Model predictive control (MPC) is regarded as the prime advanced control method for a wide class ofindustrial 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 industryis primarily due to its unique constraint handlingcapability. This book investigates how the basicframework of model predictive control can beextended to handle uncertainty in the problem datawhile 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 complexityand optimality. Using the concept of parametricprogramming, a practical low-complexity algorithm is presented which ensures robust closed-loop stabilitywithout severely compromising optimality. The bookshould be useful for researchers in the areas ofrobust 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.