Rapid model prediction control method for air separation unit based on FPAA simulation neural network

A model predictive control, air separation plant technology, applied in adaptive control, general control system, control/regulation system, etc., can solve the problems of analog circuit parameters that cannot be updated online and low real-time performance

Active Publication Date: 2018-11-23
ZHEJIANG UNIV
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Problems solved by technology

Although the traditional numerical solution algorithm of quadratic programming has a wide range of applications, most of them involve operations such as matrix inversion and decomposition, so the real-time performance is low, so

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  • Rapid model prediction control method for air separation unit based on FPAA simulation neural network
  • Rapid model prediction control method for air separation unit based on FPAA simulation neural network
  • Rapid model prediction control method for air separation unit based on FPAA simulation neural network

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[0113] Such as figure 1 As shown, the present invention uses SDNN to solve the QP problem of MPC control to control the product purity of the air separation unit. The fast MPC control method based on FPAA simulation neural network is implemented as follows:

[0114] (1) Offline calculation and simulation circuit QP solver construction

[0115] Given MPC parameters: prediction time domain P; control time domain M; controlled variable weighted matrix Q y ; Control increment weighting matrix Q Δu . According to the model of the controlled process, the number of MPC control variables is n u , The number of controlled variables n y , The number of state variables n x Wait for the parameters to initialize. Consider here the controlled process described by the state space model, namely:

[0116]

[0117] among them, Is the controlled variable, Is the control variable, Is a state variable. In practical applications, it is often necessary to control the increment, so in order to obtain ...

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Abstract

The invention discloses a rapid model prediction control method for an air separation unit based on an FPAA simulation neural network. The method comprises two parts: offline calculation and online calculation. The offline calculation comprises the calculation of an MPC control parameter and the construction of an analog circuit QP solver; The online calculation comprises state updating, unconstrained optimization, calculation of translation transformation and scale transformation parameters, and QP solving of the analog circuit. The method employs a continuous neural network for solving a QPproblem, and has the natural parallelism. The method solves a problem that the analog circuit achieves the signal restriction of a continuous neural network through the translation transformation andscale transformation. On the basis, the circuit employs an FPAA for designing the analog circuit, thereby achieving the rapid solving of the QP in the MPC. Compared with an existing technology, the method effectively irons out the defects of a discrete neural network, and solves the problems in a better way that a conventional numerical method is low in MPC solving speed and is poorer in real-timeperformance.

Description

technical field [0001] The present invention relates to the fast model predictive control (Fast MPC) field of air separation unit, particularly relate to a kind of fast model predictive control framework based on FPAA simulation neural network, it is characterized in that adopting FPAA simulation neural network to solve the QP problem in MPC, has High real-time performance. Background technique [0002] Model predictive control (Model Predictive Control, referred to as MPC) has been widely used in petroleum, chemical and other process fields due to its excellent constraint optimization control ability in complex multivariable systems. In essence, the constraint optimization control capability of MPC is mainly generated from the online solution of the quadratic programming (Quadratic Programming, QP) problem with constraints. Although the traditional numerical solution algorithm of quadratic programming has a wide range of applications, most of them involve operations such a...

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 徐祖华赵均黄彦春陈铭豪邵之江
Owner ZHEJIANG UNIV
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