Identification of nonlinear process models in an LPV framework

Driven by the current economical needs, developments in process design and control point out that deliberate operation of chemical process requires better models and control designs than what is offered by the traditional Linear Time-Invariant (LTI) framework. In this paper an identification approach based on Linear Parameter-Varying (LPV) models is introduced for process systems which enables the use of powerful LPV control synthesis tools. LPV systems represent an intermediate step between LTI and nonlinear descriptions as they are capable of describing the system over its whole operating range but preserve many advantages of LTI descriptions. Estimation of LPV models is efficiently solvable by using series expansion type of model structures, like orthonormal basis function models. Advantageous properties of this approach and modeling paradigm are investigated with respect to process models and the added value over LTI models is demonstrated via an example of a continuous stirred tank reactor.