Design of a state-based nonlinear controller

Abstract

A developer of thermofluid simulation software requires algorithms which are used to design and implement PI controllers at some operating points of nonlinear industrial processes. In general, the algorithm should be applicable to multivariable plant models which may be nonlinear. In some areas there is a hesitancy to use controllers for nonlinear processes which use neural networks or fuzzy logic or a combination thereof. PI controllers are also standard in various SCADA systems. Since control normally takes place around an operating point, a linearised model is obtained. A controller designed for a particular operating point, may not be suitable for other operating points. Since a multitude of variables are to be controlled in the plant, the problem becomes more acute. In this research, a methodology is derived for the design of multivariable control using PI controllers. The parameters of the controllers depend on the operating point, and are therefore nonlinear. The behaviour is deterministic in a classical control sense around a range of operating points. This should remove concerns of non-deterministic behaviour as attached to neural networks due to the lack of stability tests for them which are industry accepted. A state-space approach leads to the development of a design methodology, which is then used to implement these algorithms. The P- and PI-controllers will be designed using traditional methods, as well as by an optimal procedure which makes use of a genetic algorithm. The GA tuning algorithm yields superior performance when compared to other methods.