Model predictive control (MPC) is regarded as the prime advanced control method for a wide class of industrial processes and perhaps one of the most significant developments in process control since the introduction of the PID controller in the early 1940's. The success of the MPC paradigm in industry is primarily due to its unique constraint handling capability. This book investigates how the basic framework of model predictive control can be extended to handle uncertainty in the problem data while maintaining stability, feasibility and low- complexity. The framework of min-max control is studied in detail with specific emphasis upon the inherent trade-off between controller complexity and optimality. Using the concept of parametric programming, a practical low-complexity algorithm is presented which ensures robust closed-loop stability without severely compromising optimality. The book should be useful for researchers in the areas of robust predictive control, linear matrix inequalities and parametric programming, and practitioners who may be considering utilizing robust MPC in low-cost embedded systems areas including automotive control, MEMS and power electronics.