||Historically, the process industry has recognized the important work of automatic control in the proper functioning of the production process. Although the preferred control strategy in most applications is the implementation of simple ties of PID (Proportional-Integral-Derivative) control, there are a number of characteristics which sometimes are not considered explicitly in the design of these PID controllers, such as delays, not measurable variables, parameter uncertainty, variance in time, nonlinearities, constraints and multivariable interactions. Many developments of modern control theory are designed to tackle these features, but the industry has been conservative in applying these tools. This has led many critics to say that there is a gap between theory and practice of control.
In industry, many processes are behind in their dynamic behavior. Although these delays are due primarily to dynamic characteristics of some systems, they may also be made by processing time or the accumulation of time delays in a number of simple dynamical systems connected in series. Typical applications in the presence of delays are communication systems, chemical processes, transportation systems, power systems, tele-operation systems and biosystems.
From classical control perspective, the presence of delays in a system helps to reduce the phase margin and hence profit margins, achieving even destabilize the closed loop response. However, the introduction of a delay may be beneficial to achieve stability in an unstable system (Stépán, 1989), which explains the five decades of interest in the stability and control of these systems (Stépán, 1989), (Bellm, 1963), (Datko, 1978), (Hale, 1993), (Diekmann, Van Gils, & Verduyn-Lunel, 1995). (Niculescu W. M.-I., 2007), (Niculescu S. I., 2001).
Furthermore, due to the difficulty of accurately model a complex process, there are always modeling errors. The development of methods to address the problem of model uncertainty is a big challenge and today there have been different approaches to tackle it. Sometimes, in an attempt to take into account all relevant dynamics and reduce modeling error, it comes to the development of increasingly complex models. However, this maneuver can lead to models that are too difficult for mathematical analysis and design of controllers.
Another common problem in the control systems is due to external disturbances. Such disturbances bring harm to the system performance, so rejection is one of the key objectives in the design of the controller. In control community of Industrial processes - like oil industry and metal industry - the production processes are usually influenced by external disturbances such as variations in raw material quality, production load fluctuations, and variations of complicated production environments. In the regulation of blood glucose in diabetic patients, for example, external disturbances are related to food intake, physical activity conducting and stress, among others.
In this work is proposed a new control strategy that combines the virtues of monitoring techniques MPC (Model Predictive Control), QFT (Quantitative Feedback Theory) and observers of disturbances (DOB), to address the delays, uncertainty in the model and external disturbances of nonlinear multivariable systems. It is intended that the proposed scheme be as simple and convenient as possible and that is validated in at least two cases of multivariable systems, that can be active power control in a wind turbine, the automatic regulation of glucose levels in patients with type 1 diabetes mellitus (T1DM) and / or control of various variables of quality in a crude distillation process.