The self-ensembling methods have achieved amazing performance for semi-supervised representation learning and domain adaptation. However, the disadvantage of these methods is that the teacher network is tightly coupled with the student network, which limits the descriptive ability of the self-ensembling model. To overcome the coupling effect between the teacher network and the student network, we propose a novel Triplet Ensemble Student-Teacher (TEST) model for unsupervised person re-identification, which consists of one teacher network T and two student networks S1 and S2 . Similar to the traditional self-ensembling model, the student network S1 is applied to update the teacher network T . Furthermore, a closed-loop learning mechanism is built in the TEST model by imposing an ensemble consistent constraint between T and S2 , and performing a heterogeneous co-teaching procedure between S1 and S2 . With the closed-loop learning mechanism, the TEST model can loosen the constraint between the teacher T and the student S1 , and enhance the descriptive ability of S1 . Besides, the knowledge exchange between S1 and S2 can ensure that the two student networks can elegantly deal with the noisy labels and avoid coupling. By training the TEST model with the clustering-generated pseudo labels, we can achieve effective and robust representation learning for unsupervised person re-identification. The evaluations on three widely-used benchmarks show that our approach can achieve significant performance compared with state-of-the-art methods.