Temperature control method and system for receiver of microwave radiometer

Abstract

The invention discloses a microwave radiometer receiver temperature control method and system, and relates to the technical field of radiometers, and the method comprises the following steps: building a first-order pure lag transfer function of a microwave radiometer receiver temperature control model according to empirical data; based on historical temperature control data, establishing and updating a fuzzy rule table of a proportionality coefficient, an integral coefficient and a differential coefficient of receiver temperature control, and adding a multi-stage integral coefficient; dynamically executing integral separation control according to a real-time error and an error change rate based on the fuzzy rule table; and the integral action range is regulated and controlled through the multi-stage integral coefficient, and the heating temperature control quantity is output. Through combination of the fuzzy PID controller and a multistage integral separation mechanism, rapid and accurate temperature control of the microwave radiometer receiver in an environment temperature sudden change scene is realized.

Description

Technical Field

[0001] This invention relates to the field of radiometer technology, and in particular to a method and system for temperature control of a microwave radiometer receiver. Background Technology

[0002] Microwave radiometers, as important remote sensing devices, have wide applications in meteorological monitoring, environmental science, and other fields. The receiver, as a key component of the radiometer system, directly determines the radiometer's main performance indicators. Among these, the radiometer's sensitivity primarily depends on the receiver's gain, and the stability of the receiver gain is mainly affected by the ambient temperature. Therefore, maintaining a constant operating temperature of the receiver is crucial to achieving high sensitivity and other performance indicators, and ensuring the stability of the receiver gain.

[0003] Traditional PID (PID control) isothermal temperature control technology is relatively mature. However, when faced with rapidly changing external ambient temperatures for microwave radiometer receivers, the traditional PID temperature control method has significant drawbacks. Because its PID coefficients are fixed, it is difficult to quickly adjust the internal temperature of the receiver when the external ambient temperature changes rapidly, thus adversely affecting the receiver's output.

[0004] Currently, although a fuzzy PID temperature control method based on multi-level integral prediction has been proposed to address this problem, in practical applications, when the external ambient temperature changes rapidly, it is still unable to effectively and quickly adjust the temperature of the radiometer receiver, which affects the accuracy of the measurement results.

[0005] It is evident that there is an urgent need for a control method that can rapidly adjust the internal temperature of the receiver according to changes in the external ambient temperature, in order to meet the high-precision temperature control requirements of microwave radiometers in complex environments. Summary of the Invention

[0006] In a first aspect, to solve the above-mentioned technical problems, a method for temperature control of a microwave radiometer receiver is provided, comprising the following steps: Based on empirical data, a first-order pure time delay transfer function is established for the temperature control model of a microwave radiometer receiver. Based on historical temperature control data, a fuzzy rule table for the proportional coefficient, integral coefficient, and derivative coefficient of receiver temperature control is established and updated, and multi-level integral coefficients are added. Based on the fuzzy rule table, integral separation control is dynamically executed according to the real-time error and the error change rate. The integral range is adjusted by the multi-level integral coefficients to output the heating temperature control quantity.

[0007] Furthermore, the first-order pure time delay transfer function satisfies the expression:

[0008] In the formula, For system gain, It is a time constant. It is a pure time delay constant.

[0009] Furthermore, the parameters of the first-order pure time delay transfer function are determined as follows: A step signal is applied to the receiver's heating device, the temperature response curve is recorded, and the step response method is used to calculate the temperature response curve. , and The value of .

[0010] Further: The input variables of the fuzzy rule table are error and error change rate, and the quantization range of error and error change rate is [-6, 6]; The output variables of the fuzzy rule table are the proportional coefficient adjustment, integral coefficient adjustment, and differential coefficient adjustment, and the quantization range of the proportional coefficient adjustment, integral coefficient adjustment, and differential coefficient adjustment is [-3, 3].

[0011] Furthermore, the input and output variables of the fuzzy rule table are divided into seven fuzzy subsets: positive large, positive medium, positive small, zero, negative small, negative medium, and negative large, and are fuzzified using triangular or trapezoidal membership functions.

[0012] Furthermore, the method for dynamically executing integral separation control includes: When the value of the error belongs to the positive large, positive medium, negative large, or negative medium fuzzy subset, and the rate of change of the error belongs to the negative large, negative medium, positive medium, or positive large fuzzy subset, the integral coefficient is set to zero.

[0013] Furthermore, the expression for the output temperature control quantity is:

[0014] In the formula, For the number of samples, This is the quantized value of the error. This represents the quantized value of the error change. The multi-level integral coefficients, , , These are the proportional coefficient, integral coefficient, and differential coefficient, respectively. , , These are the adjustment amounts for the proportional coefficient, integral coefficient, and derivative coefficient, respectively.

[0015] A second aspect of the present invention provides a temperature control system for a microwave radiometer receiver, comprising: Temperature sensor, used to collect the receiver's temperature in real time; A fuzzy PID controller is configured to perform the steps of the method; The heating execution module adjusts the heating power based on the output of the fuzzy PID controller.

[0016] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention breaks through the limitations of fixed coefficients in traditional PID controllers by combining a fuzzy PID controller with a multi-level integral separation mechanism. When the external ambient temperature changes rapidly, the control quantity can be dynamically optimized based on the error and the rate of change of the error, realizing rapid and accurate temperature control of the microwave radiometer receiver in scenarios with sudden changes in ambient temperature. At the same time, the fuzzy rules combined with multi-level integral parameters can dynamically adjust the intensity of the integral action according to the temperature deviation. That is, when the error is small, the integral action is enhanced to effectively eliminate steady-state error; when the error is large, the integrator is turned off to prevent system oscillation, thus balancing control accuracy and stability. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the sorting process disclosed in an embodiment of the present invention; Figure 2 This is a schematic diagram of the simulation model disclosed in an embodiment of the present invention; Figure 3 The above is a simulation comparison curve of an embodiment of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] This invention aims to provide a temperature control method for a microwave radiometer receiver, which is particularly suitable for scenarios with rapidly changing ambient temperatures. The following detailed description of the specific steps is provided.

[0021] S1. Based on empirical data, establish the first-order pure time delay transfer function of the microwave radiometer receiver temperature control model.

[0022] In this embodiment, the transfer function of the temperature control model of the receiver system is analyzed. Based on previous empirical data, it is assumed that the transfer function of the receiver temperature control model is a first-order system with an added pure time delay element, and its first-order pure time delay transfer function satisfies the expression:

[0023] In the formula, For system gain, It is a time constant. The time constant is the pure time delay. The parameters of the first-order pure time delay transfer function are determined by applying a step signal to the receiver's heating device, recording the temperature response curve, and calculating the parameters using the step response method. , and The value of .

[0024] Specifically, a step signal is added to the receiver heating device of the actual microwave radiometer, and the receiver temperature changes over time are recorded. The recorded experimental data are plotted in software, and the first-order pure time delay transfer function of the temperature control model is calculated from the graph plotted from the actual data using the step response method.

[0025] Based on the first-order pure time delay transfer function, the step response of the microwave radiometer temperature control system in the Laplace domain can be expressed as:

[0026] here This represents the system response in the Laplace domain. Indicates control signal The Laplace transform of .

[0027] The time-domain transformation process of the control system is shown in the following formula:

[0028]

[0029] In the formula, The amplitude of the step input, This represents the inverse Laplace transform.

[0030] As can be seen from the equation above, when At that time, the step response approaches the value at the time. In the transfer function The value can be obtained from the following equation get:

[0031]

[0032] S2. Based on historical temperature control data, establish and update a fuzzy rule table of proportional coefficient, integral coefficient and derivative coefficient of receiver temperature control, and add multi-level integral coefficients.

[0033] Specifically, to adapt to complex environments, a fuzzy logic controller is introduced for real-time updating of PID coefficients. The fuzzy controller consists of three parts: fuzzification, fuzzy inference, and defuzzification. The fuzzification part converts the clear input into a fuzzy subset. In this embodiment, triangular or trapezoidal membership functions are used for fuzzification.

[0034] The input variables of the fuzzy rule table are error and error rate of change, both with a quantization range of [-6, 6]. The output variables are proportional coefficient adjustment, integral coefficient adjustment, and differential coefficient adjustment, all with a quantization range of [-3, 3]. Both the input and output variables of the fuzzy rule table are divided into seven fuzzy subsets: positive large, positive medium, positive small, zero, negative small, negative medium, and negative large. The input and output variables of the fuzzy rule table have the same membership function.

[0035] S21. Analyze past receiver heating data and establish temperature control... , and A fuzzy rule table.

[0036] Let the error be... The error change rate is PB represents positive, PM represents positive, PS represents positive, ZO represents negative, NS represents negative, NM represents negative, and NB represents negative.

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] in:

[0044] In the formula, , , These are the proportional coefficient, integral coefficient, and differential coefficient, respectively. , , These are the adjustment amounts for the proportional coefficient, integral coefficient, and derivative coefficient, respectively. The coefficients for the introduced integral are denoted as .

[0045] S22. Conduct experimental simulations based on previous fuzzy rule tables to continuously update the rules, and add multi-level integral parameters.

[0046] S3. Based on the fuzzy rule table, integral separation control is dynamically executed according to the real-time error and the rate of change of error.

[0047] Based on the aforementioned fuzzy rules and update rules, the integrator can adjust according to the input error. and error change rate To turn it on or off. Specifically: when the error value belongs to a positive large, positive medium, negative large, or negative medium fuzzy subset, and the error change rate belongs to a negative large, negative medium, positive medium, or positive large fuzzy subset, the integral coefficient will be... Setting it to zero controls the separation of the integrator. To express the control process of this optimized fuzzy logic controller, subtle adjustments were made to the fuzzy rule table. Simultaneously, parameters for multi-level integration were added to collectively control the integral magnitude.

[0048] S4. The integral range is adjusted by using multi-level integral coefficients to output the heating temperature control quantity.

[0049] In this embodiment, the PID output control quantity satisfies the expression:

[0050] In the formula, For the number of samples, This is the quantized value of the error. The quantized value representing the change in error. This is a multi-level integration coefficient, and its value can be defined by freely setting the range of multi-level integration intervals; , , These are the proportional coefficient, integral coefficient, and differential coefficient, respectively. , , These are the adjustment amounts for the proportional coefficient, integral coefficient, and derivative coefficient, respectively.

[0051] In optimization methods, integral coefficients The update conditions are not only affected by errors The influence is also affected by error changes. The effect of error, i.e. When the coefficients are large, the algorithm uses multi-level integral coefficients. Defined as 0, the fuzzy output is 0, and thus the integral coefficient is... Set to zero; no integration is needed at this point; error changes. That is also true. Therefore, As an integral coefficient, it is affected by multi-level integral coefficients and fuzzy output.

[0052] In a further embodiment, experimental simulation is performed based on the established temperature control model transfer function and temperature control fuzzy rule table. The simulation principle diagram is shown below. Figure 2 As shown, the Smith predictor is introduced, and the simulation comparison results are as follows. Figure 3 As shown, comparing the traditional PID control method with Smith-fuzzy PID control, the results are as follows: The microwave radiometer temperature control method proposed in this invention outperforms other indicators such as temperature tracking accuracy and settling time. Specifically, the method provided by this invention can respond to temperature changes more quickly, enabling the receiver temperature to rapidly approach the target temperature, and after reaching a steady state, the temperature fluctuation is smaller, resulting in higher control accuracy.

[0053] In addition, the present invention also provides a temperature control system for a microwave radiometer receiver, which mainly includes a temperature sensor, a fuzzy PID controller and a heating execution module. The temperature sensor is used to collect the temperature of the receiver in real time; the fuzzy PID controller is configured to execute the steps of the method; and the heating execution module adjusts the heating power according to the output of the fuzzy PID controller.

[0054] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for temperature control of a microwave radiometer receiver, characterized in that, Includes the following steps: Based on empirical data, a first-order pure time delay transfer function is established for the temperature control model of a microwave radiometer receiver. Based on historical temperature control data, a fuzzy rule table for the proportional coefficient, integral coefficient, and derivative coefficient of receiver temperature control is established and updated, and multi-level integral coefficients are added. Based on the fuzzy rule table, integral separation control is dynamically executed according to the real-time error and the error change rate. The integral range is adjusted by the multi-level integral coefficients to output the heating temperature control quantity.

2. The microwave radiometer receiver temperature control method according to claim 1, characterized in that, The first-order pure time delay transfer function satisfies the expression: In the formula, For system gain, It is a time constant. It is a pure time delay constant.

3. The microwave radiometer receiver temperature control method according to claim 2, characterized in that, The parameters of the first-order pure time delay transfer function are determined as follows: A step signal is applied to the receiver's heating device, the temperature response curve is recorded, and the step response method is used to calculate the temperature response curve. , and The value of .

4. The microwave radiometer receiver temperature control method according to claim 1, characterized in that: The input variables of the fuzzy rule table are error and error change rate, and the quantization range of error and error change rate is [-6, 6]; The output variables of the fuzzy rule table are the proportional coefficient adjustment, integral coefficient adjustment, and differential coefficient adjustment, and the quantization range of the proportional coefficient adjustment, integral coefficient adjustment, and differential coefficient adjustment is [-3, 3].

5. The microwave radiometer receiver temperature control method according to claim 4, characterized in that, The input and output variables of the fuzzy rule table are divided into seven fuzzy subsets: positive large, positive medium, positive small, zero, negative small, negative medium, and negative large, and are fuzzified using triangular or trapezoidal membership functions.

6. The microwave radiometer receiver temperature control method according to claim 5, characterized in that, The method for dynamically executing integral separation control includes: When the value of the error belongs to the positive large, positive medium, negative large, or negative medium fuzzy subset, and the rate of change of the error belongs to the negative large, negative medium, positive medium, or positive large fuzzy subset, the integral coefficient is set to zero.

7. The microwave radiometer receiver temperature control method according to claim 5, characterized in that, The expression for the output temperature control quantity is: In the formula, For the number of samples, This is the quantized value of the error. This represents the quantized value of the error change. The multi-level integral coefficients, , , These are the proportional coefficient, integral coefficient, and differential coefficient, respectively. , , These are the adjustment amounts for the proportional coefficient, integral coefficient, and derivative coefficient, respectively.

8. A temperature control system for a microwave radiometer receiver, characterized in that, include: Temperature sensor, used to collect the receiver's temperature in real time; A fuzzy PID controller configured to perform the steps of the method according to any one of claims 1-7; The heating execution module adjusts the heating power based on the output of the fuzzy PID controller.

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