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Design method of LQG controller of LPV system based on data driving

A data-driven, design method technology, applied in adaptive control, general control system, control/regulation system, etc., can solve the problems of hysteresis, inconvenient calculation, large order of large-scale dynamic system, etc., to reduce the degree of interference, The effect of improving control accuracy

Active Publication Date: 2021-03-19
JIANGSU UNIV
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Problems solved by technology

[0004] Large-scale dynamic systems often have a large order, which is extremely inconvenient to calculate, and serious hysteresis will appear in the case of low computing power

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  • Design method of LQG controller of LPV system based on data driving
  • Design method of LQG controller of LPV system based on data driving
  • Design method of LQG controller of LPV system based on data driving

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Embodiment Construction

[0016] The present invention will be further described below in conjunction with accompanying drawing.

[0017] Such as figure 1 Shown, the design of controller of the present invention comprises as follows:

[0018] Step1: Establish system equations

[0019] For large dynamic systems with time-varying parameters, the following system equations are established:

[0020] x(t;q(t))=A(q(t)x(t;q(t))+Bu(t;q(t))+w(t)

[0021] y(t; q(t)) = Cx(t; q(t)) + v(t)

[0022] Among them, assuming all t>0, the system matrix A(q(t))∈R n×n By the time-varying parameter q(t)∈R d Determined, so as to obtain the time-varying system matrix, the input matrix B∈R n×n and the output matrix C ∈ R n×n are considered fixed. Control input u(t; q(t)) ∈ R m , the controlled output y(t; q(t))∈R p , and the state quantity x(t; q(t))∈R n Depending on the parameters, w(t), v(t) are the measurement noise and observation noise in the stochastic process, respectively. The present invention assumes that ...

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Abstract

The invention discloses a design method for an LQG controller of an LPV system based on data driving. The method comprises steps of S1, building a system equation, carrying out the equal-time-intervalsampling of sensor inputs under different parameter conditions, carrying out the order reduction of the sensor inputs through a POD method, and obtaining an order reduction basis; S2, under the condition of different parameters, a system filtering algebra Riccati equation and a controller algebra Riccati equation being calculated respectively, and a filter gain and a controller gain being obtained; S3, a database being established, reduced-order bases of all prior parameters and the two gains obtained in the S2 being in one-to-one correspondence and stored in the database, the reduced-order bases serving as learning bases of a machine learning classifier, a proper machine learning model being selected, the learning bases in the database serving as a training set, the two corresponding gain parameters serving as labels, and the classifier model being designed; wherein the classifier model is the LQG controller. The controller can be used online in real time. According to the method, the stable reaction speed and control precision of the system are effectively improved, and the interference can be greatly reduced.

Description

technical field [0001] The invention belongs to the field of large-scale linear dynamic system controller design, and in particular relates to the design of a reduced-order controller for a data-driven parameter time-varying switching system. Background technique [0002] Large-scale dynamic switching systems are often highly sensitive to certain control parameters. The performance of the control system largely depends on the response speed of the system to the sudden change of these parameters. In the case of ensuring the system sensitivity, the stability of the system is guaranteed. Therefore, for this type of linear parameter-varying system (LPV system), it is very important to select an appropriate control method to satisfy the controller that depends on specific time-varying switching parameters. Model-based feedback control provides an elegant and mathematically sound approach to controller design. Data-driven feedback gain control is a mathematically reasonable and h...

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Application Information

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IPC IPC(8): G05B13/04G05B13/02
CPCG05B13/041G05B13/0265G05B13/027
Inventor 潘公宇丁聪张晓曼王功强
Owner JIANGSU UNIV
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