With the development of deep learning technologies, recent research on real-world noisy image denoising has achieved a considerable improvement in performance. However, a common limitation for existing approaches is the imbalanced trade-off between denoising accuracy and efficiency. To address this problem, we propose a robust and efficient denoiser, called a hierarchical-based PID-attention denoising network (HPDNet), to flexibly deal with the sophisticated noise. The core of our algorithm is the PID-attentive recurrent network (PAR-Net) whose framework mainly consists of the LSTM network and PID controller. PAR-Net inherits the advantages of both the attentive recurrent network and control action, which can encourage more discriminatory feature representations. This learning procedure is implemented within a feedback control system, allowing a faster and more robust means to enhance feature discriminability. Furthermore, by decomposing the noisy image and stacking the PAR-Nets, our PAR-Net can work on a progressively hierarchical framework, and hence obtain multi-scale features and manageable successive refinements. On several widely used datasets, the proposed HPDNet demonstrates high efficiency, while delivering a better perceptually appealing image quality over state-of-the-art image denoising methods.