Model predictive control (MPC) is a control method (or group of control methods) which make explicit use of a model of the process to obtain the control signal by minimizing an objective function. Control law is easy to implement and requires little computation, its derivation is more complex than that of the classical PID controllers. The main benefit of MPC is its constraint handling capacity: unlike most other control strategies, constraints on inputs and outputs can be incorporated into the MPC optimization (Camacho, E., 2004). Another benefit of MPC is its ability to anticipate to future events as soon as they enter the prediction horizon. The implementation supposes good knowledge of system for the purpose of model creation using the system identification. Modeling and identification as a methodology dates back to Galileo (1564-1642), who also is important as the founder of dynamics (Johanson, R., 1993). Identification has many aspects and phases. In our work we use the parametric identification of real system using the measured data from control centre. For the purpose of identification it is interesting to describe the sought process using inputoutput relations. The general procedure for estimation of the process model consists of several steps: determination of the model structure, estimation of parameters and verification of the model. Finally we can convert the created models to any other usable form. This chapter gives an introduction to model predictive control, and recent development in design and implementation. The controlled object is an urban tunnel tube. The task is to design a control system of ventilation based on traffic parameters, ie to find relationship between traffic intensity, speed of traffic, atmospheric and concentration of pollutants inside the tunnel. Nowadays the control system is designed as tetra-positional PID controller using programmable logic controllers (PLC). More information about safety requirements for critical processes control is mentioned in the paper (Ždánsky, J., Rástočný, K. and Záhradník, J., 2008). The ventilation system should be optimized for chosen criteria. Using of MPC may lead to optimize the control way for chosen criteria. Even more we can predict the pollution in the tunnel tube according to appropriate model and measured values. This information is used in the MPC controller as measured disturbances. By introducing predictive control it will be made possible to greatly reduce electric power consumption while keeping the degree of pollution within the allowable limit. www. intechopen. com