Fuzzy-PID controller on ANFIS, NN-NARX and NN-NAR system identification models for cylinder vortex induced vibration

In this paper, Fuzzy-PID controller on nonlinear system identification models for cylinder due to vortex induced vibration (VIV) has been presented well. Nonlinear system identification models generated after extracting the input-output data from previous paper. The nonlinear model consisted into three methods: Neural Network (NN-NARX) based on the Nonlinear Auto-Regressive with External (Exogenous) Input, Neural Network (NN-NAR) based on the Nonlinear Auto-Regressive and Adaptive Neuro-Fuzzy Inference System (ANFIS). The work has been divided into two main parts: generating the system identification models to predict the system dynamic behavior and using Fuzzy-PID controller to suppress the cylinder vibration arising from the vortices. For system identification models, the best representation for NAR and NARX models has been chosen depend on two variables which are Number of hidden neurons (NE) and number of delay (ND) then using mean Square Error (MSE) to find the best model. Whereas, calculating the lowest MSE when the ND equal to 2 and the value of NE ranging 1-11 then fixing NE which is giving the lowest MSE and calculating it when the ND ranging 1-11. While, for ANFIS model the process consisted of find the lowest MSE at particular number of membership function (MF) with two inputs and generalized bell shape as a type of MF. For the second part, Fuzzy-PID used to attenuate the effect of vortices on the cylinder on the best representation for all methods. However, the consequences presented that the lowest MSE of NAR model equal 2.8452×10-9 when the NE = 6 and ND = 4. While the best model of the NARX method recorded MSE = 1.2714×10-9 at NE and ND equal to 8 and 2 respectively. Also, the lowest MES for ANFIS model recorded 2.5635×10-13 when the MF equal to 2 for input and output. From another hand, Fuzzy-PID controller has been succeeded to reduce the vortex induced vibration on cylinder for all models but particularly on ANFIS model

Finite element method for dynamic modelling of an underwater flexible single-link manipulator

In order to control the angular displacement of the hub and to suppress the vibration at the end point of an underwater flexible single-link manipulator system efficiently, it is required to obtain an adequate model of the structure. In this study, a mathematical model of an underwater flexible single-link manipulator system has been developed and modelled as a pinned-free, an Euler-Bernoulli flexible beam using finite element method based on Lagrangian approach analysis. Damping, hub inertia and payload are incorporated in the dynamic model, which is then represented in a state-space form. The simulation algorithm was developed using matlab and its performance, on the basis of accuracy in characterizing the behavior of the manipulator, is assessed

Neuro-fuzzy identification of an internal combustion engine

Dynamic modeling and identification of an internal combustion engine (ICE) model is presented in this paper. Initially, an analytical model of an internal combustion engine simulated within SIMULINK environment is excited by pseudorandom binary sequence (PRBS) input. This random signals input is chosen to excite the dynamic behavior of the system over a large range of frequencies. The input and output data obtained from the simulation of the analytical model is used for the identification of the system. Next, a parametric modeling of the internal combustion engine using recursive least squares (RLS) technique within an auto-regressive external input (ARX) model structure and a nonparametric modeling using neuro-fuzzy modeling (ANFIS) approach are introduced. Both parametric and nonparametric models verified using one-step-ahead (OSA) prediction, mean squares error (MSE) between actual and predicted output and correlation tests. Although both methods are capable to represent the dynamic of the system very well, it is demonstrated that ANFIS gives better prediction results than RLS in terms of mean squares error achieved between the actual and predicted signals

Intelligent Model for Endpoint Accelerations of Two link Flexible Manipulator Using a Deep Learning Neural Network

This article investigates a two-link flexible manipulator (TLFM) that can be modelled utilizing a deep learning neural network. The system was classified under a multiple-input multiple-output (MIMO) system. In the modelling stage of this study, the TLFM dynamic models were divided into single-input single-output (SISO) models. Since coupling impact was assumed to be minimised, the characterizations of TLFM were defined independently in each model. Two discrete SISO models of a flexible two link manipulator were developed using the torque input and the endpoint accelerations of each link. The input-output data pairs were collected from experimental work and utilised to establish the system model. The Long Short-Term Memory (LSTM) algorithm optimised using Particle Swarm Optimization (PSO) was selected as the model structure due to the system's high degree of nonlinearity. The identification of the TLFM system utilizing LSTM optimised by PSO was successful, according to the high-performance result of PSO. Using LSTM-PSO, it is demonstrated that both link 1 and 2 models are accurately identified and that their performance in terms of MSE for links endpoint acceleration 1 and 2 is within a 95% confidence interval

Vibration suppression of the horizontal flexible plate using proportional– integral–derivative controller tuned by particle swarm optimization

This paper presents the development of an active vibration control for vibration suppression of the horizontal flexible plate structure using proportional–integral–derivative controller tuned by a conventional method via Ziegler–Nichols and an intelligent method known as particle swarm optimization algorithm. Initially, the experimental rig was designed and fabricated with all edges clamped at the horizontal position of the flexible plate. Data acquisition and instrumentation systems were designed and integrated into the experimental rig to collect input–output vibration data of the flexible plate. The vibration data obtained through experimental study was used to model the system using system identification technique based on auto-regressive with exogenous input structure. The plate system was modeled using particle swarm optimization algorithm and validated using mean squared error, one-step ahead prediction, and correlation tests. The stability of the model was assessed using pole zero diagram stability. The fitness function of particle swarm optimization algorithm is defined as the mean squared error between the measured and estimated output of the horizontal flexible plate system. Next, the developed model was used in the development of an active vibration control for vibration suppression on the horizontal flexible plate system using a proportional–integral–derivative controller. The proportional–integral–derivative gains are optimally determined using two different ways, the conventional method tuned by Ziegler–Nichols tuning rules and the intelligent method tuned by particle swarm optimization algorithm. The performances of developed controllers were assessed and validated. Proportional–integral–derivative-particle swarm optimization controller achieved the highest attenuation value for first mode of vibration by achieving 47.28 dB attenuation as compared to proportional–integral–derivative-Ziegler–Nichols controller which only achieved 34.21 dB attenuation

Active vibration control of a flexible plate via active force control strategy

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 structures using Active Force Control (AFC) method is studied, experimentally. The AVC-AFC 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-AFC possessed the ability to attenuate vibration of the flexible structure

Intelligent evolutionary controller for flexible robotic arm

Robotic is one of the key technologies towards Industrial Revolution 4.0. Robotic system, especially robotic arm have received tremendous demand in various fields especially manufacturing industry. Robotic arm is highly needed to enhance production, improve output, reduce human error and the most importantly, earn more profit with fast return on investment. The current industrial robotic arm, not only they are very expensive and required specialist for maintenance, they are also very heavy and difficult to manoeuvre. These facts are the reason why robotic solution are still unaffordable in most small and medium manufacturing industries in developing countries. Despite all the drawbacks, there is still a pressing need to employ robotics solution with the inherent problems of worker-related issues and output quality. Today, work requires a nimble and versatile robot and yet remain reliable. Operating robots should be simpler, where the learning curve is less steep. The user interface should be friendly and intuitive. Recently, there is a growing interest in employing lightweight, stronger and more flexible robotic arm in various fields. However, lightweight robot arm can be more easily influenced by unwanted vibrations, which may lead to problems including fatigue, instability and performance reduction. These problems may eventually cause damage to the highly stressed structures. This research focused on the development of the intelligent evolutionary controller algorithms for controlling flexible robotic arm manipulator. The controller algorithm has been formulated for trajectory planning control and vibration cancelation utilizing intelligent evolutionary algorithms such as Particle Swarm Algorithm and Artificial Bees Colony. The developed evolutionary algorithms have been implemented and experimentally verified using robotic arm manipulator experimental rig. The performances of these intelligent evolutionary controllers were found to be far better than the conventional method in term of input tracking, trajectory control and vibration cancelation

Active vibration control using pole placement method of a flexible plate structure optimized by genetic algorithm

Active vibration control (AVC) of flexible structure has given remarkable attention in recent years due to its importance in engineering applications. This research investigates the application of system identification to model a dynamic system of flexible plate structure for active vibration control purpose. A second order ARX model optimised by genetic algorithm (GA) is employed to represent the dynamical system and then feedback controller using pole placement method is exploited to stabilise the system and attenuate the disturbance vibration. The result indicates that pole placement method has capability to ensure the stability of the system while suppressing the disturbance vibration of flexible plate system

Intelligent model for endpoint accelerations of two link flexible manipulator using a deep learning neural network

This article investigates a two-link flexible manipulator (TLFM) that can be modelled utilizing a deep learning neural network. The system was classified under a multiple-input multiple-output (MIMO) system. In the modelling stage of this study, the TLFM dynamic models were divided into single-input single-output (SISO) models. Since coupling impact was assumed to be minimised, the characterizations of TLFM were defined independently in each model. Two discrete SISO models of a flexible two link manipulator were developed using the torque input and the endpoint accelerations of each link. The input-output data pairs were collected from experimental work and utilised to establish the system model. The Long Short-Term Memory (LSTM) algorithm optimised using Particle Swarm Optimization (PSO) was selected as the model structure due to the system's high degree of nonlinearity. The identification of the TLFM system utilizing LSTM optimised by PSO was successful, according to the high-performance result of PSO. Using LSTM-PSO, it is demonstrated that both link 1 and 2 models are accurately identified and that their performance in terms of MSE for links endpoint acceleration 1 and 2 is within a 95% confidence interval

Modeling and simulation of an active vibration control system for a flexible structure using finite difference method

This paper is focused on modeling and simulation of an active vibration control (AVC) system for a rectangular flexible thin plate with all clamped edge through the use of position feedback. The plate system was first modeled using Finite Difference (FD) approach. Then, the validity of the obtained model was investigated through comparative studies between the plate natural frequencies predicted by th e model and the exact values of resonance modes. The control algorithm was then implemented within the FD simulation platform. Different types of disturbances were applied to excite the plate system at excitation point and the performance of the controller in reducing the unwanted vibration was evaluated. Results of the study demonstrate the effectiveness of the proposed control strategy to attenuate the unwanted vibrations of the flexible thin plate system