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Payload-specific vibration control may be suitable for a particular task but lacks generality and transferability required for adapting to the various payload. Self-decoupling and robust vibration control are the crucial problems to achieve payload-agnostic vibration control. However, there are problems still unsolved. In this article, we present a maglev vibration isolation platform (MVIP), which aims to attenuate vibration in the payload-agnostic task under a dynamic environment. Since efforts trying to suppress disturbance will encounter inevitable coupling problems, we analyzed the reasons resulting in it and proposed unique and effective solutions. To achieve payload-agnostic vibration control, we proposed a new control strategy, which is the main contribution of this article. It consists of a self-construct radial basis function neural network inversion (SRBFNNI) decoupling scheme and hybrid adaptive feed-forward internal model control (HAFIMC). The former one enables the MVIP to create a self inverse model with little prior knowledge and achieving self-decoupling. For the unique structure of MVIP, the vibration control problem is stated and addressed by the proposed HAFIMC, which utilizes the adaptive part to deal with the periodical disturbance and the internal mode part to deal with the stability.