GENERALIZED PREDICTIVE CONTROL USING GENETIC ALGORITHMS (GAGPC)

Generalized predictive controllers (GPCs) have been successfully appli ed in process control during the last decade. The performance of unsta ble, non-minimum-phase, or linear processes with dead-time are improve d with this type of controller. However, the kind of process that can be controlled, or the kind of optimization method used to derive the c ontroller, can present important restrictions: the performance index m ust be quadratic, and the model of the process must be linear and with out actuator constraints. In other words, GPCs are limited when used t o control real industrial processes. In this paper the genetic algorit hms (GA) technique is used for optimization in GPCs. As this technique is robust under the presence of nonlinear structures in the cost func tion and constraints, it will be shown that a GPC optimized using the GA technique (GAGPC) can perform better in a real industrial environme nt. (C) 1998 Elsevier Science Ltd. All rights reserved.