Recent decades have witnessed a trend that control theoretical techniques are widely leveraged in various areas, e.g., design and analysis of computational models. From the control theoretical perspective, a computational method for solving an algebraic can be deemed as a controller, of which the error should converge to zero, and subsequently, the controller’s output is identical to the theoretical solution of the algebraic equation. This chapter makes progress along this direction by applying control theoretical techniques to construct a new computational neural dynamics for the perturbed nonstationary quadratic programming (QP) with time-varying parameters. Specifically, to break the limitations of continuous-time models in handling nonstationary problems, the presented discrete neural dynamics model is functional to robustly deal with noises. In addition, a modified Newton iteration model and an improved gradient-based neural dynamics model are also established by referring to the superior structural technology of the first neural dynamics from the viewpoint of control, where the chief breakthrough is their excellent convergence and robustness over the traditional models. Numerical experiments are conducted to demonstrate the advantages of the presented neural dynamics models for the perturbed nonstationary QP.