Forecasts will play an increasingly important role in the next generation of control systems. In nominal conditions, predictions of system dynamics, human behavior and environmental conditions can be used by the control algorithm to improve the performance of the resulting system. However, in practice, constraint satisfaction, performance guarantees and real-time computation are challenged by the complexity of the engineered system and uncertainty in the environment where the system operates. In this talk I will present the theory and tools that we have developed over the past ten years for the systematic design of predictive controllers for uncertain linear and nonlinear systems. I will first provide an overview of our theoretical efforts. Then, I will focus on our recent results in stochastic predictive control design for smart buildings. More info on: www.mpc.berkeley.edu