A decentralized model predictive controller applicable for some systems which exhibit different dynamic characteristics in different channels was presented in this paper. These systems can be regarded as combinations of a fast model and a slow model, the response speeds of which are in two-time scale. Because most practical models used for control are obtained in the form of transfer function matrix by plant tests, a singular perturbation method was firstly used to separate the original transfer function matrix into two models in two-time scale. Then a decentralized model predictive controller was designed based on the two models derived from the original system. And the stability of the control method was proved. Simulations showed that the method was effective.

This paper presents a model predictive control (MPC) method for a single supply chain (SC) unit. In order to make the most profit, the prediction of demand and the policy of inventory are the key factors for an SC unit. The ordering is a manipulate variable for an SC unit, and the pricing is introduced as another manipulate variable in this paper. The supposed price has determinate effect on the demand. The demand then can be regarded as a determinate part and a stochastic part. The inventory is regarded as a probability problem because of the stochastic demand. The two manipulate variables bring different dynamics into the SC unit. An MPC is designed, and the two‐time scale characteristic of the two manipulate variables is considered simultaneously for the SC unit. By comparing with MPC without dynamic pricing policy and order‐up‐to‐level policy, we found that the simulation results approve the validity of the MPC with dynamic pricing policy. © 2012 Curtin University of Technology and John Wiley & Sons, Ltd.

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A nonlinear model predictive control (NMPC) algorithm based on a BP-ARX combination model is proposed for multivariable nonlinear systems whose static nonlinearity between inputs and outputs could be obtained. The dynamic behavior of the system is described by a parameter varying ARX model, whose parameters are estimated on-line with recursive leastsquares algorithm and rescaled properly according to a BP neural network representing the system static nonlinearity. The construction of the BP-ARX model and a constrained NMPC algorithm based on the BP-ARX model are elaborated. The effectiveness of the proposed method is demonstrated by simulation on a multivariable chemical reactor system.