State-space approach to nonlinear predictive generalized minimum variance control

Grimble, M.J. and Majecki, P.M., EPSRC on the Platform Grant Project No EP/C526422/1 (Funder) (2010) State-space approach to nonlinear predictive generalized minimum variance control. International Journal of Control, 83 (8). pp. 1529-1547. ISSN 0020-7179 (https://doi.org/10.1080/00207171003736303)

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Abstract

A Nonlinear Predictive Generalized Minimum Variance (NPGMV) control algorithm is introduced for the control of nonlinear discrete-time multivariable systems. The plant model is represented by the combination of a very general nonlinear operator and also a linear subsystem which can be open-loop unstable and is represented in state-space model form. The multi-step predictive control cost index to be minimised involves both weighted error and control signal costing terms. The solution for the control law is derived in the time-domain using a general operator representation of the process. The controller includes an internal model of the nonlinear process but because of the assumed structure of the system the state observer is only required to be linear. In the asymptotic case, where the plant is linear, the controller reduces to a state-space version of the well known GPC controller.

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