Gap multi-model weighting function parameter self-tuning method

A technology of parameter self-tuning and weighting function, which is applied in adaptive control, instrument, control/regulation system, etc., can solve the complex and tedious problem of multi-model weighting function parameter tuning, reduce the dependence on prior knowledge, and optimize control performance. , to avoid cumbersome effects

Pending Publication Date: 2019-11-12
HOHAI UNIV CHANGZHOU
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to overcome the complex and cumbersome defect of the existing gap...

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  • Gap multi-model weighting function parameter self-tuning method
  • Gap multi-model weighting function parameter self-tuning method
  • Gap multi-model weighting function parameter self-tuning method

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Embodiment Construction

[0027] In order to make the objectives, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the following The described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0028] The technical solutions of the present invention will be further described below in conjunction with the drawings and specific implementations.

[0029] A gap multi-model weighting function parameter self-tuning method, specifically for a continuous stirred reactor system (CSTR) simulation and analysis.

[0030]...

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Abstract

The invention discloses a gap multi-model weighting function parameter self-tuning method, which is characterized in that the characteristics of a nonlinear system are analyzed, scheduling variables capable of reflecting system operating conditions are selected, a model set of an approximate nonlinear system is constructed by using a multi-model decomposition algorithm, a sub-controller is designed based on each sub-model in the model set, a weighting function of the sub-controller comprises a reciprocal of a gap distance between the nonlinear system and each sub-model at a current moment, theweighting function has a unique tuning parameter, and an optical value of the tuning parameter is obtained by optimizing an integral absolute error value between closed-loop output and reference input of the nonlinear system. The gap multi-model weighting function parameter self-tuning method has the advantages of avoiding the cumbersomeness of manual parameter tuning of the weighting function, reducing the dependence of parameter tuning on prior knowledge, and optimizing the control performance of a closed-loop system.

Description

Technical field [0001] The invention relates to a method for self-tuning parameters of gap multi-model weighting functions, and belongs to the technical field of multi-model control of nonlinear systems. Background technique [0002] The multi-model control method has natural advantages in dealing with the control problems of nonlinear systems with relatively wide operating ranges and relatively large interference signals. The multi-model control method based on the decomposition-synthesis principle can effectively transform complex nonlinear control problems into a series of simple linear control problems through decomposition; then, the nonlinear control problems can be solved by solving this series of linear control problems. [0003] The multi-model method mainly includes three key steps: multi-model decomposition, local control design, and multi-model synthesis. Among them, multi-model synthesis usually adopts two methods of hard handover and soft handover to synthesize sub-c...

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 杜静静陈俊风李建
Owner HOHAI UNIV CHANGZHOU
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