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The purpose of this study is to verify whether digital transformation strategy (DTS) could improve the organizational performance and provide a comprehensive analysis for enterprises on the necessity of implementing digital transformation in the context of China and draw on the perspectives of “Skewed conflict,” “minority dissent theory” and “too-much-of-a-good-thing.” This study investigates the curvilinear moderating role of cognitive conflict between DTS and performance.

An empirical investigation was used to collect a large sample data of Chinese enterprises’ digital transformation. A multiple linear regression analysis with SPSS was used to test the proposed hypotheses such as the inverted U-shaped moderating effect of the cognitive conflict.

In the Chinese context, DTS has a positive relationship on the short- and long-term financial performance. Moreover, this relationship was moderated by cognitive conflict such that the relationship between DTS and short-term financial performance could be further enhanced under the moderate cognitive conflict; however, the relationship between DTS and long-term financial performance was considerably influenced for higher cognitive conflict.

Based on the co-evolution of the information technology/information system (IT/IS) and business strategy, this study clarified the relationships among DTS, digital strategy and business and information technology strategies. By focusing on corporate strategy, this study further examined the effect of digital transformation on both short- and long-term financial performance. To further reveal the micro-psychological mechanisms underlying the effect of DTS on organizational performance, this study confirmed the inverted U-shaped moderating effect of the top management team’s cognitive conflict. Therefore, this research provides a new theoretical perspective for future research in the field of IT/IS, DTS and digital strategy.

Mobile robots with independent wheel control face challenges in steering precision, motion stability and robustness across various wheel and steering system types. This paper aims to propose a coordinated torque distribution control approach that compensates for tracking deviations using the longitudinal moment generated by active steering.

Building upon a two-degree-of-freedom robot model, an adaptive robust controller is used to compute the total longitudinal moment, while the robot actuator is regulated based on the difference between autonomous steering and the longitudinal moment. An adaptive robust control scheme is developed to achieve accurate and stable generation of the desired total moment value. Furthermore, quadratic programming is used for torque allocation, optimizing maneuverability and tracking precision by considering the robot’s dynamic model, tire load rate and maximum motor torque output.

Comparative evaluations with autonomous steering Ackermann speed control and the average torque method validate the superior performance of the proposed control strategy, demonstrating improved tracking accuracy and robot stability under diverse driving conditions.

When designing adaptive algorithms, using models with higher degrees of freedom can enhance accuracy. Furthermore, incorporating additional objective functions in moment distribution can be explored to enhance adaptability, particularly in extreme environments.

By combining this method with the path-tracking algorithm, the robot’s structural path-tracking capabilities and ability to navigate a variety of difficult terrains can be optimized and improved.

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.