This work considers the problem of control of nonlinear process systems subject to input constraints and faults in the control actuators. Faults are considered that preclude the possibility of continued operating at the nominal equilibrium point and a framework (which we call the safe-parking framework) is developed to enable efficient resumption of nominal operation upon fault-recovery. To this end, first Lyapunov-based model predictive controllers, that allow for an explicit characterization of the stability region subject to constraints on the manipulated input, are designed. The stability region characterization is utilized in selecting 'safe-park' points from the safe-park candidates (equilibrium points subject to failed actuators). Specifically, a candidate parking point is termed a safe-park point if (1) the process state at the time of failure resides in the stability region of the safe-park candidate (subject to depleted control action), and (2) the safe-park candidate resides within the stability region of the nominal control configuration. Performance considerations, such as ease of transition from and to the safe-park point and cost of running the process at the safe-park point, are then quantified and utilized in choosing the optimal safe-park point. The proposed framework is illustrated using a chemical reactor example and robustness with respect to parametric uncertainty and disturbances is demonstrated on a styrene polymerization process. (C) 2008 Elsevier Ltd. All rights reserved.