Deep space missions are characterized by severely constrained communication links and often require intervention from Ground to overcome the difficulties encountered during the mission. An adequate Ground control could be compromised due to communication delays and required Ground decision-making time, endangering the system, although safing procedures are strictly adhered to. To meet the needs of future missions and increase their scientific return, space systems will require an increased level of autonomy on-board. We propose a comprehensive approach to on-board autonomy relying on model-based reasoning. This approach encompasses in a uniform formal framework many important reasoning capabilities needed to achieve autonomy (such as plan generation, plan validation, plan execution and monitoring, fault detection identification and recovery, run-time diagnosis, and model validation). The controlled platform is represented symbolically, and the reasoning capabilities are seen as symbolic manipulation of such formal model. In this approach we separate out the discrete control parts and the continuous parts of the domain model (e.g., resources such as the power consumed or produced and the data acquired during an execution of a certain action) to facilitate the deliberative actions. The continuous part is associated to the discrete part by means of the resource estimation functions, that are taken into account while validating the generated plan and while monitoring the execution of the current plan. We have developed a prototype of this framework and we have plugged it within an Autonomous Reasoning Engine. This engine has been evaluated on two case studies inspired by real-world ongoing projects: a planetary rover and an orbiting spacecraft. We have performed a characterization of the approach in terms of reliability, availability and performances both on a desktop platform and on a spacecraft simulator.