Interval type-2 fuzzy singular system control method and device based on disturbance observer

Abstract

The invention discloses an interval type-2 fuzzy singular system control method and device based on a disturbance observer, and the method comprises the steps: obtaining physical constraint structures in different working condition modes, and constructing a type-2 fuzzy model of partition constraint mapping based on the physical constraint structures. And establishing residual mapping for each fuzzy interval of the type-2 fuzzy model, performing interval identification according to the residual mapping through a disturbance observer, and outputting an interval identification result and a disturbance estimator. And calling a type-2 fuzzy model to generate a compensation item of each interval based on the interval identification result and the disturbance estimator. And generating a to-be-executed control law in each working condition mode according to each compensation amount in the compensation item. And in response to the control law, the working condition mode switching is adjusted in real time based on the interval recognition result and the compensation item. According to the method, the singular structure interval to which the current working condition belongs is identified, and the obtained compensation item selects the corresponding compensation channel according to the current identification interval, so that the compensation amount is prevented from being mapped to the failed constraint direction.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, specifically to a control method and apparatus for a type II fuzzy singular system based on an interference observer. Background Technology

[0002] In systems where the physical constraints corresponding to the singular matrix change with operating conditions, the singular structure can change instantaneously, making it difficult for disturbance observers to adapt. Currently, the singularity of many engineering systems is not fixed, but rather caused by changes in physical constraints due to operating conditions. For example, when an electro-hydraulic servo system enters an emergency bypass path, the loop instantaneously loses some constraints; the aerodynamic constraint coefficients of aircraft control surfaces can change abruptly in different atmospheric boundary layers; and on continuous production lines, certain fixtures / support arms may "go offline or online" at different stages. In these scenarios, the physical constraint relationships corresponding to the original singular matrix can switch within milliseconds. However, disturbance observers are residual channels built based on fixed singular structures. When physical constraints change, their internal structures may exhibit the following problems: the residuals are amplified into misjudged disturbances; the original coupling relationships no longer hold; compensation terms are incorrectly mapped to invalid channels; ultimately, the control law may completely reverse compensation at the moment of operating condition switching, or even exacerbate system deviations. This scenario of "singular structure changing with operating conditions" is a typical engineering problem that is difficult to solve using existing control methods. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to overcome the aforementioned deficiencies and propose a control method and apparatus for a type II fuzzy singular system based on an interference observer.

[0004] The present invention adopts the following technical solution.

[0005] The first aspect of this invention discloses a control method for an interval type-2 fuzzy singular system based on an interference observer, the method comprising: Obtain the physical constraint structure under different working conditions, and construct a type II fuzzy model of partition constraint mapping based on the physical constraint structure; A residual mapping is established for each fuzzy interval of the type II fuzzy model, and the interval is identified by the disturbance observer based on the residual mapping. The interval identification result and disturbance estimate are output. Based on the interval identification results and disturbance estimates, the type II fuzzy model is invoked to generate compensation terms for each interval; Based on the compensation amounts in the compensation items, control laws to be executed under each working condition mode are generated. In response to the control law, the switching of operating modes is adjusted in real time based on the interval identification results and compensation terms.

[0006] Furthermore, the step of obtaining the physical constraint structure under different operating conditions and constructing a type-II fuzzy model of partition constraint mapping based on the physical constraint structure includes: Obtain the physical constraint parameter vectors under different working conditions, and integrate the physical constraint parameter vectors into the physical constraint structure according to the working condition type. The physical constraint parameter vectors are composed of multiple constraint parameters. For each physical constraint parameter vector in the physical constraint structure, a corresponding singular structure matrix is ​​constructed, and a maximum / minimum value operation is performed on the singular structure matrix to generate the corresponding interval envelope; Based on the interval envelope, construct the interval type II fuzzy antecedent, and generate fuzzy rules for each interval envelope based on the interval type II fuzzy antecedent.

[0007] Furthermore, the step of establishing a residual mapping for each fuzzy interval of the type-II fuzzy model, and performing interval identification based on the residual mapping using a disturbance observer, and outputting the interval identification result and disturbance estimate, includes: A fuzzy rule base is extracted from the type II fuzzy model, and each fuzzy rule in the fuzzy rule base is deconstructed to determine the rule body and antecedent interval corresponding to each fuzzy rule. The rule body and the antecedent interval are converted into feature vectors according to a unified set format, and the residual mapping matrix of the current interval is constructed based on each element in the feature vector. The residual deviation of the current interval is calculated based on the residual mapping matrix, and the criterion function of the current interval is determined based on the residual deviation.

[0008] Furthermore, the step of establishing a residual mapping for each fuzzy interval of the type-II fuzzy model, and performing interval identification based on the residual mapping using a disturbance observer, and outputting the interval identification result and disturbance estimate, further includes: The criterion function is called to calculate the criterion value for each interval, so as to select the working condition interval with the smallest criterion value and mark the working condition interval to obtain the interval identifier; The operating condition interval with interval identifier is input to the disturbance observer, so that the disturbance observer selects the residual mapping matrix corresponding to the operating condition interval, and calculates the disturbance estimate based on the residual mapping matrix.

[0009] Furthermore, the step of generating a compensation term for each interval based on the interval identification result and the perturbation estimate, by calling the type II fuzzy model, includes: Select the fuzzy rule corresponding to the interval identifier from the fuzzy rule base to determine the antecedent interval and output channel number corresponding to the interval identifier, and generate the interval structure vector by combining the perturbation estimate; Construct a compensation gain matrix and calculate the basic compensation amplitude based on each compensation gain coefficient in the compensation gain matrix; Based on the interval structure vector, a compensation direction mask vector is constructed, and the output channel of the basic compensation amplitude is filtered through the compensation direction mask vector to obtain the direction constraint compensation vector. The center value of the current interval is calculated, and the disturbance amplitude is calculated based on the disturbance estimate. A smoothing coefficient is constructed based on the deviation between the disturbance amplitude and the center value. The smoothing coefficient is used to smooth the direction constraint compensation vector.

[0010] Furthermore, the step of generating the control law to be executed under each operating condition mode based on the compensation amounts in the compensation item includes: Obtain the actual physical constraints and the set reference physical constraints, and integrate the actual physical constraints, the reference physical constraints, and the compensation terms into an initial structure vector; The tracking error of each output channel is calculated based on the initial structure vector, and a proportional control gain vector is introduced to construct a basic control law function based on the proportional control gain vector and the tracking error. The basic control law function is used to calculate the basic control vector. The compensation interval weights are constructed based on the basic control vector, and the intermediate control quantity after compensation is determined based on the compensation interval weights. The intermediate control quantity is then adaptively scaled and saturated to obtain the control law.

[0011] Furthermore, in response to the control law, based on the interval identification result and compensation term, the switching of operating modes is adjusted in real time, including: An execution gain vector is introduced into the actuator, and an execution input vector is generated based on the execution gain vector and the control law; Historical operating condition parameters are obtained, and the discretized singular system state equation is called to predict future operating condition parameters based on the historical operating condition parameters, so as to determine the tracking error and evaluation index after switching operating conditions based on the future operating condition parameters. Based on the tracking error and evaluation indicators after switching operating modes, the corresponding set thresholds are compared to adjust the operating mode switching in real time.

[0012] The second aspect of this invention discloses a control device for an interval type-2 fuzzy singular system based on an interference observer, used to implement the control method for an interval type-2 fuzzy singular system based on an interference observer as described in any one of the first aspects, the device comprising: The model building module is used to obtain the physical constraint structure under different working conditions and to build a type II fuzzy model of partition constraint mapping based on the physical constraint structure. The interval identification module is used to establish a residual mapping for each fuzzy interval of the type II fuzzy model, and to perform interval identification based on the residual mapping by the disturbance observer, and output the interval identification result and the disturbance estimate. The compensation term generation module is used to generate a compensation term for each interval by calling the type II fuzzy model based on the interval identification results and the disturbance estimate. The control law generation module is used to generate control laws to be executed under each working condition mode based on the compensation amounts in the compensation items. The control law execution module is used to adjust the operating mode switching in real time in response to the control law, based on the interval identification result and compensation term.

[0013] A third aspect of the present invention discloses a terminal, including a processor and a storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method described in the first aspect.

[0014] A fourth aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0015] The beneficial effects of the present invention are as follows: Compared with the prior art, the present invention has the following advantages: (1) This invention obtains the physical constraint structure of the system under different operating conditions, such as whether the constraint force exists, whether it is bypassed, and whether it is applied by the clamp / support arm under each condition. Based on these discrete operating conditions, it constructs the antecedent intervals of the interval type II fuzzy model, so that each antecedent interval corresponds to a different singular structure envelope. The model defines independent singular coupling terms, residual dynamic terms, and effective constraint channels in each antecedent interval. At the same time, a corresponding residual mapping is established for each fuzzy interval through the interference observer to determine the switching criterion. The singular structure interval to which the current operating condition belongs is identified according to the instantaneous response change of the system, and the compensation amount is calculated independently for each interval. The obtained compensation term selects the corresponding compensation channel according to the currently identified interval, which effectively avoids the compensation amount being mapped to the failed constraint direction.

[0016] (2) This invention generates a control law that can be stably executed under each operating mode by using the directionality of the compensation term, interval weights, and singular structure envelopes. The control law adaptively scales the interval switching to avoid control saturation and reverse jumps in the compensation direction during the switching moment, while maintaining the ability to suppress singular coupling terms. In addition, as the operating mode switches, the control law is adjusted in real time through the previous interval identification mechanism and directionality compensation mechanism, so that the system remains stable in transient phases such as constraint online / offline, bypass activation, and aerodynamic changes, and achieves the desired output tracking effect, ultimately obtaining a system response that satisfies stability, controllability, and switching robustness. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the control method for a type II fuzzy singular system based on an interference observer provided by the present invention.

[0018] Figure 2 This is a schematic diagram of the control device for a type II fuzzy singular system based on an interference observer provided by the present invention. Detailed Implementation

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

[0020] like Figure 1 As shown, in one embodiment, a control method for an interval type-2 fuzzy singular system based on a disturbance observer includes the following steps: Step S110: Obtain the physical constraint structure under different working conditions, and construct a type II fuzzy model of partition constraint mapping based on the physical constraint structure.

[0021] In some embodiments, the control method for a type-2 fuzzy singular system based on an interference observer provided by the present invention includes the following steps in step S110: Step S111: Obtain the physical constraint parameter vectors under different working conditions, and integrate the physical constraint parameter vectors into a physical constraint structure according to the type of working condition. The physical constraint parameter vector is composed of multiple constraint parameters.

[0022] Step S112: Construct a corresponding singular structure matrix for each physical constraint parameter vector in the physical constraint structure, and perform maximum and minimum value operations on the singular structure matrix to generate the corresponding interval envelope.

[0023] Step S113: Construct interval type II fuzzy antecedents based on interval envelopes, and generate fuzzy rules for each interval envelope based on interval type II fuzzy antecedents.

[0024] In a specific embodiment, the interval type-2 fuzzy singular system control method based on an interference observer provided by the present invention includes steps 1 to 5: Step 1: Construct a partitionable constraint mapping interval type II fuzzy singular structure model.

[0025] First, the physical constraint structure of the system under different operating conditions is obtained, including whether constraint forces exist, are bypassed, and are applied by fixtures / support arms in each condition. Then, based on these discrete operating conditions, antecedent intervals of a type-II fuzzy model are constructed, with each antecedent interval corresponding to a different singular structure envelope. The model constructed in this step defines independent singular coupling terms, residual dynamic terms, and effective constraint channels within each antecedent interval, including the following sub-steps: Sub-step 1.1: Collect the physical constraint structure under the working condition mode and form a set of constraint parameters.

[0026] Specifically, the actual measured or system-defined constraint states under different working conditions are organized into a parameter set, and the constraint parameter vector for each working condition is stored in the form of a triplet, so that the singular structure changes can be directly derived from the vector.

[0027] The set of constraint parameters is represented as follows: ; In the formula, This represents the actual number of operating conditions, and is a positive integer. Indicates operating mode The physical constraint parameter vector; vector Q k =[ q k1 , q k2 , q k3 ] , Determine whether there is a constraint force under this working condition (take 0 or 1); This indicates whether the constraint is bypassed under this operating condition (0 indicates no bypass, 1 indicates bypass). Add constraints (0 or 1) to indicate whether there are clamps or support arms under this working condition.

[0028] Sub-step 1.2: Establish a set of singular structure determination matrices based on the set of constraint parameters.

[0029] Specifically, for the set of constraint parameters Each vector in Construct the corresponding singular structure matrix The matrix construction method is entirely determined by the existence of constraints, without using any inference algorithms. Finally, the resulting set of singular structure matrices is represented as follows: ; In the formula, Indicates operating mode The singular structure matrix below; for A square matrix is ​​defined as follows: ; In the matrix, the first row indicates whether the primary constraint exists, and the existence of the primary constraint only represents the primary constraint. To prevent other factors from affecting the determination of the primary constraint, other elements in the first row are set to "0". The second row indicates whether the bypass channel violates the constraint, and "0" is used for the same reason. The third row indicates whether the fixture support forms an additional singular coupling, and "0" is used for the same reason.

[0030] Sub-step 1.3: Divide each singular structure matrix into interval envelopes to form an antecedent interval set.

[0031] Specifically, for each matrix Perform min-max operations to form an interval envelope that covers the dynamic range of the singular structure. Interval envelope This represents a bounding structure consisting of the maximum and minimum values ​​of all elements in all matrices. Intervals are used to construct the antecedents of type II fuzzy intervals. The output set of antecedent intervals is represented as: ; In the formula, the interval I k =[ L k , U k ] ; Singular structure matrix The smallest non-zero value among all elements; Singular structure matrix The maximum value among all elements.

[0032] Then, based on the aforementioned set of antecedent intervals, we can derive: ; ; The minimum and maximum values ​​are range operations of matrix element values.

[0033] Sub-step 1.4: Construct a fuzzy rule base containing singular coupling and residual dynamics based on the interval set.

[0034] Specifically, for each interval Generation rules The output parameter triplet of the rule body The fuzzy rule base is directly determined based on the singular structure properties of the antecedent interval, without involving estimation or algorithm optimization. The final output fuzzy rule base is represented as follows: ; Among them, rules The form is: ; It should be noted that the output parameters The triples consist of three parts: H k [1] The magnitude of the singular coupling term under the interval is extracted from the constraint matrix; H k [2] Represents the residual dynamic term within the interval, given by the system hardware characteristics (the constant range can be set to 0 to 10). H k [3] Indicates the valid constraint channel number (from 1 to 3) within the interval.

[0035] Step S120: Establish a residual mapping for each fuzzy interval of the type II fuzzy model, and use the disturbance observer to identify the interval based on the residual mapping, outputting the interval identification result and disturbance estimate.

[0036] In some embodiments, the control method for an interval type-2 fuzzy singular system based on an interference observer provided by the present invention includes the following steps in step S120: Step S121: Extract the fuzzy rule base from the type II fuzzy model, and deconstruct each fuzzy rule in the fuzzy rule base to determine the rule body and antecedent interval corresponding to each fuzzy rule.

[0037] Step S122: Convert the rule body and the antecedent interval into feature vectors according to a unified set format, and construct the residual mapping matrix of the current interval based on each element in the feature vector.

[0038] Step S123: Calculate the residual deviation of the current interval based on the residual mapping matrix, and determine the criterion function of the current interval based on the residual deviation.

[0039] In some embodiments, the control method for an interval type-2 fuzzy singular system based on a disturbance observer provided by the present invention further includes the following steps in step S120: Step S124: Call the criterion function to calculate the criterion value of each interval, select the working condition interval with the smallest criterion value, and mark the working condition interval to obtain the interval identifier.

[0040] Step S125: Input the operating condition interval with interval identifier into the disturbance observer to call the disturbance observer to select the residual mapping matrix corresponding to the operating condition interval, and calculate the disturbance estimate based on the residual mapping matrix.

[0041] In a specific embodiment, the interval-based type-2 fuzzy singular system control method provided by the present invention includes step 2, which involves constructing a disturbance observer structure with pattern recognition capabilities based on a partitioned singular structure model. The partitioned singular structure model obtained in step 1 is input into the observer construction unit. Instead of using a single residual structure, the observer establishes a corresponding residual mapping for each fuzzy interval and simultaneously constructs a switching criterion. Based on the instantaneous response change of the system, the singular structure interval to which the current operating condition belongs is identified, and finally, the identification result of the current interval and the disturbance estimate corresponding to that interval are output. This includes the following sub-steps: Sub-step 2.1 deconstructs the interval and rule outputs in the partitioned singular structure model into a set of feature vectors that can be used for observation.

[0042] Specifically, for each rule in the rule base Deconstruct each antecedent interval and rule output, and convert them into vectors according to the above format. , forming a set of feature vectors .

[0043] The feature vector set is represented as follows: ; Each feature vector is represented as: V 2 (k) =[ a k , b k , c k ] ; In the formula, The lower bound of the antecedent interval is derived from the regular interval. I k [ L k ] ; The upper bound of the antecedent interval is derived from the regular interval. I k [ U k ] ; To effectively constrain channel numbers, the triplet from the rule output is used. H k [3] , indicating which channel of the system has a singular structure effect (values ​​from 1 to 3).

[0044] Sub-step 2.2: Construct a set of interval residual mapping matrices based on the set of feature vectors.

[0045] Specifically, based on the feature vector The three elements are directly filled into the three diagonals of the matrix to form the residual structure corresponding to the interval. Finally, the set of interval residual mappings for all intervals is output, which consists of the residual mapping matrix corresponding to each interval.

[0046] The set of interval residual mappings is represented as follows: ; Each residual mapping matrix in the set is defined as: ; In the matrix above, the first row represents the residual response weight corresponding to the lower bound of the interval; the second row represents the residual response weight corresponding to the upper bound of the interval; and the third row represents the influence relationship between the constraint channel and the residual channel.

[0047] Sub-step 2.3: Construct a set of criteria functions for detecting operating condition switching.

[0048] Specifically, the current system output vector Left-multiply each residual mapping matrix and calculate with The difference between the values ​​is the residual deviation degree entering the interval, and the final output is a set of criteria functions composed of the criteria functions of each interval.

[0049] The set of criterion functions is represented as follows: ; Each criterion function is defined as: ; In the formula, This represents the system's current instantaneous output vector, measured in real time by the system's sensors, and is in the following form: Y 2 =[ y 1 , y 2 , y 3 ] ; The three components in the above formula represent the instantaneous response of the system in three directions or three sensing channels, respectively.

[0050] Sub-step 2.4: Identify the current interval based on the criterion set and output the interval identifier.

[0051] Specifically, all criterion values ​​are compared, the one with the smallest amplitude is selected as the current operating condition interval of the system, and a number is output. That is, the interval number with the smallest criterion value, represented as: ; Sub-step 2.5 outputs the perturbation estimation results based on the interval identifier and forms the observer output structure.

[0052] Specifically, the selection and identification interval The corresponding residual structure is used to calculate the perturbation estimate through matrix mapping, and the interval labels are combined to form the total output. , represented as: D 2 =[ K 2 , W 2 ] ; The disturbance estimation vector is expressed as: ; In the formula, This is the disturbance estimation result corresponding to the current interval, a 3-dimensional vector; Let be the residual mapping matrix of the current optimal interval; the product of the two represents mapping the instantaneous output of the system to the disturbance estimate of that interval.

[0053] Step S130: Based on the interval identification results and the disturbance estimate, call the type II fuzzy model to generate the compensation term for each interval.

[0054] In some embodiments, the control method for an interval type-2 fuzzy singular system based on an interference observer provided by the present invention includes the following steps in step S130: Step S131: Select the fuzzy rule corresponding to the interval identifier in the fuzzy rule base to determine the antecedent interval and output channel number corresponding to the interval identifier, and generate the interval structure vector by combining the perturbation estimate.

[0055] Step S132: Construct the compensation gain matrix and calculate the basic compensation amplitude based on the compensation gain coefficients in the compensation gain matrix.

[0056] Step S133: Based on the interval structure vector, construct the compensation direction mask vector, and use the compensation direction mask vector to filter the output channels of the basic compensation amplitude to obtain the direction constraint compensation vector.

[0057] Step S134: Calculate the center value of the current interval and calculate the disturbance amplitude based on the disturbance estimate. Construct a smoothing coefficient based on the deviation between the disturbance amplitude and the center value. The smoothing coefficient is used to smooth the direction constraint compensation vector.

[0058] In a specific embodiment, the interval type II fuzzy singular system control method based on a disturbance observer provided by the present invention includes step 3, which involves dynamically generating interval type II fuzzy compensation terms based on the interval identifier to form a total output and a partitioned singular structure model. The interval identification results and disturbance estimates output in step 2 are input into the compensation term generation unit, and the compensation amount is independently calculated for each interval in conjunction with the partitioned singular structure model from step 1. The compensation term selects the corresponding compensation channel according to the currently identified interval to avoid mapping the compensation amount to an invalid constraint direction. The output of this compensation term includes: the currently effective compensation direction, the compensation magnitude of the current interval, and the cross-interval smoothing amount. This includes the following sub-steps: Sub-step 3.1: Parse the interval identifier to form the total output and the partition singular structure model, and extract the structure and perturbation information of the current interval.

[0059] Specifically, based on the interval identifiers provided by the observer. Retrieve the corresponding rule from the rule base. Read its predecessor interval [ L K 2 , U K 2 ] and the valid channel number in the output structure and the perturbation estimation vector Combined into a structure vector , represented as: T 3 (1) =[ L K 2 , U K 2 , C K 2 , W 2 ] ; In the formula, For rules The lower bound of the corresponding antecedent interval; For rules The upper bound of the corresponding antecedent interval; For rules The output contains the valid constraint channel number, with a value range from 1 to 3; To reuse the input perturbation estimation vector, W 2 =[ w 1 , w 2 , w 3 ] .

[0060] Sub-step 3.2: Calculate the basic compensation magnitude vector for the current interval based on the disturbance estimation.

[0061] Specifically, first, construct the compensation gain matrix: ; In the formula, These are the compensation gain coefficients for the three channels, ranging from 0 to 1, and are pre-tuned according to the actual system's ability to withstand the compensation intensity.

[0062] Therefore, the basic compensation amplitude is calculated by the following formula: ; Right now ; Sub-step 3.3 involves selecting the compensation direction based on the effective constraint channels to form a directional constraint compensation vector.

[0063] Specifically, first, construct the orientation mask vector: M 3 =[ m 1 , m 2 , m 3 ] ; The mask element is composed of the valid constraint channel number. The decision, according to the rules, is as follows: ; in, This is the channel index, with values ​​ranging from 1 to 3.

[0064] Then, the basic compensation amplitude is filtered channel by channel using the mask vector to obtain the directional constraint compensation vector: Finally, it is equivalent to writing it in matrix form: ; In the formula, the symbol This indicates element-wise multiplication.

[0065] Sub-step 3.4: Calculate the cross-interval smoothing coefficient based on the interval location and disturbance intensity.

[0066] Specifically, first, calculate the center value of the current interval. : ; Then, based on the perturbation estimation vector W 2 =[ w 1 , w 2 , w 3 ] Calculate the disturbance amplitude : ; Then, the deviation between the disturbance amplitude and the center of the interval is calculated: ; Finally, the smoothing coefficient is constructed: ; in, The smoothing sensitivity coefficient is a positive number, and its empirical range is from 0 to 10.

[0067] Based on the obtained smoothing coefficients, output the smoothed compensation vector: ; Right now ; Sub-step 3.5 combines the current compensation direction, amplitude, and smoothing result to form the final interval type II fuzzy compensation term.

[0068] Specifically, compensation vectors are obtained through directional constraints. Extract the positive and negative directions of compensation for each channel to form a direction vector. The fundamental magnitude vector obtained in sub-step 3.2 The smoothed compensation vector obtained in sub-step 3.4 Together, they form a compensation structure that includes direction, amplitude, and smoothing results. The final interval type II fuzzy compensation term structure vector representation is as follows: C 3 =[ F 3 , A 3 , S 3 ] ; in, F 3 =[ f 1 , f 2 , f 3 ] The compensation direction vector is defined as: ;Right now ; This structure can be directly invoked in subsequent control law design steps to determine the direction of action and final applied value of the control input on each physical channel.

[0069] Step S140: Generate the control laws to be executed under each working condition mode based on the compensation amounts in the compensation items.

[0070] In some embodiments, the control method for an interval type-2 fuzzy singular system based on an interference observer provided by the present invention includes the following steps in step S140: Step S141: Obtain the actual physical constraints and the set reference physical constraints, and integrate the actual physical constraints, reference physical constraints and compensation terms into an initial structure vector.

[0071] Step S142: Calculate the tracking error of each output channel based on the initial structure vector, and introduce the proportional control gain vector to construct the basic control law function based on the proportional control gain vector and the tracking error. The basic control law function is used to calculate the basic control vector.

[0072] Step S143: Construct compensation interval weights based on the basic control vector, and determine the intermediate control quantity after compensation based on the compensation interval weights, so as to adaptively scale and saturate limit the intermediate control quantity to obtain the control law.

[0073] In a specific embodiment, the interval type-2 fuzzy singular system control method based on an interference observer provided by the present invention, step 4, is based on... Construct a control law that adapts to sudden changes in operating conditions. The compensation term output from step 3... The input control law generation unit generates a control law that can be stably executed under each operating mode by utilizing the directionality of the compensation term, interval weights, and singular structure envelope. This control law adaptively scales for interval switching, preventing control saturation and reverse jumps in the compensation direction during switching, while maintaining the ability to suppress singular coupling terms. It includes the following sub-steps: Sub-step 4.1, parse the compensation structure It also incorporates a reference trajectory and the current output.

[0074] Specifically, from the compensation items Disassemble directly from At the same time, the reference vector provided by the higher-level control will be used. Output vector of field measurement Package them together to form a unified structure vector It is used for subsequent calculations of error and basic control law.

[0075] Wherein, reference vector Represented as: R 4 =[ r 1 , r 2 , r 3 ] ; In the formula, These are reference values ​​for the 1st, 2nd, and 3rd physical channels, provided by upper-level planning or settings, with the value range determined based on operating conditions.

[0076] Output vector of field measurement Represented as: Y 4 =[ y 1 , y 2 , y 3 ] ; In the formula, It is obtained by real-time measurement from the sensor.

[0077] Structure Vector Represented as: T 4 (1) =[ F 3 , A 3 , S 3 , R 4 , Y 4 ] ; Sub-step 4.2, based on Calculate the basic tracking control law without compensation.

[0078] Specifically, first, calculate the tracking error of the three channels: ; In the formula, For the first Tracking error of the channel; For the first Channel reference value; For the first Current output of the channel.

[0079] Next, a proportional control gain vector is introduced: K 4 =[ k 1 , k 2 , k 3 ] ; In the formula, each A positive number indicates the th Channel base tracking gain, range selectable as .

[0080] The basic control laws are given proportionally: ; In the formula, For the first Basic control parameters for the channel, excluding compensation; For the first Channel proportional gain; This represents the error for the corresponding channel.

[0081] The final output base control vector is: U b =[ u b1 , u b2 , u b3 ] ; Sub-step 4.3: Based on the compensation direction and the interval weight, the compensation amount is superimposed to form an intermediate control vector.

[0082] Specifically, firstly, based on the compensation vector and basic compensation amplitude The relative size of the interval weights is used to construct the interval weights, let: S 3 =[ s 1 , s 2 , s 3 ], A 3 =[ a 1 , a 2 , a 3 ] ; Calculate the compensation strength and the base amplitude strength: ; ; Set a very small positive constant. Used to prevent the denominator from being zero, the range is as follows: .

[0083] Next, construct the interval weighting coefficients: ; In the formula, The interval weight reflects the proportion of the current compensation in the control law, and its value is between 0 and 1; To compensate for the strength; Basic compensation strength; To prevent small quantities with a denominator of zero.

[0084] Subsequently, the compensation vector is weighted according to the interval weights and then superimposed onto the basic control law: ; In the formula, This refers to the intermediate control quantity after compensation is added; Basic control quantity; For interval weights; For smoothing compensation amount.

[0085] Finally, output the intermediate control vector: U c =[ u c1 , u c2 , u c3 ] ; Sub-step 4.4 involves adaptively scaling and saturating the intermediate control vector to obtain the final control law.

[0086] Specifically, firstly, to prevent excessively large control inputs due to sudden changes in operating conditions, a scaling factor is introduced into the intermediate control inputs. The maximum allowable control amplitude for each channel is set as follows: U max =[ u max 1 , u max 2 , u max 3 ] ; in, It is a positive number, determined by the physical limits of the actuator.

[0087] Next, construct the scaling factor for each channel: ; In the formula, For the first Channel scaling factor; The absolute value of the intermediate control quantity; To prevent small values ​​with a denominator of zero, the numerical range can be from 0.0001 to 0.01.

[0088] Therefore, the scaled control quantity is expressed as: ; Finally, a hard saturation constraint is applied to form the final control law: ; Combine the results from the three channels: U 4 =[ u 1 , u 2 , u 3 ] ; In step S150, in response to the control law, the operating mode switching is adjusted in real time based on the interval identification results and compensation terms.

[0089] In some embodiments, the control method for a type-2 fuzzy singular system based on an interference observer provided by the present invention includes the following steps in step S150: Step S151: Introduce the execution gain vector into the actuator, and generate the execution input vector based on the execution gain vector and the control law.

[0090] Step S152: Obtain historical operating condition parameters and call the discretized singular system state equation to predict future operating condition parameters based on the historical operating condition parameters, so as to determine the tracking error and evaluation index after switching operating conditions based on the future operating condition parameters.

[0091] Step S153: Based on the tracking error and evaluation indicators after switching operating modes, compare with the corresponding set thresholds and make real-time adjustments to the operating mode switching.

[0092] In a specific embodiment, the interval type-2 fuzzy singular system control method based on an interference observer provided by the present invention, step 5, involves... This is applied to the physical system to achieve stable control of the switching singular structure. The control law output from step 4... Input the physical system control path. As the operating mode switches, the control law is adjusted in real time through the identification mechanism in step 2 and the directional compensation mechanism in step 3, ensuring system stability during transient phases such as constraint online / offline states, bypass activation, and aerodynamic abrupt changes, while achieving the desired output tracking effect. The final system response, satisfying stability, controllability, and switching robustness, is obtained, including the following sub-steps: Sub-step 5.1 maps the control law to actuator inputs.

[0093] Specifically, considering the driving gain and hardware conversion relationship of the actuator, an execution gain vector is introduced: G 5 =[ g 5,1 , g 5,2 , g 5,3 ] ; In the formula, A positive number represents the proportional coefficient from the control quantity to the actuator input, and its value range can be set as follows: This is given by the calibration or instruction manual.

[0094] Then, the actuator input is calculated using the following formula: ; In the formula, For the first The actual input quantity of each actuator (such as valve opening percentage, motor voltage, etc.); For the first Channel control law output; This is the conversion coefficient between the control quantity and the actuator.

[0095] Finally, the combined output is: U 5 =[ v 1 , v 2 , v 3 ] ; Sub-step 5.2 updates the state and obtains the current output under the singular system physical model.

[0096] Specifically, first, obtain the system state vector at the previous sampling time: X 5 (k)=[ x 1 (k), x 2 (k), ... , x n (k)] ; In the formula, The system state dimension; Indicates the first The state at the current moment The value is recorded from the previous iteration.

[0097] Next, the state equations of the singular system are discretized (here, the "compensated equivalent model" is actually used at the execution layer): ; ; In the formula, The system state matrix has dimensions of . It is obtained through discretization modeling; The input matrix has dimensions of . This maps the three execution channels to state increments; The output matrix has dimensions of . The system state is mapped to three output channels; the matrix elements are determined by the system identification or design phase and are constants.

[0098] Finally, based on the current time and execution input Calculate the state at the next moment. Then through the output matrix Calculate .

[0099] Sub-step 5.3: Calculate the tracking error and stability evaluation index under the switching conditions.

[0100] Specifically, first, calculate the tracking error of the three channels: ; In the formula, For the first The tracking error of the channel at the current moment; For the first Channel reference value; This is the current output value.

[0101] Next, construct the error weight vector: Q 5 =[ q 1 , q 2 , q 3 ] ; In the formula, `0` is a non-negative constant, representing the weight of the corresponding channel error on the overall stability index; its value can be set to `0`. , i=1,2,3.

[0102] Construct the control quantity change weight vector: H 5 =[ h 1 , h 2 , h 3 ] ; in, It is a non-negative constant used to limit the impact caused by rapid changes in control. Its value range can also be set from 0.0 to 10.0.

[0103] Let the control law at the previous time step be: U 4 (k)=[ u 1 (k), u 2 (k), u 3 (k)] ; The control law at the current moment is: U 4 (k+1)=[ u 1 (k+1), u 2 (k+1), u 3 (k+1)] ; Define control increment: ; The comprehensive index of stability and smoothness is defined as follows: ; In the formula, This is a comprehensive indicator for the current moment; the smaller the value, the smaller the tracking error and the smoother the control changes. The square of the error reflects the degree of deviation. To control the square of the change, reflecting the smoothness of the control.

[0104] Sub-step 5.4: Judge the control effect based on the evaluation indicators and output the final system response result.

[0105] Specifically, first, set an upper limit for the allowed comprehensive index, which is a positive number, given by engineering experience or design requirements. For example, it can be calculated based on the system's maximum allowable error and maximum control variation, and then judge the current index. If the current index... If the upper limit is not exceeded, the system control under the current operating condition switching state is considered acceptable and stable; if the upper limit is exceeded, the upper-level parameter tuning or alarm mechanism can be triggered in actual engineering. Finally, the state vector, output vector, error vector, and evaluation index are packaged into a unified system response result vector as the final output result.

[0106] The following describes the control device for a type II fuzzy singular system based on an interference observer provided by the present invention. The control device for a type II fuzzy singular system based on an interference observer described below can be referred to in correspondence with the control method for a type II fuzzy singular system based on an interference observer described above.

[0107] like Figure 2 As shown, in one embodiment, a control device for an interval type II fuzzy singular system based on an interference observer includes a model building module, an interval identification module, a compensation term generation module, a control law generation module, and a control law execution module.

[0108] The model building module is used to obtain the physical constraint structure under different working conditions and to build a type II fuzzy model of partition constraint mapping based on the physical constraint structure.

[0109] It should be noted that the physical constraint structure consists of all singular structure matrices and the interval envelope corresponding to each singular structure matrix. Each singular structure matrix is ​​constructed from the physical constraint parameter vector corresponding to that singular structure matrix. The interval envelope of each matrix is ​​the enclosing interval formed by the operation of the maximum and minimum values ​​of each element in the corresponding matrix.

[0110] The interval identification module is used to establish a residual mapping for each fuzzy interval of the type II fuzzy model, and to perform interval identification based on the residual mapping through the disturbance observer, outputting the interval identification results and disturbance estimates.

[0111] The compensation term generation module is used to generate compensation terms for each interval based on the interval identification results and the disturbance estimate by calling the type II fuzzy model.

[0112] The control law generation module is used to generate control laws to be executed under each working condition based on the compensation amounts in the compensation items.

[0113] The control law execution module is used to respond to the control law and make real-time adjustments to the switching of operating modes based on the interval identification results and compensation terms.

[0114] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0115] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0116] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0117] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0118] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0119] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0120] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0121] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0122] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0123] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A control method for interval type-2 fuzzy singular systems based on disturbance observer, characterized in that, The method comprises: acquiring physical constraint structures under different working condition modes, and constructing a two-type fuzzy model of partition constraint mapping based on the physical constraint structures; establishing a residual error mapping for each fuzzy interval of the two-type fuzzy model, and performing interval identification according to the residual error mapping through an interference observer to output an interval identification result and a disturbance estimation; based on the interval identification result and the disturbance estimation, calling the two-type fuzzy model to generate a compensation term for each interval; generating a control law to be executed under each working condition mode according to each compensation amount in the compensation terms; in response to the control law, adjusting the working condition mode switching in real time based on the interval identification result and the compensation term.

2. The control method of the interval type-2 fuzzy singular system based on the disturbance observer according to claim 1, characterized in that, The acquisition of the physical constraint structures under different working condition modes, and the construction of the two-type fuzzy model of partition constraint mapping based on the physical constraint structures, comprises: acquiring physical constraint parameter vectors under different working condition modes, and integrating the physical constraint parameter vectors into the physical constraint structures according to the types of the working condition modes, wherein each physical constraint parameter vector is composed of multiple constraint parameters; constructing a singular structure matrix for each physical constraint parameter vector in the physical constraint structures, and performing maximum and minimum operations on the singular structure matrix to generate a corresponding interval envelope; constructing an interval two-type fuzzy antecedent according to the interval envelope, and generating a fuzzy rule for each interval envelope based on the interval two-type fuzzy antecedent.

3. The control method of the interval type-2 fuzzy singular system based on the disturbance observer according to claim 1, characterized in that, The establishment of the residual error mapping for each fuzzy interval of the two-type fuzzy model, and the interval identification according to the residual error mapping through the interference observer to output the interval identification result and the disturbance estimation, comprises: extracting a fuzzy rule base from the two-type fuzzy model, and deconstructing each fuzzy rule in the fuzzy rule base to determine a rule body and an antecedent interval corresponding to each fuzzy rule; converting the rule body and the antecedent interval into a feature vector in a unified setting format, and constructing a residual error mapping matrix of the current interval based on each element in the feature vector; calculating a residual error deviation of the current interval according to the residual error mapping matrix, and determining a criterion function of the current interval based on the residual error deviation.

4. The control method of the interval type-2 fuzzy singular system based on the disturbance observer according to claim 3, characterized in that, The establishment of the residual error mapping for each fuzzy interval of the two-type fuzzy model, and the interval identification according to the residual error mapping through the interference observer to output the interval identification result and the disturbance estimation, further comprises: calling the criterion function to calculate criterion values of each interval to select a working condition interval with the smallest criterion value, and marking the working condition interval to obtain an interval identifier; inputting the working condition interval with the interval identifier into the interference observer to call the interference observer to select a residual error mapping matrix corresponding to the working condition interval, and calculating the disturbance estimation based on the residual error mapping matrix.

5. The control method of the interval type-2 fuzzy singular system based on the disturbance observer according to claim 4, characterized in that, The calling of the two-type fuzzy model to generate a compensation term for each interval based on the interval identification result and the disturbance estimation, comprises: selecting a fuzzy rule corresponding to the interval identifier in the fuzzy rule base to determine an antecedent interval and an output channel number corresponding to the interval identifier, and generating an interval structure vector in combination with the disturbance estimation; construct a compensation gain matrix, and calculate a basic compensation amplitude according to each compensation gain coefficient in the compensation gain matrix; based on the interval structure vector, construct a compensation direction mask vector, and perform output channel screening on the basic compensation amplitude through the compensation direction mask vector to obtain a direction-constrained compensation vector; calculate a center value of the current interval, and calculate a disturbance amplitude according to the disturbance estimator, so as to construct a smoothing coefficient based on the deviation of the disturbance amplitude and the center value, the smoothing coefficient being used for smoothing processing of the direction-constrained compensation vector.

6. The control method of the interval type-2 fuzzy singular system based on the disturbance observer according to claim 1, wherein, the control law to be executed in each working condition mode is generated according to each compensation amount in the compensation term, including: acquire actual physical constraints and set reference physical constraints, and integrate the actual physical constraints, reference physical constraints and the compensation term into an initial structure vector; based on the initial structure vector, calculate the tracking error of each output channel, and introduce a proportional control gain vector to construct a basic control law function according to the proportional control gain vector and the tracking error, the basic control law function being used for calculating a basic control vector; construct a compensation interval weight according to the basic control vector, and determine an intermediate control quantity after compensation based on the compensation interval weight, so as to perform adaptive scaling and saturation limitation on the intermediate control quantity to obtain the control law.

7. The control method of the interval type-2 fuzzy singular system based on the disturbance observer according to claim 1, characterized in that, in response to the control law, real-time adjustment is performed on the working condition mode switching based on the interval identification result and the compensation term, including: introduce an execution gain vector into the actuator, and generate an execution input vector according to the execution gain vector and the control law; acquire historical working condition parameters, and call a discretized singular system state equation to predict future working condition parameters according to the historical working condition parameters, so as to determine the tracking error and evaluation index after switching the working condition mode based on the future working condition parameters; based on the tracking error and evaluation index after switching the working condition mode, compare the corresponding set threshold value to real-time adjust the working condition mode switching.

8. An interval type-2 fuzzy singular system control device based on disturbance observer, characterized in that, The device for implementing the interval two-type fuzzy singular system control method based on the disturbance observer of any one of claims 1 to 7 comprises: a model construction module, configured to acquire physical constraint structures in different working condition modes, and construct a two-type fuzzy model of partitioned constraint mapping based on the physical constraint structures; an interval identification module, configured to establish a residual error mapping for each fuzzy interval of the two-type fuzzy model, and perform interval identification through a disturbance observer according to the residual error mapping to output an interval identification result and a disturbance estimator; a compensation term generation module, configured to generate a compensation term for each interval based on the interval identification result and the disturbance estimator by calling the two-type fuzzy model; a control law generation module, configured to generate a control law to be executed in each working condition mode according to each compensation amount in the compensation term; a control law execution module, configured to, in response to the control law, real-time adjust the working condition mode switching based on the interval identification result and the compensation term.

9. A terminal comprising a processor and a storage medium; characterized in that: the storage medium is used to store instructions; The processor is configured to operate on the instructions to perform the steps of the method according to any of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program, which when executed by the processor, implements the steps of the method according to any of claims 1-7.

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