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The purpose of this paper is to solve the challenging problem of automatic carrier landing with the presence of environmental disturbances. Therefore, a global fast terminal sliding mode control (GFTSMC) method is proposed for automatic carrier landing system (ACLS) to achieve safe carrier landing control.

First, the framework of ACLS is established, which includes flight glide path model, guidance model, approach power compensation system and flight controller model. Subsequently, the carrier deck motion model and carrier air-wake model are presented to simulate the environmental disturbances. Then, the detailed design steps of GFTSMC are provided. The stability analysis of the controller is proved by Lyapunov theorems and LaSalle’s invariance principle. Furthermore, the arrival time analysis is carried out, which proves the controller has fixed time convergence ability.

The numerical simulations are conducted. The simulation results reveal that the proposed method can guarantee a finite convergence time and safe carrier landing under various conditions. And the superiority of the proposed method is further demonstrated by comparative simulations and Monte Carlo tests.

The GFTSMC method proposed in this paper can achieve precise and safe carrier landing with environmental disturbances, which has important referential significance to the improvement of ACLS controller designs.

Diffusion studies investigate the propagation of behavior, attitudes, or beliefs across a networked population. Some behavior is binary, e.g., whether or not to install solar panels, while other behavior is continuous, e.g., wastefulness with plastic. Similarly, attitudes and beliefs often allow nuance, but can become practically binary in polarized environments. We argue that this property of behavior and attitudes – whether they are binary or continuous – should critically affect whether a population becomes homogenous in its adoption of that behavior. Models show that only continuous behavior converges across a network. Specifically, binary behavior allows local convergence, as multiple states can be local majorities. Continuous behavior becomes uniform across the network through a logic of communicating vessels. We present a model comparing the diffusion of both types of behavior and report on a laboratory experiment that tests it. In the model, actors have to distribute an investment over two options, while a majority receives information that points to the optimal option and a minority receives misguided information that points toward the other option. We predict that when adjacent persons receive misguided information this can hinder convergence toward optimal investment behavior in small networked groups, especially when subjects cannot split their investment, i.e., binary choice. Results falsify our theoretical predictions: Although investment decisions are significantly negatively affected by local majorities only in the binary condition, this difference with the continuous condition is not itself significant. Binary and continuous behavior therefore achieve comparable incidences of optimal investment in the experiment. The failure of the theoretical predictions appears due to a substantial level of error in decision-making, which prevents local majorities from locking in on a suboptimal behavior.

This paper aims to explore the strategic and evolving role of human resource management (HRM) directors within the context of underdeveloped institutional arrangements. The study focuses on India and conducts a comparative analysis of the roles of HRM directors in both multinational enterprises (MNEs) and domestic firms.

Survey-based data from the HRM directors of 252 enterprises were gathered for the comparative analysis, including both multinational and domestic enterprises.

HRM directors in MNEs lack the proficiency required to effectively fulfil their strategic role. In addition, there has been a notable shift in the responsibilities of HRM directors in MNEs, with increased emphasis on labour movements and trade union negotiations, as opposed to traditional human resource (HR) activities. This shift suggests that the role of HRM in MNEs operating in India has been influenced by local isomorphic forces, rather than following a “pendulum swing” between home and host country institutional pressures. The prevalence of informality in the Indian institutional arrangements may act as a strong counterforce to integrating the strategic agency of MNEs' home country HRM directors into the organizational structure. Despite facing resistance from the local institutional context, HRM directors in MNEs are responding with a pushback, prioritizing labour movements and trade union negotiations over core HRM activities.

The study highlights the broader implications for theory and practice, shedding light on the challenges faced by HRM directors in navigating incoherent institutional arrangements. It emphasizes the need for a deeper understanding of local forces in shaping HRM practices within multinational settings.

We contribute to the comparative HRM literature by elaborating on power struggles that HRM directors face amid the dichotomies of formal power and authority that are encoded in the organizational structure versus culturally contingent power that can be accrued from engaging in informality. We also highlight their engagement in prolonged institutional mediation and change, which serves as a compensatory mechanism for the institutional shortfalls they encounter within the context of emerging markets.

To provide high torques needed to move a robot’s links, electric actuators are followed by a transmission system with a high transmission rate. For instance, gear ratios of 100:1 are often used in the joints of a robotic manipulator. This results into an actuator with large mechanical impedance (also known as nonback-drivable actuator). This in turn generates high contact forces when collision of the robotic mechanism occur and can cause humans’ injury. Another disadvantage of electric actuators is that they can exhibit overheating when constant torques have to be provided. Comparing to electric actuators, pneumatic actuators have promising properties for robotic applications, due to their low weight, simple mechanical design, low cost and good power-to-weight ratio. Electropneumatically actuated robots usually have better friction properties. Moreover, because of low mechanical impedance, pneumatic robots can provide moderate interaction forces which is important for robotic surgery and rehabilitation tasks. Pneumatic actuators are also well suited for exoskeleton robots. Actuation in exoskeletons should have a fast and accurate response. While electric motors come against high mechanical impedance and the risk of causing injuries, pneumatic actuators exhibit forces and torques which stay within moderate variation ranges. Besides, unlike direct current electric motors, pneumatic actuators have an improved weight-to-power ratio and avoid overheating problems.

The aim of this paper is to analyze a nonlinear optimal control method for electropneumatically actuated robots. A two-link robotic exoskeleton with electropneumatic actuators is considered as a case study. The associated nonlinear and multivariable state-space model is formulated and its differential flatness properties are proven. The dynamic model of the electropneumatic robot is linearized at each sampling instance with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. Within each sampling period, the time-varying linearization point is defined by the present value of the robot’s state vector and by the last sampled value of the control inputs vector. An H-infinity controller is designed for the linearized model of the robot aiming at solving the related optimal control problem under model uncertainties and external perturbations. An algebraic Riccati equation is solved at each time-step of the control method to obtain the stabilizing feedback gains of the H-infinity controller. Through Lyapunov stability analysis, it is proven that the robot’s control scheme satisfies the H-infinity tracking performance conditions which indicate the robustness properties of the control method. Moreover, global asymptotic stability is proven for the control loop. The method achieves fast convergence of the robot’s state variables to the associated reference trajectories, and despite strong nonlinearities in the robot’s dynamics, it keeps moderate the variations of the control inputs.

In this paper, a novel solution has been proposed for the nonlinear optimal control problem of robotic exoskeletons with electropneumatic actuators. As a case study, the dynamic model of a two-link lower-limb robotic exoskeleton with electropneumatic actuators has been considered. The dynamic model of this robotic system undergoes first approximate linearization at each iteration of the control algorithm around a temporary operating point. Within each sampling period, this linearization point is defined by the present value of the robot’s state vector and by the last sampled value of the control inputs vector. The linearization process relies on first-order Taylor series expansion and on the computation of the associated Jacobian matrices. The modeling error which is due to the truncation of higher-order terms from the Taylor series is considered to be a perturbation which is asymptotically compensated by the robustness of the control algorithm. To stabilize the dynamics of the electropneumatically actuated robot and to achieve precise tracking of reference setpoints, an H-infinity (optimal) feedback controller is designed. Actually, the proposed H-infinity controller for the model of the two-link electropneumatically actuated exoskeleton achieves the solution of the associated optimal control problem under model uncertainty and external disturbances. This controller implements a min-max differential game taking place between: (i) the control inputs which try to minimize a cost function which comprises a quadratic term of the state vector’s tracking error and (ii) the model uncertainty and perturbation inputs which try to maximize this cost function. To select the stabilizing feedback gains of this H-infinity controller, an algebraic Riccati equation is being repetitively solved at each time-step of the control method. The global stability properties of the H-infinity control scheme are proven through Lyapunov analysis.

Pneumatic actuators are characterized by high nonlinearities which are due to air compressibility, thermodynamics and valves behavior and thus pneumatic robots require elaborated nonlinear control schemes to ensure their fast and precise positioning. Among the control methods which have been applied to pneumatic robots, one can distinguish differential geometric approaches (Lie algebra-based control, differential flatness theory-based control, nonlinear model predictive control [NMPC], sliding-mode control, backstepping control and multiple models-based fuzzy control). Treating nonlinearities and fault tolerance issues in the control problem of robotic manipulators with electropneumatic actuators has been a nontrivial task.

The novelty of the proposed control method is outlined as follows: preceding results on the use of H-infinity control to nonlinear dynamical systems were limited to the case of affine-in-the-input systems with drift-only dynamics. These results considered that the control inputs gain matrix is not dependent on the values of the system’s state vector. Moreover, in these approaches the linearization was performed around points of the desirable trajectory, whereas in the present paper’s control method the linearization points are related with the value of the state vector at each sampling instance as well as with the last sampled value of the control inputs vector. The Riccati equation which has been proposed for computing the feedback gains of the controller is novel, so is the presented global stability proof through Lyapunov analysis. This paper’s scientific contribution is summarized as follows: (i) the presented nonlinear optimal control method has improved or equally satisfactory performance when compared against other nonlinear control schemes that one can consider for the dynamic model of robots with electropneumatic actuators (such as Lie algebra-based control, differential flatness theory-based control, nonlinear model-based predictive control, sliding-mode control and backstepping control), (ii) it achieves fast and accurate tracking of all reference setpoints, (iii) despite strong nonlinearities in the dynamic model of the robot, it keeps moderate the variations of the control inputs and (iv) unlike the aforementioned alternative control approaches, this paper’s method is the only one that achieves solution of the optimal control problem for electropneumatic robots.

The use of electropneumatic actuation in robots exhibits certain advantages. These can be the improved weight-to-power ratio, the lower mechanical impedance and the avoidance of overheating. At the same time, precise positioning and accurate execution of tasks by electropneumatic robots requires the application of elaborated nonlinear control methods. In this paper, a new nonlinear optimal control method has been developed for electropneumatically actuated robots and has been specifically applied to the dynamic model of a two-link robotic exoskeleton. The benefit from using this paper’s results in industrial and biomedical applications is apparent.

A comparison of the proposed nonlinear optimal (H-infinity) control method against other linear and nonlinear control schemes for electropneumatically actuated robots shows the following: (1) Unlike global linearization-based control approaches, such as Lie algebra-based control and differential flatness theory-based control, the optimal control approach does not rely on complicated transformations (diffeomorphisms) of the system’s state variables. Besides, the computed control inputs are applied directly on the initial nonlinear model of the electropneumatic robot and not on its linearized equivalent. The inverse transformations which are met in global linearization-based control are avoided and consequently one does not come against the related singularity problems. (2) Unlike model predictive control (MPC) and NMPC, the proposed control method is of proven global stability. It is known that MPC is a linear control approach that if applied to the nonlinear dynamics of the electropneumatic robot, the stability of the control loop will be lost. Besides, in NMPC the convergence of its iterative search for an optimum depends on initialization and parameter values selection and consequently the global stability of this control method cannot be always assured. (3) Unlike sliding-mode control and backstepping control, the proposed optimal control method does not require the state-space description of the system to be found in a specific form. About sliding-mode control, it is known that when the controlled system is not found in the input-output linearized form the definition of the sliding surface can be an intuitive procedure. About backstepping control, it is known that it cannot be directly applied to a dynamical system if the related state-space model is not found in the triangular (backstepping integral) form. (4) Unlike PID control, the proposed nonlinear optimal control method is of proven global stability, the selection of the controller’s parameters does not rely on a heuristic tuning procedure, and the stability of the control loop is assured in the case of changes of operating points. (5) Unlike multiple local models-based control, the nonlinear optimal control method uses only one linearization point and needs the solution of only one Riccati equation so as to compute the stabilizing feedback gains of the controller. Consequently, in terms of computation load the proposed control method for the electropneumatic actuator’s dynamics is much more efficient.

This paper aims to explore the evolutionary dynamics of innovation ecosystems in the food industry by adopting both open innovation and convergence approaches to derive practical policy implications to develop impactful innovation ecosystems to promote food production sustainably.

Starting from the cutting-edge case of Foodvalley in the Netherlands, the study adopts a backcasting approach to reach a future vision of food ecosystems from a sustainable food production perspective. The authors set the backcasting analysis in four steps: (1) description of the present and trends analysis, (2) selection of trustworthy criteria and goals, (3) development of future images and (4) analysis of how to reach the images.

The trends analysis has identified three existing innovation fields – protein shift, circular agrifood and food and health – and two strategic directions – convergence and localness decrease. The study reports how a long-term commitment may lead the valley toward a best future scenario.

The study suggests that policymakers and stakeholders can promote innovation strategies in sustainable food production ecosystems by encouraging collaboration between different sectors, reducing regulatory barriers, attracting innovative actors, and investing in education and training programs. To achieve measurable environmental and social impact outcomes, policies should promote entrepreneurship and create an enabling environment that encourages innovation and risk-taking.

The originality of this paper concerns the backcasting perspective applied to the analysis of the food ecosystem. This approach facilitates the identification of a path direction for successfully managing open innovation and industry convergence toward a desirable future of sustainable food production.

For privacy protection, federated learning based on data separation allows machine learning models to be trained on remote devices or in isolated data devices. However, due to the limited resources such as bandwidth and power of local devices, communication in federated learning can be much slower than in local computing. This study aims to improve communication efficiency by reducing the number of communication rounds and the size of information transmitted in each round.

This paper allows each user node to perform multiple local trainings, then upload the local model parameters to a central server. The central server updates the global model parameters by weighted averaging the parameter information. Based on this aggregation, user nodes first cluster the parameter information to be uploaded and then replace each value with the mean value of its cluster. Considering the asymmetry of the federated learning framework, adaptively select the optimal number of clusters required to compress the model information.

While maintaining the loss convergence rate similar to that of federated averaging, the test accuracy did not decrease significantly.

By compressing uplink traffic, the work can improve communication efficiency on dynamic networks with limited resources.

The purpose of this study is to propose a framework for the convergence of maturity model and education and evaluation in accounting.

The present research was conducted in two phases. In the first phase, to determine the indicators of convergence of the maturity model and education and evaluation in accounting, a Meta-Synthesis method was used. The conceptual model includes two dimensions of “Teaching and learning processes” and “Evaluation methods"; five levels of initial, repeatable, defined, managed and optimized; and a total number of 35 indicators. In the second phase, a questionnaire was developed, and academics as accounting faculty members in Iranian public universities were employed to fill out the questionnaire electronically and present a final framework. Having received the questionnaires, 66 questionnaires were analyzed statistically.

The results showed that the two dimensions of “Teaching and learning processes” and “Evaluation methods” considering initial, repeatable, defined, managed and optimized levels include 35 indicators, which form a framework for the convergence of maturity model and education and evaluation in accounting. The results show that both dimensions have positive and significant regression path coefficients in the convergence model. Moreover, the dimension of teaching and learning processes has the highest regression path coefficient indicating a greater impact on the convergence model. Besides, all five levels have positive and significant regression path coefficients with dimensions. Finally, in this study, all indicators were prioritized according to five levels.

Due to the success of maturity models and the urgent developments that require transformative improvements in accounting education, maturity models can respond to the challenges associated with education and learning in accounting. Thus, conceiving an image of the convergence of maturity model, education and evaluation in accounting seems imperative which has been scarcely investigated previously.

Motivated by the growing and urgent demands for a unified set of internationally accepted, and high-quality environmental, social and governance (hereafter ESG) disclosure standards, this exploratory study aims to propose a roadmap for setting out the proper technical groundwork for global ESG disclosure standards.

An exploratory study is conducted to gain initial understanding and insights into establishing a worldwide set of standards for reporting on sustainability, as this topic has not been extensively studied. This study examines the viewpoints of various stakeholders, including sustainability practitioners, academics and organizations focused on ESG issues, to generate knowledge that is more solid than knowledge produced when one group of stakeholders work alone.

The results revealed that there is an ongoing and incompatible debate regarding several conceptual and practical challenges for setting a unified set of ESG disclosure standards.

The study results provide multidimensional insights for regulatory parties and standard-setters to develop a high-quality package of global ESG reporting standards. This, in turn, enables different groups of stakeholders to understand the firm’s impact on the environment, society and economy.

Research into this timely and relevant global issue is considered an appealing area of study and deserves significant attention. Thereby, working on this topic merits remarkable attention. Furthermore, this exploratory article provides valuable and informative suggestions for creating a unified and high-quality set of internationally accepted sustainability reporting standards.

Robotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.

This study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.

Experiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.

A new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.