Parametric Optimization Of Magneto-Rheological Fluid Damper Using Particle Swarm Optimization

This paper presents a parametric modeling of a magneto-rheological (MR) damper using a Particle Swarm Optimization (PSO) method. The objective of this paper is to optimize the parameter values of the MR fluid damper behavior using the Bouc-Wen model. The parametric identification was imposed beforehand in replicating the behavior of the MR fluid damper. The algebraic function from a number of hysteresis models was steered by comparing selected models: Bingham, Bouc-Wen and BoucWen by Kwok. A simulation method was operated in investigating these models by employing MATLAB reliant from the model intricacy. The experimental data was presented in terms of the time histories of the displacement, the velocity and the force parameters, measured for both constant and variable current settings and at a selected frequency applied to the damper. The model parameters were determined using a set of experimental measurements corresponding to different current constant values. It has been shown that the MR damper model’s response via the proposed approach is in good agreement with the MR damper test rig counterpar

Optimal design of a quadratic parameter varying vehicle suspension system using contrast-based Fruit Fly Optimisation

In the UK, in 2014 almost fifty thousand motorists made claims about vehicle damages caused by potholes. Pothole damage mitigation has become so important that a number of car manufacturers have officially designated it as one of their priorities. The objective is to improve suspension shock performance without degrading road holding and ride comfort. In this study, it is shown that significant improvement in performance is achieved if a clipped quadratic parameter varying suspension is employed. Optimal design of the proposed system is challenging because of the multiple local minima causing global optimisation algorithms to get trapped at local minima, located far from the optimum solution. To this end an enhanced Fruit Fly Optimisation Algorithm − based on a recent study on how well a fruit fly’s tiny brain finds food − was developed. The new algorithm is first evaluated using standard and nonstandard benchmark tests and then applied to the computationally expensive suspension design problem. The proposed algorithm is simple to use, robust and well suited for the solution of highly nonlinear problems. For the suspension design problem new insight is gained, leading to optimum damping profiles as a function of excitation level and rattle space velocity

A Novel Strain Stiffening Model for Magnetorheological Elastomer Base Isolator and Parameter Estimation Using Improved Particle Swarm Optimization

In order to fully utilize the advantages of magnetorheological elastomer (MRE) base isolator for seismic protection of civil structures, a high fidelity model should be established to characterize its nonlinear hysteresis for its implementation in structural control. In this paper, a novel strain stiffening model is developed to capture this unique characteristic. In this model, a strain stiffening component, which described the unique viscos-elastic behavior of the device, is incorporated with a Voigt element, which portrays the solid-material behavior. The new model, as an attractive feature, maintains a relationship between the isolator parameters and physical force-displacement nonlinear phenomenon and decreases the complexity in other existing models. In addition to the proposed model, an improved optimization algorithm based on particle swarm optimization (IPSO) is designed to identify the model parameters by utilizing experimental force-displacement-velocity data acquired from various loading conditions. In this new algorithm, the mutation operation in genetic algorithm is utilized for helping the model solution avoiding the local optimum. The superiority of the proposed model and parameter solving algorithm is validated by comparing them with the classical Bouc-Wen model and other optimization algorithms through the error analysis, respectively. The comparison results show that the proposed model can exactly predict the force-displacement and force-velocity responses at both small and large displacements, and has a smaller root-mean-square (MSE) error than the Bouc-Wen model. Compared with other optimization algorithm, the IPSO not only has a faster convergence rate, but also obtains the satisfactory parameters identification results

Optimization of Sliding Mode Control using Particle Swarm Algorithm for an Electro-hydraulic Actuator System

The dynamic parts of electro-hydraulic actuator (EHA) system are widely applied in the industrial field for the process that exposed to the motion control. In order to achieve accurate motion produced by these dynamic parts, an appropriate controller will be needed. However, the EHA system is well known to be nonlinear in nature. A great challenge is carried out in the EHA system modelling and the controller development due to its nonlinear characteristic and system complexity. An appropriate controller with proper controller parameters will be needed in order to maintain or enhance the performance of the utilized controller. This paper presents the optimization on the variables of sliding mode control (SMC) by using Particle Swarm Optimization (PSO) algorithm. The control scheme is established from the derived dynamic equation which stability is proven through Lyapunov theorem. From the obtained simulation results, it can be clearly inferred that the SMC controller variables tuning through PSO algorithm performed better compared with the conventional proportionalintegral-derivative (PID) controller

Tuning of different controlling techniques for magnetic suspending system using an improved bat algorithm

In this paper, design of proportional- derivative (PD) controller, pseudo-derivative-feedback (PDF) controller and PDF with feedforward (PDFF) controller for magnetic suspending system have been presented. Tuning of the above controllers is achieved based on Bat algorithm (BA). BA is a recent bio-inspired optimization method for solving global optimization problems, which mimic the behavior of micro-bats. The weak point of the standard BA is the exploration ability due to directional echolocation and the difficulty in escaping from local optimum. The new improved BA enhances the convergence rate while obtaining optimal solution by introducing three adaptations namely modified frequency factor, adding inertia weight and modified local search. The feasibility of the proposed algorithm is examined by applied to several benchmark problems that are adopted from literature. The results of IBA are compared with the results collected from standard BA and the well-known particle swarm optimization (PSO) algorithm. The simulation results show that the IBA has a higher accuracy and searching speed than the approaches considered. Finally, the tuning of the three controlling schemes using the proposed algorithm, standard BA and PSO algorithms reveals that IBA has a higher performance compared with the other optimization algorithm