Natural Frequency Analysis Of All Edges Clamped Flexible Thin Plate

In this paper, the analysis of natural frequencies for all clamped edges rectangular flexible thin plate is carried out using Finite Difference (FD) and Finite Element (FE) approaches. According to the literatures, the differential equation of plate was obtained by considering the Kirchhoff hypotheses and Newton’s law. The dynamic differential model is developed by using the FD to obtain the natural frequencies of given plate; for this purpose, a displacement model is converted to combination of sine and cosine functions in form of Fast Fourier Series. In second method, modes of vibration are driven by FE method using the ABAQUS software. The obtained natural frequencies of both methods are evaluated and compared with previous literatures; the outcomes can explain that the improved FD method’s results are more accurate in compare with FE method’s

PID controller for NARX and ANFIS models of marine pipe cylinder undergoes vortex induced vibration

PID controller discrete time on marine cylinder pipe risers which represented based on system identification models under vortex induced vibration (VIV) has been verified in this work. Input-output data have been provided from the experimental setup of previous paper. Two System identification methods used to create models which are: Neural Network based on Nonlinear Auto-Regressive External (Exogenous) Input (NARX) and Adaptive Neuro-Fuzzy Inference System (ANFIS). NARX and ANFIS models have selected based on Mean Square Error (MSE) technique. While, PID controller has been applied to overcome on the pipe cylinder oscillation for all models. Also, the controller performance has been compared on each model during from tuning the controller parameter (KP, KI and KD) depending on the heuristic method and validation the gain values based on Mean Square Error (MSE) technique. Finally, the consequences demonstrated that the ANFIS model better than the NARX model to forecast the dynamic behavior of the system. By contrast, PID controller has been managed to decrease the pipe cylinder fluctuation for all models specially the NARX model system identificatio

Experimental evaluation of active vibration control of a flexible plate using proportional gain controller

Active Vibration Control (AVC) is well known nowadays as an optimum technique in vibration suppression of flexible structures. Due to the complexity of the dynamics system of flexible structures, vibration control process is quite a challenge. In this paper, the vibration control of flexible plate using classical proportional feedback gain controller method is studied, experimentally. The AVC-P controller design is implemented to a full clamped flexible plate system to evaluate its vibration attenuation performance. The system's dynamic model considering the collocated placement of the sensor and actuator is derived within the LabVIEW environment. The first five frequencies of vibration mode were obtained. The result indicated that the AVC-P controller propose possessed the ability to attenuate vibration of the flexible structure

Nonlinear dynamic modelling of flexible beam structures using neural networks

This paper investigates the utilisution of back propagation neural networlu (NNs) for modelling flexible beam structures infixed-free mode; a simple repsentation of an aircrufr wing or robot arm. A comparative performance of the NN model and conventional recursive least square scheme, in characterising the system is carried out in the time and frequency domains. Simulated results demonstrate that using NN approach the system is modelled better than with the conventional linear modelling approach. The developed neuro-modelling approach will firther be utilized in the design and implementation of suitable controllers, for vibration slippression in such system

Semi-active suspension system using MR damper with PSO skyhook and sensitivity analysis of PID controller

This paper introduces the use of Particle Swarm Optimization (PSO) algorithm to tune Skyhook controller & Sensitivity Analysis method to tune PID controller for semi-active suspension system in furtherance of increasing and enhancing the ride comfort and vehicle stability. The performance of skyhook and PID controller are optimized by PSO and Sensitivity Analysis respectively. The mean square error (MSE) of the system is set as an objective function for optimization process of the proposed controller. The performances of proposed controllers are compared with the passive system in terms of sprung displacement & sprung acceleration. The bump & hole and random road profile is set as a disturbance of the system. The simulated results reflect that the proposed controllers offer a significant improvement in ride comfort and vehicle stability

Implementation of PID based controller tuned by Evolutionary Algorithm for Double Link Flexible Robotic Manipulator

The paper investigates the development of intelligent hybrid collocated and non-collocated PID controller for hub motion and end point vibration suppression of doublelink flexible robotic manipulator. The system was modeled using multi-layer perceptron neural network structure based on Nonlinear Autoregressive Exogenous (NARX) model. The hybrid controllers are incorporated with optimization algorithm that is ABC and PSO to find out the parameters of the PID controllers. Numerical simulation was carried out in MATLAB/Simulink to evaluate the system in term of tracking capability and vibration suppression for both links. Performance of the controllers are compared with the hybrid PID-PID Ziegler Nichols (ZN) controller in term of input tracking and vibration suppression. The results show that PSO revealed the superiority over ABC in controlling the system. The system managed to reach desired angle for both hub at lower overshoot using proposed method. Meanwhile, the vibration reduction shows great improvement for both link 1 and 2. This signifies that, the PSO algorithm is very effective in optimizing the PID parameters

Optimization Of Pid Controller For Double-Link Flexible Robotic Manipulator Using Metaheuristic Algorithms

This paper investigates the optimization approach of PID controller for double-link flexible robotic manipulator using metaheuristic algorithm. This research focus on population-based metaheuristic that is particle swarm optimization (PSO) and artificial bees algorithm (ABC) to tune the PID control parameters of the system. In the tuning process, the number of iteration was set and the number of particles was varied. The tuning process was interrupted once the convergence value of Mean Square Error (MSE) was achieved. For PSO, it was found that when the number of iteration increased, or the number of particles were set to higher values, there were no significant improvement of MSE. Results showed that 25 iterations were required for MSE to converge for hub angle and 20 iterations were required for MSE to converge for endpoint acceleration. Meanwhile, it was discovered that ABC portrayed the same pattern with PSO whereby when the number of iteration increased or the number of colony sizes were set to higher values, there were no significant improvement of MSE. From the results, 15 iterations were required for MSE to converge for hub angle and 25 iterations were required for MSE to converge for end-point acceleration. The performance of the algorithm was validated by evaluating the performance of the controllers in comparison with the conventional controller that is Ziegler Nichols (ZN) in term of input tracking capability and vibration suppression for both links. The system managed to reach desired angle for both hub angle 1 and 2. Besides, vibration reduction shows great improvement for both link 1 and 2. This signifies that, the PSO and ABC algorithm are very effective in optimizing the PID parameters

Utilizing P-Type ILA in tuning Hybrid PID Controller for Double Link Flexible Robotic Manipulator

The usage of robotic manipulator with multi-link structure has a great influence in most of the current industries. However, controlling the motion of multi-link manipulator has become a challenging task especially when the flexible structure is used. Currently, the system utilizes the complex mathematics to solve desired hub angle with the coupling effect and vibration in the system. Thus, this research aims to develop the controller for double-link flexible robotics manipulator (DLFRM) with the improvement on hub angle position and vibration suppression. The research utilized DLFRM modeling based on NARX model structure estimated by neural network. In the controllers’ development, this research focuses on adaptive controller. PType iterative learning algorithm (ILA) control scheme is implemented to adapt the controller parameters to meet the desired performances when there are changes to the system. The hybrid PID-PID controller is developed for hub motion and end point vibration suppression of each link respectively. The controllers are tested in MATLAB/Simulink simulation environment. The performance of the controller is compared with the fixed hybrid PID-PID controller in term of input tracking and vibration suppression. The results indicate that the proposed controller is effective to move the double-link flexible robotic manipulator to the desired position with suppression of the vibration at the end of the double-link flexible robotic manipulator structure

Modelling of Flexible Manipulator System Using Flower Pollination Algorithm

The study of the flexible manipulator system (FMS) has attracted many researchers due to its superiority of light weight and faster system response. Flexible manipulator system is an improvement from its rigid structure, however it can be easily vibrated when it subjected to disturbance. If the advantages of FMS are not to be sacrificed, an accurate model and efficient control system must be developed. Thus, this study presents an approach of evolutionary swarm algorithm via flower pollination algorithm (FPA) to model the dynamic system of flexible manipulator structure. An experimental rig of flexible manipulator system was developed for input-output acquisition. Then, this input-output data was fed to system identification method to obtain a dynamic model of flexible manipulator system utilizing evolutionary algorithm with linear auto regressive with exogenous (ARX) model structure. The result obtained through flower pollination algorithm was then compared with conventional method known as least square (LS) algorithm in terms of mean square error (MSE), correlation test and pole-zero diagram. The best MSE achieved by LS modeling for endpoint acceleration and hub angle positioning are 0.0075 and 0.0028, respectively. While, the best MSE produced by flower pollination algorithm for endpoint acceleration and hub angle positioning are 0.0063 and 0.0020, respectively. It is reveals that the performance of intelligence algorithm is superior than conventional algorithm

A Single Objective Flower Pollination Algorithm for Modeling the Horizontal Flexible Plate System

Flexible plate structure is a chosen technology used for many applications since past decades ago. However, this structure has a disadvantage that needs to be avoided which is easy to vibrate. Thus, this project presents the modelling of horizontal flexible plate system using bio-inspired flower pollination algorithm. The objective is to obtain an accurate model of the real system in the simulated environment. The collected of real vibration data through experimental study was then utilized to develop the dynamic system model based on linear autoregressive with exogenous (ARX) model structure and optimized by flower pollination algorithm (FPA). The algorithm is a novel bio-inspired optimization algorithm that mimics the real-life processes of the flower pollination. The gained model in this simulation is approved utilizing the most minimal mean squared error, correlation tests, and pole zero graph stability due to check the robustness of the model. The performance of the developed model was then compared with the conventional algorithm known as recursive least square (RLS). The best model achieved in this study will be used as a platform of controller development using active vibration control technique