Model predictive control (MPC) has a long history in the ?eld of control en- gineering. It is one of the few areas that has received on-going interest from researchers in both the industrial and academic communities. Four major as- pects of model predictive control make the design methodology attractive to both practitioners and academics. The ?rst aspect is the design formulation, which uses a completely multivariable system framework where the perfor- mance parameters of the multivariable control system are related to the engi- neering aspects of the system; hence, they can be understood and ‘tuned’ by engineers. The second aspect is the ability of the method to handle both ‘soft’ constraints and hard constraints in a multivariable control framework. This is particularly attractive to industry where tight pro?t margins and limits on the process operation are inevitably present. The third aspect is the ability to perform on-line process optimization. The fourth aspect is the simplicity of the design framework in handling all these complex issues. This book gives an introduction to model predictive control, and recent developments in design and implementation. Beginning with an overview of the ?eld, the book will systematically cover topics in receding horizon con- trol, MPC design formulations, constrained control, Laguerre-function-based predictive control, predictive control using exponential data weighting, refor- mulation of classical predictive control, tuning of predictive control, as well as simulation and implementation using MATLAB and SIMULINK as a platform. Both continuous-time and discrete-time model predictive control is presented in a similar framework.