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Top1. Introduction And Literature Review
Conventional Hydraulic Brakes (CHB) are commonly used for automotive brakes but report response time of around 200-300 ms (Park, Stoikov, da Luz, & Suleman, 2006) due to which stopping time and stopping distance of the vehicle increases. Magnetorheological Brakes (MRB) based on Magnetorheological Fluids (MRF) owing to their response time of 15-20 ms are being explored for automotive applications (Park, Stoikov, da Luz, & Suleman, 2006; Karakoc, Park, Suleman, 2008; Nguyen & Choi, 2010). MRB, on the application of input current, produces a braking torque which is used to decelerate the vehicle. Park et al. (2006), Karakok et al. (2008), Nguyen and Choi (2010) have explored MRB for automotive applications. Sukhwani and Hirani (2008) have characterized MRB behavior experimentally for various levels of input current and speeds. Some optimization studies on MR brake have been reported as well (Assadsangabi, Daneshmand, Vahdati, Eghtesad, & Bazargan-Lari, 2011; Younis, Karakoc, Dong, Park, Suleman, 2011). Patil et al. (2016) have presented thermal analysis for automotive MR brake. Patil and Sawant (2014) have reported reliability studies on MRB envisaged for automotive application. Though till date, MRBs have not been commercialized for vehicular application, as is the case of MR dampers, a breakthrough in MR fluid technology shall pave the way for the realization of MRBs for automobiles.
Antilock brake systems (ABS) are commonly being used with CHB for avoiding wheel lock thereby increasing the stability and safety of a vehicle. Literature reports a variety of studies on CHB based MRBs. Sun et al. (2015) presented a novel nonlinear observer based on a vehicle dynamics model, and a simplified tire model is introduced to provide estimates of longitudinal and lateral vehicle velocities and the tire-road friction coefficient for vehicle safety control systems, specifically ABS control. Zheng et al. (2011) proposed a method to identify road condition using the first pressurization time, the dropped wheel speed and the slope of the dropped speed at the end of decompression. Choi (2008) executed a new type of ABS algorithm in which rear wheels are controlled to create limit cycles around the peak friction slip points. Yafu and Wang (2008) described a semi-physical simulation system model for an ABS application. Lin and Ting (2007) utilized backstepping control design schemes for quarter car model of non-linear ABS based on active suspension. Patil et al. (2016) developed new strategies considering an uncertainty estimation of disturbance due to inertial delay control (IDC) and initial observer delay (IDO) for ABS. Verghese et al. (2009) performed analysis of integrated control of ABS and collision avoidance system (CAS). Hossein and Mehdi (2010) have done work on optimization-based braking torque control law for ABS using the prediction of the wheel slip response from a continuous non-linear vehicle dynamics model. Poursamad (2009) adopted neural network (NN) based hybrid controller for ABS which controls the wheel slip, such that the braking force is maximized and steerability is maintained during braking. Baek et al. (2008) present application method of a sliding mode wheel slip controller for ABS that improves the vehicle response and increases the safety on the slippery road. All these studies have been done for CHB based ABS and present various control approaches to improve the braking performance of a vehicle. However, it will be of interest to evaluate the braking performance of MRB based ABS and improve the same with the help of appropriate control strategy.