Adaptive fuzzy sliding mode control method, device and equipment for temperature and pressure of cremator

Abstract

The present application relates to the technical field of automatic control, and discloses a kind of cremator temperature and pressure self-adapting fuzzy sliding mode control method, device, equipment and storage medium.The method comprises: according to pre-constructed temperature and pressure expected trajectory function, the tracking error value of cremator real-time temperature and pressure parameter is calculated;According to historical experience data, the system estimated initial value is obtained;Using the pre-configured parameter adaptive estimation law, the tracking error value is estimated according to the system estimated initial value to obtain the estimated parameter vector;Using the fuzzy sliding mode controller of estimated parameter vector parameter adjustment, the temperature and pressure control instruction of the target cremator is calculated according to the tracking error value;Using the supervision control function of estimated parameter vector parameter configuration, the temperature and pressure control instruction is predicted and supervised identification;When supervision passes, the temperature and pressure control of the target cremator is carried out according to the temperature and pressure control instruction.The present application can improve the accuracy and response speed of cremator pressure regulating.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology, and in particular to an adaptive fuzzy sliding mode control method, apparatus, equipment, and computer-readable storage medium for temperature and pressure in cremation machines. Background Technology

[0002] Because the cremation process in the furnace is nonlinear and strongly coupled, and many parameters affect the cremation, such as the weight of the remains, airflow, and the amount of fuel required, all of these significantly impact cremation efficiency and exhaust emissions. Currently, to better control furnace temperature, the industry mostly uses sensors to acquire actual parameters within the cremator furnace. Then, variable structure control methods are used to determine the temperature and pressure deviations and their rate of change, thereby adjusting controllers for airflow, fuel flow, and other parameters.

[0003] However, the complexity and robustness of variable structure control methods require a long design cycle for control and switching algorithms, and external noise and interference have a significant impact on the stability of the system, leading to a reduction in the performance of the spark system. Summary of the Invention

[0004] This invention provides an adaptive fuzzy sliding mode control method, device, equipment, and storage medium for temperature and pressure of a cremator, the main purpose of which is to improve the accuracy and response speed of the pressure regulation of the cremator.

[0005] To achieve the above objectives, the present invention provides an adaptive fuzzy sliding mode control method for temperature and pressure in a cremator, comprising:

[0006] The real-time temperature and pressure parameters in the target cremator are obtained, and the tracking error value of the real-time temperature and pressure parameters is calculated based on the pre-constructed temperature and pressure expected trajectory function.

[0007] Based on the real-time temperature and pressure parameters and the expected temperature and pressure trajectory function, the historical experience data of the target cremator is queried to obtain the initial system estimate. Then, using the pre-configured parameter adaptive estimation law, the tracking error value is estimated based on the initial system estimate to obtain the estimated parameter vector.

[0008] The parameters of the pre-constructed fuzzy sliding mode controller are adaptively adjusted according to the estimated parameter vector, and the temperature and pressure control command of the target cremator is calculated using the fuzzy sliding mode controller after parameter adjustment, based on the tracking error value.

[0009] The parameters of the pre-constructed supervised control function are configured using the estimated parameter vector and the tracking error value;

[0010] The temperature and pressure control command is predicted and identified using the supervisory control function with configured parameters.

[0011] When the temperature and pressure control command is passed, the target cremator is subjected to temperature and pressure control according to the temperature and pressure control command.

[0012] Optionally, the step of using the parameter-configured supervisory control function to predict and supervise the identification of the temperature and pressure control command includes:

[0013] Obtain pre-built supervisory control functions, preset given parameters, and pre-built supervisory control terms;

[0014] The supervisory control function is used to perform supervisory calculations on the temperature and pressure regulation command to obtain the supervisory control value;

[0015] When the monitoring control value is less than or equal to the given parameter, a zero product operation is performed on the monitoring control item to generate a monitoring failure instruction;

[0016] When the monitoring control value is greater than the given parameter, a monitoring pass instruction is generated using the monitoring control item, and the monitoring pass instruction is used to enhance the temperature and pressure control instruction.

[0017] Optionally, before adaptively adjusting the parameters of the pre-constructed fuzzy sliding mode controller based on the estimated parameter vector, the method further includes:

[0018] Based on the temperature and pressure relationship in the main combustion chamber of the target cremator, a second-order nonlinear function is obtained;

[0019] Based on the pre-configured function experience configuration rules, the second-order nonlinear function is converted into a temperature and pressure dynamic model:

[0020] Obtain the pre-constructed temperature and pressure expected trajectory function, and transform the temperature and pressure dynamic model according to the temperature and pressure expected trajectory function and the predefined tracking error value calculation function to obtain the tracking error dynamic model;

[0021] The tracking error dynamics model is transformed based on the pre-configured sliding mode variables and the first derivative is calculated to obtain the sliding mode-based dynamic equations.

[0022] Based on the fuzzy logic control algorithm and the sliding mode-based dynamic equation, a fuzzy sliding mode controller is obtained.

[0023] Optionally, the step of querying historical experience data of the target cremator based on the real-time temperature and pressure parameters and the expected temperature and pressure trajectory function to obtain the initial system estimate includes:

[0024] Acquire pre-constructed historical incineration data of crematoriums, and perform noise reduction processing on the historical incineration data of crematoriums according to a preset data preprocessing method to obtain clean data;

[0025] The clean data was used to train an experiential knowledge neural network.

[0026] The trained empirical knowledge neural network is used to perform initial value prediction of system parameters on the real-time temperature and pressure parameters and the expected temperature and pressure trajectory function to obtain the initial value prediction of the system.

[0027] Optionally, the step of using a pre-configured adaptive parameter estimation law to estimate the tracking error value based on the initial system prediction to obtain an estimated parameter vector includes:

[0028] Based on the projection operator, a projection adaptive estimation law for the fuzzy system estimation parameters based on the initial predicted values ​​of the system is constructed;

[0029] Using the aforementioned projection adaptive estimation law, an estimated parameter vector is obtained based on the tracking error value.

[0030] Optionally, the step of using the parameter-adjusted fuzzy sliding mode controller to calculate the temperature and pressure control command of the target cremator based on the tracking error value includes:

[0031] The tracking error value is imported into the fuzzy sliding mode controller after parameter adjustment to obtain the control output value vector;

[0032] Obtain the membership function corresponding to the control output value vector, and calculate the membership degree of the control output vector based on the membership function;

[0033] The temperature and pressure control command is calculated based on the membership degree using a fuzzy inference algorithm.

[0034] Optionally, calculating the tracking error value of the real-time temperature and pressure parameters based on the pre-constructed temperature and pressure expected trajectory function includes:

[0035] Obtain the real-time time node, and based on the time node, query the temperature and pressure expected trajectory function to obtain the expected temperature and pressure value;

[0036] The tracking error value is obtained by calculating the difference between the expected temperature and pressure values ​​and the real-time temperature and pressure parameters.

[0037] To address the aforementioned problems, the present invention also provides an adaptive fuzzy sliding mode control device for temperature and pressure in crematoriums, the device comprising:

[0038] The real-time temperature and pressure error monitoring module is used to acquire the real-time temperature and pressure parameters in the target cremator and calculate the tracking error value of the real-time temperature and pressure parameters based on the pre-constructed temperature and pressure expected trajectory function.

[0039] The adaptive parameter estimation module is used to query the historical experience data of the target cremator based on the real-time temperature and pressure parameters and the expected temperature and pressure trajectory function to obtain the initial system estimate, and to use the pre-configured adaptive parameter estimation law to estimate the tracking error value based on the initial system estimate to obtain the estimated parameter vector.

[0040] The fuzzy sliding mode control command generation module is used to adaptively adjust the parameters of the pre-constructed fuzzy sliding mode controller according to the estimated parameter vector, and use the parameter-adjusted fuzzy sliding mode controller to calculate the temperature and pressure control command of the target cremator based on the tracking error value.

[0041] The instruction supervision module is used to configure the parameters of the pre-constructed supervision control function using the estimated parameter vector and the tracking error value, and to perform predictive supervision and identification of the temperature and pressure control instruction using the parameter-configured supervision control function.

[0042] The execution command control temperature and pressure module is used to control the temperature and pressure of the target cremator according to the temperature and pressure control command when the temperature and pressure control command is passed.

[0043] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0044] At least one processor; and,

[0045] A memory communicatively connected to the at least one processor; wherein,

[0046] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the adaptive fuzzy sliding mode control method for temperature and pressure of the cremator described above.

[0047] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one computer program, which is executed by a processor in an electronic device to implement the aforementioned adaptive fuzzy sliding mode control method for temperature and pressure in a cremator.

[0048] This invention provides a method for real-time acquisition of the real-time temperature and pressure parameters of the target cremator, which is then compared with a pre-constructed desired temperature and pressure trajectory function to calculate the difference. This allows the system to continuously acquire tracking error values, improving system response speed. A fuzzy sliding mode controller then generates instructions based on the tracking error values, bringing the real-time temperature and pressure of the target cremator closer to the desired trajectory. The fuzzy sliding mode algorithm in the controller can handle the system's nonlinear characteristics and uncertainties. Furthermore, an adaptive algorithm updates the controller parameters before generating control instructions, improving the accuracy of the fuzzy sliding mode controller. Additionally, this application introduces a supervisory control function to monitor the control instructions generated by the fuzzy sliding mode controller, thereby enhancing control effectiveness and improving system stability. Therefore, this invention provides an adaptive fuzzy sliding mode control method, device, equipment, and storage medium for cremator temperature and pressure, which can improve the accuracy and response speed of cremator voltage regulation. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating an adaptive fuzzy sliding mode control method for temperature and pressure in a cremator provided in an embodiment of the present invention.

[0050] Figure 2 This is a detailed flowchart of one step in the adaptive fuzzy sliding mode control method for temperature and pressure of a cremator provided in an embodiment of the present invention;

[0051] Figure 3 This is a detailed flowchart of one step in the adaptive fuzzy sliding mode control method for temperature and pressure of a cremator provided in an embodiment of the present invention;

[0052] Figure 4 This is a functional block diagram of an adaptive fuzzy sliding mode control device for temperature and pressure in a cremator provided in an embodiment of the present invention;

[0053] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the adaptive fuzzy sliding mode control method for temperature and pressure of the cremator according to an embodiment of the present invention.

[0054] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0055] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0056] This application provides an adaptive fuzzy sliding mode control method for temperature and pressure in crematoriums. In this application, the executing entity of the adaptive fuzzy sliding mode control method for temperature and pressure in crematoriums includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the adaptive fuzzy sliding mode control method for temperature and pressure in crematoriums can be executed by software or hardware installed on a terminal device or a server device. The software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0057] Reference Figure 1 The diagram shown is a flowchart illustrating an adaptive fuzzy sliding mode control method for temperature and pressure in a cremator provided in an embodiment of the present invention. In this embodiment, the adaptive fuzzy sliding mode control method for temperature and pressure in a cremator includes:

[0058] S1. Obtain the real-time temperature and pressure parameters in the target cremator, and calculate the tracking error value of the real-time temperature and pressure parameters according to the pre-constructed temperature and pressure expected trajectory function.

[0059] In this embodiment of the invention, the temperature and pressure in the combustion chamber of the cremator have the greatest impact on combustion efficiency. Therefore, this invention requires real-time control of the temperature and pressure in the cremator. The mathematical model of the dynamic system of the central temperature and pressure in the main combustion chamber of the cremator can be represented as the following second-order nonlinear system:

[0060]

[0061] Where T is the main combustion chamber temperature and P is the main combustion chamber pressure; The first derivative of the main combustion chamber temperature. The second derivative of the main combustion chamber temperature. The first derivative of the main combustion chamber pressure Second derivative of main combustion chamber pressure; τ i >0 (i = 1, 2, 3, 4) represents the state delay time constant; f ij l ij n ij h ij and g ijThe coupling functions related to temperature and pressure variables are included in the target cremator system model, i = 1, 2; j = 1, 2; u1 represents the fuel oil regulation amount in the main combustion chamber, u2 represents the air-to-air ratio regulation amount in the main combustion chamber, and d1 and d2 represent external disturbances.

[0062] Furthermore, the temperature and pressure expected trajectory function described in this embodiment of the invention is obtained based on business type and empirical knowledge, so that under the temperature and pressure expected trajectory function, the combustion efficiency of the cremator is the highest or the pollution level is the lowest.

[0063] The desired temperature and pressure trajectory function is:

[0064] y d =[T d ,P d ] T

[0065] Where T d and P d It is a piecewise continuous and differentiable function.

[0066] In this embodiment of the invention, real-time temperature and pressure parameters in the target cremator can be obtained through various sensors.

[0067] Furthermore, in this embodiment of the invention, a comparator can be used to calculate the difference between the real-time temperature and pressure parameters and the expected temperature and pressure trajectory function.

[0068] In detail, in this embodiment of the invention, calculating the tracking error value of the real-time temperature and pressure parameters based on the pre-constructed temperature and pressure expected trajectory function includes:

[0069] Obtain the real-time time node, and based on the time node, query the temperature and pressure expected trajectory function to obtain the expected temperature and pressure value;

[0070] The tracking error value is obtained by calculating the difference between the expected temperature and pressure values ​​and the real-time temperature and pressure parameters.

[0071] The error calculation expression is as follows:

[0072] e(t) = r(t) - y(t)

[0073] Where e(t) is the tracking error value at time t, r(t) is the expected value at time t in the temperature and pressure expected trajectory function, y(t) is the real-time temperature and pressure parameter at time t, and t is the time node.

[0074] In this embodiment of the invention, if the tracking error value is not zero, it indicates that the target cremator needs to be intervened in temperature and pressure regulation.

[0075] Furthermore, in order to improve the accuracy and response speed of the cremator's voltage regulation, this embodiment of the invention proposes a method combining adaptive control, fuzzy control, and sliding mode control. Adaptive control is used to adjust the controller parameters according to the dynamic characteristics of the system to adapt to the control requirements under different operating conditions. Fuzzy control uses fuzzy logic and fuzzy rules to handle the uncertainty and nonlinear characteristics of the system, achieving precise control of the system. Sliding mode control introduces a sliding surface to achieve fast response and robustness of the system.

[0076] Fuzzy logic and sliding mode techniques can be combined to generate a fuzzy sliding mode controller. The construction process is as follows:

[0077] For details, please refer to the following: Figure 2 As shown in this embodiment of the invention, before adaptively adjusting the parameters of the pre-constructed fuzzy sliding mode controller according to the adaptive parameters, the method further includes:

[0078] S101. Based on the temperature and pressure relationship in the main combustion chamber of the target cremator, a second-order nonlinear function is obtained;

[0079] S102. Based on the pre-configured function experience configuration rules, the second-order nonlinear function is converted into a temperature and pressure dynamic model:

[0080] S103. Obtain the pre-constructed temperature and pressure expected trajectory function, and perform function transformation on the temperature and pressure dynamic model according to the temperature and pressure expected trajectory function and the predefined tracking error value calculation function to obtain the tracking error dynamic model;

[0081] S104. The tracking error dynamic model is transformed according to the pre-configured sliding mode variables and the first derivative is calculated to obtain the dynamic equation based on sliding mode.

[0082] S105. Based on the fuzzy logic control algorithm and the sliding mode-based dynamic equation, a fuzzy sliding mode controller is obtained.

[0083] As described above, the expression for a second-order nonlinear system is as follows: In this embodiment of the invention, the empirical configuration rule for the function is defined as follows: τ is defined as follows. i Bounded but unknown, f ij l ij n ij h ij Each function is a bounded function; g ij ≠0 and can be decomposed into in For the known part, The uncertain part; d1 and d2 are unknown bounded functions and satisfy... in Given: The system state vector is configured as x = [T, P]T , where the [] T Let y1 be the transpose symbol, and let y1 = x. G = G 0 +G Δ The Δ symbol represents an unknown symbol.

[0084] Therefore, the dynamic temperature and pressure model can be expressed as:

[0085]

[0086]

[0087] in,

[0088] Then, based on the desired temperature and pressure trajectory function and the predefined tracking error value, the function e1 = y1 - y is calculated. d and

[0089] In this invention, m is denoted as s =m+G Δ u, then the temperature and pressure dynamic model can be transformed into a tracking error dynamic model:

[0090]

[0091]

[0092] Then, the present invention introduces sliding mode technology into the dynamic model, obtains the pre-configured sliding mode variable s, and transforms the tracking error dynamic model and performs first-order derivative calculation to obtain the sliding mode-based dynamic equation.

[0093] Where s = e2 + Λe1 = [s1, s2] T Let Λ be a 2×2 positive definite matrix chosen by the designer of the cremator temperature and pressure control system. Then, based on the sliding mode dynamic equation:

[0094]

[0095] Furthermore, this embodiment of the invention introduces fuzzy technology into sliding mode technology, and designs a fuzzy sliding mode controller as follows:

[0096]

[0097] in,

[0098]

[0099] m is calculated by the adaptive fuzzy logic system s The estimated vector, θm1 =[θ a1 ,θ a2 ,…,θ aM ] T and θ m2 =[θ b1 ,θ b2 ,…,θ bM ] T Let ξ be the estimated parameter vector of two fuzzy logic systems. lj (s) is a fuzzy basis function vector and n is the number of input variables in the fuzzy system, and M is the number of fuzzy membership functions; u s For the supervisory control term, sign(s) = [sign(s1), sign(s2)] T The sign function vector satisfies sign(s) = 0 when s = 0; K is a 2×2 positive definite matrix chosen by the designer.

[0100] In this embodiment of the invention, based on the characteristics of the system (blowing, oil supply, pressurization, pollutant emission, etc.) and the control objectives (temperature, pressure), a membership function for the output variable is designed. The membership function can be triangular, trapezoidal, or Gaussian in shape, etc., to describe the fuzzy set of the output variable. Subsequently, control commands can be calculated based on the membership degree, such as controlling the blower and oil quantity.

[0101] S2. Based on the real-time temperature and pressure parameters and the expected temperature and pressure trajectory function, query the historical experience data of the target cremator to obtain the initial system estimate, and use the pre-configured parameter adaptive estimation law to estimate the tracking error value based on the initial system estimate to obtain the estimated parameter vector.

[0102] In detail, in this embodiment of the invention, the step of querying the historical experience data of the target cremator based on the real-time temperature and pressure parameters and the expected temperature and pressure trajectory function to obtain the system's initial estimated value includes:

[0103] Acquire pre-constructed historical incineration data of crematoriums, and perform noise reduction processing on the historical incineration data of crematoriums according to a preset data preprocessing method to obtain clean data;

[0104] The clean data was used to train an experiential knowledge neural network.

[0105] The trained empirical knowledge neural network is used to perform initial value prediction of system parameters on the real-time temperature and pressure parameters and the expected temperature and pressure trajectory function to obtain the initial value prediction of the system.

[0106] In this embodiment of the invention, the historical incineration data of the cremator is the data retained by various sensors when incinerating various items; the data preprocessing method is to perform standardized operations on the data, including filtering, outlier elimination and noise reduction, and interpolation filling.

[0107] This invention provides a method for obtaining clean data through preprocessing, followed by machine learning using a transformer neural network to construct empirical knowledge and rules. Furthermore, methods other than machine learning, such as statistical analysis and data mining, can be used to extract patterns and regularities in the combustion process of the cremator.

[0108] Then, based on empirical knowledge from neural networks or data mining, some possible parameters of the cremator system under the current state can be obtained as initial values ​​for system prediction.

[0109] Furthermore, the present invention can adaptively estimate the rate using pre-configured parameters and adaptively adjust the tracking error value based on the initial value estimated by the system.

[0110] In detail, in this embodiment of the invention, the step of using a pre-configured adaptive parameter estimation law to estimate the tracking error value based on the initial system estimate to obtain an estimated parameter vector includes:

[0111] Based on the projection operator, a projection adaptive estimation law for the fuzzy system estimation parameters based on the initial predicted values ​​of the system is constructed;

[0112] Using the aforementioned projection adaptive estimation law, an estimated parameter vector is obtained based on the tracking error value.

[0113] The projection operator is a linear operator used to project a vector onto a subspace spanned by another vector. It can decompose a vector into two parts: a projection vector on the target subspace and a perpendicular vector on the orthogonal complement of the target subspace, such as temperature and pressure in this application.

[0114] Specifically, in this embodiment of the invention, the system's estimated initial value is marked as... Then obtain the pre-built projection operator:

[0115]

[0116] Then, based on the projection operator, a projection adaptive estimation law for the fuzzy system estimation parameters is constructed:

[0117]

[0118] Where, γ i >0、 The parameters are determined by the designer based on combustion experience, i = 1, 2; according to S1 above, T is the temperature at the center of the combustion chamber of the cremator, and s is the sliding mode variable, s = e2 + Λe1 = [s1, s2] T , where Λ is a 2×2 positive definite matrix chosen by the designer. ξ(s) represents the fuzzy sliding mode vector.

[0119] In this embodiment of the invention, after the projection adaptive estimation law is constructed, the estimated parameter vector can be updated using optimization algorithms such as gradient descent and least squares based on the projection adaptive estimation law and the tracking error value, thus obtaining the estimated parameter vector. The estimated parameter vector can then be sent to the pre-constructed supervision function and the aforementioned constructed fuzzy sliding mode controller, enabling both components to adaptively update parameters, thereby improving performance.

[0120] S3. Based on the estimated parameter vector, the parameters of the pre-constructed fuzzy sliding mode controller are adaptively adjusted, and the temperature and pressure control command of the target cremator is calculated using the fuzzy sliding mode controller after parameter adjustment, based on the tracking error value.

[0121] In this embodiment of the invention, the fuzzy sliding mode controller operates using fuzzy rules. The number of fuzzy rules is related to the number of system states (e.g., wind speed, oil volume, etc.) and the number of intervals divided for each state. Gaussian membership functions are used to calculate the real-time estimate of model uncertainty for each state. Therefore, this embodiment of the invention uses an adaptive law to update the center point and width parameters of the Gaussian membership function in real time, adjusting the coverage and approximation capabilities of the membership function. After adaptive parameter adjustment, the fuzzy sliding mode controller exhibits faster response speed and stability.

[0122] In detail, in this embodiment of the invention, the step of using the fuzzy sliding mode controller with adjusted parameters to calculate the temperature and pressure control command of the target cremator based on the tracking error value includes:

[0123] The tracking error value is imported into the fuzzy sliding mode controller after parameter adjustment to obtain the control output value vector;

[0124] Obtain the membership function corresponding to the control output value vector, and calculate the membership degree of the control output vector based on the membership function;

[0125] The temperature and pressure control command is calculated based on the membership degree using a fuzzy inference algorithm.

[0126] In this embodiment of the invention, the output value of the fuzzy sliding mode controller is obtained according to the definition of the membership function in S1 above, and the membership degree of the output variable is calculated. The membership degree represents the degree to which the output variable belongs to each membership function, and can be calculated using fuzzy inference methods to obtain control instructions. The control instructions can be specific numerical values ​​or fuzzy sets. The fuzzy inference algorithm is one of the core methods of fuzzy logic, used to calculate the membership degree of the output variable based on fuzzy rules and the membership degree of the input variables; it will not be elaborated upon here.

[0127] S4. Configure the parameters of the pre-constructed supervisory control function using the estimated parameter vector and the tracking error value.

[0128] In this embodiment of the invention, to ensure the accuracy and stability of the supervised control function, the same estimated parameter vector needs to be used for parameter configuration as the supervised fuzzy sliding mode controller.

[0129] S5. Use the supervisory control function after parameter configuration to predict and supervise the identification of the temperature and pressure regulation command.

[0130] For details, please refer to the following: Figure 3 As shown in this embodiment of the invention, the step of using the supervised control function with configured parameters to predict and supervise the identification of the temperature and pressure control command includes:

[0131] S51. Obtain the pre-built supervisory control function, the preset given parameters, and the pre-built supervisory control items;

[0132] S52. The supervisory control function is used to perform supervisory calculations on the temperature and pressure regulation command to obtain the supervisory control value;

[0133] S53. Determine whether the monitoring control value is greater than a preset given parameter;

[0134] When the supervisory control value is less than or equal to the given parameter, S54, perform a zero-product operation on the supervisory control item to generate an unsupervised instruction;

[0135] When the monitoring control value is greater than the given parameter, S55, a monitoring instruction is generated using the monitoring control item, and the monitoring instruction is used to regulate the temperature and pressure control instruction.

[0136] Specifically, the pre-constructed supervisory control function in this embodiment of the invention is as follows: For the error term, s T 's' represents the state item, and then the designer configures the given parameters based on experience and knowledge.

[0137] In this embodiment of the invention, a monitoring and control item is also designed as follows:

[0138]

[0139] in, and U is the upper bound estimate, i.e., the control command generated by the fuzzy sliding mode controller of the controlled term, based on f in S1 above. ij l ij n ij h ij and g ij As can be seen from the coupling functions related to temperature and pressure variables included in the system model, F, N, L, and H are the values ​​of the coupling functions related to temperature and pressure variables included in the system model.

[0140] In this embodiment of the invention, the temperature and pressure control command is calculated using the supervisory control function to obtain the supervisory control value, and then it is determined whether the supervisory control value is greater than the given parameter.

[0141] When the monitored control value is less than or equal to the given parameter, a zero-product operation is performed on the monitored control item, indicating a small error, and an unsupervised instruction is generated; when the monitored control value is greater than the given parameter, indicating a large error, a monitored instruction is generated using the monitored control item, and the monitored instruction is used to enhance the temperature and pressure control instruction. That is, when Time I * =1, when At that time, I * =0, u s =0.

[0142] When the temperature and pressure control command is "no monitoring", the target cremator is controlled for temperature and pressure according to the temperature and pressure control command.

[0143] When the temperature and pressure control command is supervised, S6, the target cremator is subjected to temperature and pressure control according to the temperature and pressure control command.

[0144] In this embodiment of the invention, when At that time, the temperature and pressure control command monitoring passed, the aforementioned It is an independent control command used to regulate and control the state of the system. For example, it may be a part of the temperature and pressure regulation command, used to enhance the control effect and system stability.

[0145] Therefore, the temperature and pressure control command can be executed, thereby changing the temperature and pressure in the cremator.

[0146] This invention provides a method for real-time temperature and pressure parameters of the target cremator, which are then compared with a pre-constructed desired temperature and pressure trajectory function to calculate the difference. This allows the system to continuously acquire tracking error values, improving system response speed. A fuzzy sliding mode controller then generates instructions based on the tracking error values, bringing the real-time temperature and pressure of the target cremator closer to the desired trajectory. The fuzzy sliding mode algorithm in the controller handles the system's nonlinear characteristics and uncertainties. Furthermore, an adaptive algorithm updates the controller parameters before generating control instructions, improving the accuracy of the fuzzy sliding mode controller. Additionally, this application introduces a supervisory control function to monitor the control instructions generated by the fuzzy sliding mode controller, thereby enhancing control effectiveness and improving system stability. Therefore, this invention provides an adaptive fuzzy sliding mode control method for cremator temperature and pressure, which improves the accuracy and response speed of cremator voltage regulation.

[0147] like Figure 4 The diagram shown is a functional block diagram of an adaptive fuzzy sliding mode control device for temperature and pressure in a cremator provided in an embodiment of the present invention.

[0148] The adaptive fuzzy sliding mode control device 100 for temperature and pressure of a cremator described in this invention can be installed in an electronic device. Depending on the functions implemented, the adaptive fuzzy sliding mode control device 100 for temperature and pressure may include a real-time temperature and pressure error monitoring module 101, an adaptive parameter estimation module 102, a fuzzy sliding mode control command generation module 103, a command supervision module 104, and an execution command to regulate temperature and pressure module 105. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and is stored in the memory of the electronic device.

[0149] In this embodiment, the functions of each module / unit are as follows:

[0150] The real-time temperature and pressure error monitoring module 101 is used to acquire the real-time temperature and pressure parameters in the target cremator and calculate the tracking error value of the real-time temperature and pressure parameters according to the pre-constructed temperature and pressure expected trajectory function.

[0151] The adaptive parameter estimation module 102 is used to query the historical experience data of the target cremator based on the real-time temperature and pressure parameters and the expected temperature and pressure trajectory function to obtain the initial system estimate, and to use the pre-configured adaptive parameter estimation law to estimate the tracking error value based on the initial system estimate to obtain the estimated parameter vector.

[0152] The fuzzy sliding mode control command generation module 103 is used to adaptively adjust the parameters of the pre-constructed fuzzy sliding mode controller according to the estimated parameter vector, and use the fuzzy sliding mode controller with adjusted parameters to calculate the temperature and pressure control command of the target cremator according to the tracking error value.

[0153] The instruction supervision module 104 is used to configure the parameters of the pre-constructed supervision control function using the estimated parameter vector and the tracking error value, and to perform predictive supervision and identification of the temperature and pressure control instruction using the supervision control function after parameter configuration.

[0154] The execution command control temperature and pressure module 105 is used to control the temperature and pressure of the target cremator according to the temperature and pressure control command when the temperature and pressure control command supervision is passed.

[0155] In detail, each module in the adaptive fuzzy sliding mode control device 100 for temperature and pressure of the cremator described in this application embodiment adopts the same approach as described above during use. Figures 1 to 3 The adaptive fuzzy sliding mode control method for temperature and pressure of cremator described in the article uses the same technical means and can produce the same technical effect, so it will not be elaborated here.

[0156] like Figure 5 The diagram shown is a schematic representation of the structure of an electronic device 1 that implements an adaptive fuzzy sliding mode control method for temperature and pressure in a cremator, according to an embodiment of the present invention.

[0157] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as an adaptive fuzzy sliding mode control program for temperature and pressure of a cremator.

[0158] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device 1, connecting various components of the electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing an adaptive fuzzy sliding mode control program for temperature and pressure in a cremator), and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0159] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed on the electronic device, such as the code of an adaptive fuzzy sliding mode control program for temperature and pressure in a cremator, but also to temporarily store data that has been output or will be output.

[0160] The communication bus 12 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0161] The communication interface 13 is used for communication between the electronic device 1 and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), or, optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.

[0162] Figure 5 Only electronic devices with components are shown; those skilled in the art will understand that... Figure 5The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0163] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0164] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0165] The adaptive fuzzy sliding mode control program for the temperature and pressure of the cremator stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0166] The real-time temperature and pressure parameters in the target cremator are obtained, and the tracking error value of the real-time temperature and pressure parameters is calculated based on the pre-constructed temperature and pressure expected trajectory function.

[0167] Based on the real-time temperature and pressure parameters and the expected temperature and pressure trajectory function, the historical experience data of the target cremator is queried to obtain the initial system estimate. Then, using the pre-configured parameter adaptive estimation law, the tracking error value is estimated based on the initial system estimate to obtain the estimated parameter vector.

[0168] The parameters of the pre-constructed fuzzy sliding mode controller are adaptively adjusted according to the estimated parameter vector, and the temperature and pressure control command of the target cremator is calculated using the fuzzy sliding mode controller after parameter adjustment, based on the tracking error value.

[0169] The parameters of the pre-constructed supervised control function are configured using the estimated parameter vector and the tracking error value;

[0170] The temperature and pressure control command is predicted and identified using the supervisory control function with configured parameters.

[0171] When the temperature and pressure control command is passed, the target cremator is subjected to temperature and pressure control according to the temperature and pressure control command.

[0172] Specifically, the specific implementation method of the processor 10 for the above instructions can be referred to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, and will not be repeated here.

[0173] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0174] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0175] The real-time temperature and pressure parameters in the target cremator are obtained, and the tracking error value of the real-time temperature and pressure parameters is calculated based on the pre-constructed temperature and pressure expected trajectory function.

[0176] Based on the real-time temperature and pressure parameters and the expected temperature and pressure trajectory function, the historical experience data of the target cremator is queried to obtain the initial system estimate. Then, using the pre-configured parameter adaptive estimation law, the tracking error value is estimated based on the initial system estimate to obtain the estimated parameter vector.

[0177] The parameters of the pre-constructed fuzzy sliding mode controller are adaptively adjusted according to the estimated parameter vector, and the temperature and pressure control command of the target cremator is calculated using the fuzzy sliding mode controller after parameter adjustment, based on the tracking error value.

[0178] The parameters of the pre-constructed supervised control function are configured using the estimated parameter vector and the tracking error value;

[0179] The temperature and pressure control command is predicted and identified using the supervisory control function with configured parameters.

[0180] When the temperature and pressure control command is passed, the target cremator is subjected to temperature and pressure control according to the temperature and pressure control command.

[0181] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0182] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0183] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0184] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0185] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0186] The blockchain referred to in this invention is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0187] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0188] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0189] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An adaptive fuzzy sliding mode control method for temperature and pressure in a cremator, characterized in that, The method includes: The real-time temperature and pressure parameters in the target cremator are obtained, and the tracking error value of the real-time temperature and pressure parameters is calculated based on the pre-constructed temperature and pressure expected trajectory function. Based on the temperature-pressure relationship in the main combustion chamber of the target cremator, a second-order nonlinear function is obtained. According to pre-configured function empirical configuration rules, the second-order nonlinear function is converted into a temperature-pressure dynamic model. A pre-constructed temperature-pressure expected trajectory function is obtained, and based on the temperature-pressure expected trajectory function and a pre-defined tracking error value calculation function, the temperature-pressure dynamic model is transformed to obtain a tracking error dynamic model. The tracking error dynamic model is transformed according to pre-configured sliding mode variables, and first-order derivative operations are performed to obtain a sliding mode-based dynamic equation. Based on the fuzzy logic control algorithm and the sliding mode-based dynamic equation, a fuzzy sliding mode controller is obtained. Acquire pre-constructed historical incineration data of crematoriums, and perform noise reduction processing on the historical incineration data of crematoriums according to a preset data preprocessing method to obtain clean data; use the clean data to train an empirical knowledge neural network; use the trained empirical knowledge neural network to perform initial value prediction of system parameters on the real-time temperature and pressure parameters and the expected temperature and pressure trajectory function to obtain the initial value of the system prediction. Based on the projection operator, a projection adaptive estimation law for the fuzzy system estimation parameters based on the initial system prediction is constructed; using the projection adaptive estimation law, the estimated parameter vector is evaluated based on the tracking error value. The fuzzy sliding mode controller is adaptively adjusted based on the estimated parameter vector; the tracking error value is imported into the fuzzy sliding mode controller after parameter adjustment to obtain the control output value vector; the membership function corresponding to the control output value vector is obtained, and the membership degree of the control output vector is calculated based on the membership function; the temperature and pressure control command of the target cremator is calculated based on the membership degree using a fuzzy inference algorithm. The estimated parameter vector and the tracking error value are used to configure the parameters of the pre-constructed supervisory control function; the supervisory control function after parameter configuration is used to perform predictive supervision and identification of the temperature and pressure control command. When the inspection is passed, the target cremator is subjected to temperature and pressure control according to the temperature and pressure control command.

2. The adaptive fuzzy sliding mode control method for temperature and pressure of crematoriums as described in claim 1, characterized in that, The step of using the configured supervisory control function to predict and supervise the identification of the temperature and pressure control command includes: acquiring a pre-constructed supervisory control function, preset given parameters, and pre-constructed supervisory control items; using the supervisory control function to perform supervisory calculation on the temperature and pressure control command to obtain a supervisory control value; when the supervisory control value is less than or equal to the given parameter, performing a zero-product operation on the supervisory control item to generate a supervisory failure command; when the supervisory control value is greater than the given parameter, using the supervisory control item to generate a supervisory success command, and using the supervisory success command to enhance the temperature and pressure control command.

3. An adaptive fuzzy sliding mode control device for temperature and pressure in a cremator, characterized in that, The device includes: a real-time temperature and pressure error monitoring module, used to acquire real-time temperature and pressure parameters in the target cremator, and calculate the tracking error value of the real-time temperature and pressure parameters based on a pre-constructed temperature and pressure expected trajectory function; and an adaptive parameter estimation module, used to obtain a second-order nonlinear function based on the temperature and pressure relationship in the main combustion chamber of the target cremator, and convert the second-order nonlinear function into a temperature and pressure dynamic model, thereby obtaining a tracking error dynamic model, a sliding mode-based dynamic equation, and a fuzzy sliding mode controller; and to acquire historical combustion data of the cremator, process it to obtain clean data through noise reduction, use the clean data to train an empirical knowledge neural network, and use the trained empirical knowledge neural network to perform initial value prediction of system parameters for the real-time temperature and pressure parameters and the temperature and pressure expected trajectory function. The system obtains initial estimated values ​​and constructs an adaptive projection estimation law based on the projection operator to evaluate and obtain an estimated parameter vector; a fuzzy sliding mode control command generation module is used to adaptively adjust the parameters of the fuzzy sliding mode controller based on the estimated parameter vector, and use the parameter-adjusted fuzzy sliding mode controller to calculate the temperature and pressure control command of the target cremator based on the tracking error value; a command supervision module is used to configure the parameters of a pre-constructed supervision control function using the estimated parameter vector and the tracking error value, and use the parameter-configured supervision control function to perform predictive supervision identification of the temperature and pressure control command; an execution command temperature and pressure control module is used to perform temperature and pressure control on the target cremator based on the temperature and pressure control command when the temperature and pressure control command supervision passes.

4. An electronic device, characterized in that, The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the adaptive fuzzy sliding mode control method for temperature and pressure of cremator as described in claim 1 or 2.

5. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the adaptive fuzzy sliding mode control method for temperature and pressure of the cremator as described in claim 1 or 2.

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