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Greenhouse environment temperature self-adaption method based on parameter identification

A technology for ambient temperature and parameter identification, applied in adaptive control, instruments, control/regulation systems, etc., can solve problems such as fast response speed, small overshoot, and short transition time

Pending Publication Date: 2021-06-08
CHINA JILIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Fuzzy control does not need to establish an accurate mathematical model for the researched object, the transition time is short, the overshoot is small, the response speed is fast, and it is superior to PID control in terms of adjustment speed and robustness, but it can only achieve rough control

Method used

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  • Greenhouse environment temperature self-adaption method based on parameter identification
  • Greenhouse environment temperature self-adaption method based on parameter identification
  • Greenhouse environment temperature self-adaption method based on parameter identification

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0080] refer to figure 1 , model reference adaptive control is a form of adaptive control. The design of the reference model can be based on the structure and control requirements of the controlled object. Make its output express the expected response to the input command, and then adjust the controller parameters through the difference between the model output and the output of the controlled object to make the difference tend to zero, that is, to make the object output approach the model output, and finally achieve complete consistency. The model reference adaptive control system in the step S2 is composed of a reference model, a controlled object, a feedback controller and an adaptive mechanism; the feedback loop formed by the controlled object and the feedback controller constitutes an inner loop, and the feedback control The controller and the adaptive mechanism form an outer loop for adjusting the parameters of the feedback controller, and the output of the reference mo...

Embodiment 2

[0083] The design of the adaptive adjustment law in this application uses the gradient method. The gradient method is one of the parameter optimization methods. Its design principle is to construct an objective function consisting of a generalized error and an adjustable function, and can be regarded as a hypersurface in the adjustable parameter space. A parameter optimization method is used to gradually reduce the objective function. The consistency requirement between the tunable system and the reference model is satisfied until the objective function reaches a minimum or near a minimum.

[0084] The reference model takes K m G(s), where K m In order to make the model output reach the gain of the desired state; when the system is disturbed, the gain K of the controlled object p changes, and its dynamic characteristics deviate from those of the reference model; due to K p The change is not measurable, in order to overcome by K p The effect caused by the drift, set the ad...

Embodiment 3

[0113] The method for verifying stability in the step S2 is:

[0114] The transfer function of the first-order system is The mathematical model of the closed-loop adaptive control system designed according to the MIT rules should be:

[0115]

[0116]

[0117]

[0118] Suppose t = t 0 when y and y m are all zero, and k c k p ≠k m , give the system a step signal with an output amplitude of R, then t 0 The output of the reference model is then:

[0119] the y m =k m R(1-e -t / T ) (1-17)

[0120] So the adaptive regulation law is:

[0121]

[0122] Taking the derivative of the open-loop generalized error equation gives:

[0123]

[0124] Substituting formula (a) into formula (b) gives:

[0125]

[0126] When t→∞, the coefficient of the third term e of Equation 1-20 tends to k p kk m R 2 , that is:

[0127]

[0128] According to Routh's criterion, it can be seen that this system of equations is asymptotically stable, that is, when t→∞, there a...

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Abstract

The invention discloses a greenhouse environment temperature self-adaption method based on parameter identification, and belongs to the technical field of intelligent agriculture. The method comprises: determining a model structure of the controlled object, and designing a corresponding controller to meet the required performance requirements; used parameter identification online estimating the parameter value of the control object, and replacing the estimated value for the parameter in the controller, so as to achieve the real effect of controlling the system; using MATLAB / SIMULINK to simulate the adaptive greenhouse temperature control system provided by the invention, and analyzing the simulation effect. Self-adaptive control belongs to one of intelligent control, and control parameters are correspondingly adjusted by continuously detecting the change condition of a controlled object, so that the whole system reaches an optimal or suboptimal state. The method can stabilize the prediction error in a certain range, timely and effectively update the prediction model, and improve the prediction and control precision of the greenhouse.

Description

technical field [0001] The invention relates to the technical field of intelligent agriculture, in particular to an adaptive method for greenhouse environment temperature based on parameter identification. Background technique [0002] Greenhouse environmental control system is a multi-coupling, nonlinear and complex dynamic system with large hysteresis performance characteristics. It is a comprehensive control technology mainly used in computers. The ultimate purpose of its control is to obtain an ideal environment conducive to crop growth. This technology can realize industrial scale production in terms of resource saving, and has the advantages of high quality, high efficiency, and low consumption. Currently, the main methods used in greenhouse intelligent control are: fuzzy control, neural network, and hybrid control. [0003] Fuzzy control does not need to establish an accurate mathematical model for the researched object. It has short transition time, small overshoot,...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 王丽娜王斌锐戴文彬刘锦杰章陈康平
Owner CHINA JILIANG UNIV
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