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Control strategy for achieving multivariable PID in PLS frame on basis of Gaussian process model

A Gaussian process model and control strategy technology, applied in the direction of electric controllers, controllers with specific characteristics, etc., can solve the problems of incomplete model establishment, poor control performance, and inability to eliminate multi-loop interaction

Inactive Publication Date: 2018-11-27
TAIYUAN UNIV OF TECH
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Problems solved by technology

[0006] The invention solves the problems that the existing control strategy cannot eliminate the mutual influence between multiple loops, the establishment of the model is incomplete, and the control performance is poor, and provides a multivariable PID control strategy based on the Gaussian process model in the PLS framework

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  • Control strategy for achieving multivariable PID in PLS frame on basis of Gaussian process model
  • Control strategy for achieving multivariable PID in PLS frame on basis of Gaussian process model
  • Control strategy for achieving multivariable PID in PLS frame on basis of Gaussian process model

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Embodiment Construction

[0078] The multivariable PID control strategy is implemented in the PLS framework based on the Gaussian process model, including the following steps:

[0079] Step 1. Give the parameters of the PID controller:

[0080] Consider a multivariate stochastic dynamical system with m inputs and l outputs: u k =[u 1k ,u 2k ,...,u mk ] T ,y k =[y 1k ,y 2k ,...,y lk ] T , where u k and y k They are the control input and the system output respectively; the setting values ​​corresponding to each moment are given as follows:

[0081] Next, the algorithm of the PID controller is given: where the error matrix is: parameter k P ,k I and k D are the proportional gain, integral time constant and differential time constant of the PID controller;

[0082] Therefore, the rate form of a discrete PID controller can be expressed as:

[0083]

[0084] where the parameter matrix and error matrix are respectively and

[0085] Step 2. In the PLS framework, use the Gaussian p...

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Abstract

The invention relates to the field of nonlinear time-varying system optimization control, in particular to a control strategy for achieving multivariable PID in a PLS frame on the basis of a Gaussianprocess model. The problems that by means of existing control strategies, interaction among multiple loops cannot be eliminated, a built model is not complete, and the control performance is poor aresolved. The control strategy for achieving multivariable PID in the PLS frame on the basis of the Gaussian process model comprises the steps that 1, parameters of a PID controller are given; 2, the GP(Gaussian process) model is used for achieving the control strategy of multivariable PID, wherein firstly, an MIMO system is subjected to decoupling, secondly, the model uncertainty is improved, thirdly, a covariance function is chosen, and the parameters are optimized; 3, the PID controller is set, wherein a gradient-based optimization algorithm is used for using the GP model for adjusting the PID controller. The control strategy for achieving multivariable PID in the PLS frame on the basis of the Gaussian process model has the advantages that the GP model is used for providing a predictionvariance, and the prediction reliability of a local random area is shown; cross coupling effects brought by a multivariable control process are taken into account, through the PLS frame, the MIMO system is decoupled into single loops, and then based on the GP model, the PID controller parameters are adjusted separately.

Description

technical field [0001] The present invention relates to the field of nonlinear time-varying system optimization control, in particular to the multivariable control strategy design of the Gaussian process (GP) model, specifically the realization of multivariable proportional-integral based on the Gaussian process model in the framework of the least squares regression method (PLS) - Derivative (PID) control strategy. Background technique [0002] The traditional PID controller has always been the most popular controller due to its simple structure, high hardware requirements, wide range of industrial applications and high efficiency of mechanical systems. The PID algorithm has a complete set of parameter setting and design methods, which are easy to be mastered by engineers and technicians; in many industrial circuits, the requirements for control speed and control accuracy are not very high, and when more emphasis is placed on the reliability of the system, using PID control ...

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Application Information

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IPC IPC(8): G05B11/42
CPCG05B11/42
Inventor 任密蜂张旭霞陈荣辉阎高伟张雯
Owner TAIYUAN UNIV OF TECH
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