Smart devices have become an important entity for many applications in daily life activities. These devices have witnessed a rapid improvement in its technology to fulfill the increasingly diverse usage demands. In the meanwhile, rotating machinery vibration analysis based on low-cost sensors has gained a considerable attraction over the last few years. For a long time, the vibration analysis of machines has been accepted as an effective solution to detect and prevent failures in complex systems to avoid the sudden malfunction. The objective of this work is to use MEMS accelerometer measurements to monitor the different level of vibration of a machine. This work presents a new technique for rotating machinery vibration analysis. It uses Fast Fourier Transformation as a feature extraction algorithm and Fuzzy Logic System (FLS) as the classifier algorithm. A smartphone accelerometer is used to collect the data from the vibrating machine. The performance of the proposed technique is tested using data from different vibration resources at a different speed of operations. The results are discussed to illustrate the various vibration levels.
Robotic manipulator is a Multi-Input Multi-Output (MIMO), highly nonlinear and coupled system. Therefore, designing an efficient controller for this system is a challenging task for the control engineers. In this paper, the Fractional Order-Fuzzy-Proportional Integral Derivative (FO-Fuzzy-PID) controller is investigated for the first three joints of robot arm (PUMA 560) for trajectory tracking problem. To study the effectiveness of FO-Fuzzy-PID controller, its performance is compared with other three non model controllers namely Fuzzy-PID, Fractional Order PID (FOPID) and conventional PID. Genetic algorithm (GA) optimization technique was used for tuning parameters of FOPID and conventional PID controllers. Simulation results clearly indicate the superiority of FO-Fuzzy-PID controller over the other controllers for trajectory tracking, better steady state and RMS errors. All controllers were tested by simulation under the same conditions using SIMULINK under MATLAB2013a.
In this article a three non-model based trajectory tracking controllers for rigid simple open chain robot manipulator are investigated. The first is a Fuzzy-PID controller, it is considered as a reference benchmark to compare its results with the others which are a proportional Derivative (PD) tuned using genetic algorithm (GA) and an Adaptive Neuro Fuzzy Inference System (ANFIS). The simulation is carried out for the first three joints of robot arm (PUMA560) aiming to track aquintic polynomial trajectory with minimum errors, and good disturbance rejection. Simulation results, shows that using Fuzzy-PID has better steady state error and RMS error than the ANFIS and PD tuned using GA. The three controllers are tested by simulated under the same conditions using SIMULINK under MATLAB2013a.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.