In the visual object tracking, the tracking algorithm based on discriminative model prediction have shown favorable performance in recent years. Probabilistic discriminative model prediction (PrDiMP) is a typical tracker based on discriminative model prediction. The PrDiMP evaluates tracking results through output of the tracker to guide online update of the model. However, the tracker output is not always reliable, especially in the case of fast motion, occlusion or background clutter. Simply using the output of the tracker to guide the model update can easily lead to drift. In this paper, we present a robust model update strategy which can effectively integrate maximum response, multi-peaks and detector cues to guide model update of PrDiMP. Furthermore, we have analyzed the impact of different model update strategies on the performance of PrDiMP. Extensive experiments and comparisons with state-of-the-art trackers on the four benchmarks of VOT2018, VOT2019, NFS and OTB100 have proved the effectiveness and advancement of our algorithm.