A reference model switching method and device based on model estimation error trend

Abstract

The application discloses a reference model switching method and device based on model estimation error trend, and the method comprises the steps of constructing a segmented model of a controlled object system, collecting system state information of the controlled object system through a sensor, extracting adjacent first and second models from a model set according to the system state information, respectively estimating first and second system state values of the first and second models, calculating errors between the system state value of the controlled object system collected by the sensor and the first and second system state values to obtain first and second deviation values, calculating a first error trend set based on the first deviation value and a second error trend set based on the second deviation value, respectively calculating first and second sub-control laws according to the first and second models, and outputting a target control law in combination with the first and second deviation values and the first and second error trend sets.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of ship power system control, and more particularly, to a reference model switching method and device based on model estimation error trend. BACKGROUND

[0002] At present, a multi-segment control parameter set is generally used in a ship power controller to match the dynamic process requirements under all working conditions. A single linear model cannot match the control requirements under all working conditions, and therefore, different reference models are generally selected for the controller according to the working condition points during operation. The switching time of the reference model has an important influence on the stable control of the system, and an incorrect switching time can even lead to divergence of the control law and failure of the system working condition switching to enter a non-steady state.

[0003] The existing model direct switching method based on preset parameters can cause sudden changes in the control law output and large fluctuations in the system state parameters during the switching process. SUMMARY

[0004] In view of at least one defect or improvement demand of the prior art, the present application provides a reference model switching method and device based on model estimation error trend, which solves the technical problems that the switching process can cause sudden changes in the control law output and large fluctuations in the system state parameters, realizes smooth switching of the reference model, and alleviates the deterioration of equipment caused by drastic changes in the control command.

[0005] To achieve the above-mentioned purpose, according to a first aspect of the present application, a reference model switching method based on model estimation error trend is provided, which comprises: constructing a segmented model of a controlled object system, and collecting system state information of the controlled object system through a sensor, wherein the segmented model comprises an initial zero dynamic model, and the system state information comprises a current working condition of the controlled object system; extracting an adjacent first model and a second model from a model set according to the system state information, and respectively estimating a first system state value and a second system state value of the first model and the second model; calculating errors between a system state value of the controlled object system collected by the sensor and the first system state value and the second system state value, respectively obtaining a first deviation value and a second deviation value, calculating a first error trend set based on the first deviation value, and calculating a second error trend set based on the second deviation value; calculating a first sub-control law and a second sub-control law according to the first model and the second model, and outputting a target control law in combination with the first deviation value and the second deviation value, the first error trend set and the second error trend set.

[0006] In one example embodiment, after the segmented model of the controlled object system is constructed and the system state information of the controlled object system is collected by the sensor, the method further comprises: the supervisory rule selects a first model and a second model adjacent to each other from a model set containing a plurality of fixed models according to the system running parameters in the system state information at each calculation step; the first model is taken as a current initial state model of the adaptive reference model, and the second model is taken as a candidate state model of the adaptive reference model.

[0007] In one example embodiment, after the errors between the system state values of the controlled object system collected by the sensor and the first system state value and the second system state value are calculated, respectively obtaining a first deviation value and a second deviation value, the method further comprises: constructing a first initial error set based on the first deviation value, wherein the first initial error set records the errors of the first model for ten continuous calculation steps starting from the algorithm; and constructing a second initial error set based on the second deviation value, wherein the second initial error set records the errors of the second model for ten continuous calculation steps starting from the algorithm.

[0008] In one example embodiment, after the second initial error set is constructed based on the second deviation value, the method further comprises: calculating a first error trend set according to the first initial error set, and counting a first negative value number, a first positive sum and a first negative sum in the first error trend set; and calculating a second error trend set according to the second initial error set, and counting a second negative value number, a second positive sum and a second negative sum in the second error trend set.

[0009] In one example embodiment, after the second error trend set is calculated according to the second initial error set, and the second negative value number, the second positive sum and the second negative sum in the second error trend set are counted, the method further comprises: in the case that the first negative value number is greater than the second negative value number, the output target control law is a first sub-control law; in the case that the first negative value number is less than the second negative value number, the output target control law is a second sub-control law; and in the case that the first negative value number is equal to the second negative value number, the output target control law is output.

[0010] In one example embodiment, in the case that the first negative value number is equal to the second negative value number, outputting the target control law comprises: determining a first weight and a second weight based on the first negative sum and the second negative sum, calculating an output weight by the first weight and the second weight; and determining the target control law by the output weight, combining the first sub-control law and the second sub-control law.

[0011] In one example embodiment, after the target control law is determined by the output weight, combining the first sub-control law and the second sub-control law, the method further comprises: limiting the output change rate of the target control law based on the first calibration value and the second calibration value, wherein the output change rate is the difference between the adjacent two output target control laws.

[0012] According to a second aspect of the present application, there is also provided a reference model switching device for estimating error trends based on models, comprising: an acquisition unit configured to construct a segmented model of a controlled object system by acquiring system state information of the controlled object system via a sensor, wherein the segmented model comprises an initial zero dynamic model, and the system state information comprises a current working condition of the controlled object system; an estimation unit configured to extract a first model and a second model adjacent to each other from a model set according to the system state information, and estimate a first system state value and a second system state value of the first model and the second model, respectively; a first calculation unit configured to calculate errors between a system state value of the controlled object system acquired by the sensor and the first system state value and the second system state value, to obtain a first deviation value and a second deviation value, respectively, to calculate a first error trend set based on the first deviation value, and to calculate a second error trend set based on the second deviation value; and a first output unit configured to calculate a first sub-control law and a second sub-control law based on the first model and the second model, respectively, to combine the first deviation value and the second deviation value, the first error trend set and the second error trend set, and to output a target control law.

[0013] According to a third aspect of the present application, there is also provided a computer readable storage medium having a computer program stored therein, wherein the computer program is configured to execute the above-mentioned reference model switching method for estimating error trends based on models when running.

[0014] According to a fourth aspect of the present application, there is also provided an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-mentioned reference model switching method for estimating error trends based on models through the computer program.

[0015] Overall, the above technical solutions conceived by the present application can achieve the following beneficial effects compared with the prior art:

[0016] (1) The present application provides a reference model switching method for estimating error trends based on models, which compares the output value of the reference model with the sensor acquisition value by simultaneously calculating the estimated output of two reference models with continuous states, determines the error change trend of each model through the historical data of the estimation error when the reference model error value exceeds the set threshold, selects the model with the error decreasing trend, and performs control law calculation and reference model switching when the error trend difference exceeds the set threshold. Through the error and error trend, the model intersection is generated to realize the smooth switching of the reference model and relieve the device deterioration caused by the dramatic change of the control command.

[0017] (2) By adopting the reference model switching method based on model estimation error trend provided by the application, the most suitable model is dynamically selected according to real-time working conditions (such as reference model error and its variation trend) in a multi-model control system. This method avoids the inadaptability caused by model switching based on fixed rules, and enhances the self-adaptive ability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0019] Figure 1 A flowchart of an optional reference model switching method based on model estimation error trend provided by the embodiments of the present application is shown in the figure.

[0020] Figure 2 A model set composition diagram of a controlled object provided by the embodiments of the present application is shown in the figure.

[0021] Figure 3 A flowchart of an optional model switching method provided by the embodiments of the present application is shown in the figure.

[0022] Figure 4 An algorithm logic block diagram of an optional reference model switching method based on model estimation error trend provided by the embodiments of the present application is shown in the figure.

[0023] Figure 5 A flowchart of an optional reference model switching method based on model estimation error trend provided by the embodiments of the present application is shown in the figure.

[0024] Figure 6 A structure diagram of an optional reference model switching device based on model estimation error trend provided by the embodiments of the present application is shown in the figure.

[0025] Figure 7 A structure diagram of an optional electronic device provided by the embodiments of the present application is shown in the figure. DETAILED DESCRIPTION

[0026] In order to make the objects, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as there is no conflict.

[0027] The terms "first", "second", "third", etc. in the specification and claims of the present application and in the above drawings are used to distinguish different objects, and are not used to describe a particular order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0028] According to an aspect of an embodiment of the present application, a reference model switching method based on model estimation error trend is provided. The following will be described in combination with Figure 1 The reference model switching method based on model estimation error trend provided by the embodiment of the present application is described.

[0029] Figure 1 is a flowchart of an optional reference model switching method based on model estimation error trend provided by the embodiment of the present application, as Figure 1 shown, the flow of the method can include the following steps:

[0030] S102, a segmented model of the controlled object system is constructed, and system state information of the controlled object system is collected through a sensor, wherein the segmented model includes an initial zero dynamic model, and the system state information includes a current working condition of the controlled object system;

[0031] S104, a first model and a second model adjacent to each other are extracted from the model set according to the system state information, and a first system state value and a second system state value of the first model and the second model are estimated respectively;

[0032] S106, errors between the system state value of the controlled object system collected by the sensor and the first system state value and the second system value are calculated, to obtain a first deviation value and a second deviation value respectively, a first error trend set is calculated based on the first deviation value, and a second error trend set is calculated based on the second deviation value;

[0033] S108, a first sub-control law and a second sub-control law are calculated according to the first model and the second model respectively, and a target control law is output in combination with the first deviation value and the second deviation value, and the first error trend set and the second error trend set.

[0034] The reference model switching method based on model estimation error trend provided by the embodiment of the present application can be applied to the scene that the reference model needs to be switched to realize high-performance index control of different working points after the working condition changes when the multi-model reference controller is applied.

[0035] In practical applications, the trend change of model estimation error is a complex and unpredictable problem. Traditional model switching methods often rely on preset thresholds or rules to determine when to switch models, which performs poorly when facing dynamic changes in error trends. Specifically, when the estimation error of a model gradually increases to a certain extent, the traditional method may trigger the switching mechanism too early, resulting in performance degradation; conversely, if the error increases to a large extent before switching, it may have already caused significant negative impact on the system. In addition, the adaptability and robustness of different models also differ. In some cases, a model may exhibit high accuracy during a certain period of time, but may become inaccurate during another period of time. This uncertainty makes it difficult for model switching strategies based on fixed rules to effectively respond to dynamic changes in practical applications.

[0036] With the advancement and development of computer technology, the degree of coupling of controlled systems has gradually increased, and control systems have gradually evolved from single-component control to multi-subsystem coupled giant system control, which has put new and higher requirements on the design method of controllers. Traditional model-based control methods cannot adapt to control under conditions of wide-range dynamic changes, significant deviation from system model ideal values, and other situations, and the robustness of adaptive control algorithms cannot achieve transient control performance that meets control requirements within the entire operating condition range. To improve transient performance within the entire operating condition range, the multi-model idea emerges, which achieves parameter coverage and dynamic switching within the entire operating condition range by constructing a model set, a control set, and supervision rules. Here, the supervision rules directly affect the transient control performance of model switching and constrain the overall performance of the system.

[0037] Multi-model switching control systems are a typical means to solve the engineering application of advanced control theory. When designing a controller, attention is generally paid to control effects while supervision rules during the switching process are ignored. Generally, a weighted method is used to calculate multiple controllers in the control set simultaneously, different weights are selected according to model errors, and the calculated values of the sub-controllers are integrated and output. This method requires a large number of tests for calibration of the weighting method of local controllers, and the stability and convergence of the system are difficult to prove. Another way is to design a switching multi-model controller to integrate the model set and the controller, which may result in excessive number of models and large amount of calculation, and is not suitable for engineering application of controllers. To solve the above problems, the present application proposes a reference model switching method based on the trend of model estimation error, which selects two adjacent models in a local area, synchronously calculates the control law output, and then assigns weights to the control law output of each model according to the calculation error and error trend of each model. This method reduces the amount of calculation while ensuring the smoothness of the model switching process and optimizing the transient performance of the switching process.

[0038] Through the steps S102 to S108, the segmented model of the controlled object system is constructed, and the system state information of the controlled object system is collected through the sensor, wherein the segmented model includes the initial zero dynamic model, and the system state information includes the current working condition of the controlled object system; the adjacent first model and second model are extracted from the model set according to the system state information, and the first system state value and the second system state value of the first model and the second model are estimated respectively; the errors between the system state value of the controlled object system collected by the sensor and the first system state value and the second system state value are calculated, and the first deviation value and the second deviation value are obtained respectively, the first error trend set is calculated based on the first deviation value, and the second error trend set is calculated based on the second deviation value; the first sub-control law and the second sub-control law are calculated according to the first model and the second model respectively, and the target control law is output by combining the first deviation value and the second deviation value, the first error trend set and the second error trend set, thereby solving the technical problems that the switching process will cause the mutation of the control law output and the large fluctuation of the system state parameters, realizing the smooth switching of the reference model, and relieving the equipment deterioration caused by the dramatic change of the control command.

[0039] In one exemplary embodiment, after the segmented model of the controlled object system is constructed, and the system state information of the controlled object system is collected through the sensor, the above method further includes:

[0040] S11, the supervisory rules select the adjacent first model and second model from the model set containing multiple fixed models according to the system running parameters in the system state information at each calculation step;

[0041] S12, the first model is used as the current initial state model of the adaptive reference model, and the second model is used as the alternative state model of the adaptive reference model.

[0042] In this embodiment, the model finite step length historical error data is used as the model switching basis, which effectively avoids the problems of excessive calculation amount of the system model set and control weight calibration. Figure 2 An optional controlled object model set composition diagram provided by the embodiment of the present application is shown in Figure 2 The nonlinear dynamic process model set of the controlled object system can include n sub-models, which are model 1 to model 5. Figure 3 A flowchart of an optional model switching method provided by the embodiment of the present application is shown in Figure 3As shown, the multi-model reference control method includes a reference model set Ω, a supervision rule l, a model error e, an error trend estimation δ, a sub-control law calculation u', a control law integration output u, and the like. In the strategy working process, the supervision rule extracts two adjacent models from the model set Ω through the system state information collected by the sensor, then estimates the system state values of the two models respectively, calculates the error between the sensor value and the estimated value, records the error and calculates the deviation value of the error. The sub-control law is calculated according to the two different models respectively, and then the control law is integrated and output according to the deviation value of the error and the change trend of the error.

[0043] Exemplarily, the supervision rule selects the first model and the second model adjacent to each other from the model set containing a plurality of fixed models according to the system operating parameters in the system state information at each calculation step, that is, after the segmented model of the controlled object system is constructed (the typical number of models in the ship power system is 6, including the initial zero dynamic model). The supervision rule can select two adjacent models M1,k (for example, the first model) and M2,k (for example, the second model) from the model set Ω containing n fixed models as the current initial state and the alternative state of the adaptive reference model according to the system operating parameters (for example, the rotating speed and the load) at each calculation step, and then convert them into the control law calculation method of the two fixed model reference adaptive control.

[0044] Through the embodiment, the dynamic switching process of the model i to the model i+1 is optimized, the smooth transition of the control law calculation reference model is realized according to the design of the supervision rule and the error estimation rule, and the robustness and adaptability of the system are ensured.

[0045] In an exemplary embodiment, after the error between the system state value of the controlled object system collected by the sensor and the first system state value and the second system state value is calculated, the first deviation value and the second deviation value are obtained respectively, and the above method further includes:

[0046] S21, constructing a first initial error set based on the first deviation value, wherein the first initial error set records the errors of the first model for ten continuous calculation steps at the beginning of the algorithm;

[0047] S22, constructing a second initial error set based on the second deviation value, wherein the second initial error set records the errors of the second model for ten continuous calculation steps at the beginning of the algorithm.

[0048] In the embodiment, Figure 4 An algorithm logic block diagram of an optional reference model switching method based on model estimated error trend provided by the embodiment of the application, Figure 5 An optional flowchart of a reference model switching method based on model estimated error trend provided by the embodiment of the application, combined with Figure 4 andFigure 5 As shown, the control law of the current control step, i.e., the first sub-control law u1'(k) and the second sub-control law u2'(k), can be calculated by M1 (the first model) and M2 (the second model) respectively according to the conventional model reference adaptive control method. According to the selected reference models M1,k and M2,k, the estimated values of the current model output state are calculated respectively According to the model estimated value of the kth step and the sensor collected value xk (the system state value), the model error ek (the first deviation value, the second deviation value) of the kth step is calculated, The initial error sets {0,…,0} and {0,…,0} are constructed respectively 1x10 From the beginning of the algorithm, the errors of the two models in the last 10 steps are recorded to construct the error sets e M1 , e M2 , i.e., the first initial error set and the second initial error set respectively.

[0049] Through the embodiment, by selecting two reference models corresponding to adjacent working condition points, the calculation complexity and resource consumption can be significantly reduced while ensuring the coverage range of the working condition parameters.

[0050] In one exemplary embodiment, after the second initial error set is constructed based on the second deviation value, the above method further comprises:

[0051] S31, calculating the first error trend set according to the first initial error set, and counting the first negative value number, the first positive value sum, and the first negative value sum in the first error trend set;

[0052] S32, calculating the second error trend set according to the second initial error set, and counting the second negative value number, the second positive value sum, and the second negative value sum in the second error trend set.

[0053] In the embodiment, after the second initial error set is constructed based on the second deviation value, the first error trend set can be calculated according to the first initial error set, and the first negative value number, the first positive value sum, and the first negative value sum in the first error trend set can be counted. Similarly, the second error trend set can be calculated according to the second initial error set, and the second negative value number, the second positive value sum, and the second negative value sum in the second error trend set can be counted.

[0054] Specifically, as shown in Figure 4 and Figure 5 , the error trend sets, i.e., the first error trend set A and the second error trend set B, can be calculated according to the error sets, i.e., the first initial error set e M1 and the second initial error set e M2 . The above two error trend sets can be represented as {δ1,…,δ9}, wherein δ1=e k -e k-1 …, δ 9＝ ek-9 -e k-10 .

[0055] Furthermore, we can count the number of the first negative value N1 in the first error trend set A, the number of the second negative value N2 in the second error trend set B, the sum of the first positive value Ps1 in the first error trend set A, the sum of the second positive value Ps2 in the second error trend set B, the sum of the negative values ​​Ns1 in the first error trend set A, and the sum of the Ns2 in the second error trend set B.

[0056] This embodiment demonstrates that under dynamically changing operating conditions, control law output control can be completed based on the reference model error and its trend, ensuring the robustness and adaptability of the multi-model reference control system during model switching. It dynamically determines the most suitable model for control based on the system's current operating state and trends, rather than statically presetting the model switching point. This allows the control strategy to better adapt to environmental or operating condition uncertainties. The adaptive model switching strategy based on error trends enables a smooth transition to the new control model, reducing the impact of switching.

[0057] In an exemplary embodiment, after calculating a second error trend set based on a second initial error set, and counting the number of second negative values, the sum of second positive values, and the sum of second negative values ​​in the second error trend set, the above method further includes:

[0058] S41, when the first negative value is greater than the second negative value, the output target control law is the first sub-control law;

[0059] S42, when the first negative value is less than the second negative value, the output target control law is the second sub-control law;

[0060] S43, when the first negative value is equal to the second negative value, output the target control law.

[0061] In this embodiment, combined with Figure 4 and Figure 5 As shown, after calculating the second error trend set based on the second initial error set, and statistically analyzing the second negative value, the second positive value, and the second negative value in the second error trend set, the ratio of the first positive value to the second positive value and the ratio of the first negative value to the second negative value can be calculated.

[0062] Specifically, the ratio of the first positive sum to the second positive sum, i.e., Ps1 / Ps2, and the ratio of the first negative sum to the second negative sum, i.e., Ns1 / Ns2, can be calculated. If Th2 > Ps1 / Ps2 > Th1, the next calculation step is entered, where Th1 ∈ [0.01, 0.9] and Th2 ∈ [1, 10], and the specific values can be adjusted according to the output result of the control law. If Ps1 / Ps2 < Th1, the calculation result of u2'(k) is abandoned and u1'(k) is directly output. If Ps1 / Ps2 > Th2, the calculation result of u1'(k) is abandoned and u2'(k) is output.

[0063] Further, in the case where the number of first negative values is greater than the number of second negative values, the output target control law is the first sub-control law, that is, if N1 > N2, u k = u1'(k); in the case where the number of first negative values is less than the number of second negative values, the output target control law is the second sub-control law, that is, if N1 < N2, u k = u2'(k); in the case where the number of first negative values is equal to the number of second negative values, the output target control law is, that is, u k = W1*u1'(k) + W2*u2'(k), and the weight coefficients W1 and W2 are described in detail in the following embodiments.

[0064] Through this embodiment, the control weights of each reference model are dynamically determined in the multi-model control system. By considering the numerical magnitude of the error and its change trend, the current control effect and future prediction can be more accurately reflected, and then the weights of the control model are adjusted, avoiding the disadvantages such as a large workload of calibrating weight parameters and reducing the dependence on testing and experiments.

[0065] In an exemplary embodiment, in the case where the number of first negative values is equal to the number of second negative values, the output target control law includes:

[0066] S51, determining the first weight and the second weight based on the first negative sum and the second negative sum, and calculating the output weight through the first weight and the second weight;

[0067] S52, using the output weight, combining the first sub-control law and the second sub-control law to determine the target control law.

[0068] In this embodiment, in the case where the number of first negative values is equal to the number of second negative values, that is, in the case where the errors decay simultaneously and have the same amplitude, the output target control law can be determined in the following manner. Optionally, the first weight and the second weight can be determined based on the first negative sum and the second negative sum, and the output weight can be calculated through the first weight and the second weight. Using the output weight, combining the first sub-control law and the second sub-control law to determine the target control law.

[0069] Exemplarily, combining Figure 4and Figure 5 As shown, the output weights W1 (the first weight) and W2 (the second weight) can be calculated, where W1 = |Ns1| / (|Ns1| + |Ns2|), W2 = 1 - W1, and combined with u k = W1 * u1'(k) + W2 * u2'(k), the specific output target control law can be obtained when the first negative value number is equal to the second negative value number.

[0070] Through this embodiment, in the method for calculating the weight factor of the error statistical value with continuous step sizes, based on statistical information, the weight factor of each reference model is calculated. The smaller the error statistical value, or the more favorable the error change trend is for achieving the control target, the higher the weight factor of this model, which means that the control strategy will be more inclined to adopt the model with better performance, and based on this, more stable model switching is achieved.

[0071] In an exemplary embodiment, after determining the target control law by using the output weights and combining the first sub-control law and the second sub-control law, the above method further includes:

[0072] S61, restricting the output change rate of the target control law based on the first calibration value and the second calibration value, where the output change rate is the difference between two adjacent target control laws of the output.

[0073] In the embodiments of the present application, after determining the target control law by using the output weights and combining the first sub-control law and the second sub-control law, in order to obtain better model switching effect, it is necessary to restrict the output change law of the target control law.

[0074] Optionally, the output change rate of the target control law can be restricted based on the first calibration value (Th3) and the second calibration value (Th4), combined with Figure 4 and Figure 5 As shown, the output change rate of u (the difference between two adjacent target control laws of the output, that is, Δu) can be restricted. First, Δu = u k - u k-1 .

[0075] If Th4 < Δu < Th3, then output uk; otherwise, if Δu > Th3, u k = u k-1 + Th3; if Δu < Th4, u k = u k-1 + Th4, where Th3 > 0 is the calibration value and Th4 < 0 is the calibration value. Then u k can be output, and u k is assigned to u k-1 , waiting for the next calculation trigger.

[0076] This embodiment utilizes a finite number of adjacent reference models for simultaneous calculation. This ensures both the breadth of model coverage of the operating conditions and effectively reduces the computational resources consumed in system state estimation during control law calculation, thus guaranteeing the real-time performance and speed of control law calculation. The adaptive model switching strategy based on error trends integrates the control laws calculated by the two models based on error values, gradually reducing the output proportion of the model with the larger error and smoothly transitioning to the new control model, minimizing the impact of the switch.

[0077] According to another aspect of the embodiments of this application, a reference model switching apparatus is also provided for implementing the above-described reference model switching method based on the trend of model estimation error. Figure 6 This is a schematic diagram of an optional reference model switching device based on the model estimation error trend according to an embodiment of this application, as shown below. Figure 6 As shown, the device may include:

[0078] The acquisition unit 602 is used to construct a segmented model of the controlled object system and acquire system state information of the controlled object system through sensors. The segmented model includes an initial zero dynamic model, and the system state information includes the current operating condition of the controlled object system.

[0079] Estimation unit 604 is used to extract adjacent first and second models from the model set according to the system state information, and estimate the first system state value and the second system state value of the first model and the second model respectively;

[0080] The first calculation unit 606 is used to calculate the error between the system state value of the controlled object system collected by the sensor and the first system state value and the second system state value, respectively to obtain a first deviation value and a second deviation value, calculate a first error trend set based on the first deviation value, and calculate a second error trend set based on the second deviation value;

[0081] The first output unit 608 is used to calculate the first sub-control law and the second sub-control law according to the first model and the second model respectively, and output the target control law by combining the first deviation value and the second deviation value, the first error trend set and the second error trend set.

[0082] It should be noted that the acquisition unit 602 in this embodiment can be used to execute the above step S102, the estimation unit 604 in this embodiment can be used to execute the above step S104, the first calculation unit 606 in this embodiment can be used to execute the above step S106, and the first output unit 608 in this embodiment can be used to execute the above step S108.

[0083] Through the above modules, a segmented model of the controlled object system is constructed, and system state information of the controlled object system is collected by sensors. The segmented model includes an initial zero dynamic model, and the system state information includes the current operating condition of the controlled object system. Based on the system state information, adjacent first and second models are extracted from the model set, and the first and second system state values ​​of the first and second models are estimated respectively. The errors between the system state values ​​of the controlled object system collected by the sensors and the first and second system state values ​​are calculated, and the first and second deviation values ​​are obtained respectively. Based on the first deviation value, a first error trend set is calculated, and based on the second deviation value, a second error trend set is calculated. The first and second sub-control laws are calculated according to the first and second models respectively. Combining the first and second deviation values, the first and second error trend sets, the target control law is output. This solves the technical problem that the switching process will cause abrupt changes in the control law output and large fluctuations in system state parameters, achieves smooth switching of the reference model, and alleviates the equipment deterioration caused by drastic changes in control commands.

[0084] In one exemplary embodiment, the apparatus further includes:

[0085] The selection module is used to supervise the rules to select adjacent first models and second models from the model set containing multiple fixed models at each calculation step based on the system operating parameters in the system status information.

[0086] The first model serves as the current initial state model of the adaptive reference model, and the second model serves as the alternative state model of the adaptive reference model.

[0087] In one exemplary embodiment, the apparatus further includes:

[0088] The first construction unit is used to construct a first initial error set based on the first deviation value, wherein the first initial error set records the errors of the first model for ten consecutive calculation steps starting from the beginning of the algorithm.

[0089] The second construction unit is used to construct a second initial error set based on the second deviation value, wherein the second initial error set records the errors of the algorithm for the first ten consecutive calculation steps of the second model.

[0090] In one exemplary embodiment, the apparatus further includes:

[0091] The second calculation unit is used to calculate the first error trend set based on the first initial error set, and to count the first negative value, the first positive value, and the first negative value in the first error trend set.

[0092] The third calculation unit is used to calculate the second error trend set based on the second initial error set, and to count the number of second negative values, the sum of second positive values, and the sum of second negative values ​​in the second error trend set.

[0093] In one exemplary embodiment, the apparatus further includes:

[0094] The second output unit is used to output the target control law as the first sub-control law when the first negative value number is greater than the second negative value number.

[0095] The third output unit is used to output the target control law as the second sub-control law when the first negative value number is less than the second negative value number.

[0096] The fourth output unit is used to output the target control law when the first negative value is equal to the second negative value.

[0097] In one exemplary embodiment, the fourth output unit includes:

[0098] The first determining module is used to determine a first weight and a second weight based on the first negative value and the second negative value, and to calculate the output weight using the first weight and the second weight.

[0099] The second determining module is used to determine the target control law by using the output weights and combining the first sub-control law and the second sub-control law.

[0100] In one exemplary embodiment, the apparatus further includes:

[0101] A limiting unit is used to limit the output change rate of the target control law based on a first calibration value and a second calibration value, wherein the output change rate is the difference between two adjacent outputs of the target control law.

[0102] It should be noted that the examples and scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a hardware environment and can be implemented by software or hardware. The hardware environment includes a network environment.

[0103] According to another aspect of the embodiments of this application, a storage medium is also provided. Optionally, in this embodiment, the storage medium can be used to execute the program code of any of the reference model switching methods based on model estimation error trends described in the embodiments of this application.

[0104] Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps:

[0105] S1. Construct a segmented model of the controlled object system and collect system state information of the controlled object system through sensors. The segmented model includes an initial zero dynamic model and the system state information includes the current operating condition of the controlled object system.

[0106] S2, based on the system state information, extract the adjacent first model and second model from the model set, and estimate the first system state value and the second system state value of the first model and the second model, respectively;

[0107] S3, calculate the error between the system state value of the controlled object system collected by the sensor and the first system state value and the second system state value, and obtain the first deviation value and the second deviation value respectively. Calculate the first error trend set based on the first deviation value and the second error trend set based on the second deviation value.

[0108] S4. Calculate the first sub-control law and the second sub-control law according to the first model and the second model respectively. Combine the first deviation value and the second deviation value, the first error trend set and the second error trend set to output the target control law.

[0109] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be repeated in this embodiment.

[0110] The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0111] According to another aspect of the embodiments of this application, an electronic device for implementing the above-described reference model switching method based on model estimation error trends is also provided. The electronic device may be a server, a terminal, or a combination thereof.

[0112] Figure 7 This is a schematic diagram of the structure of an optional electronic device according to an embodiment of this application, such as... Figure 7 As shown, it includes a processor 702, a communication interface 704, a memory 706, and a communication bus 708. The processor 702, communication interface 704, and memory 706 communicate with each other via the communication bus 708.

[0113] Memory 706 is used to store computer programs;

[0114] When processor 702 executes a computer program stored in memory 706, it performs the following steps:

[0115] S1. Construct a segmented model of the controlled object system and collect system state information of the controlled object system through sensors. The segmented model includes an initial zero dynamic model and the system state information includes the current operating condition of the controlled object system.

[0116] S2, based on the system state information, extract the adjacent first model and second model from the model set, and estimate the first system state value and the second system state value of the first model and the second model, respectively;

[0117] S3, calculate the error between the system state value of the controlled object system collected by the sensor and the first system state value and the second system state value, and obtain the first deviation value and the second deviation value respectively. Calculate the first error trend set based on the first deviation value and the second error trend set based on the second deviation value.

[0118] S4. Calculate the first sub-control law and the second sub-control law according to the first model and the second model respectively. Combine the first deviation value and the second deviation value, the first error trend set and the second error trend set to output the target control law.

[0119] Optionally, the communication bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 The symbol is represented by a single thick line, but this does not indicate that there is only one bus or one type of bus. The communication interface is used for communication between the aforementioned electronic device and other devices.

[0120] The memory may include RAM, or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0121] As an example, the memory 706 described above may include, but is not limited to, the acquisition unit 602, the estimation unit 604, the first calculation unit 606, and the first output unit 608 in the reference model switching device based on the model estimation error trend. Furthermore, it may include, but is not limited to, other module units in the reference model switching device based on the model estimation error trend, which will not be elaborated upon in this example.

[0122] The processors mentioned above can be general-purpose processors, including but not limited to: CPU (Central Processing Unit), NP (Network Processor), etc.; they can also be DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0123] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be repeated here.

[0124] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0125] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0126] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0127] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0129] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0130] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0131] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

[0132] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0133] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A reference model switching method based on the trend of model estimation error, characterized in that, include: Construct a segmented model of the controlled object system, and collect system state information of the controlled object system through sensors. The segmented model includes an initial zero dynamic model, and the system state information includes the current operating condition of the controlled object system. Based on the system state information, the adjacent first and second models are extracted from the model set, and the first system state value and the second system state value of the first model and the second model are estimated respectively. The error between the system state value of the controlled object system collected by the sensor and the first system state value and the second system state value is calculated to obtain the first deviation value and the second deviation value respectively. The first error trend set is calculated based on the first deviation value, and the second error trend set is calculated based on the second deviation value. Calculate the first sub-control law and the second sub-control law based on the first model and the second model respectively, and output the target control law by combining the first deviation value and the second deviation value, the first error trend set and the second error trend set. The method further includes: Count the number of the first negative values ​​in the first error trend set, and count the number of the second negative values ​​in the second error trend set; When the first negative value is greater than the second negative value, the output target control law is the first sub-control law; When the first negative value is less than the second negative value, the output target control law is the second sub-control law; If the first negative value is equal to the second negative value, the target control law is output.

2. The reference model switching method based on the model estimation error trend as described in claim 1, characterized in that, After constructing a segmented model of the controlled object system and collecting system state information of the controlled object system through sensors, the method further includes: The supervision rule selects the adjacent first model and the second model from the model set containing multiple fixed models at each calculation step based on the system operating parameters in the system status information. The first model serves as the current initial state model of the adaptive reference model, and the second model serves as the alternative state model of the adaptive reference model.

3. The reference model switching method based on the model estimation error trend as described in claim 1, characterized in that, After calculating the error between the system state value of the controlled object system collected by the calculation sensor and the first system state value and the second system state value, respectively, and obtaining the first deviation value and the second deviation value, the method further includes: A first initial error set is constructed based on the first deviation value, wherein the first initial error set contains rolling records of the errors of the first model for the first ten consecutive calculation steps at the beginning of the algorithm. A second initial error set is constructed based on the second deviation value, wherein the second initial error set contains rolling records of the errors of the second model for the first ten consecutive calculation steps of the algorithm.

4. The reference model switching method based on the model estimation error trend as described in claim 3, characterized in that, After constructing the second initial error set based on the second deviation value, the method further includes: Calculate the first error trend set based on the first initial error set, and count the first negative value, the first positive value, and the first negative value in the first error trend set; The second error trend set is calculated based on the second initial error set, and the number of second negative values, the sum of second positive values, and the sum of second negative values ​​in the second error trend set are counted.

5. The reference model switching method based on the trend of model estimation error as described in claim 1, characterized in that, When the first negative value is equal to the second negative value, the output of the target control law includes: The first weight and the second weight are determined based on the sum of the first negative value and the sum of the second negative value, and the output weight is calculated using the first weight and the second weight. The target control law is determined by using the output weights and combining the first sub-control law and the second sub-control law.

6. The reference model switching method based on the trend of model estimation error as described in any one of claims 1-5, characterized in that, After determining the target control law by utilizing the output weights and combining the first sub-control law and the second sub-control law, the method further includes: The output rate of change of the target control law is limited based on a first calibration value and a second calibration value, wherein the output rate of change is the difference between two consecutive outputs of the target control law.

7. A reference model switching device based on model estimation error trend, executing the reference model switching method based on model estimation error trend as described in claim 1, characterized in that, include: The acquisition unit is used to construct a segmented model of the controlled object system and to acquire system state information of the controlled object system through sensors. The segmented model includes an initial zero dynamic model, and the system state information includes the current operating condition of the controlled object system. The estimation unit is used to extract adjacent first and second models from the model set based on the system state information, and estimate the first system state value and the second system state value of the first model and the second model, respectively. The first calculation unit is used to calculate the error between the system state value of the controlled object system collected by the sensor and the first system state value and the second system state value, respectively, to obtain a first deviation value and a second deviation value, to calculate a first error trend set based on the first deviation value, and to calculate a second error trend set based on the second deviation value; The first output unit is used to calculate the first sub-control law and the second sub-control law according to the first model and the second model respectively, and output the target control law by combining the first deviation value and the second deviation value, the first error trend set and the second error trend set.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method of any one of claims 1 to 6.

9. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method of any one of claims 1 to 6 through the computer program.

Images