A multi-model generalized predictive control system and its control method based on dynamic optimization

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 effects of eliminating interference, improving adjustment capabilities, and improving transient performance

Inactive Publication Date: 2017-03-15
SHANGHAI JIAOTONG UNIV
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  • 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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  • A multi-model generalized predictive control system and its control method based on dynamic optimization
  • A multi-model generalized predictive control system and its control method based on dynamic optimization
  • A multi-model generalized predictive control system and its control method based on dynamic optimization

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Experimental program
Comparison scheme
Effect test

Embodiment

[0102] The S1 dynamic optimization layer is set as the following process mathematical model:

[0103]

[0104]

[0105]

[0106] x(0)=[300] T ,0≤δ(k)≤30,k∈[0,k f ] (11)

[0107] In the formula, f is the economic objective function, x A ,x B are two parameters related to the key variable δ(k).

[0108] Divide the time interval into the same 10 segments, and then set the PSO parameter to set the number of particles to m=10, dimension D=10, and learning factor c 1 =2,c 2 =2, 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.

[0109] The S2MPC layer controlled object is expressed as:

[0110] y(k)+a 1 y(k-1)+a 2 y(k-2)=b 1 u(k-1)+b 2 u(k-2)+ξ(k) / Δ

[0111] The number of control steps is taken as 300, a 1 =-1.8, a 2 =1.2,b 1 =1,b 2 =2, the system parameters jump at step 150. jump to ...

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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

technical field [0001] The 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 for the...

Claims

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Application Information

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