Process control, automatic linear systems

Nonlinearity In addition, it is well known that the process kinetics shows a highly nonlinear behavior. This a serious drawback in instrumentation and automatic control because, in contrast to linear systems where the observability can be established independently of the process inputs, the nonlinear systems must accomplish with the detectability condition depending on the available on-line measurements, including process inputs in the case of non autonomous systems [23]. 

An adaptive control system can automatically modify its behaviour according to the changes in the system dynamics and disturbances. They are applied especially to systems with non-linear and unsteady characteristics. There are a number of actual adaptive control systems. Programmed or scheduled adaptive control uses an auxiliary measured variable to identify different process phases for which the control parameters can be either programmed or scheduled. The "best" values of these parameters for each process state must be known a priori. Sometimes adaptive controllers are used to optimise two or more process outputs, by measuring the outputs and fitting the data with empirical functions. 

Currently the design of control systems relies heavily upon an explicit mathematical model of the system. However, such a model is often very difficult and sometimes impossible to find. For this reason, conventional control systems are usually based on a linearized and highly simplified mathematical model of the dynamics of the system. From another point of view, robust control incorporates the varying or uncertain parameters of the model. This work considers the application of a robust control methodology that allows the controller to automatically adjust to changing process variables and thereby provide a imiform response over a wide range of operating conditions. 

---