Semi-active control system for magneto-rheological damper based on the identification model with fuzzy neural network

Semi-active suspensions that dissipate energy through controllable dampers have been used in trains, tractors and off-road vehicles in the last decades. Magneto-rheological (MR) fluids have been widely applied as a new material in the field of vibration control. Magneto-rheological (MR) damper is an extremely ideal semi-active control device compared with traditional semi-active damping control device for its superiorities of rapid response (in millisecond), far less response time than sampling time, and almost no time lag caused by the control devices. However, its complicated dynamic hysteresis characteristics vary with the currents imposed on it, resulting in the difficulty in establishing the mathematical model which can truly describe its dynamic behavior. Besides, an effective and precision mathematical model can be of help for constructing the semi-active control law. In this paper, the system identification method based on the theory of fuzzy neural network (FNN) is employed to identify the mathematical model that can accurately reflect the dynamic hysteresis characteristics of magneto-rheological (MR) damper. Under the semi-active control law, the structure on the damper can be stabilized at the fastest rate with an optimal current calculated by the identified mathematical model imposed on magneto-rheological (MR) damper. Consequently, vibration reduction can be effectively realized

State of the art of control schemes for smart systems featuring magneto-rheological materials

This review presents various control strategies for application systems utilizing smart magneto-rheological fluid (MRF) and magneto-rheological elastomers (MRE). It is well known that both MRF and MRE are actively studied and applied to many practical systems such as vehicle dampers. The mandatory requirements for successful applications of MRF and MRE include several factors: advanced material properties, optimal mechanisms, suitable modeling, and appropriate control schemes. Among these requirements, the use of an appropriate control scheme is a crucial factor since it is the final action stage of the application systems to achieve the desired output responses. There are numerous different control strategies which have been applied to many different application systems of MRF and MRE, summarized in this review. In the literature review, advantages and disadvantages of each control scheme are discussed so that potential researchers can develop more effective strategies to achieve higher control performance of many application systems utilizing magneto-rheological materials

A Magnetorheological Damper with Embedded Piezoelectric Force Sensor: Experiment and Modeling

This chapter describes configuration, fabrication, calibration and performance tests of the devised self-sensing MR damper firstly. Then, a black-box identification approach for modeling the forward and inverse dynamics of the self-sensing MR damper is presented, which is developed with the synthesis of NARX model and neural network within a Bayesian inference framework to have the ability of enhancing generalization.Department of Civil and Environmental Engineerin

Neutral network-PID control algorithm for semi-active suspensions with magneto-rheological damper

In this paper, a semi-active suspension control system based on Magneto-Rheological (MR) damper is designed for a commercial vehicle to improve the ride comfort and driving stability. A mathematical model of MR damper based on the Bouc-Wen hysteresis model is built. The mathematical model could precisely describe the characteristics of MR damper compared with the bench test results. The neural network-PID controller is designed for the semi-active suspension systems. According to the numerical results, the proposed controller can constrain vehicle vibrations and roll angle significantly. A detailed multi-body dynamic model of the light vehicle with four semi-active suspensions are established, and an actual vehicle handling and stability tests are carried out to verify the control performances of the proposed controller. It can be concluded that MR semi-active suspension systems can play a key role in coordination between the ride comfort and handling stability for the commercial vehicle

A Novel Inverse Dynamic Model for a Magnetorheological Damper based on Network Inversion

Semi-active suspensions based on magnetorheological (MR) dampers are receiving significant attention specially for control of vibration isolation systems. The nonlinear hysteretic behaviour of MR dampers can cause serious problems in controlled systems such as instability and loss of robustness. Most of the developed controllers determine the desired damping forces which should be produced by the MR damper. Nevertheless, the MR damper behaviour can only be controlled in terms of the applied current (or voltage). In addition to this, it is necessary to develop an adequate inverse dynamic model in order to calculate the command current (or voltage) for the MR damper to generate the desired forces as close as possible to the optimal ones. Due to MR dampers are highly nonlinear devices, the inverse dynamics model is difficult to obtain. In this paper, a novel inverse MR damper model based on a network inversion to estimate the necessary current (or voltage) such as the desired force is exerted by the MR damper is presented. The proposed inverse model is validated carrying out experimental tests. In addition, a comparison of simulated tests with other damper controllers is also presented. Results show the effectiveness of the network inversion for inverse modeling of an MR damper, so that the proposed inverse model can act as a damper controller to generate the command current (or voltage) to track the desired damping force.This work was supported by the funds provided by the Spanish Government through the CICYT projects TRA2008-05373/AUT and TRA2011-28548-C02-01.Publicad

Robust Adaptive Controls of a Vehicle Seat Suspension System

This work proposes two novel adaptive fuzzy controllers and applies them to vibration control of a vehicle seat suspension system subjected to severe road profiles. The first adaptive controller is designed by considering prescribed performance of the sliding surface and combined with adaptation laws so that robust stability is guaranteed in the presence of external disturbances. As for the second adaptive controller, both the H-infinity controller and sliding mode controller are combined using inversely fuzzified values of the fuzzy model. In order to evaluate control performances of the proposed two adaptive controllers, a semi-active vehicle suspension system installed with a magneto-rheological (MR) damper is adopted. After determining control gains, two controllers are applied to the system and vibration control performances such as displacement at the driver’s position are evaluated and presented in time domain. In this work, to demonstrate the control robustness two severe road profiles of regular bump and random step wave are imposed as external disturbances. It is shown that both adaptive controllers can enhance ride comfort of the driver by reducing the displacement and acceleration at the seat position. This excellent performance is achieved from each benefit of each adaptive controller; accurate tracking performance of the first controller and fast convergence time of the second controller

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