Planning and controlling complex project portfolios is a challenging task. These portfolios are subject to a number of potential risk sources coupled with resource constraints, intricate precedence relationships, and penalties for project delays. For this reason, it is fundamental that optimal strategies for the allocation of the available resources are constantly adopted by the decision makers to ensure that their projects are completed within limits of time and cost. Moreover, the uncertainty that affects these projects has to be taken into account for effective resource allocation decisions. Within this context, this work proposes a robust optimization-based methodology for planning and controlling project portfolios under uncertainty. The method combines models and algorithms for multiple resource allocation problems under the same robust optimization framework. In this approach, the uncertainty environment is modeled as an adversary that selects the worst-case combination of risks for any decision maker s actions. Subsequently, the main goal of the decision maker is to determine optimal resource allocation plans for minimizing a particular objective subject to the assumption that the adversary s worst-combination of risks will materialize. The approach also provides a way to control the degree of conservatism of the solutions. For each studied problem, a solution strategy is developed through a reformulation scheme from a compact min-max formulation to a cut-generation algorithm. Several computational experiments are conducted, providing key insights that drive the design of the referred portfolio planning and control methodology. The ineffectiveness of traditional critical path analysis under worst-case realizations of uncertain activities durations and the importance of taking integrated resource allocation decisions in the context of project portfolios, are examples of the key findings of the experiments. The application of the methodology is demonstrated in a case study of a portfolio aimed at the construction of two refineries. This example presents the capabilities of the developed techniques in a practical context.