Ensuring the safe operation of neural network-controlled systems in the presence of uncertain measurements is a critical challenge. Inaccurate state estimation can lead to unsafe controller behavior, necessitating safety guarantees that can effectively handle measurement errors. Existing methods rely on pre-determining the measurement error bound \(\epsilon \) and synthesizing \(\epsilon \) -robust barrier certificates based on this bound. However, in practical applications, \(\epsilon \) is often unavailable during the design phase, which limits the applicability of existing techniques. This paper addresses the problem from a novel perspective by proposing an iterative method for synthesizing robust barrier certificates. First, we synthesize a barrier certificate for a given system, and then the maximum tolerable error bound and the most vulnerable region is calculated through an optimization problem. Second, we design an iterative optimization framework that progressively strengthens the barrier certificate by repairing its most vulnerable regions, resulting in certificates with increasingly larger tolerable error bounds. Experiments on benchmark examples demonstrate that the proposed approach can generate barrier certificates that are more robust than those of state-of-the-art work.