Search results

This research aims to present a novel cooperative control architecture designed specifically for roads with variations in height and curvature. The primary objective is to enhance the longitudinal and lateral tracking accuracy of the vehicle.

In addressing the challenges posed by time-varying road information and vehicle dynamics parameters, a combination of model predictive control (MPC) and active disturbance rejection control (ADRC) is employed in this study. A coupled controller based on the authors’ model was developed by utilizing the capabilities of MPC and ADRC. Emphasis is placed on the ramifications of road undulations and changes in curvature concerning control effectiveness. Recognizing these factors as disturbances, measures are taken to offset their influences within the system. Load transfer due to variations in road parameters has been considered and integrated into the design of the authors’ synergistic architecture.

The framework's efficacy is validated through hardware-in-the-loop simulation. Experimental results show that the integrated controller is more robust than conventional MPC and PID controllers. Consequently, the integrated controller improves the vehicle's driving stability and safety.

The proposed coupled control strategy notably enhances vehicle stability and reduces slip concerns. A tailored model is introduced integrating a control strategy based on MPC and ADRC which takes into account vertical and longitudinal force variations and allowing it to effectively cope with complex scenarios and multifaceted constraints problems.

This paper aims to address the longitudinal control problem for person-following robots (PFRs) for the implementation of this technology.

Nine representative car-following models are analyzed from PFRs application and the linear model and optimal velocity model/full velocity difference model are qualified and selected in the PFR control.

A lab PFR with the bar-laser-perception device is developed and tested in the field, and the results indicate that the proposed models perform well in normal person-following scenarios.

This study fills a gap in the research on PRFs longitudinal control and provides a useful and practical reference on PFRs longitudinal control for the related research.

To ensure the stability of the flying wing layout unmanned aerial vehicle (UAV) during flight, this paper uses the radial basis function neural network model to analyse the stability of the aforementioned aircraft.

This paper uses a linear sliding mode control algorithm to analyse the stability of the UAV's attitude in a level flight state. In addition, a wind-resistant control algorithm based on the estimation of wind disturbance with a radial basis function neural network is proposed. Through the modelling of the flying wing layout UAV, the stability characteristics of a sample UAV are analysed based on the simulation data. The stability characteristics of the sample UAV are analysed based on the simulation data.

The simulation results indicate that the UAV with a flying wing layout has a short fuselage, no tail with a horizontal stabilising surface and the aerodynamic focus of the fuselage and the centre of gravity is nearby, which is indicative of longitudinal static instability. In addition, the absence of a drogue tail and the reliance on ailerons and a swept-back angle for stability result in a lack of stability in the transverse direction, whereas the presence of stability in the transverse direction is observed.

The analysis of the stability characteristics of the sample aircraft provides the foundation for the subsequent establishment of the control model for the flying wing layout UAV.

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 paper aims to investigate the addition of airdrop capability to a commuter aircraft and its consequences on the reversible flight control system.

Airdrop was modeled to include its effect on aerodynamics and flight control system. A mathematical model was also developed for the reversible longitudinal flight control system of a regional commuter aircraft using the available geometry, mass property and kinematics. The model was incorporated into a general multi‐body dynamics code and validated using existing manufacturer's data which included recorded data from flight. The airdrop simulation results showed that the flight control system is affected in two steps. In the first step, the movement of the load required a forward force by the pilot. In this step, the elevator power was a key factor and had to be increased to allow the pilot to keep the aircraft in trim position during the airdrop. In the second step, a sudden forward shift of centre of gravity required an abrupt change in the direction of applied force. The maximum allowable force and control column movement had to be checked. In the case under study, they did not impose any difficulty.

The result showed that a special consideration had to be taken into account when an aircraft with reversible flight control system was to be used for airdrop mission.

This paper investigates the behaviour of a reversible flight control system during airdrop operation through analysis and simulation.

The purpose of this paper is to discuss why public sector reforms hybridize during implementation processes, consequences on accountability relations and practitioners’ and policymakers’ reactions to these changes.

The paper considers experiences from three initiatives related to the governance reform in the Norwegian hospital sector. Data were collected via interviews and document studies, and all three cases were longitudinal studies.

Unexpected consequences of reform initiatives and contextual changes are causing controls to hybridize and having profound effects on accountability relations. However, the gradually alignment of controls in a dynamic pattern of hybridization enables the balancing of conflicts in the chain of accountabilities. Hybrid controls are observed to emerge as stronger than the initial ideal control models. The longitudinal studies of control hybridization illuminate the sector’s survival in the long run, as they allow for adaptation to changes in contexts.

This work augments leaders’ understanding of how governmental strategies may follow diverse paths and yield results that diverge from intentions. Narrow accountability bases inhibit the government from implementing political decisions through agencies. Conversely, agents must relate to direct control from authorities. The predictability of agents’ decision space is reduced, and the control process becomes more ambiguous.

Through connecting what happens in agencies with accountabilities in the political level, it is possible to study the flexible nature of accountability relations and why controls hybridize. The paper underlines the need for longitudinal studies to describe complex patterns of reform initiatives.

The purpose of this paper is to design an innovative autonomous carrier landing system (ACLS) using novel robust adaptive preview control (RAPC) method, which can assure safe and successful autonomous carrier landing under the influence of airwake disturbance and irregular deck motion. To design a deck motion predictor based on an unscented Kalman filter (UKF), which predicts the touchdown point, very precisely.

An ACLS is comprising a UKF based deck motion predictor, a previewable glide path module and a control system. The previewable information is augmented with the system and then latitude and longitudinal controllers are designed based on the preview control scheme, in which the robust adaptive feedback and feedforward gain’s laws are obtained through Lyapunov stability theorem and linear matrix inequality approach, guarantying the closed-loop system’s asymptotic stability.

The autonomous carrier landing problem is solved by proposing robust ACLS, which is validated through numerical simulation in presence of sea disturbance and time-varying external disturbances.

The ACLS is designed considering the practical aspects of the application, presenting superior performance with extended robustness.

The novel RAPC, relative motion-based guidance system and deck motion compensation mechanism are developed and presented, never been implemented for autonomous carrier landing operations.

The purpose of this paper is to develop a nonlinear control system for flight trajectory control of flapping Micro Aerial Vehicles (MAVs), subjected to wind.

In the dynamic study and fabrication of the MAV, biomimetic principles are considered as the best inspiration for the MAV's flight as well as design constraints. The blade element theory, which is a two‐dimensional quasi‐steady state method, is modified to consider the effect of MAV's translational and rotational velocity. A quaternion‐based dynamic wrench method is then developed for the dynamic system.

The flapping flight dynamics is highly nonlinear and the system is under‐actuated, so any linear control strategy fails to meet any desired maneuver for trajectory tracking. In this study, a controller with quaternion‐based feedback linearization method is designed for the dynamical averaged system. It is shown that the original system is bonded to a stable limit cycle with desired amplitude and the controller inputs are bounded.

The effectiveness of a synthesized controller is proved for the cruse and the Cuban‐8 maneuver.

The authors' major contribution is developing feedback linearization quaternion‐based controller and deriving some essential mathematics for implementing quaternion model in the synthesis of controller. A piezoelectric‐actuated wing model is developed for the control system. Results of cursing and turning modes of the flight indicate the stability of the flight. Finally, an appropriate controller is designed for the Cuban‐8 maneuver so that the MAV would follow the trajectory with a bounded fluctuation.

When entrepreneurship scholars and policy makers turned their attention to entrepreneurial ventures during the COVID-19 pandemic (2019–2023), its full effects on entrepreneurial firms and systems presented radically challenging questions and unresolved puzzles. In this paper, the authors shed light on these questions and puzzles with a large-scale empirical examination of the pandemic's overall effects on entrepreneurs, entrepreneurial firms, entrepreneurial environments and responses with a view toward success and failure over time.

The authors adopt a broad exploratory approach and examine different perspectives to develop a deeper understanding of the COVID-19 pandemic's effects on entrepreneurs, entrepreneurial firms, entrepreneurial environments and responses especially regarding the success and failure of entrepreneurial ventures during the pandemic. Thus, the authors built a dataset with 10 survey waves from 2020 to 2021, with an average of 7,000 Brazilian entrepreneurial ventures (SMEs) in each wave of the survey. The authors used this data to examine their performance and survival.

The findings suggest that the increase of the COVID-19 virus contagion per se did not severely affect entrepreneurial ventures' performance and survival. However, the worsening of the COVID-19 pandemic did weaken entrepreneurial ventures' performance and survival. Moreover, the findings suggest that entrepreneur education has an inverted U-shaped relationship with entrepreneurial ventures performance. Indigenous, Brown and Black entrepreneurs experienced decreased entrepreneurial ventures survival compared to White entrepreneurs. While entrepreneurial ventures that adopted digital technologies and had access to loans increased their performance and survival during the COVID-19 pandemic, those who failed in these aspects experienced negative performance and survival effects. Thus, although the COVID-19 pandemic severely impacted many entrepreneurial ventures and even forced some to close, others survived and even prospered during the environmental shock.

The paper sheds light on a little understood topic: entrepreneurial venture success and failure in the COVID-19 pandemic.

The purpose of this paper is to propose a new algorithm for pendulum‐like oscillation control of an unmanned rotorcraft (UR) in a reconnaissance mission and improve the stabilizing performance of the UR's hover and stare.

The algorithm is based on linear‐quadratic regulator (LQR), of which the determinable parameters are optimized by the artificial bee colony (ABC) algorithm, a newly developed algorithm inspired by swarm intelligence and motivated by the intelligent behaviour of honey bees.

The proposed algorithm is tested in a UR simulation environment and achieves stabilization of the pendulum oscillation in less than 4s.

The presented algorithm and design strategy can be extended for other types of complex control missions where relative parameters must be optimized to get a better control performance.

The ABC optimized control system developed can be easily applied to practice and can safely stabilize the UR during hover and stare, which will considerably improve the stability of the UR and lead to better reconnaissance performance.

This research presents a new algorithm to control the pendulum‐like oscillation of URs, whose performance of hover and stare is a key issue when carrying out new challenging reconnaissance missions in urban warfare. Simulation results show that the presented algorithm performs better than traditional methods and the design process is simpler and easier.