Interior point algorithm based LPV (Linear Parameter Varying) model nonlinear predicating control method

Abstract

The invention discloses an interior point algorithm based LPV (Linear Parameter Varying) model nonlinear predicating control method. The method comprises the steps of linearizing a complex mechanism model of a system at a set working point to obtain a plurality of linear sub-models; selecting weight functions; weighting each linear sub-model to obtain a global approximation model of the system, namely, PLV model; treating the LPV model as the predicating model and selecting the secondary performance index function to create a nonlinear predicating control topic; solving the optimal topic by the interior point algorithm during rolling optimizing so as to obtain the optimal control sequence for achieving the nonlinear predication control. Compared with the prior art, the method has the advantages that the LPV-based full-simultaneous direct solving is performed, so that the solving precision is high, and the algorithm needs a little time; the control effect is obvious; the transition process of the system is reduced; the resource consumption is reduced; the control quality of the system is particularly obviously improved under a large variable working condition range.

Description

technical field [0001] The invention belongs to the field of industrial process control and relates to an LPV model nonlinear predictive control method based on an interior point method. Background technique [0002] In actual industry, with the continuous pursuit of production efficiency and stricter environmental protection, industrial processes have become more and more complex, and most of them have strong nonlinear characteristics, and are complex chemical objects with constraints on control quantities. Using a single linear model to describe such a system and design a controller cannot meet the control performance requirements, and even cause system instability. Therefore, it is necessary to explore multi-model methods. The linear variable parameter model (LPV) in the multi-model method has the advantages of simple algorithm and can use the main factors that cause the nonlinearity of the system as scheduling variables. Therefore, the present invention uses the LPV mode...

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Problems solved by technology

Moreover, when the QP subproblem has inequality constraints, the efficiency of QP solution will be reduced. When the problem scale or inequality constraints increase, the problem scale will increase exponentially, the algorithm will take a long time, the amount of online calculation is relatively large, and the solution accuracy is not high.

If there are a large number of boundary constraints in the optimization proposition, the solution of the QP problem in the multi-step linearization method will become a bottleneck

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