CN-121995775-A - Self-adaptive control method, system and device for environmental test equipment

CN121995775ACN 121995775 ACN121995775 ACN 121995775ACN-121995775-A

Abstract

The invention relates to an environment test equipment self-adaptive control method, system and device, which comprises the following steps of S1, collecting environment detection data and corresponding actuator data according to physical quantity to be controlled, S2, selecting or constructing a control object according to the physical quantity to be controlled, S3, performing on-line identification operation when identification conditions are met, otherwise, directly executing step S4, performing feedforward compensation and feedback according to the value of the quantity to be estimated, performing feedforward compensation and feedback compensation calculation, superposing feedforward compensation and feedback compensation to obtain a control instruction, and S5, performing constraint allocation of the actuator, and performing saturation constraint processing on the control instruction to obtain the control quantity of the corresponding actuator. The method can perform joint online identification on the to-be-estimated quantity only by perturbation excitation, automatically adapt to different loads and sample states, adopt saturation constraint processing, and has low calculated quantity of models and algorithms and compromise precision, speed and engineering realizability.

Inventors

XIE YUKE
MA HONGWEI
ZHANG NI
YU ZHENHAO
HUANG JIE

Assignees

成都天奥技术发展有限公司

Dates

Publication Date: 20260508
Application Date: 20260411

Claims (10)

1. The self-adaptive control method of the environment test equipment is characterized by comprising the following steps of: S1, data acquisition, namely acquiring environment detection data and data of corresponding actuators according to physical quantity to be controlled; s2, selecting or constructing a control object according to the physical quantity to be controlled; s3, performing online identification operation when the identification condition is met, otherwise, directly executing the step S4; Superposing perturbation excitation on the corresponding executor under the identification constraint, and estimating to obtain the to-be-estimated quantity by a self-adaptive estimation method according to the data acquired in the step S1 based on a discrete regression model constructed by the control object; S4, feedforward and feedback, namely, performing feedforward compensation and feedback compensation calculation according to the value of the to-be-estimated quantity, and superposing the feedforward compensation and the feedback compensation to obtain a control instruction; And S5, performing constraint distribution on the executor, namely performing saturation constraint processing on the control instruction to obtain the control quantity of the corresponding executor.
2. The method for adaptively controlling environmental test equipment according to claim 1, wherein said physical quantity to be controlled includes a temperature; The environment detection data comprise the temperature in the equipment and the temperature outside the equipment, and the data of the corresponding executor comprise the data of a heating executor and a refrigerating executor of the environment test equipment; the step S2 is to select the temperature as a control object; The to-be-estimated quantity comprises equivalent heat capacity, equivalent heat transfer coefficient and self-heating power of the measured object.
3. The method for adaptively controlling environmental test equipment according to claim 1, wherein said physical quantity to be controlled includes humidity; the environment detection data comprise relative humidity in the equipment and temperature in the equipment, and the data of the corresponding executors comprise data of a humidifying executor and a dehumidifying executor of the environment test equipment; The step S2 is to calculate the humidity ratio by using the humidity and the temperature and take the humidity ratio as a control object; The to-be-estimated quantity comprises equivalent moisture content, moisture exchange admittance and moisture release/absorption source items of the measured object.
4. The method for adaptively controlling environmental test equipment according to claim 1, wherein the discrete regression model is: ; in the formula, Represent the first The subsampling moment controls the observed amount of the rate of change of the object, Represent the first The sub-sampling instants control the transpose of the regression vector of the object, Represent the first Controlling regression parameter vector of the object at subsampling moment; The regression vector of the control object is: ; in the formula, Represent the first The subsampling moments control the regression vector of the object, Represent the first The sub-sampling instants correspond to the net equivalent contribution provided by the actuators, Represent the first The data of the control object in the device at the sub-sampling instant, Represent the first Sub-sampling data of an external environment control object of the equipment at the moment; The regression parameter vector of the control object is set as: ; In the middle of 、、 Respectively is Is included in the three components of (a); The to-be-estimated quantity is calculated by an estimated value of a regression parameter vector of the control object; the estimated value of the regression parameter vector is estimated by an adaptive estimation method.
5. The adaptive control method of an environmental test apparatus according to claim 1, wherein the adaptive estimation method is a recursive least squares estimation method; the recursive least square estimation method adopts forgetting factors; The updating formula of the regression parameter vector by the recursive least square estimation method is as follows: ; ; ; in the formula, And (3) with Respectively represent the first Secondary and tertiary The sub-sampling instants identify the covariance matrix, Represent the first The sub-sampling instants identify the gain vector, Represent the first The subsampled moment forgetting factor, And (3) with Respectively represent the first Secondary and tertiary Regression parameter vector at sub-sampling time.
6. The method for adaptively controlling environmental test equipment according to claim 5, wherein said forgetting factor is an adaptive forgetting factor; the adaptive forgetting factor is adjusted according to an adjustment factor, wherein the adjustment factor comprises one of a control error and a regression residual error; The adaptive rule of the adaptive forgetting factor is that the forgetting factor is reduced when the absolute value of the adjusting factor is increased, and conversely, the forgetting factor is increased.
7. The method for adaptively controlling environmental test equipment according to claim 1, wherein said feedforward compensation includes steady-state feedforward, dynamic feedforward and offset compensation; The feedback compensation is generated by calculating a gain scheduling PID parameter; In the step S5, recalculating anti-integral saturation correction is carried out on the PID integral term based on the difference before and after saturation; In the step S5, when there are two corresponding actuators and the functions of the two actuators are opposite, performing mutual exclusion allocation; The mutual exclusion allocation means that when the control instruction is positive, only the forward actuator is allowed to output and the reverse actuator is set to zero, and when the control instruction is negative, only the reverse actuator is allowed to output and the forward actuator is set to zero.
8. An environment test equipment self-adaptive control system is used for executing the environment test equipment self-adaptive control method according to any one of claims 1-7, and is characterized by comprising a data acquisition module, a perturbation excitation module, an online identification module, a feedforward and feedback module and an actuator constraint distribution module, wherein the data acquisition module is used for completing data acquisition, the perturbation excitation module is used for overlapping perturbation excitation on an actuator, the online identification module is used for estimating a to-be-estimated quantity based on a discrete regression model, the feedforward and feedback module is used for calculating feedforward compensation and feedback compensation, and the actuator constraint distribution module is used for carrying out saturation constraint processing.
9. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of a method for adaptively controlling an environmental test equipment according to any one of claims 1-7.
10. An environmental test equipment adaptive control device comprising a processor and a memory, characterized in that the memory stores a computer program which, when executed by the processor, implements the steps of an environmental test equipment adaptive control method as claimed in any one of claims 1 to 7.

Description

Self-adaptive control method, system and device for environmental test equipment Technical Field The invention relates to the field of environmental test equipment control, in particular to a self-adaptive control method, a self-adaptive control system and a self-adaptive control device for environmental test equipment. Background The environmental test box (including high-low temperature box, temperature and humidity box, rapid temperature change box, etc.) adopts fixed parameter PID control. In an actual test, the problems of overshoot, long stability time, large track tracking error, need of manual repeated setting and the like of a fixed PID under different loads are caused due to the fact that the heat capacity difference, the self-heating strength difference and the humidity release/absorption characteristic difference of a measured object are obvious, and the problems are more prominent under a rapid temperature change and temperature humidity coupling scene, so that the test consistency and the repeatability are reduced. The existing self-tuning PID or experience gain scheduling method generally does not perform joint online identification on the 'equivalent heat capacity-heat transfer-self-heating' and the 'equivalent moisture capacity-wet exchange-wet source item', or needs larger disturbance to destroy test tolerance, and meanwhile, the problem of integral saturation caused by saturation of an actuator, heating/refrigeration mutual exclusion and humidification/dehumidification mutual exclusion is not always solved, and the precision, the speed and the engineering feasibility are difficult to be considered. Disclosure of Invention The invention aims to overcome the defects of the prior art and provide a self-adaptive control method, a self-adaptive control system and a self-adaptive control device for environmental test equipment, which only need perturbation excitation to realize joint on-line identification, and adopt saturation constraint processing to realize precision, speed and engineering realizability. The aim of the invention is realized by the following technical scheme: an adaptive control method of environmental test equipment comprises the following steps: S1, data acquisition, namely acquiring environment detection data and data of corresponding actuators according to physical quantity to be controlled; s2, selecting or constructing a control object according to the physical quantity to be controlled; s3, performing online identification operation when the identification condition is met, otherwise, directly executing the step S4; Superposing perturbation excitation on the corresponding executor under the identification constraint, and estimating to obtain the to-be-estimated quantity by a self-adaptive estimation method according to the data acquired in the step S1 based on a discrete regression model constructed by the control object; S4, feedforward and feedback, namely, performing feedforward compensation and feedback compensation calculation according to the value of the to-be-estimated quantity, and superposing the feedforward compensation and the feedback compensation to obtain a control instruction; And S5, performing constraint distribution on the executor, namely performing saturation constraint processing on the control instruction to obtain the control quantity of the corresponding executor. Further, the physical quantity to be controlled includes a temperature; The environment detection data comprise the temperature in the equipment and the temperature outside the equipment, and the data of the corresponding executor comprise the data of a heating executor and a refrigerating executor of the environment test equipment; the step S2 is to select the temperature as a control object; The to-be-estimated quantity comprises equivalent heat capacity, equivalent heat transfer coefficient and self-heating power of the measured object. Further, the physical quantity to be controlled includes humidity; the environment detection data comprise relative humidity in the equipment and temperature in the equipment, and the data of the corresponding executors comprise data of a humidifying executor and a dehumidifying executor of the environment test equipment; The step S2 is to calculate the humidity ratio by using the humidity and the temperature and take the humidity ratio as a control object; The to-be-estimated quantity comprises equivalent moisture content, moisture exchange admittance and moisture release/absorption source items of the measured object. Further, the discrete regression model is: ; in the formula, Represent the firstThe subsampling moment controls the observed amount of the rate of change of the object,Represent the firstThe sub-sampling instants control the transpose of the regression vector of the object,Represent the firstControlling regression parameter vector of the object at subsampling moment; The regression vector of the control object is: in the formula, Rep