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Multi-model generalized predictive control system based on dynamic optimization and control method thereof

A generalized predictive control and dynamic optimization technology, applied in general control systems, control/regulation systems, adaptive control, etc., can solve problems such as multi-model matching, and achieve the goal of eliminating interference, reducing system cost consumption, and improving system economic benefits. Effect

Inactive Publication Date: 2013-12-04
SHANGHAI JIAO TONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the existence of random noise, it is difficult for the conventional multi-model to match the actual process characteristics

Method used

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  • Multi-model generalized predictive control system based on dynamic optimization and control method thereof
  • Multi-model generalized predictive control system based on dynamic optimization and control method thereof
  • Multi-model generalized predictive control system based on dynamic optimization and control method thereof

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Experimental program
Comparison scheme
Effect test

Embodiment

[0104]

[0105] The S1 dynamic optimization layer is set as the following process mathematical model:

[0106]

[0107]

[0108]

[0109] (11)

[0110] In the formula, is the economic objective function, for the key variable related two parameters.

[0111] Divide the time interval into the same 10 segments, and then the PSO parameter sets the number of particles to take ,dimension , learning factor , the maximum number of iterations is 500. The set value of the key variable is obtained through dynamic optimization, and this set value is used as the reference trajectory of the multi-model generalized predictive controller.

[0112] The controlled object of S2 MPC layer is expressed as:

[0113]

[0114] The number of control steps is taken as 300, , , , , the system parameters jump at step 150. jump to , , constant. for The fixed model of uniformly distributed white noise is taken as , , , There are 10 in t...

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Abstract

A multi-model generalized predictive control system based on dynamic optimization comprises a dynamic optimization layer, an MPC layer and a basic control layer. The dynamic optimization layer is located in the upper layer and calculates the optimization value of a critical control variable as the optimal set value of the MPC layer; the MPC layer is located in the lower layer and adjusts a to-be-optimized variable based on a rolling optimization prediction algorithm on the condition that the to-be-optimized variable satisfies a model dynamic behavior, thereby tracking the optimal set value obtained in the S1; the basic control layer is located in the bottom layer and transmits the final optimization value of the to-be-optimized variable to an actuating mechanism. According to the present invention, the cost consumption of the system is reduced, the economic benefit of the system is improved, the transient performance and the model parameter jump adjusting capability of the system can be improved, at the same time, the interference of disturbance on the output of the system can be eliminated effectively.

Description

[0001] Technical Field The present invention relates to the field of control optimization, in particular to a design method of a multi-model generalized predictive control system based on dynamic optimization. Background technique [0002] The market competition intensified by the globalization of production makes the requirements for reducing cost consumption and improving economic benefits increasingly high. The optimization of the operational performance of the process can create huge benefits for the production of enterprises, so more effective and advanced optimization and control strategies are required to be applied to related industrial equipment. The traditional process optimization technology based on the steady-state model has made extraordinary achievements, and it has a good optimization effect on the process whose model is not strongly time-varying. However, in the actual industry, the time-varying phenomenon of the model often occurs, which makes it difficult fo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
Inventor 王昕宋治强
Owner SHANGHAI JIAO TONG UNIV
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