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This paper aims to study a parameter tuning method for the active disturbance rejection control (ADRC) to improve the anti-interference ability and position tracking of the performance of the servo system, and to ensure the stability and accuracy of practical applications.

This study proposes a parameter self-tuning method for ADRC based on an improved glowworm swarm optimization algorithm. The algorithm is improved by using sine and cosine local optimization operators and an adaptive mutation strategy. The improved algorithm is then used for parameter tuning of the ADRC to improve the anti-interference ability of the control system and ensure the accuracy of the controller parameters.

The authors designed an optimization model based on MATLAB, selected examples of simulation and experimental research and compared it with the standard glowworm swarm optimization algorithm, particle swarm algorithm and artificial bee colony algorithm. The results show that the response time of using the improved glowworm swarm optimization algorithm to optimize the auto-disturbance rejection control is short; there is no overshoot; the tracking process is relatively stable; the anti-interference ability is strong; and the optimization effect is better.

The innovation of this study is to improve the glowworm swarm optimization algorithm, propose a sine and cosine, local optimization operator, expand the firefly search space and introduce a new adaptive mutation strategy to adaptively adjust the mutation probability based on the fitness value, improve the global search ability of the algorithm and use the improved algorithm to adjust the parameters of the active disturbance rejection controller.

The purpose of this paper is to investigate the impact of price adjustment speed on the stability of Bertrand–Nash equilibrium in the context of a dual-channel supply chain competition.

The paper considers a dual-channel supply chain comprising a manufacturer, a traditional retailer and an online retailer. A two-dimensional discrete dynamical system is used to examine the Bertrand competition between the retailers. The retailers are assumed to follow bounded rational expectations. Local stability of Bertrand–Nash equilibrium is investigated with respect to the price adjustment speed.

As the price adjustment speed increases, the stability of Bertrand–Nash equilibrium is lost, leading to complex chaotic dynamics. The results showed that chaotic dynamics deteriorates the profit of the retailers. The authors also found that the chaos can be controlled using an adaptive adjustment mechanism and the retailers enjoy higher profit when the chaos is controlled.

This study helps retail managers to choose an appropriate price adjustment speed to maximize profit.

The heterogeneity of the retailers is not considered in the studies involving dynamics of retailer competition. This paper contributes to the literature by considering the operational difference between a traditional retailer and an online retailer, i.e. price adjustment speed. In addition, the study establishes a link between price adjustment speed and profit.

This study aims to propose a consensus model that considers dynamic trust and the hesitation degree of the expert's evaluation, and the model can provide personalized adjustment advice to inconsistent experts.

The trust degree between experts will be affected by the decision-making environment or the behavior of other experts. Therefore, based on the psychological “similarity-attraction paradigm”, an adjustment method for the trust degree between experts is proposed. In addition, we proposed a method to measure the hesitation degree of the expert's evaluation under the multi-granular probabilistic linguistic environment. Based on the hesitation degree of evaluation and trust degree, a method for determining the importance degree of experts is proposed. In the feedback mechanism, we presented a personalized adjustment mechanism that can provide the personalized adjustment advice for inconsistent experts. The personalized adjustment advice is accepted readily by inconsistent experts and ensures that the collective consensus degree will increase after the adjustment.

The results show that the consensus model in this paper can solve the social network group decision-making problem, in which the trust degree among experts is dynamic changing. An illustrative example demonstrates the feasibility of the proposed model in this paper. Simulation experiments have confirmed the effectiveness of the model in promoting consensus.

The authors presented a novel dynamic trust consensus model based on the expert's hesitation degree and a personalized adjustment mechanism under the multi-granular probabilistic linguistic environment. The model can solve a variety of social network group decision-making problems.

This paper aims to provide a simple and flexible calibration method of parallel manipulators for improving the position accuracy only using partial pose information.

The overall idea of this method is to use BP neural network to fit the relationship between calibration parameters and measurement parameters and then adjust calibration parameters according to measurements.

The calibration method significantly improves the position accuracy of the six-axis parallel manipulator. Simulation shows that the accuracy can be improved by increasing the number of positions consisted of samples to train BP neural network, and when the position number is increased, the descent velocity of fitting error is decreased.

The method is general for various parallel mechanisms and simple for measurement process. It can be applied to the calibration of various mechanisms without analyzing the mathematical relationship between measurements and calibration parameters. The measurement parameters can be flexibly selected to simplify measurement process, which saves calibration cost and time.

This study presents the improvements of the multicomponent Armstrong–Frederick model with multiplier (MAFM) performance through a numerical optimisation methodology available in a commercial software. Moreover, this study explores the application of a multiobjective optimisation technique for the determination of the parameters of the constitutive models using uniaxial experimental data gathered from aluminium alloy 7075-T6 specimens. This approach aims to improve the overall accuracy of stress–strain response, for not only symmetric strain-controlled loading but also asymmetrically strain- and stress-controlled loading.

Experimental data from stress- and strain-controlled symmetric and asymmetric cyclic loadings have been used for this purpose. The analysis of the influence of the parameters on simulation accuracy has led to an adjustment scheme that can be used for focused optimisation of the MAFM model performance. The method was successfully used to provide a better understanding of the influence of each model parameter on the overall simulation accuracy.

The optimisation identified an important issue associated with competing ratcheting and mean stress relaxation objectives, highlighting the issues with arriving at a parameter set that can simulate ratcheting and mean stress relaxation for load cases not reaching at complete relaxation.

The study uses a strain-life fatigue application to demonstrate the importance of incorporating a technique such as the presented multiobjective optimisation method to arrive at robust parameters capable of accurately simulating a variety of transient cyclic phenomena.

The proposed methodology improves the accuracy of cyclic plasticity phenomena and strain-life fatigue simulations for engineering applications. This study is considered a valuable contribution for the engineering community, as it can act as starting point for further exploration of the benefits that can be obtained through material parameter optimisation methodologies for models of the MAFM class.

Proposes a cost model for parameter design in quality engineering. In this model the total cost is minimized to obtain the best design. The total cost includes quality loss, cost for input characteristic, and cost for objective characteristic adjustment. Presents the best mean level of an input characteristic for the parameter design, which has a quadratic relationship between an input and an objective characteristic.

Although wave soldering is a long established technique, the touch‐up percentage is still unacceptably high. This leads to high touch‐up costs and too low product quality. One cause lies in the fact that the complexity of the boards is increasing all the time (greater component density, greater diversity, more leads and smaller pitches). The main reason, however, lies in the lack of a fundamental process technology background. This becomes even more serious now that soldering processes are becoming more critical. The touch‐up percentage can and must be lowered through accurate process adjustment and control. A further improvement in process adjustment and control will give a considerable improvement in soldering quality in the short term. In this way, the actions are successful, although benefits can be achieved only if the improvements are consolidated. Further improvements can be achieved through systematic application of design rules.

This study attempts to provide a systematic theoretical analysis of the portfolio selection approach to the determination of inter‐regional and international capital flows, and to identify the implications of this analysis for the appropriate specification of short‐run econometric models of the foreign exchange market.