Rapid model prediction control method

Abstract

The invention relates to a method for rapid predicting controlling a model which includes steps as follows: 1) matrix converting an inequality constraint aggregate; 2) selecting compression matrix parameter of the inequality constraint aggregate; 3) constructing the inequality constraint aggregate compression matrix; 4) mapping changing of the constraint aggregate based on compression matrix; 5) rolling horizon strategies of model predicting optimization problem; 6) applying of rapid control model predicting control rule. The invention only convert to the constraint aggregate and dose not refer to any conversion of waited optimization variable that can reduce scale of MPC optimization control problem efficiently, decrease the on-line calculating amount, increase calculating efficiency, and does not effect calculation inputted by optimization control, can ensure MPC optimization control function. The method has few waited parameter, simple construction method, is easy to on line debugging, and is suitable for industry process, for example petrochemical engineering, and also is suitable for high real-time control field, for example car and robot.

Description

A Fast Model Predictive Control Method technical field The invention relates to a model predictive control (MPC, Model Predictive Control) method, in particular to a fast model predictive control method for a high real-time controlled object. Background technique At present, due to the advantages of online optimization of linear or nonlinear systems, MPC is not only widely used in petrochemical and other industrial processes, but also in high real-time control fields such as automobiles and robots. In the field of high real-time control, limited by the controlled object and performance index, MPC can be attributed to solving a constrained nonlinear programming problem, and its online optimization calculation is not only cumbersome but also time-consuming. With the increase of the scale of the MPC optimization problem, when the calculation time exceeds the control period, the optimal control input cannot be obtained due to the incomplete solution of the optimization problem...

AI Technical Summary

Problems solved by technology

Under normal circumstances, the use of the direct dimension reduction method not only does not help to improve the computational efficiency of MPC, but also affects the calculation of the optimal control input, weakening the optimal control function of MPC

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