Robotic systems require high accuracy in manipulating objects. Positioning errors are influenced by geometric tolerances and various sources. This paper introduces a new technique based on the interior-point algorithm optimisation method to allocate tolerances to the geometric parameters of a robot. This method consists of three steps. First, a method for modelling the kinematic problem as well as the geometric errors must be used. The Denavit–Hartenberg rule is the most suitable method for this modelling case. Then, a mathematical model for tolerance allocation is developed and used as a nonlinear multivariable optimisation problem. Finally, the “interior-point” algorithm is used to solve this optimisation problem. The accuracy and efficiency of the proposed method, in determining the tolerance allocations for a Delta parallel robot, is illustrated via calculation and simulation results. The values of the dimensional tolerances found are optimal. As a result, these values always keep the accuracy less than or equal to the imposed value.

This research focuses on employing Recurrent Neural Networks (RNN) to prognosis a wind turbine operation’s health from collected vibration time series data, by using several memory cell variations, including Long Short Time Memory (LSTM), Bilateral LSTM (BiLSTM), and Gated Recurrent Unit (GRU),...

This paper presents an innovative solution to address agricultural irrigation needs through a hybrid renewable energy system (HRES) that was specifically designed for a farm located in the Skikda region of Algeria. This system is tailored to irrigate 830 fruit trees spread across 3 hectares with a total perimeter of 770 m. The proposed approach integrates two main renewable energy sources (while eliminating the use of traditional batteries for electrical energy storage): solar and wind. Instead, a large water reservoir is employed as an energy storage medium in the form of potential energy. Utilizing gravity, this reservoir directly powers the irrigation system for the fruit trees, thereby reducing the costs and environmental impacts associated with conventional batteries. This innovative design not only enhances sustainability, but also improves the system’s energy efficiency. To ensure precise and customized sizing of the system for the irrigation area, a detailed mathematical modeling of the key system components (solar panels, wind turbines, and reservoir) was conducted. This modeling identifies the critical design variables required to meet technical specifications and irrigation needs. A multi-objective optimization approach was then developed to determine the optimal configuration of the HRES, and this was achieved by considering both technical and economic constraints. The optimization algorithm used was tailored to the formulated problem, ensuring reliable and applicable results. The robustness of the optimization approach was shown by the precise match between energy production (24 kWh at 16,119.40 $) and the minimum demand. This alignment prevents over- or under-designing the system, which increases costs and reduces energy use. The findings highlight the relevance and effectiveness of the proposed methodology, demonstrating its practical utility and significant potential for generalization and adaptation to different agricultural zones with varying conditions. This work paves the way for sustainable and innovative solutions for agricultural irrigation, particularly in remote areas or regions lacking traditional energy infrastructure.