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A non-iterative analytical approach is investigated to plan the safe wire tension distribution along with the cables in the redundant cable-driven parallel robots. The proposed algorithm considers not only tracking the desired trajectory but also protecting the system against possible failures. This method is used to optimize the non-negative wire tensions through the cables which are constrained based on the workspace conditions. It also maintains both actuators' torque and cables' tensile strength boundary limits. The pseudo-inverse problem solution leads to an n-dimensional convex problem, which is related to the robot degrees of redundancy. In this paper, a comprehensive solution is presented for a 1–3 degree(s) of redundancy in wire-actuated robots. To evaluate the effectiveness of this method, it is verified through an experimental study on the RoboCab cable robot in the infinity trajectory tracking task. As a matter of comparison, some standard methods like Active-set and sequential quadratic programming are also presented and the average elapsed time for each method is compared to the proposed algorithm. [ABSTRACT FROM AUTHOR]

Background and Objectives: One of the problems in juice membrane clarification is the accumulation and deposition of rejected compounds on membrane surfaces or inside its pores which results in a membrane fouling. Materials and Methods: Several parameters can have influence on fouling in one hand and prediction of juice permeates flux during the membrane processing is of importance in industrial applications on the other hand. Therefore, providing a model able to predict the permeate flux having the value of effective input parameters seems to be useful. In this regard, several artificial methods can be used. In contrast, the Fuzzy Inference System (FIS) has been proposed as a reliable and appropriate machine learning method to predict the output parameter with acceptable performance. In this study, a FIS will be used to model the permeate flux based on five input variables: the transmitted membrane pressure, feed flow rate, processing time, membrane pore size, and membrane type. For this purpose, a fuzzy system is trained using the laboratory data and then, appropriate membership functions for the input and output variables and fuzzy rules are extracted for the proper prediction of permeate flux. Results: Results were shown that the normalized mean squared errors for the prediction of permeate flux in the membrane processing are 0.0055 and 0.0081 for the Mixed Cellulose Ester (MCE) and the Polyvinylidene Flouride (PVDF) membranes, respectively. Conclusions: In total, the fuzzy inference model which is presented in this study has been able to predict the pomegranate juice permeate flux with an acceptable error compared with the laboratory data. [ABSTRACT FROM AUTHOR]