The focus of this paper is to develop a semi-parallel control method using an inversion of identification model of a magnetorheological (MR) fluid damper along with a smart predictor controller (SPC) for a damping system using that damper and an electrohydraulic actuator (EHA) in order to realize the real time position/force control of the industrial task requiring interaction with the environment. The inverse model of MR fluid damper is established base on a self-tuning Lyapunov-based fuzzy (STLF) model. This STLF model is designed in the form of a center average fuzzy interference system, of which the fuzzy rules are planted based on the Lyapunov stability condition. In addition, in order to optimize the STLF model, the back propagation learning rules are used to adjust the fuzzy weighting net. Meanwhile, the SPC is constructed using a nonlinear PID controller (NPID) base on feedforward neural network and a smart Grey-Markov predictor (SGMP): Here, the NPID controller is built to drive the system to desired targets. Additionally, a learning mechanism with robust checking conditions is implemented into the NPID in order to optimize online its parameters with respect to the control error minimization. Besides, the SGMP with self-tuning ability of the predictor step size takes part in, first, estimating the system.