This workshop introduces its audience to the theory, design and applications of model predictive control (MPC) under uncertainty. The workshop provides conceptual and technical principles governing rigorous and computationally effective methods for design of MPC under set–membership and probabilistic uncertainty. The theoretical fundamentals are carefully introduced and studied within the frameworks of robust MPC and stochastic MPC. The technical foundations are complemented with a discussion of related design and practical aspects as well as with an overview of effective computations based on convex and reliable real–time optimization. Thus, the workshop provides a unique and unified exposure to key constituents of MPC under uncertainty. It also allows for a comprehensive understanding of highly powerful and applicable robust and stochastic MPC algorithms that have seen real–life transition from theory to practice in several important applications within aerospace and automotive industries. Finally, a carefully designed panel discussion at the workshop assesses the current state of affairs in, and identifies relevant future research directions for, MPC under uncertainty.