Semi-active Vibration Control Using a Magneto Rheological (MR) Damper with Particle Swarm Optimization

2015 ◽  
Vol 40 (3) ◽  
pp. 747-762 ◽  
Author(s):  
Wei Huang ◽  
Jian Xu ◽  
Da-yong Zhu ◽  
Ying-lei Wu ◽  
Jian-wei Lu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Vibration Control ◽  
Active Vibration Control ◽  
Particle Swarm ◽  
Mr Damper ◽  
Swarm Optimization ◽  
Active Vibration ◽  
Magneto Rheological

Modelling and control of horizontal flexible plate using particle swarm optimization

2018 ◽  
Vol 7 (2.29) ◽  
pp. 13
Author(s):  
Muhamad Sukri Hadi ◽  
Hanim Mohd Yatim ◽  
Intan Zaurah Mat Darus
Keyword(s):  
Particle Swarm Optimization ◽  
Vibration Control ◽  
Active Vibration Control ◽  
Particle Swarm ◽  
Flexible Plate ◽  
Input Output ◽  
Swarm Optimization ◽  
Vibration Data ◽  
Active Vibration ◽  
Experimental Rig

This paper presents the modeling and active vibration control using an evolutionary swarm algorithm known as particle swarm optimization. Initially, a flexible plate experimental rig was designed and fabricated with all clamped edges as boundary conditions constrained at horizontal position. The purpose of the experimental rig development is to collect the input-output vibration data. Next, the data acquisition and instrumentation system were designed and integrated with the experimental rig. Several procedures were conducted to acquire the input-output vibration data. The collected vibration data were then utilized to develop the system model. The parametric modeling using particle swarm optimization was devised using an auto regressive model with exogenous model structure. The developed model was validated using mean square error, one step ahead prediction, correlation tests and pole-zero diagram stability. Then, the developed model was used for the development of controller using an active vibration control technique. It was found that particle swarm optimization based on the active vibration control using Ziegler-Nichols method has successfully suppressed the unwanted vibration of the horizontal flexible plate system. The developed controller achieved the highest attenuation value at the first mode of vibration which is the dominant mode in the system with 34.37 dB attenuation. 

Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper

2016 ◽  
Vol 23 (3) ◽  
pp. 501-514 ◽  
Author(s):  
Mat Hussin Ab Talib ◽  
Intan Zaurah Mat Darus
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Firefly Algorithm ◽  
Ride Comfort ◽  
Particle Swarm ◽  
Mr Damper ◽  
Suspension System ◽  
System Response ◽  
Swarm Optimization ◽  
Magneto Rheological

This paper presents a new approach for intelligent fuzzy logic (IFL) controller tuning via firefly algorithm (FA) and particle swarm optimization (PSO) for a semi-active (SA) suspension system using a magneto-rheological (MR) damper. The SA suspension system’s mathematical model is established based on quarter vehicles. The MR damper is used to change a conventional damper system to an intelligent damper. It contains a magnetic polarizable particle suspended in a liquid form. The Bouc–Wen model of a MR damper is used to determine the required damping force based on force–displacement and force–velocity characteristics. The performance of the IFL controller optimized by FA and PSO is investigated for control of a MR damper system. The gain scaling of the IFL controller is optimized using FA and PSO techniques in order to achieve the lowest mean square error (MSE) of the system response. The performance of the proposed controllers is then compared with an uncontrolled system in terms of body displacement, body acceleration, suspension deflection, and tire deflection. Two bump disturbance signals and sinusoidal signals are implemented into the system. The simulation results demonstrate that the PSO-tuned IFL exhibits an improvement in ride comfort and has the smallest MSE for acceleration analysis. In addition, the FA-tuned IFL has been proven better than IFL–PSO and uncontrolled systems for both road profile conditions in terms of displacement analysis.

Research of active vibration control optimal disposition based on CMQPSO

2021 ◽  
pp. 1-8
Author(s):  
Xiangzhong Meng ◽  
Ying Ma ◽  
Qiang Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Vibration Control ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Cloud Model ◽  
Active Vibration Control ◽  
Sensors And Actuators ◽  
Swarm Optimization ◽  
Active Vibration

The adaptive quantum particle swarm optimization algorithm based on cloud model and the multi-island genetic algorithm [15] have obvious advantages in convergence speed to solve the sensor optimization problem, and can effectively achieve global optimization. Due to the installation of sensors and actuators, the electromechanical coupling coefficient of intelligent structures is changed, which affects the vibration energy of structures. In this paper, the reserved energy index of structural vibration control system is taken as the objective optimization function. The position, number, length and control gain of sensors and actuators of active vibration control system are optimized. The adaptive Quantum-behaved Particle Swarm Optimization algorithm in cloud model(CMQPSO) is used as the optimization strategy, and the cantilever beam is taken as an example. This approach is verified its effectiveness and feasibility. It is found that excellent optimization results are obtained.

scholarly journals An adaptive LQR controller based on PSO and maximum predominant frequency approach for semi-active control scheme using MR damper

2018 ◽  
Vol 19 (1) ◽  
pp. 109
Author(s):  
Gaurav Kumar ◽  
Ashok Kumar ◽  
Ravi S. Jakka
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Mr Damper ◽  
Control Force ◽  
Linear Quadratic ◽  
Swarm Optimization ◽  
Predominant Period ◽  
Weighting Matrix ◽  
Lqr Problem ◽  
Lqr Controller

In the linear quadratic regulator (LQR) problem, the generation of control force depends on the components of the control weighting matrix R. The value of R is determined while designing the controller and remains the same later. Amid a seismic event, the responses of the structure may change depending the quasi-resonance occurring between the structure and the earthquake signal. In this situation, it is essential to update the value of R for conventional LQR controller to get optimum control force to mitigate the vibrations due to the earthquake. Further, the constant value of the weighting matrix R leads to the wastage of the resources using larger force unnecessarily where the structural responses are smaller. Therefore, in the quest of utilizing the resources wisely and to determine the optimized value of the control weighting matrix R for LQR controller in real time, a maximum predominant period τpmax and particle swarm optimization-based method is presented here. This method comprises of four different algorithms: particle swarm optimization (PSO), maximum predominant period approach τpmax to find the dominant frequency for each window, clipped control algorithm (CO) and LQR controller. The modified Bouc-Wen phenomenological model is taken to recognize the nonlinearities in the MR damper. The assessment of the advised method is done on a three-story structure having a MR damper at ground floor subjected to three different near fault historical earthquake time histories. The outcomes are equated with those of simple conventional LQR. The results establish that the advised methodology is more effective than conventional LQR controllers in reducing inter-story drift, relative displacement, and acceleration response.

SEMI-ACTIVE VIBRATION CONTROL OF A ROTATING SHAFT BY USING A MAGNETO RHEOLOGICAL DAMPER

2015 ◽  
Author(s):  
Aldemir Ap Cavalini Jr ◽  
Edson Hideki Koroishi ◽  
Adriano Silva Borges ◽  
Luiz Gustavo Pereira ◽  
Valder Steffen Jr
Keyword(s):  
Vibration Control ◽  
Active Vibration Control ◽  
Rotating Shaft ◽  
Active Vibration ◽  
Magneto Rheological
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