State observation model construction method and device, computer equipment and storage medium
By constructing an error compensation term based on the transfer function, the dynamic bias of the state observation model is eliminated, the output problem of the pure integrator in the dynamic process is solved, and high-precision and high-stability state observation is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-27
- Publication Date
- 2026-04-14
AI Technical Summary
The existing state observation model has significant dynamic biases during dynamic processes. In particular, the pure integrator is sensitive to initial conditions and DC bias causes output saturation, which affects the stability and accuracy of the observation model.
By constructing a transfer function based on the first and second state observation models, the output correlation is determined, an error compensation term is generated, and a target state observation model is constructed in conjunction with the stabilized transfer function to eliminate dynamic bias.
It achieves high-precision and high-stability state observation during dynamic processes, effectively eliminates the effects of initial value drift and DC bias of pure integrator, and improves the observation accuracy and stability of the system.
Smart Images
Figure CN121863933A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of motor control technology, and in particular to a method, apparatus, computer equipment, and storage medium for constructing a state observation model. Background Technology
[0002] In fields such as high-performance motor control, inertial navigation, and industrial process control, accurate observation of the system's internal states (such as flux linkage, velocity, and attitude angles) is crucial for achieving precise closed-loop control. The state observation model, as the core algorithm, directly determines the dynamic response, stability, and accuracy of the entire system.
[0003] In practical engineering, flux linkage observation methods based on voltage models are widely regarded as an ideal reference model due to their simple structure and the fact that their observation results are largely unaffected by time-varying parameters (such as motor resistance and inductance) in the medium and high speed ranges. However, this ideal pure integrator has two significant drawbacks in actual digital or analog systems: the pure integrator is extremely sensitive to initial conditions; if the initial value is not zero, it will cause a continuous DC offset in the output, which cannot automatically return to zero; the actual acquired voltage signal often contains a certain DC bias component, and the pure integrator will continuously accumulate this DC error, eventually causing output saturation and rendering the observation model invalid.
[0004] To address the inherent problems of pure integrators, an improved scheme employing a dual low-pass filter structure has been proposed in related technologies. The basic principle of this scheme is to select two low-pass filters with different cutoff frequencies and proportional coefficients to stabilize the input voltage signal separately, and then subtract the weighted sum of their outputs. Theoretically, by rationally designing the filter parameters, errors caused by the initial value and DC bias can be completely offset. However, due to the phase lag and amplitude attenuation characteristics of the filters, the output of this state observation model exhibits significant dynamic deviations during system dynamic processes (such as sudden changes in motor speed or rapid load changes). Summary of the Invention
[0005] Based on this, a method, apparatus, computer device, and storage medium for constructing a state observation model are provided to solve the problem of significant dynamic deviation in the output of state observation models in related technologies.
[0006] In a first aspect, the present invention provides a method for constructing a state observation model, the method comprising: Based on the transfer functions of the first state observation model and the second state observation model, the output correlation between the first state observation model and the second state observation model is determined. The accuracy of the output state of the first state observation model is higher than that of the output state of the second state observation model, but lower than the stability of the second state observation model when outputting the state. Based on the output correlation, an error compensation term is generated, which represents the output error between the second state observation model and the first state observation model. Based on the error compensation term and the second state observation model, a target state observation model is constructed.
[0007] In one embodiment, the method for constructing the first state observation model includes: Determine the physical quantities in the device under test that form an integral relationship with the measured state quantity; The physical quantity is integrated to construct the first state observation model.
[0008] In one embodiment, the method for constructing the second state observation model includes: Construct a stabilization transfer function, wherein the stabilization transfer function is used to eliminate the initial value drift and DC bias of the first state observation model output; Based on the stabilized transfer function and the first state observation model, the second state observation model is constructed.
[0009] In one embodiment, determining the output correlation between the first state observation model and the second state observation model based on their respective transfer functions includes: Obtain the first transfer function of the first state observation model and the second transfer function of the second state observation model; Calculate the ratio between the first transfer function and the second transfer function to obtain the output correlation relationship.
[0010] In one embodiment, generating an error compensation term based on the output correlation includes: Based on the aforementioned proportional relationship, the difference between the output of the first state observation model and the output of the second state observation model is calculated. The error compensation term is generated based on the difference relationship.
[0011] In one embodiment, generating the error compensation term based on the difference relationship includes: The difference relationship is decomposed into a main deviation term and at least one derived deviation term, wherein the derived deviation term is generated by the main deviation term and a corresponding dynamic coefficient. The principal deviation term is stabilized to obtain a stable principal deviation term, and the dynamic coefficient is stabilized to obtain a stable dynamic coefficient. Based on the stable principal deviation term and the stable dynamic coefficient, a stable derived deviation term is generated; The error compensation term is generated based on the stable main deviation term and the stable derived deviation term.
[0012] In one embodiment, constructing the target state observation model based on the error compensation term and the second state observation model includes: Obtain the error compensation terms corresponding to each coordinate axis observation channel of the second state observation model; Based on each of the aforementioned error compensation terms, the outputs of each of the aforementioned coordinate axis observation channels are compensated to generate the target state observation model.
[0013] Secondly, the present invention provides a state observation model construction apparatus, the apparatus comprising: The determination module is used to determine the output correlation between the first state observation model and the second state observation model based on their respective transfer functions, wherein the accuracy of the output state of the first state observation model is higher than the accuracy of the output state of the second state observation model, but lower than the stability of the second state observation model when outputting the state. The generation module is used to generate an error compensation term based on the output correlation, wherein the error compensation term characterizes the output error between the output of the second state observation model and the first state observation model; A construction module is used to construct a target state observation model based on the error compensation term and the second state observation model.
[0014] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the state observation model construction method of the first aspect described above.
[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the state observation model construction method of the first aspect described above.
[0016] The aforementioned method, apparatus, computer equipment, and storage medium for constructing state observation models construct error compensation terms based on the output correlation determined by the transfer functions of the first and second state observation models, which is unaffected by instantaneous observation biases. This ensures that the generated error compensation terms accurately characterize the theoretical deviation characteristics of the second state observation model relative to the first state observation model, rather than the apparent deviations mixed with actual noise and dynamic errors. Finally, the error compensation terms are combined with the second state observation model to form the target state observation model, which can effectively eliminate the dynamic observation bias of the second state observation model, thereby achieving high-precision and high-stability state observation during the dynamic process of the system. Attached Figure Description
[0017] Figure 1 This is a flowchart illustrating a method for constructing a state observation model in one embodiment; Figure 2 This is a control architecture diagram of the second stator flux linkage observation model in one embodiment; Figure 3 This is a control architecture diagram of the target stator flux linkage observation model in one embodiment; Figure 4 This is a structural block diagram of a state observation model construction device in one embodiment; Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. The specific operational methods in the method embodiments can also be applied to the device embodiments or system embodiments. It should be noted that in the description of this invention, "multiple" is understood as "at least two". "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing together, or B existing alone. A connected to B can represent: A and B directly connected, or A and B connected through C. Furthermore, in the description of this invention, terms such as "first" and "second" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or order.
[0019] Before introducing the state observation model construction method provided by this invention, the technical background of this invention will be described in detail below for ease of understanding.
[0020] To address the inherent problems of pure integrators, an improved scheme employing a dual low-pass filter structure has been proposed in related technologies. The basic principle of this scheme is to select two low-pass filters with different cutoff frequencies and proportional coefficients to stabilize the input voltage signal separately, and then subtract the weighted sum of their outputs. Theoretically, by properly designing the filter parameters, errors caused by the initial value and DC bias can be completely offset. However, due to the phase lag and amplitude attenuation characteristics of the filters, the output of this state observation model exhibits significant dynamic deviations during system dynamic processes (such as sudden changes in motor speed or rapid load changes).
[0021] The technical solution provided by the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
[0022] Figure 1 This is a flowchart illustrating a state observation model construction method in one embodiment. This process can be executed by a state observation model construction device, which can be implemented in software, hardware, or a combination of both. Figure 1 As shown, the process includes the following steps: S101, Based on the transfer functions of the first state observation model and the second state observation model, determine the output correlation between the first state observation model and the second state observation model. The accuracy of the output state of the first state observation model is higher than that of the output state of the second state observation model, but lower than the stability of the output state of the second state observation model. S102, Based on the output correlation, an error compensation term is generated. The error compensation term represents the output error between the output of the second state observation model and the first state observation model. S103, Based on the error compensation term and the second state observation model, construct the target state observation model.
[0023] Among them, the output correlation is used to characterize the deterministic functional relationship between the high-precision output of the first-state observation model and the high-stability output of the second-state observation model.
[0024] The state observation model construction method of this invention serves as a general state estimation solution and can be widely applied to scenarios involving high-precision and high-stability observation of the internal state of various industrial equipment. Typical applications include, but are not limited to: observation of rotor and stator flux linkages in motors, observation of carrier attitude angles in inertial navigation systems, and real-time tension observation of materials in winding and tension control.
[0025] Taking stator flux linkage observation in an induction motor as an example: Based on the observed stator flux linkage, the induction motor torque can be estimated in real time. In the electric drive system of new energy vehicles, real-time and accurate acquisition of the induction motor's output torque is crucial for achieving high-performance control, improving energy efficiency, and ensuring functional safety. This torque directly serves core functions such as control strategy optimization, torque closed-loop feedback, and online fault diagnosis. Currently, there are two main technical approaches to torque acquisition: Direct measurement method: Relies on torque sensor. Although it has high accuracy, it has inherent disadvantages such as high cost, installation limitations, and measurement accuracy being easily affected by dynamic speed.
[0026] Indirect calculation method: Based on the motor model, stator flux linkage is estimated online using easily acquired signals such as voltage and current. However, traditional stator flux linkage observation models (such as pure integrators and their various improved stabilization models) have inherent defects: the former is extremely sensitive to DC bias and initial values, while the latter, although solving the stability problem, inevitably produces phase lag and amplitude attenuation due to the introduction of low-pass filtering and other components. When the induction motor is under dynamic operating conditions with rapidly changing speed or load, these defects will cause significant deviations in the flux linkage observation values, ultimately directly affecting the accuracy of torque estimation.
[0027] Therefore, developing a high-precision magnetic flux observation module that can both ensure observation stability and effectively compensate for dynamic errors has become a key technical bottleneck for improving the overall performance of electric drive systems.
[0028] The state observation model construction method in this invention constructs an error compensation term based on the output correlation determined by the transfer functions of the first and second state observation models, which is unaffected by instantaneous observation bias. This ensures that the generated error compensation term accurately characterizes the theoretical deviation characteristics of the second state observation model relative to the first state observation model, rather than the apparent deviation mixed with actual noise and dynamic errors. Finally, the error compensation term is combined with the second state observation model to form the target state observation model, which can effectively eliminate the dynamic observation bias of the second state observation model, thereby achieving high-precision and high-stability state observation during the dynamic process of the system.
[0029] In one embodiment, an exemplary method for constructing a first-state observation model is illustrated, including but not limited to: Identify the physical quantities in the device under test that form an integral relationship with the state quantity under test, and perform integral operations on these physical quantities to construct a first state observation model. The first state observation model includes observation channels with different coordinate axes.
[0030] Taking the observation of stator flux linkage in an induction motor as an example: the state variable to be measured is the stator flux linkage ψ. The physical quantities that form an integral relationship with the stator flux linkage in the induction motor include the voltage U, resistance R, and current I of the induction motor. The integral relationship is: U = R × I + dψ / dt.
[0031] Integrating the above physical quantities to construct the first stator flux linkage observation model, which includes, but is not limited to: To obtain the electric drive parameters of the induction motor, at least the three-phase current (i) must be included. a i b i c ), bus voltage U dc Stator resistance R s .
[0032] Then, (i) a i b i c The current I is transformed into the first current I in a two-phase stationary coordinate system (α-β axis) through the Clarke transformation. α Second current I β , among which, I α and I β Specifically: .
[0033] Meanwhile, based on the duty cycle (s) of the motor controller switching transistor a s b s c ) and U dc Calculate the three-phase voltage (U) applied to the induction motor. a U b U c ), where U a U b U c Specifically: And U a U b U c Through the Clarke transformation, it is converted into the first voltage U along the α-β axis. α Second voltage U β , among which, U α and U β Specifically: .
[0034] Therefore, based on I α I β U α and U β The observation model of the first stator flux linkage (ψ) under the α-β axis is constructed as follows: The first stator flux linkage observation model includes an α-axis observation channel and a β-axis observation channel.
[0035] Using the above method, a pure integral first-state observation model is constructed, laying an idealized theoretical foundation for the subsequent construction of a high-precision composite observation model.
[0036] In one embodiment, an exemplary method for constructing a second-state observation model is illustrated, including but not limited to: A stabilization transfer function is constructed, which is used to eliminate the initial value drift and DC bias in the output of the first-state observation model. The stabilization function can be a low-pass filter, two parallel low-pass filters with different cutoff frequencies, or a series combination of a low-pass filter and a high-pass filter, depending on the situation. No specific limitation is made here. By reasonably designing the filter parameters, the errors caused by the initial value and DC bias can be completely eliminated in theory.
[0037] Then, based on the stabilized transfer function and the first-state observation model, the second-state observation model is constructed.
[0038] Taking the observation of stator flux linkage in an induction motor as an example: the excitation frequency of the motor (the fundamental frequency of voltage and current) is the angular velocity ω of the internal magnetic field of the motor rotating in space. e Therefore, when designing filter parameters, ω can be used as a basis. e Determining the filter's cutoff frequency ensures that the stator flux linkage observation model maintains optimal approximation accuracy near the current fundamental frequency throughout the entire operating speed range. Wherein, ω e The methods for obtaining it can be: Obtain the rotor angular frequency ω of the induction motor r The slip angular frequency ω is calculated based on the torque command and the rotor magnetic field command. s Therefore, based on ω r and ω s ω was calculated e =ω r +ω s .
[0039] To address the issues of initial integration value and DC drift in a pure integrator, a low-pass filter can be used instead. In this case, the cutoff frequency of the low-pass filter can be set to ω. e This allows the low-pass filter to function as a pure integrator in a synchronously rotating coordinate system, achieving ideal integration with no distortion at the fundamental frequency while effectively filtering out high-frequency noise. Specifically: Based on the Laplace transform, the first stator flux linkage observation model is transformed into a first stator flux linkage observation model in the frequency domain: , where ψ s Represents ψsα and ψ sβ U s U sα and U sβ I s Indicate I sα and I sβ s represents the Laplace operator.
[0040] Based on the principle of the low-pass filter flux linkage observation model, s in the first stator flux linkage observation model in the frequency domain is replaced with s+ω. e U s -I s R s Transformed into X(s), ψ s Transforming into Y(s), we obtain the Laplace observation model: The Laplace observation model is discretized using the backward Euler method to obtain the first discrete observation model: and .
[0041] Will Substituting into the first discrete observation model, we obtain the second discrete observation model: ; Predefined input quantity Let B be the DC error. Substituting this into the second discrete observation model, we obtain the third discrete observation model: ; Clearly, in the third discrete observation model, when λ2 < 1 and k approaches infinity (i.e., in steady state), simplifying the third discrete observation model yields the fourth discrete observation model: ; Based on the fourth discrete observation model, it can be deduced that although a low-pass filter can eliminate the influence of the initial integral value, it cannot eliminate the influence of DC error; it can only reduce the influence of DC error. A higher cutoff frequency results in a smaller output DC bias, but an excessively high cutoff frequency leads to excessive output amplitude and phase deviations, thus worsening system stability.
[0042] Alternatively, a low-pass filter can be connected in series with a high-pass filter to replace the pure integrator and compensate for it. This can effectively avoid the inherent defects of the pure integrator. However, the compensation strategies proposed by these methods are based on steady-state conditions. These methods will not be applicable in dynamic conditions. Therefore, the output results of the above flux linkage observation model may have large errors when the stator flux linkage of the induction motor changes dynamically.
[0043] Based on this, a second stator flux linkage observation model based on dual low-pass filters is adopted. This method can eliminate the influence of initial integration values and DC errors on stator flux linkage observation, and also extends the applicable domain of the compensation strategy from the steady-state range to the dynamic range. This is referred to as the dual low-pass method. Its basic idea is to select two different cutoff frequencies (such as aω) e and bω e The values of a and b are determined by the situation and are not limited here. A low-pass filter with proportional coefficients (such as λa and λb, λa-λb=1, the specific values are determined by the situation and are not limited here) filters the input signal and then subtracts it, thereby completely eliminating the influence of DC error and initial integral value.
[0044] like Figure 2 As shown, the second stator flux linkage observation model based on the dual low-pass filter can be expressed as: ,Right now To facilitate the calculation of the error compensation term, we set λa-λb=1 and discretize the second stator flux linkage observation model to obtain the fifth discrete observation model: ; When k approaches infinity, i.e., in steady state, λa-λb=1 and Substituting into the fifth discrete observation model, we obtain the sixth discrete observation model: ; The sixth discrete observation model is simplified to obtain the seventh discrete observation model: ; When the system is in steady state, the seventh discrete observation model is simplified to the eighth discrete observation model: .
[0045] It can be inferred from the eighth discrete observation model that the second stator flux linkage observation model based on the dual low-pass filter can not only eliminate the influence of the initial integral value, but also eliminate the influence of DC error, thus solving the problem of the first stator flux linkage observation model being affected by DC error. However, the dual low-pass method also brings the problems of output flux linkage amplitude error and phase shift, so it is necessary to compensate for them.
[0046] By introducing a proprietary stabilization transfer function and organically combining it with the basic first-state observation model, a second-state observation model is constructed. This eliminates the steady-state error caused by initial value drift and DC bias, thereby achieving a high-order unification of stability and accuracy of the observation model under all operating conditions.
[0047] In one embodiment, it is exemplarily illustrated that, in S101, the output correlation between the first state observation model and the second state observation model is determined based on their respective transfer functions, including but not limited to: Obtain the first transfer function of the first state observation model and the second transfer function of the second state observation model, and calculate the proportional relationship between the first transfer function and the second transfer function to obtain the output correlation relationship.
[0048] Taking the observation of stator flux linkage in an induction motor as an example: the first transfer function is The second transfer function is: The output association relationship is: .
[0049] By directly calculating the ratio of the transfer functions of the first-state observation model to the second-state observation model using the above method, the output correlation between the two models is accurately established. From the perspective of the transfer characteristics of the system's essence, the theoretical deviation law between the outputs of the two models is revealed, rather than relying solely on the actual output signal that is susceptible to noise and dynamic process interference. This lays a stable and reliable theoretical foundation for the subsequent construction of high-precision error compensation terms.
[0050] In one embodiment, by way of example, in S102, an error compensation term is generated based on the output correlation, including but not limited to: Based on the proportional relationship between the first transfer function and the second transfer function, the difference between the output of the first state observation model and the output of the second state observation model is calculated, and an error compensation term is generated based on this difference.
[0051] Taking the observation of stator flux linkage in an induction motor as an example: Based on It can be seen that, The difference between the outputs of the first stator flux linkage observation model and the second stator flux linkage observation model is then determined. and based on Generate error compensation terms.
[0052] By combining the established theoretical deviation relationship with the real-time observation signal using the above method, the theoretical output difference between models is transformed into a concrete and usable error compensation term, thus providing a direct basis for achieving accurate closed-loop correction of the dynamic error of the second-state observation model.
[0053] In one embodiment, an error compensation term is exemplarily illustrated based on the difference relationship, including but not limited to: The difference between the outputs of the first-state observation model and the second-state observation model is decomposed into a primary bias term and at least one derived bias term. The derived bias term is generated by operating the primary bias term with a corresponding dynamic coefficient. Then, the primary bias term is stabilized to obtain a stable primary bias term, and the dynamic coefficient is stabilized to obtain stable dynamic coefficients. Based on the stable primary bias term and the stable dynamic coefficients, stable derived bias terms are generated (e.g., by multiplying the stable primary bias term with the stable dynamic coefficients). Finally, based on the stable primary bias term and the stable derived bias term, an error compensation term is generated (e.g., by adding the stable primary bias term to the stable derived bias term).
[0054] The stabilization process refers to replacing the integral transfer function in the main deviation term and the dynamic coefficient with the transfer function of the corresponding filter. The filter can be a low-pass filter, a band-pass filter, or multiple low-pass filters, and the cutoff frequencies of the filters can be the same or different, depending on the situation, which is not limited here.
[0055] Taking the observation of stator flux linkage in an induction motor as an example: Decomposed into a main deviation term and a derived deviation term ,in, yes and Multiply them to get the result.
[0056] As can be seen from the above equation, both deviation terms contain integral terms. Since the pre-pass low-pass filter has already filtered out the back EMF DC bias, only the influence of the initial phase of pure integration needs to be considered in the deviation terms. Here, both cutoff frequencies are taken as cω. e (The specific value of c depends on the situation and is not limited here.) Replacing the pure integral with a low-pass filter can eliminate this effect, resulting in the stable main deviation term: The stable dynamic coefficient is: Based on the stable principal deviation term and the stable dynamic coefficient, the stable derived deviation term is generated as follows: Finally, based on and The error compensation term is generated as follows: .
[0057] Based on the above derivation, and using the error compensation term and the second stator flux linkage observation model, the target stator flux linkage observation model is constructed as follows: ,like Figure 3 The diagram shown is a control architecture diagram of a target stator flux linkage observation model in one embodiment.
[0058] Using the above method, the output difference relationship between the first and second observation models is systematically decomposed into primary and derived deviation terms, which are then stabilized and reconstructed to ultimately generate error compensation terms. By performing refined and structured decoupling and stabilization processing on the composite deviation signal, high-frequency noise and dynamic fluctuation interference in the original difference relationship are effectively removed. This results in error compensation terms that possess both high accuracy and strong robustness, significantly improving the stability and disturbance rejection capability of the final state observation model under complex dynamic conditions.
[0059] In one embodiment, it is illustrated by way of example that in S103, a target state observation model is constructed based on the error compensation term and the second state observation model, including but not limited to: Obtain the error compensation terms corresponding to each coordinate axis observation channel of the second state observation model, and independently compensate the output of each coordinate axis observation channel based on each error compensation term to generate the target state observation model.
[0060] Taking stator flux linkage observation in an induction motor as an example: The second stator flux linkage observation model includes α-axis and β-axis stator flux linkage observation channels. These channels are independent of each other. Following the error compensation term generation method described above, a first error compensation term corresponding to the α-axis stator flux linkage observation channel and a second error compensation term corresponding to the β-axis stator flux linkage observation channel are generated respectively. Thus, the output of the α-axis stator flux linkage observation channel is compensated based on the first error compensation term, and simultaneously, the output of the β-axis stator flux linkage observation channel is compensated based on the second error compensation term, to generate the target stator flux linkage observation model. During actual flux linkage changes, the calculation of flux linkage components on each axis will not affect each other.
[0061] By using the above method, error compensation is implemented in parallel on each coordinate axis observation channel, which effectively overcomes the problems of inter-channel coupling interference and dynamic response mismatch. Thus, while ensuring system stability, the overall observation accuracy and dynamic coordination of the target state observation model in the entire working domain are significantly improved.
[0062] Therefore, in practical applications of estimating induction motor torque based on stator flux linkage, by collecting the current and voltage signals of the induction motor and inputting them into the aforementioned target state observation model, the observed values of the α-axis stator flux linkage can be obtained. and β-axis stator flux linkage observations and based on and The torque of the induction motor can be calculated. , where P is the number of pole pairs of the motor.
[0063] It should be understood that, although Figure 1The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
[0064] In one embodiment, such as Figure 4 As shown, a state observation model construction device is provided, including: a determination module 401, a generation module 402, and a construction module 403, wherein: The determination module 401 is used to determine the output correlation between the first state observation model and the second state observation model based on their respective transfer functions. The accuracy of the output state of the first state observation model is higher than that of the output state of the second state observation model, but lower than the stability of the output state of the second state observation model. The generation module 402 is used to generate an error compensation term based on the output correlation. The error compensation term represents the output error between the output of the second state observation model and the first state observation model. Module 403 is used to construct a target state observation model based on the error compensation term and the second state observation model.
[0065] Specific limitations regarding the state observation model construction device can be found in the limitations of the state observation model construction method described above, and will not be repeated here. Each module in the aforementioned state observation model construction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.
[0066] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data for constructing state observation models. The network interface communicates with external terminals via a network. When the computer program is executed by the processor, it implements a state observation model construction method. The display screen can be an LCD screen or an e-ink display screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device casing, or an external keyboard, touchpad, or mouse.
[0067] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0068] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: Based on the transfer functions of the first state observation model and the second state observation model, the output correlation between the first state observation model and the second state observation model is determined. The accuracy of the output state of the first state observation model is higher than that of the output state of the second state observation model, but lower than the stability of the output state of the second state observation model. Based on the output correlation, an error compensation term is generated, which represents the output error between the second state observation model and the first state observation model. Based on the error compensation term and the second-state observation model, a target state observation model is constructed.
[0069] In one embodiment, the processor, when executing a computer program, also performs the following steps: Determine the physical quantities in the device under test that form an integral relationship with the measured state quantity; Integral operations are performed on the physical quantities to construct a first-state observation model.
[0070] In one embodiment, the processor, when executing a computer program, also performs the following steps: Construct a stable transfer function, which is used to eliminate the initial value drift and DC bias of the first-state observation model output; Based on the stabilized transfer function and the first-state observation model, a second-state observation model is constructed.
[0071] In one embodiment, the processor, when executing a computer program, also performs the following steps: Obtain the first transfer function of the first state observation model and the second transfer function of the second state observation model; Calculate the ratio between the first transfer function and the second transfer function to obtain the output correlation.
[0072] In one embodiment, the processor, when executing a computer program, also performs the following steps: Based on the output correlation, the difference between the output of the first state observation model and the output of the second state observation model is calculated; An error compensation term is generated based on the difference relationship.
[0073] In one embodiment, the processor, when executing a computer program, also performs the following steps: The difference relationship is decomposed into a main deviation term and at least one derived deviation term, wherein the derived deviation term is generated by the main deviation term and a corresponding dynamic coefficient. The main deviation term is stabilized to obtain a stable main deviation term, and the dynamic coefficient is stabilized to obtain a stable dynamic coefficient. Based on the stable principal deviation term and the stable dynamic coefficient, a stable derived deviation term is generated; An error compensation term is generated based on the stable main deviation term and the stable derived deviation term.
[0074] In one embodiment, the processor, when executing a computer program, also performs the following steps: Obtain the error compensation terms corresponding to each coordinate axis observation channel of the second state observation model; Based on each error compensation term, the output of each coordinate axis observation channel is compensated to generate the target state observation model.
[0075] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: Based on the transfer functions of the first state observation model and the second state observation model, the output correlation between the first state observation model and the second state observation model is determined. The accuracy of the output state of the first state observation model is higher than that of the output state of the second state observation model, but lower than the stability of the output state of the second state observation model. Based on the output correlation, an error compensation term is generated, which represents the output error between the second state observation model and the first state observation model. Based on the error compensation term and the second-state observation model, a target state observation model is constructed.
[0076] In one embodiment, when the computer program is executed by a processor, it also performs the following steps: Determine the physical quantities in the device under test that form an integral relationship with the measured state quantity; Integral operations are performed on the physical quantities to construct a first-state observation model.
[0077] In one embodiment, when the computer program is executed by a processor, it also performs the following steps: Construct a stable transfer function, which is used to eliminate the initial value drift and DC bias of the first-state observation model output; Based on the stabilized transfer function and the first-state observation model, a second-state observation model is constructed.
[0078] In one embodiment, when the computer program is executed by a processor, it also performs the following steps: Obtain the first transfer function of the first state observation model and the second transfer function of the second state observation model; Calculate the ratio between the first transfer function and the second transfer function to obtain the output correlation.
[0079] In one embodiment, when the computer program is executed by a processor, it also performs the following steps: Based on the output correlation, the difference between the output of the first state observation model and the output of the second state observation model is calculated; An error compensation term is generated based on the difference relationship.
[0080] In one embodiment, when the computer program is executed by a processor, it also performs the following steps: The difference relationship is decomposed into a main deviation term and at least one derived deviation term, wherein the derived deviation term is generated by the main deviation term and a corresponding dynamic coefficient. The main deviation term is stabilized to obtain a stable main deviation term, and the dynamic coefficient is stabilized to obtain a stable dynamic coefficient. Based on the stable principal deviation term and the stable dynamic coefficient, a stable derived deviation term is generated; An error compensation term is generated based on the stable main deviation term and the stable derived deviation term.
[0081] In one embodiment, when the computer program is executed by a processor, it also performs the following steps: Obtain the error compensation terms corresponding to each coordinate axis observation channel of the second state observation model; Based on each error compensation term, the output of each coordinate axis observation channel is compensated to generate the target state observation model.
[0082] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0083] 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.
[0084] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.
Claims
1. A method for constructing a state observation model, characterized in that, The method includes: Based on the transfer functions of the first state observation model and the second state observation model, the output correlation between the first state observation model and the second state observation model is determined. The accuracy of the output state of the first state observation model is higher than that of the output state of the second state observation model, but lower than the stability of the second state observation model when outputting the state. Based on the output correlation, an error compensation term is generated, wherein the error compensation term represents the output error between the second state observation model and the first state observation model; Based on the error compensation term and the second state observation model, a target state observation model is constructed.
2. The method according to claim 1, characterized in that, The method for constructing the first state observation model includes: Determine the physical quantities in the device under test that form an integral relationship with the measured state quantity; The physical quantity is integrated to construct the first state observation model.
3. The method according to claim 1, characterized in that, The method for constructing the second state observation model includes: Construct a stabilization transfer function, wherein the stabilization transfer function is used to eliminate the initial value drift and DC bias of the first state observation model output; Based on the stabilized transfer function and the first state observation model, the second state observation model is constructed.
4. The method according to claim 1, characterized in that, The step of determining the output correlation between the first state observation model and the second state observation model based on their respective transfer functions includes: Obtain the first transfer function of the first state observation model and the second transfer function of the second state observation model; Calculate the ratio between the first transfer function and the second transfer function to obtain the output correlation relationship.
5. The method according to claim 4, characterized in that, The step of generating an error compensation term based on the output correlation includes: Based on the aforementioned proportional relationship, the difference between the output of the first state observation model and the output of the second state observation model is calculated. The error compensation term is generated based on the difference relationship.
6. The method according to claim 5, characterized in that, The step of generating the error compensation term based on the difference relationship includes: The difference relationship is decomposed into a main deviation term and at least one derived deviation term, wherein the derived deviation term is generated by the main deviation term and a corresponding dynamic coefficient. The principal deviation term is stabilized to obtain a stable principal deviation term, and the dynamic coefficient is stabilized to obtain a stable dynamic coefficient. Based on the stable principal deviation term and the stable dynamic coefficient, a stable derived deviation term is generated; The error compensation term is generated based on the stable main deviation term and the stable derived deviation term.
7. The method according to claim 1, characterized in that, The construction of the target state observation model based on the error compensation term and the second state observation model includes: Obtain the error compensation terms corresponding to each coordinate axis observation channel of the second state observation model; Based on each of the aforementioned error compensation terms, the outputs of each of the aforementioned coordinate axis observation channels are compensated to generate the target state observation model.
8. A state observation model construction device, characterized in that, The device includes: The determination module is used to determine the output correlation between the first state observation model and the second state observation model based on their respective transfer functions, wherein the accuracy of the output state of the first state observation model is higher than the accuracy of the output state of the second state observation model, but lower than the stability of the second state observation model when outputting the state. The generation module is used to generate an error compensation term based on the output correlation, wherein the error compensation term characterizes the output error between the output of the second state observation model and the first state observation model; A construction module is used to construct a target state observation model based on the error compensation term and the second state observation model.
9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.