**1. Introduction**

382 Smart Actuation and Sensing Systems – Recent Advances and Future Challenges

[85] J. B. F. Spencer et al., Smart dampers for seismic protection of structures: a full-scale

study, *2nd World Conference on Structural Control*, Kyoto, Japan, 1998, 417–426.

Vibration suppression is considered as a key research field in civil engineering to ensure the safety and comfort of their occupants and users of mechanical structures. To reduce the system vibration, an effective vibration control with isolation is necessary. Vibration control techniques have classically been categorized into two areas, passive and active controls. For a long time, efforts were made to make the suspension system work optimally by optimizing its parameters, but due to the intrinsic limitations of a passive suspension system, improvements were effective only in a certain frequency range. Compared with passive suspensions, active suspensions can improve the performance of the suspension system over a wide range of frequencies. Semi-active suspensions were proposed in the early 1970s [1], and can be nearly as effective as active suspensions. When the control system fails, the semi-active suspension can still work under passive conditions. Compared with active and passive suspension systems, the semi-active suspension system combines the advantages of both active and passive suspensions because it provides better performance when compared with passive suspensions and is economical, safe and does not require either higher-power actuators or a large power supply as active suspensions do [2].

In early semi-active suspension, many researches on variable orifice dampers had been done ([3-4]). With these damper types, regulation on of the damping force can be achieved by adjusting the orifice area in the oil-filled damper, thus changing the resistance to fluid flow, but adjusting the speed is slow because of mechanical motion limitations. Another class of semi-active suspension uses controllable fluids. Two fluids that are viable contenders for development of controllable dampers are: electrorheological (ER) fluids and magnetorheological (MR) fluids. Although the discovery of both ER and MR fluids dates back to the late 1940's, researchers have primarily concentrated on ER fluids for civil engineering applications ([5-8]). Recently developed MR fluids appear to be an attractive alternative to ER fluids for use in controllable fluid dampers [9-15].

© 2012 Truong and Ahn, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Figure 1.** MR fluid – Working principle.

The initial discovery and development of MR fluid can be credited to Jacob Rainbow at the US National Bureau of Standards in the late 1940s [9,10]. These fluids are suspensions of micron-sized, magnetizable particles in an appropriate carrier liquid [11-15]. Normally, MR fluids are free flowing liquids having consistency similar to that of motor oil. However, in the presence of an applied magnetic field, the iron particles acquire a dipole moment aligned with the external field which causes particles to form linear chains parallel to the field, as shown in Fig. 1. This phenomenon can solidify the suspended iron particles and restrict the fluid movement. Consequently, yield strength is developed within the fluid. The degree of change is related to the magnitude of the applied magnetic field, and can occur only in a few milliseconds. A typical MR fluid contains 20-40% by volume of relatively pure, soft iron particles, e.g., carbonyl iron. These particles are suspended in mineral oil, synthetic oil, water or glycol. A variety of proprietary additives similar to those found in commercial lubricant are commonly added to discourage gravitational settling and promote suspension, enhance lubricity, modify viscosity, and inhibit wear. The ultimate strength of an MR fluid depends on the square of the saturation magnetization of the suspended particles. The key to a strong MR fluid is to choose a particle with a large saturation magnetization. The best available particles are alloys of iron and cobalt that have saturation magnetization of about 2.4 Tesla. Unfortunately, such alloys are prohibitively expensive for most practical applications. The best practical particles are simply pure iron, as they have saturation magnetization of 2.15 Tesla. Virtually all other metals, alloys and oxides have saturation magnetization significantly lower than that iron, resulting in substantially weaker MR fluids. Typically, the diameter of the magnetizable particles is 3 to 5 microns. Functional MR fluids may be made with larger particles; however, particle suspension becomes increasingly more difficult as the size increases. Smaller particles that are easier to suspend could be used, but the manufacture of such particles is difficult. Commercial quantities of relatively inexpensive carbonyl iron are generally limited to sizes greater than 1 or 2 microns.

Due to the special behavior of MR fluid, it has been used for a vast of applications such as: dampers, shock absorbers, rotary brakes, clutches, prosthetic devices, polishing and grinding devices, etc. Among them, MR fluid dampers, which utilize the advantages of MR fluids, are semi-active control devices that are widely used in the modern industry nowadays. A typical MR damper includes MR fluid, a pair of wires, a housing, a piston, a magnetic coil, and an accumulator as displayed in Fig. 2a. Here, the MR fluid is housed within the cylinder and flows through a small orifice. The magnetic coil is built in the piston or on the housing. When a current is supplied to the coil, the particles are aligned and the fluid changes from the liquid state to the semi-solid state within milliseconds. Consequently, a controllable damping force is produced. The force procedured by a MR damper depends on magnetic field induced by the current in the damper coil and the piston velocity as in Fig. 2b. It is capable of generating a force with magnitude sufficient for rapid response in largescale applications [16-18], while requiring only a battery for power [13]. Additionally, these devices offer highly reliable operations and their performance is relatively insensitive to temperature fluctuations or impurities in the fluid [12]. As a result, there has been active research and development of MR fluid dampers and their applications [9-19,21-29,32,35,36].

**Figure 2.** General configuration of a MR fluid damper.

384 Smart Actuation and Sensing Systems – Recent Advances and Future Challenges

**Figure 1.** MR fluid – Working principle.

(a) Without Magnetic Field (b)With Magnetic Field

The initial discovery and development of MR fluid can be credited to Jacob Rainbow at the US National Bureau of Standards in the late 1940s [9,10]. These fluids are suspensions of micron-sized, magnetizable particles in an appropriate carrier liquid [11-15]. Normally, MR fluids are free flowing liquids having consistency similar to that of motor oil. However, in the presence of an applied magnetic field, the iron particles acquire a dipole moment aligned with the external field which causes particles to form linear chains parallel to the field, as shown in Fig. 1. This phenomenon can solidify the suspended iron particles and restrict the fluid movement. Consequently, yield strength is developed within the fluid. The degree of change is related to the magnitude of the applied magnetic field, and can occur only in a few milliseconds. A typical MR fluid contains 20-40% by volume of relatively pure, soft iron particles, e.g., carbonyl iron. These particles are suspended in mineral oil, synthetic oil, water or glycol. A variety of proprietary additives similar to those found in commercial lubricant are commonly added to discourage gravitational settling and promote suspension, enhance lubricity, modify viscosity, and inhibit wear. The ultimate strength of an MR fluid depends on the square of the saturation magnetization of the suspended particles. The key to a strong MR fluid is to choose a particle with a large saturation magnetization. The best available particles are alloys of iron and cobalt that have saturation magnetization of about 2.4 Tesla. Unfortunately, such alloys are prohibitively expensive for most practical applications. The best practical particles are simply pure iron, as they have saturation magnetization of 2.15 Tesla. Virtually all other metals, alloys and oxides have saturation magnetization significantly lower than that iron, resulting in substantially weaker MR fluids. Typically, the diameter of the magnetizable particles is 3 to 5 microns. Functional MR fluids may be made with larger particles; however, particle suspension becomes increasingly more difficult as the size increases. Smaller particles that are easier to suspend could be used, but the manufacture of such particles is difficult. Commercial quantities of relatively inexpensive

carbonyl iron are generally limited to sizes greater than 1 or 2 microns.

Due to the special behavior of MR fluid, it has been used for a vast of applications such as: dampers, shock absorbers, rotary brakes, clutches, prosthetic devices, polishing and However, major drawbacks that hinder MR fluid damper applications are their nonlinear force/displacement and hysteretic force/velocity characteristics. Therefore, one of the challenges involved in creating high efficiencies for MR fluid damper applications, especially in damping control field is to develop an accurate model that can take full advantages of the unique features of this device and to design proper control algorithms in order to improve the system working performances.

With MR fluid dampers modeling technologies, both parametric and non-parametric models have been built by researchers to describe the MR fluid damper behaviors. Savaresi *et al* [19] made a comparison of both the parametric and non-parametric methods and then developed a complete framework for the development of an accurate model of MRdampers. Parametric models based on mechanical idealizations have been proposed such as Bingham model, Bouc-Wen model, phenomenological model, and others [20-25]. The Bingham model [20] represents the dry-friction as a signum function on the damper velocity and may be considered as a simple model for describing the hysteresis characteristic. The Bouc-Wen model uses a differential equation to depict the non-linear hysteresis with

moderate complexity and is widely applied in building controls. Once the characteristic parameters of the Bouc-Wen model are determined, the model can obtain the linearity and the smoothness of the transition from the pre-yield to the post-yield region. One of the major problems in the Bouc-Wen model is the accurate determination of its characteristic parameters which is obtained by using optimization or trial error techniques. Consequently, these techniques demand high computational cost to generate the model parameters. Moreover, the fact that each set of constant parameters is valid only for single vibration conditions makes the Bouc-Wen model inappropriate for varying excitation environments. Therefore, many researches on how to develop a MR fluid damper model for higher accuracy and higher adaptability in estimating the behavior of the damper have been done. Spencer *et al* [21] successfully developed a phenomenological model to improve the model accuracy with an additional internal dynamical variable. Choi and Lee [22] designed a hysteresis damper model based on a polynomial and a curve fitting to predict better the damping force when compared with conventional models. Dominguez *et al* [23] proposed a methodology to find out the characteristic parameter of Bouc-Wen model and then designed a new non-linear model to simulate the behavior of the MR fluid dampers. Kwok *et al* designed a hysteretic model based on a particle swarm optimization [24] or using GA technique [25] to modify the Bonc-Wen model and identify the characteristic parameters of the models. The effectiveness of these models with their identification process was proved through the experimental test data. However, the parametric modeling methods require assumptions regarding the structure of the mechanical model that simulates the system's behavior. These approaches could be divergent if the initial assumptions for the model structures are flawed or if the proper constraints are not applied to the parameters [24,25]. Unrealistic parameters such as negative mass or stiffness may be obtained [29].

On the contrary, non-parametric methods could avoid these drawbacks of the parametric approaches for modeling both the linear, nonlinear, and hysteretic systems with high adaptability. For modeling MR fluid dampers, some researches have been done. Chang and Roschke [26] proposed a non-parametric model using multilayer perceptron neural network with optimization method for a satisfactory representation of a damper behavior. Schurter and Roschke [27] investigated the modeling of MR fluid dampers with an adaptive neurofuzzy inference system. The fuzzy structure was simple for modeling; nevertheless, the training model process relied on input and output information on MR fluid dampers and took much computation time. Wang and Liao [28,29] explored the modeling of MR fluid dampers by using a trained direct identification based on recurrent neural network. Although, the designed models could predict the dynamic responses of the dampers with high precision, the model architectures and the training methods were complex.

Once an accurate model for the MR fluid damper is built, it is very useful to investigate the damper characteristics before applying to suspensions. In addition, the well-done model can effectively function as a virtual sensor to estimate the damping force which is used for closed-loop damping control systems with a self-sensing behavior. Self-sensing describes the technique of using a transducer to both actuate and sense concurrently [30,31]. Compared to typical self-sensing damping control systems using separated or integrated actuators and sensors [32], this technique can offer several advantages. A reduction in the number of sensing and actuation devices, and associated power, wiring and interfacing, immediately reduces cost and complexity. A sensorless damping control system can also offer higher robustness than the corresponding conventional system in which the failure occurs due to faults in sensor hardware, reading/wiring signal, or measurement noises.

For these reasons and the current demands in MR fluid damper applications, this chapter includes mainly two contents: modeling and damping control technologies. The first one is to revise several typical MR fluid damper modeling methodologies as well as to develop an effective direct modeling method based on a so-called black-box model (BBM) [35,36]. This BBM using a self-tuning fuzzy system based on neural technique is designed to model simply MR fluid dampers and then can be apply to damping control systems as a virtual force sensor. The BBM built in the form of simple fuzzy mapping laws is considered to estimate directly the MR damping force with respect to the MR damper characteristics. In order to improve the accuracy of the suggested model, the back propagation learning rules based on gradient descent method was used to train the fuzzy parameters to minimize the modeling error function. Input information for the model training process is the current supplied for the MR fluid damper and its dynamic responses. The effectiveness of the BBM method as well as the self-sensing ability of a damping system using this model was clearly verified in a comparison with the other methods through modeling and experimental investigations on two damper test rigs. The comparison results show that the BBM has satisfactorily representative ability for the behavior of MR fluid damper with small computational requirement and it can be successfully used as the virtual force sensor for damping systems. The second content is to present a novel damping control methodology which is called force-sensorless damping control. This control technique is based on the developed BBM, and its inverse back-box model, IBBM, which were designed for any given MR fluid damper, to apply to general systems using this damper for damping control with force self-sensing behavior. The IBBM was derived from the optimized BBM and suggested for usage as an effective force controller. In addition, the IBBM structure is online adjusted with respect to the control error to improve the system performance. Consequently, the closed-loop force controller, based on the 'virtual' force sensor - BBM and the adaptive force controller – IBBM, was built for the force-sensorless damping system. Simulations and real-time experiments have been finally carried out to verify that the designed models have satisfactorily representative ability for the behavior of MR fluid damper with small computational requirement and they were successfully applied for forcesensorless control of the damping systems.
