Neutral network theory-based non-linear system adaptive proportional integral control method

A proportional-integral control, nonlinear system technology, applied in adaptive control, general control system, control/regulation system, etc., can solve the problems of closed-loop system instability, dynamic performance degradation, etc.

Active Publication Date: 2017-05-10
CHONGQING UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Saturation is one of the potential problems of actuators in control systems. If the input to the actuator reaches a certain limit, the entire system will enter saturation, and further increases in input will have no effect on the output of the actuator.
The saturation of the actuator will reduce the dynamic performance of the system, and even lead to the instability of the closed-loop system

Method used

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  • Neutral network theory-based non-linear system adaptive proportional integral control method
  • Neutral network theory-based non-linear system adaptive proportional integral control method
  • Neutral network theory-based non-linear system adaptive proportional integral control method

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Embodiment Construction

[0054] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0055] In this embodiment, the nonlinear system adaptive proportional-integral control method based on neural network theory includes the following steps:

[0056] Step 1, establishing a mathematical model of a nonlinear system containing actuator saturation;

[0057] The nonlinear system with actuator saturation has the following form:

[0058]

[0059]

[0060] y=x 1

[0061] u=H(v)

[0062] Where: x i =[x i1 ,...,x im ] T ∈ R m ,i=1,...,n,x∈R mn is the state vector of the system; y∈R m Represents the output vector of the system; u∈R m Represents the input vector of the system; F(x)∈R m Represents the nonlinear function of the system; B(x,t)∈R m×m represents the control gain matrix of the system; F d (x,t)∈R m Represents the uncertainty part and external disturbance part of the system model; H(v)∈R n Denotes the control vector fo...

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Abstract

The invention discloses a neutral network theory-based non-linear system adaptive proportional integral control method. The method comprises the following steps: in a first step, a mathematical model for a non-linear system is built; in a second step, a smooth function is used for approximating a non-smooth performer saturation function; in a third step, a neutral network adaptive proportional plus integral controller is designed for control. According to the method disclosed in the invention, as for a non-linear system with input saturation, the smooth function is used for approximating the performer saturation function, a BLF is referenced, that neutral network input is maintained in a bounded compact set range can be ensured, and normal operation of a neutral network can be ensured; compared with a conventional PI gain adjustment, an adjustment method put forward in the invention is advantageous in that 1) proportion integral gain of the PI controller is not a fixed constant but a time variant; 2) proportional gain and integral gain are not designed individually but associated with each other via a certain coefficient, and therefore system analysis can be facilitated; 3) the method has certain robustness for nondeterminacy and input saturation of the system.

Description

technical field [0001] The invention relates to the fields of neural network technology, PI control and nonlinear system control, in particular to a nonlinear system adaptive proportional integral control method based on neural network theory. Background technique [0002] With the rapid development of science and technology, people put forward higher and higher requirements for the dynamics and stability of the control system, so the control problem of nonlinear systems has always been a research hotspot in the field of control theory. For uncertain nonlinear dynamic systems, traditional control theory cannot provide effective analysis and design tools, that is, it cannot satisfy the control problems under the condition of incomplete environment and system dynamic characteristics. If uncertain factors (unknown parameters, factors ignored in modeling, measurement errors, external disturbances, dynamic modeling, etc.) are not considered in the controller design, it may cause ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 宋永端郭俊侠谭觅钱基业伏进
Owner CHONGQING UNIV
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