An efficient finite element formulation of dynamics for a flexible robot with different type of joints

If two adjacent links of a flexible robot are connected via a revolute joint or a fixed prismatic joint, the relative motion of the next link will depend on both the joint motion and the elastic displacement of the distal end of the previous link. However, if the two adjacent links are connected via a sliding prismatic joint, the relative motion of the next link will depend additionally on the elastic deformation distributed along the previous link. Therefore, formulation of the motion equations for a multi-link flexible robot consisting of the revolute joints, the fixed prismatic joints and the sliding prismatic joints is challenging. In this study, the finite element kinematic and dynamic formulation was successfully developed and validated for the flexible robot, in which a transformation matrix is proposed to describe the kinematics of both the joint motion and the link deformation. Additionally, a new recursive formulation of the dynamic equations is introduced. As compared with the previous methods, the time complexity of the formulation is reduced by O(2η), where η is the number of finite elements on all links. The numerical examples and experiments were implemented to validate the proposed kinematic and dynamic modelling method

New Transfer Learning Approach Based on a CNN for Fault Diagnosis

Induction motors operate in difficult environments in the industry. Monitoring the performance of motors in such circumstances is significant, which can provide a reliable operation system. This paper intends to develop a new model for fault diagnosis based on the knowledge of transfer learning using the ImageNet dataset. The development of this framework provides a novel technique for the diagnosis of single and multiple induction motor faults. A transfer learning model based on a VGG-19 convolutional neural network (CNN) was implemented, which provided a quick and fast training process with higher accuracy. Thermal images with different induction motor conditions were captured with the help of an FLIR camera and applied as inputs to investigate the proposed model. The implementation of this task involved the use of a VGG-19 CNN-based pre-trained network, which provides autonomous features learning based on minimum human intervention. Next, a dense-connected classifier was applied to predict the true class. The experimental results confirmed the robustness and reliability of the developed technique, which was successfully able to classify the induction motor faults, achieving a classification accuracy of 99.8%. The use of a VGG-19 network allowed the attributes to be automatically extracted and associated with the decision-making part. Furthermore, this model was further compared with other applications based on related topics; it successfully proved its superiority and robustness

Honey Bees Inspired Optimization Method: The Bees Algorithm

Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem

Genetic and particle swarm optimization algorithms based direct torque control for torque ripple attenuation of induction motor

This paper introduces analysis, control, and comparison of two benchmarking optimization approaches called Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Direct Torque Control (DTC) of a three-phase Induction Motor (IM). This study aims to determine the most efficient and robust of the two different metaheuristic optimization techniques including PID-PSO and PID-GA for DTC of IM. The purpose of the proposed control technique that has been presented is to get over the most significant drawback of DTC, which is a high level of torque output. The issue of torque ripples needs to be reduced to a significant amount using the two proposed control methods PSO-DTC and GA-DTC. As a result, PSO-DTC is the most applicable scheme. The proposed PID-PSO of DTC provided an excellent work performance for IM system drive. The comparison results of the suggested control methods showed a significant improvement of the control system compared to the classical DTC. The result is a high fidelity estimate of electromagnetic torque and speed for computation of motor parameters. A high ripple suppression capability was achieved by the PSO-DTC, which was measured at 22.5 % out of 47.28 % for the traditional approach. Both proposed control schemes were implemented using MATLAB/Simulink platform

Design and development of a robust vision-based tactile sensor

For robots to perform advanced manipulation of objects, touch is a critical source of information, and a high-quality tactile sensor is essential. Image-based optical tactile sensors, and its inheritances, which have soft touch interfaces, can provide high-resolution tactile images of the contact geometry, contact pressure, and slip conditions. However, due to the lack of robustness provided by the current tactile sensors, the ability to grasp hard or sharp objects is minimal. In this work, we propose an image-based optical tactile sensor and overcome the above limitation of poor robustness by introducing a latex layer on the touch interface. We use a combination of silicone elastomer covered with a latex material and an acrylic sheet to support the silicone elastomer. A camera placed at the bottom of the sensor housing captures the deformation of the elastomer surface illuminated by an inner light. To evaluate the performance, we carried out a series of experiments. First, we evaluated the mechanical characteristics of the silicone elastomer with three types of coating, namely latex membrane, metallic coating, and no coating. The proposed latex membrane clearly outperformed the other two in terms of robustness. Second, we carried out the force-displacement experiments quantitatively to further study the sensitivity and robustness. Last, we validated the sensor performance in terms of its spatial resolution by applying the VGG-19 neural network for classifying touch patterns captured by the sensor. Overall, the proposed sensor achieved the desired robustness, sensitivity, and spatial resolution performance

The Bees Algorithm Tuned Sliding Mode Control for Load Frequency Control in Two-Area Power System

This paper proposes a design of Sliding Mode Control (SMC) for Load Frequency Control (LFC) in a two-area electrical power system. The mathematical model design of the SMC is derived based on the parameters of the investigated system. In order to achieve the optimal use of the proposed controller, an optimisation tool called the Bees Algorithm (BA) is suggested in this work to tune the parameters of the SMC. The dynamic performance of the power system with SMC employed for LFC is studied by applying a load disturbance of 0.2 pu in area one. To validate the supremacy of the proposed controller, the results are compared with those of recently published works based on Fuzzy Logic Control (FLC) tuned by Teaching–Learning-Based Optimisation (TLBO) algorithm and the traditional PID optimised by Lozi map-based Chaotic Optimisation Algorithm (LCOA). Furthermore, the robustness of SMC-based BA is examined against parametric uncertainties of the electrical power system by simultaneous changes in certain parameters of the testbed system with 40% of their nominal values. Simulation results prove the superiority and the robustness of the proposed SMC as an LFC system for the investigated power system

Different fuzzy control configurations tuned by the Bees Algorithm for LFC of two-area power system

This study develops and implements a design of the Fuzzy Proportional Integral Derivative with filtered derivative mode (Fuzzy PIDF) for Load Frequency Control (LFC) of a two-area interconnected power system. To attain the optimal values of the proposed structure’s parameters which guarantee the best possible performance, the Bees Algorithm (BA) and other optimisation tools are used to accomplish this task. A Step Load Perturbation (SLP) of 0.2 pu is applied in area one to examine the dynamic performance of the system with the proposed controller employed as the LFC system. The supremacy of Fuzzy PIDF is proven by comparing the results with those of previous studies for the same power system. As the designed controller is required to provide reliable performance, this study is further extended to propose three different fuzzy control configurations that offer higher reliability, namely Fuzzy Cascade PI − PD, Fuzzy PI plus Fuzzy PD, and Fuzzy (PI + PD), optimized by the BA for the LFC for the same dual-area power system. Moreover, an extensive examination of the robustness of these structures towards the parametric uncertainties of the investigated power system, considering thirteen cases, is carried out. The simulation results indicate that the contribution of the BA tuned the proposed fuzzy control structures in alleviating the overshoot, undershoot, and the settling time of the frequency in both areas and the tie-line power oscillations. Based on the obtained results, it is revealed that the lowest drop of the frequency in area one is −0.0414 Hz, which is achieved by the proposed Fuzzy PIDF tuned by the BA. It is also divulged that the proposed techniques, as was evidenced by their performance, offer a good transient response, a considerable capability for disturbance rejection, and an insensitivity towards the parametric uncertainty of the controlled system

A new enhancement of the k-NN algorithm by Using an optimization technique

Of a number of ML (Machine Learning) algorithms, k-nearest neighbour (KNN) is among the most common for data classification research, and classifying diseases and faults, which is essential due to frequent alterations in the training dataset, in which it would be expensive using most methods to construct a different classifier every time this happens. Therefore, KNN can be used effectively as it does not require a residual classifier to be constructed in advance. KNN offers ease of use and can be applied across a broad variation spectrum. Here, a novel KNN classification approach is put forward using the Bayesian Optimization Algorithm (BOA) for optimisation. This paper seeks to make classification more accurate and suggest alterations of nearest neighbour K value to use information about dataset structure and the similarity measure of distance. The findings of experimental work based on the University of California Irvine (UCI) repository datasets in general shows improved performance of classifiers compared with conventional KNN and give greater reliability without a significant time cost to speed