Generalized predictive control using genetic algorithms (GAGPC)
Generalized predictive controllers (GPCs) have been successfully applied in process control during the last decade. The performance of unstable, non-minimum-phase, or linear processes with dead-time are improved with this type of controller. However, the kind of process that can be controlled, or the kind of optimization method used to derive the controller, can present important restrictions: the performance index must be quadratic, and the model of the process must be linear and without actuator constraints. In other words, GPCs are limited when used to control real industrial processes. In this paper the genetic algorithms (GA) technique is used for optimization in GPCs. As this technique is robust under the presence of nonlinear structures in the cost function and constraints, it will be shown that a GPC optimized using the GA technique (GAGPC) can perform better in a real industrial environment.
predictive control genetic algorithms optimization actuator constraints digital control nonlinear processes