints. The proposed method performs satisfactorily in experiments, including assembly and in situ measurement.

This paper aims to develop a method for tuning the parameters of the active disturbance rejection controller (ADRC) for fixed-wing unmanned aerial vehicles (UAVs). The bald eagle search (BES) algorithm has been improved, and a cost function has been designed to enhance the optimization efficiency of ADRC parameters.

A six-degree-of-freedom nonlinear model for a fixed-wing UAV has been developed, and its attitude controller has been formulated using the active disturbance rejection control method. The parameters of the disturbance rejection controller have been fine-tuned using the collaborative mutual promotion bald eagle search (CMP-BES) algorithm. The pitch and roll controllers for the UAV have been individually optimized to obtain the most effective controller parameters.

Inspired by the salp swarm algorithm (SSA), the interaction among individual eagles has been incorporated into the CMP-BES algorithm, thereby enhancing the algorithm's exploration capability. The efficient and accurate optimization ability of the proposed algorithm has been demonstrated through comparative experiments with genetic algorithm, particle swarm optimization, Harris hawks optimization HHO, BES and modified bald eagle search algorithms. The algorithm's capability to solve complex optimization problems has been further proven by testing on the CEC2017 test function suite. A transitional function for fitness calculation has been introduced to accelerate the ability of the algorithm to find the optimal parameters for the ADRC controller. The tuned ADRC controller has been compared with the classical proportional-integral-derivative (PID) controller, with gust disturbances introduced to the UAV body axis. The results have shown that the tuned ADRC controller has faster response times and stronger disturbance rejection capabilities than the PID controller.

The proposed CMP-BES algorithm, combined with a fitness function composed of transition functions, can be used to optimize the ADRC controller parameters for fixed-wing UAVs more quickly and effectively. The tuned ADRC controller has exhibited excellent robustness and disturbance rejection capabilities.

The CMP-BES algorithm and transitional function have been proposed for the parameter optimization of the active disturbance rejection controller for fixed-wing UAVs.

The purpose of this study is to model the global dynamic hysteresis properties with an improved Jiles–Atherton (J-A) model through a unified set of parameters.

First, the waveform scaling parameters β, λ_k and λ_c are used to improve the calculation accuracy of hysteresis loops at low magnetic flux density. Second, the Riemann–Liouville (R-L) type fractional derivatives technique is applied to modified static inverse J-A model to compute the dynamic magnetic field considering the skin effect in wideband frequency magnetization conditions.

The proposed model is identified and verified by modeling the hysteresis loops whose maximum magnetic flux densities vary from 0.3 to 1.4 T up to 800 Hz using B30P105 electrical steel. Compared with the conventional J-A model, the global simulation ability of the proposed dynamic model is much improved.

Accurate modeling of the hysteresis properties of electrical steels is essential for analyzing the loss behavior of electrical equipment in finite element analysis (FEA). Nevertheless, the existing inverse Jiles–Atherton (J-A) model can only guarantee the simulation accuracy with higher magnetic flux densities, which cannot guarantee the analysis requirements of considering both low magnetic flux density and high magnetic flux density in FEA. This paper modifies the dynamic J-A model by introducing waveform scaling parameters and the R-L fractional derivative to improve the hysteresis loops’ simulation accuracy from low to high magnetic flux densities with the same set of parameters in a wide frequency range.

Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

This study assesses postreplanting oil palm farming risks, analyzes seed procurement parameters, investigates seed institutions' performance factors and develops a framework for improved sustainability.

Incorporating data from 219 smallholder farmers in designated replanting areas, our study comprehensively evaluates seed supply performance, examining the roles of stakeholders and identifying potential risks in seed management. We assess these risks using the Risk Priority Number (RPN) methodology and Multidimensional Scaling (MDS) techniques.

The results show that the timing and quantity of oil palm seed supply have a relatively small impact on postreplanting failure risk. To mitigate this risk, focus on monitoring seed purity using high-quality Tenera oil palm-type seeds and early detection technology. Encourage seed-producing cooperatives to become legal seed producers for an inclusive system and consider smallholders' variety preferences.

This study’s significance lies in its comprehensive assessment of the risks associated with oil palm replanting on smallholder plantations, detailed analysis of critical parameters in seed procurement, investigation into the performance of palm oil seed institutions across various dimensions and development of a strategic framework to strengthen inclusive seed institutions for sustainable oil palm farming. This strategy holds valuable potential for the development of oil palm in Indonesia, particularly in expediting the smallholders' replanting program.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2023-0811

Aiming at the problem of insufficient adaptability of robot motion planners under the diversity of end-effector constraints, this paper proposes Transformation Cross-sampling Framework (TC-Framework) that enables the planner to adapt to different end-effector constraints.

This work presents a standard constraint methodology for representing end-effector constraints as a collection of constraint primitives. The constraint primitives are merged sequentially into the planner, and a unified constraint input interface and constraint module are added to the standard sampling-based planner framework. This approach enables the realization of a generic planner framework that avoids the need to build separate planners for different end-effector constraints.

Simulation tests have demonstrated that the planner based on TC-framework can adapt to various end-effector constraints. Physical experiments have also confirmed that the framework can be used in real robotic systems to perform autonomous operational tasks. The framework’s strong compatibility with constraints allows for generalization to other tasks without modifying the scheduler, significantly reducing the difficulty of robot deployment in task-diverse scenarios.

This paper proposes a unified constraint method based on constraint primitives to enhance the sampling-based planner. The planner can now adapt to different end effector constraints by opening up the input interface for constraints. A series of simulation tests were conducted to evaluate the TC-Framework-based planner, which demonstrated its ability to adapt to various end-effector constraints. Tests on a physical experimental system show that the framework allows the robot to perform various operational tasks without requiring modifications to the planner. This enhances the value of robots for applications in fields with diverse tasks.