Model Predictive Control (MPC) has a long history in control engineering. It is one of the few areas that has received on-going interest from researchers in both the industrial and academic communities. Three major aspects of model predictive control make the design methodology attractive. The first is the design formulation, which uses a completely multivariable system framework where the performance parameters of the multivariable control system are related to the engineering aspects of the system; hence, they can be understood and "tuned" by engineers. The second aspect is the ability to handle "soft" constraints and hard constraints in a multivariable control framework. This is particularly attractive to industry where tight profit margins and limits on the process operation are present. The third aspect is the ability to perform process on-line optimization. MPC systems are designed using linear models unless a nonlinear model is explicitly stated. Nonlinear MPC is conceptually similar to its linear counterpart except that nonlinear models are deployed for the prediction and optimization. However, because of its computational intensity and complexity, nonlinear MPC is not widely applied. Instead, gain scheduled control system techniques have found success in the area of predictive control of nonlinear plants. This one-day short-course will begin by introducing and reinforcing the basic concepts in the design of an effective linear predictive controller. This is followed by the design of a gain scheduled predictive controller: (i) linearization of a nonlinear plant model according to operating conditions; (ii) design of linear predictive controllers using the family of linear models; (iii) gain scheduled predictive control law that will optimize a multiple model objective function with constraints and ensure smooth transitions (i.e. bumpless transfer); (iv) simulation and experimental validation of the gain scheduled MPC with constraints using MATLAB and Simulink as a platform.