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This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification.

First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified.

Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models.

This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.

This paper aims to propose a forcefree control algorithm that is based on a dynamic model with full torque compensation is proposed to improve the compliance and flexibility of the direct teaching of cooperative robots.

Dynamic parameters identification is performed first to obtain an accurate dynamic model. The identification process is divided into two steps to reduce the complexity of trajectory simplification, and each step contains two excitation trajectories for higher identification precision. A nonlinear friction model that considers the angular displacement and angular velocity of joints is proposed as a secondary compensation for identification. A torque compensation algorithm that is based on the Hogan impedance model is proposed, and the torque obtained by an impedance equation is regarded as the command torque, which can be adjusted. The compensatory torque, including gravity torque, inertia torque, friction torque and Coriolis torque, is added to the compensation to improve the effect of forcefree control.

The model improves the total accuracy of the dynamic model by approximately 20% after compensation. Compared with the traditional method, the results prove that the forcefree control algorithm can effectively reduce the drag force approximately 50% for direct teaching and realize a flexible and smooth drag.

The entire algorithm is verified by the laboratory-developed six degrees-of-freedom cooperative robot, and it can be applied to other robots as well.

A full torque compensation is performed after parameters identification, and a more accurate forcefree control is guaranteed. This allows the cooperative robot to be dragged more smoothly without external sensors.

The purpose of this paper is to propose an improved differential evolution algorithm (DEA) suitable for motor’s model identification.

The mutation operation of the standard DEA is improved, and the adaptive coefficient is designed to adjust the optimization process.

The application of motor model identification shows that the proposed improved DEA is more robust, with higher modeling accuracy and efficiency, and is more suitable for motor identification modeling applications. Compared with the ultrasonic motor model established by using particle swarm algorithm, the model established in this paper has higher precision.

This paper explores an improved DEA suitable for motor identification modeling. The algorithm can not only obtain the optimal solution but also effectively reduce the iterative generations and time required in the process of optimization identification.

The management of stakeholder involvement within innovation projects is a task of growing importance. The purpose of this paper is to present a method for the first challenge in stakeholder management: the identification of those stakeholders to be involved in innovation projects.

Analysis of stakeholder literature leads to the conclusion that stakeholder identification is considered a problem of classification. Although the availability of a classification model is necessary, it is argued that for a classification model to be of use in identifying stakeholders, such a model needs to be supplemented with an identification procedure for identifying real world parties. Furthermore, a classification model should fit the context the stakeholders are identified for, in this case for innovation projects. These insights have led to the development of a classification model fitting the innovation context, and to the embedding of this model, along with a matching identification procedure, in an identification method.

A partial and integral evaluation of the method on four cases showed its efficacy in the managerial practice of identifying stakeholders within innovation projects.

The method as proposed in the paper can be used for identifying stakeholders in innovation projects. The method can be considered a first step in managing stakeholder involvement.

The purpose of this paper is to explore the use of a multidimensional model from the field of media reception for analyzing how a value statement in a Danish windmill company led to employee identification as well as organizational antecedents that influenced this identification. Further, the paper evaluates whether the proposed model can enhance understanding of the study of employee identification in a specific organizational situation and context.

The identification model extends a multidimensional model for media reception originally proposed by Schrøder in the field of media reception studies. The proposed model includes the following reception dimensions: comprehension, discrimination, implementation, motivation, and position.

This paper illustrates how employees from a Danish windmill company receive a value statement. A systematic application of the multidimensional model makes it possible to gain detailed insight into the active and complex process of employee identifications with organizational texts and how they may fluctuate in a specific context.

This analysis only focuses on the reception of a value statement. Future research could include the analysis of employee readings of other types of organizational texts.

The multidimensional identification model is an extension of a media reception model and is new in the field of organizational identification. The model offers a method for analyzing the complexity and multiplicity of employee readings of different types of organizational texts. This may be crucial for both researchers and managers as the model may help to uncover the antecedents that influence how employees receive organizational texts while taking the historical and situational context of the organization into consideration.

The purpose of this paper considers optimal input signal design for flutter model parameters identification, as input signal is the first step during the whole identification process. According to the constructed flutter stochastic model with observed noises, separable least squares identification and set membership identification are proposed to identify those unknown model parameters for statistical noise and unknown but bounded noise, respectively. The common trace operation with respect to the asymptotic variance matrix is minimized to solve the power spectral for the optimal input signal in the framework of statistical noise. Moreover, for the unknown bout bounded noise, the radius of information, corresponding to the established parameter uncertainty interval, is minimized to give the optimal input signal.

First, model identification for aircraft flutter is reviewed as one problem of parameter identification and this aircraft flutter model corresponds to one stochastic model, whose input signal and output are corrupted by external noises. Second, for aircraft flutter statistical model with statistical noise, separable least squares identification is proposed to identify the unknown model parameters, then the optimal input signal is designed to satisfy one given performance function. Third, for aircraft flutter model with unknown but bounded noise, set membership identification is proposed to solve the parameter set for each unknown model parameter. Then, the optimal input signal is designed by applying the idea of the radius of information with unknown but bounded noise.

This aircraft flutter model corresponds to one stochastic model, whose input signal and output are corrupted by external noises. Then identification strategy and optimal input signal design are studied for aircraft flutter model parameter identification with statistical noise and unknown but bounded noise, respectively.

To the best knowledge of the authors, this problem of the model parameter identification for aircraft flutter was proposed by their previous work, and they proposed many identification strategies to identify these model parameters. This paper proposes two novel identification strategies and opens a new subject about optimal input signal design for statistical noise and unknown noise, respectively.

The purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method.

This method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel.

The algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight.

This paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.

The purpose of this paper is to contribute to our understanding of the antecedents of organizational identification. Specifically, this paper aims to integrate two perspectives developed within the social identity domain, labeled “cognitive” and “relational,” by comparing and reconciling their relationship organizational identification.

This study uses a survey method and a structured questionnaire to collect data from people working in a call center. The hypotheses were tested on a sample of 743 employees by using structural equation models and Hayes’ (2017) bootstrapping procedure.

The results provide evidence for a mediational model in which the attractiveness of organizational images (cognitive representations) mediates the relationship between perceived justice (relational judgments) and organizational identification.

The data were obtained from a single source in a cross-sectional design, which may inflate common method variance. To address threats to validity, the authors employed several procedures, the results of which revealed that no parameters corresponding to the hypotheses changed in sign or significance, thus suggesting that the presence of method bias, if any, was nonconsequential.

Not only does perceived justice relate to the sense of belonging to an organization, but it also contributes to shaping the long-term cognitive representations of the company. In particular, both HR and line managers should be aware that in this respect, the interactional dimension of justice shows the strongest effect.

Building on and enlarging the scope of the extant literature, the findings contribute to our knowledge of how relational judgments shape cognitive images about the company, influencing, in turn, the individual–organization relationship.

This paper aims to build an accurate mathematical model which is necessary for control design and attitude estimation of a miniature unmanned rotorcraft and its subsequent conversion to an autonomous vehicle.

Frequency-domain system identification of a small-size flybar-less remote controlled helicopter is carried out based on the input–output data collected from flight tests of the instrumented vehicle. A complete six degrees of freedom quasi-steady dynamic model is derived for hover and cruise flight conditions.

The veracity of the developed model is ascertained by comparing the predicted model responses to the actual responses from flight experiments and from statistical measures. Dynamic stability analysis of the vehicle is carried out using eigenvalues and eigenvectors. The identified model represents the vehicle dynamics very well in the frequency range of interest.

The model needs to be augmented with additional terms to represent the high-frequency dynamics of the vehicle.

Control algorithms developed using the first principles model can be easily reconfigured using the identified model, because the model structure is not altered during identification.

This paper gives a practical solution for model identification and stability analysis of a small-scale flybar-less helicopter. The estimated model can be easily used in developing control algorithms.

The chapter considers parameter identification, estimation, and model diagnostics in medium scale DSGE models from a frequency domain perspective using the framework developed in Qu and Tkachenko (2012). The analysis uses Smets and Wouters (2007) as an illustrative example, motivated by the fact that it has become a workhorse model in the DSGE literature. For identification, in addition to checking parameter identifiability, we derive the non-identification curve to depict parameter values that yield observational equivalence, revealing which and how many parameters need to be fixed to achieve local identification. For estimation and inference, we contrast estimates obtained using the full spectrum with those using only the business cycle frequencies to find notably different parameter values and impulse response functions. A further comparison between the nonparametrically estimated and model implied spectra suggests that the business cycle based method delivers better estimates of the features that the model is intended to capture. Overall, the results suggest that the frequency domain based approach, in part due to its ability to handle subsets of frequencies, constitutes a flexible framework for studying medium scale DSGE models.