IntroductionShaking tables are being increasingly used in earthquake engineering research field and remain one of the preferred tools for seismic testing [1]. Their main objective is to replicate a wide range of a desired motion with a high degree of accuracy, in order to generate meaningful and reliable results. However, despite the significant progress achieved in shake table design and control technology during the last decades, number of challenges still exist [2]. These challenges fall into two categories: (i) economical and practical challenges and (ii) technical challenges. In the first category we can cite the problem of the full-scale realistic tests of large structural systems which are constrained by the limited financial resources and capacities of the existing laboratories. The second category includes control issues, consideration of different loading conditions other than dynamic base motion and soil-structure interaction effects in shaking-table testing.Undoubtedly, the control of shaking table is the key element of the whole system and one of the most difficult technical challenges. In fact, the modern multiple degree-of-freedom shaking table with a resonating and eccentric specimen has the characteristics of a strongly coupled multiple input, multiple output dynamic system [3]. In such a system, servovalves characteristics, oil column resonance, frictions in the system, cross coupling between degrees of freedom, specimen compliance and noise in feedback transducers, are some of the predominant sources of nonlinearities that can adversely affect the accurate control, stability and high fidelity signal reproduction, of the shaking table.Various control techniques have been developed in order to provide a shaking table with optimum performance and high level of motion control. The earliest methods use conventional linear controller, such as closed-loop PID, deltaP and feed-forward control algorithm [4,5], and three variable control (TVC) [6,7]. However, due to the complexity of the problem the need for more sophisticated control systems become necessary. With advances in computing technologies, followed by the breakthrough development of the digital signal processing and realtime operations, methods based on hybrid control strategies