So far, many control algorithms have been developed for singularly perturbed systems. However, in many industrial processes, enforcing closed-loop fast-slow dynamics for peculiarly non-separable ones is a prior request and a crucial issue to be resolved. Aiming at the above problem, this paper presents two dual-level model predictive control (MPC) algorithms for two-timescale dynamical systems with unknown bounded disturbance and input constraint. The proposed algorithms, each one composed of two regulators working in slow and fast time scales, are designed to generate closed-loop separable dynamics at the high and low levels. As a key feature, the proposed algorithms are not only suitable for singularly perturbed systems, but also capable of imposing separable closed-loop performance for dynamics that are non-separable and strongly coupled. The recursive feasibility and convergence properties are proven under suitable assumptions. The simulation results on controlling a Boiler Turbine (BT) system, including the comparisons with other classic controllers are reported, which show the effectiveness of the proposed algorithms.