An ultra-high-speed motor inductive enhancement type optimal sliding mode control system for air compressor

Abstract

The application relates to an ultra-high-speed motor inductive enhancement type optimal sliding mode control system for an air compressor, which comprises an air compressor coupling control model, an enhanced optimal sliding mode speed controller and an improved harmonic suppression sliding mode speed observer; the air compressor coupling control model is based on a curve fitted according to air compressor test data, and outputs enhanced optimal performance evaluation index parameters; the enhanced optimal sliding mode speed controller establishes an enhanced optimal sliding mode surface, and outputs target quadrature axis current; the improved harmonic suppression sliding mode speed observer is based on a boundary layer adaptive sliding mode control function and a voltage injection harmonic elimination algorithm, and outputs air compressor angular velocity and angle observation values, reduces current harmonics, and realizes position sensorless control. The application effectively solves the problem that the speed control effect is poor in a traditional optimal sliding mode control system, the traditional optimal sliding mode control system is not applicable to actual air compressor operation conditions, has high working condition adaptability, and realizes rapid and accurate speed regulation of the air compressor under the condition of position sensorless.

Description

Technical Field

[0001] This invention relates to the field of air compressor control technology, specifically to a sensorless enhanced optimal sliding mode control system for ultra-high speed motors used in air compressors. Background Technology

[0002] Turbine air compressors are widely used in industrial fields with high flow and high pressure due to their advantages such as high efficiency, stability, low noise, and simple maintenance, especially in the field of hydrogen fuel cell technology. Turbine air compressors accelerate air through high-speed rotating impellers, converting the kinetic energy of the air into pressure energy, thereby increasing the air pressure and achieving the purpose of compressing air.

[0003] As a key component of fuel cell systems, the turbo air compressor is subject to torque coupling at its compression and expansion ends, as well as pressure and flow coupling. Under such multivariate coupling, it is difficult to establish a precise mathematical model. In addition, the speed fluctuation and response delay of the ultra-high-speed motor will directly lead to unstable output flow and pressure of the turbo air compressor if the speed cannot be adjusted quickly and accurately, thus affecting the power output of the fuel cell. Therefore, it is of great significance to construct a model that can describe the output characteristics of the turbo air compressor and design a control system with anti-torque interference and fast response for the efficient operation of the turbo air compressor and fuel cell system. Summary of the Invention

[0004] The purpose of this invention is to provide a sensorless enhanced optimal sliding mode control system for ultra-high speed motors used in air compressors. This control system effectively solves the problem that the speed control effect of traditional optimal sliding mode control systems is poor and not suitable for actual air compressor operating conditions. It has high adaptability to operating conditions and realizes rapid and accurate speed adjustment of air compressors under sensorless conditions.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a sensorless enhanced optimal sliding mode control system for an ultra-high-speed motor used in air compressors, comprising an air compressor coupled control model, an enhanced optimal sliding mode speed controller, and an improved harmonic suppression sliding mode speed observer; the air compressor coupled control model outputs enhanced optimal performance evaluation index parameters based on curves fitted from air compressor test data; the enhanced optimal sliding mode speed controller establishes an enhanced optimal sliding surface by combining the enhanced optimal performance evaluation index with the extended state space equation, and outputs the target quadrature-axis current; the improved harmonic suppression sliding mode speed observer outputs air compressor angular velocity and angle observation values ​​based on a boundary layer adaptive sliding mode control function and a voltage injection harmonic elimination algorithm, reducing current harmonics and realizing sensorless control of the air compressor.

[0006] Furthermore, the air compressor coupling control model incorporates the target speed signal and the actual speed observation signal into the physical curves fitted to the compression end and expansion end of the air compressor, as well as the optimal efficiency operating curve of the air compressor. By setting gas flow sensors at the outlet of the air compressor compression end, gas flow sensors at the inlet of the expansion end, temperature sensors at the air inlet of the air compressor, and temperature sensors at the inlet of the expansion end, the model monitors the air flow signals at the inlet of the compression end, the air flow signals at the inlet of the expansion end, the atmospheric temperature signals, and the gas temperature signals at the inlet of the expansion end in real time, and finally outputs enhanced optimal performance evaluation index parameters.

[0007] Furthermore, the implementation method of the air compressor coupling control model includes:

[0008] 1) Using the Jensen & Kristensen modeling method, based on actual test data of the turbo air compressor, the flow rate, pressure ratio, speed, and power consumption curves of the compression end and expansion end are fitted with the optimal efficiency as the benchmark. By receiving the target speed signal or vehicle speed signal, and combining the sampling signals of the gas flow sensor located at the inlet of the compression end, the gas flow sensor located at the inlet of the expansion end, the gas temperature sensor located near the inlet of the compression end, and the gas temperature sensor located at the inlet of the expansion end, the air compressor parameters for control are output.

[0009] 2) Calculate the motor drive torque parameter λ, the optimal performance evaluation index of the enhanced air compressor, according to the following equation. Te :

[0010]

[0011] In the formula, P n This refers to the number of pole pairs of the air compressor motor. air compressor motor flux linkage, J cp The moment of inertia of the air compressor;

[0012] 3) Calculate the compression torque parameter λ at the compression end of the air compressor as the optimal performance evaluation index for the enhanced type according to the following equation. cp :

[0013]

[0014] In the formula, W cp T is the mass flow rate of the gas at the outlet of the compression end. atm For atmospheric temperature, c c P is the isobaric specific heat capacity of the gas at the compression end. rr ρ is the target compression ratio at the compression end. c η is the specific heat ratio of the gas at the compression end. cp For compression efficiency;

[0015] 4) Calculate the expansion torque parameter λ at the expansion end of the air compressor as the optimal performance evaluation index for the enhanced type according to the following equation. ep :

[0016]

[0017] In the formula, W ep T is the mass flow rate of the gas at the expansion end inlet. tep c is the inlet gas temperature at the expansion end. e η is the isobaric specific heat capacity of the gas at the expansion end. ep For the expansion end efficiency, P er ρ is the target expansion ratio at the expansion end. e This represents the specific heat ratio of the gas at the expansion end.

[0018] Furthermore, the enhanced optimal sliding mode speed controller receives the speed observation signal from the improved harmonic suppression sliding mode speed observer, extracts the system matrix and control matrix based on the air compressor speed-torque state space equation, and establishes an extended state space equation; based on the speed sample data and subjective-objective entropy weight analysis data of traditional sliding mode control, combined with the compression torque index parameters at the compression end and the expansion torque index parameters at the expansion end, an enhanced optimal performance evaluation index is established; combined with the extended state space equation and the enhanced performance evaluation index, enhanced control information is coupled out, and finally an enhanced optimal sliding mode surface is established, receiving the target speed information and outputting the target quadrature-axis current value; the target current is applied to the PI controller of the current loop, combined with the harmonic voltage injection signal of the improved harmonic suppression sliding mode speed observer, and then combined with space vector pulse width modulation, outputting a switching pulse signal to the inverter to generate AC current and drive the motor to rotate.

[0019] Furthermore, the implementation method of the enhanced optimal sliding mode speed controller includes:

[0020] 1) Using the control variable as the state input, establish the extended state equations according to the following equations:

[0021]

[0022] In the formula, X ω To expand the state vector, A ω To expand the system matrix, B ω To expand the input matrix, U ω To extend the control input vector, U is the control input vector, μ1 is the parameter greater than 0, and μ2 is the parameter greater than 0;

[0023] 2) Construct the extended state-space matrix according to the following equation:

[0024]

[0025] In the formula, X is the state vector, A is the system matrix, B is the input matrix, and I is the identity matrix;

[0026] 3) Establish the enhanced optimal performance evaluation index G according to the following equation:

[0027]

[0028] In the formula, γ1 is the weighting coefficient obtained from the subjective and objective entropy weighting analysis data, γ2 is the weighting coefficient obtained from the subjective and objective entropy weighting analysis data, and ω ref Let x be the target mechanical angular velocity. 10 These are the linearized working point matrix elements at the current time.

[0029] 4) Based on the enhanced optimal performance evaluation index and the extended state-space equation, the enhanced control information is obtained, and the solution to the Riccati equation of the control information is obtained according to the following equation:

[0030]

[0031] In the formula, M is a solution to the Riccati equation;

[0032] 5) Establish the enhanced optimal sliding surface S(X) according to the following equation. ω ):

[0033]

[0034] Furthermore, the improved harmonic suppression sliding mode speed observer receives the Alpha and Beta axis voltage signals in the stationary coordinate system. Based on the improved sliding mode control function, it observes the back electromotive force of the motor in the stationary coordinate system, outputs the observed values ​​of the air compressor angle and angular velocity, and adds the angle observation value to the harmonic extraction equation to extract the harmonic components of the motor current at the current moment. Using the PI control algorithm, it outputs the harmonic compensation voltage, thereby reducing the harmonics of the current while realizing the sensorless control of the air compressor and improving the accuracy of the sensorless control.

[0035] Furthermore, the implementation method of the improved harmonic suppression sliding mode speed observer includes:

[0036] 1) Establish the speed observation sliding mode function Z according to the following equation. α Z β :

[0037]

[0038] In the formula, s α For the Alpha axis observation error sliding surface, s β M is the sliding surface for Beta-axis observation error. α M is the adaptive saturation function for the alpha-axis boundary layer. βR is the adaptive saturation function for the Beta-axis boundary layer. α For the Alpha axis boundary layer adaptive reaching law, R β For the Beta-axis boundary layer adaptive reaching law;

[0039] 2) Define the boundary layer adaptive saturation function M according to the following equation:

[0040]

[0041] In the formula, s is the observation error sliding surface, β is a parameter greater than 0, and r is the sliding observation error;

[0042] 3) Define the boundary layer adaptive reaching law R according to the following equation:

[0043]

[0044] In the formula, ε is a parameter greater than 0, η is a parameter greater than 0, and λ is a parameter greater than 0;

[0045] 4) Substitute the observed back EMF of the motor into the arctangent function to obtain the observed angular position of the motor; based on the observed angular position of the motor, extract the harmonic components of the motor current, introduce them into the harmonic elimination PI algorithm, output the harmonic current compensation voltage value, and superimpose it with the target voltage output by the current loop PI control algorithm, and output them together to SVPWM to generate a switching pulse signal.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The sensorless enhanced optimal sliding mode control system for ultra-high-speed motors in air compressors provided by this invention adopts an air compressor coupled control model, establishing a control-oriented electromechanical coupling model of the turbine air compressor that couples the static characteristics of the compression end and expansion end as well as the dynamic characteristics of the motor. This provides a physical explanation of the system behavior and has a certain degree of versatility. It considers the influence of load torque, fully utilizes the advantage that the air compressor's flow rate can be easily measured by sensors, treats the flow rate as a constant and updates it in real time, and converts the efficiency based on the inlet and outlet temperatures, so as to achieve good dynamic and steady-state performance of the air compressor under any complex operating conditions.

[0048] The sensorless enhanced optimal sliding mode control system for ultra-high-speed motors in air compressors provided by this invention employs an enhanced optimal sliding mode speed controller. It merges the state vector and control vector of the original state equation to construct a new state vector, establishing an extended state equation that incorporates a completely new system state, ensuring superior control performance. Furthermore, it establishes enhanced performance evaluation indicators, based on actual operating conditions and subjective / objective entropy weight analysis, covering the impact of controller performance on turbine air compressor output, as well as the performance requirements for turbine air compressor speed control. These indicators are used to evaluate the controller's performance, achieving good dynamic and steady-state performance under any complex operating conditions.

[0049] The sensorless enhanced optimal sliding mode control system for ultra-high-speed motors in air compressors provided by this invention employs an improved harmonic suppression sliding mode speed observer. Based on the boundary layer adaptive saturation function, it ensures that the boundary layer thickness does not increase indefinitely, enabling the system to smoothly converge to the switching plane. Based on the boundary layer adaptive reaching law, when the error approaches the sliding surface, the approach speed is automatically adjusted; when the error moves away from the sliding surface, the reaching rate also increases, but not excessively. When the observation error approaches 0, both the boundary layer and the reaching rate adaptively approach 0, accelerating system stability. The PI harmonic voltage injection method based on current harmonic extraction reduces the harmonic components of the motor current, improving the accuracy of speed observation. It reduces system chattering caused by speed observation, saves on the cost of position sensors, and reduces the overall size of the air compressor. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the sensorless enhanced optimal sliding mode control system for ultra-high speed motors used in air compressors, according to an embodiment of the present invention.

[0051] Figure 2 This is a schematic diagram of the air compressor coupling control model in an embodiment of the present invention.

[0052] Figure 3 This is a schematic diagram of the enhanced optimal sliding mode speed controller in an embodiment of the present invention.

[0053] Figure 4 This is a schematic diagram of the improved harmonic suppression sliding mode speed observer in an embodiment of the present invention.

[0054] Figure 5 This is a comparison chart of the control effects of the traditional optimal sliding mode and the present invention when facing a step target speed signal in an embodiment of the present invention.

[0055] Figure 6 This is a comparison chart of the control effects of the traditional optimal sliding mode and the present invention when facing a stepped target speed signal in an embodiment of the present invention. Detailed Implementation

[0056] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0057] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0058] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0059] like Figure 1 As shown, this embodiment provides a sensorless enhanced optimal sliding mode control system for an ultra-high-speed motor used in air compressors, including an air compressor coupled control model, an enhanced optimal sliding mode speed controller, and an improved harmonic suppression sliding mode speed observer. The air compressor coupled control model outputs enhanced optimal performance evaluation index parameters based on curves fitted from air compressor test data. The enhanced optimal sliding mode speed controller combines the enhanced optimal performance evaluation index with the extended state space equation to establish an enhanced optimal sliding surface and outputs the target quadrature-axis current. The improved harmonic suppression sliding mode speed observer, based on a boundary layer adaptive sliding mode control function and a voltage injection harmonic elimination algorithm, outputs the air compressor's angular velocity and angle observation values, reducing current harmonics and achieving sensorless control of the air compressor.

[0060] like Figure 2 As shown, the air compressor coupled control model incorporates the target speed signal and the actual speed observation signal into the physical curves of the air compressor's compression end, expansion end, and optimal efficiency operating curve. It also monitors the air flow signals at the compression end outlet, expansion end inlet, air temperature, and expansion end inlet in real time by installing gas flow sensors at the air compressor's air inlet and expansion end inlet, ultimately outputting enhanced optimal performance evaluation index parameters.

[0061] Specifically, the implementation method of the air compressor coupling control model includes:

[0062] 1) Using the Jensen & Kristensen modeling method, based on actual test data of the turbo air compressor, the flow rate, pressure ratio, speed, and power consumption curves of the compression end and expansion end are fitted with the optimal efficiency as the benchmark. By receiving the target speed signal or vehicle speed signal, and combining the sampling signals of the gas flow sensor located at the inlet of the compression end, the gas flow sensor located at the inlet of the expansion end, the gas temperature sensor located near the inlet of the compression end, and the gas temperature sensor located at the inlet of the expansion end, the air compressor parameters for control are output.

[0063] This model, derived from fundamental physical and thermodynamic principles, is a semi-mechanistic, semi-empirical model capable of describing the relationship between air compressor flow rate, pressure ratio, and speed. Using this method, a physical explanation of system behavior can be provided, and it possesses a degree of universality.

[0064] 2) Calculate the motor drive torque parameter λ, the optimal performance evaluation index of the enhanced air compressor, according to the following equation. Te :

[0065]

[0066] In the formula, P n This refers to the number of pole pairs of the air compressor motor. air compressor motor flux linkage, J cp This represents the rotational inertia of the air compressor.

[0067] 3) Calculate the compression torque parameter λ at the compression end of the air compressor as the optimal performance evaluation index for the enhanced type according to the following equation. cp :

[0068]

[0069] In the formula, W cp T is the mass flow rate of the gas at the outlet of the compression end. atm For atmospheric temperature, c c P is the isobaric specific heat capacity of the gas at the compression end. rr ρ is the target compression ratio at the compression end. c η is the specific heat ratio of the gas at the compression end. cp For compression efficiency.

[0070] 4) Calculate the expansion torque parameter λ at the expansion end of the air compressor as the optimal performance evaluation index for the enhanced type according to the following equation. ep :

[0071]

[0072] In the formula, W ep T is the mass flow rate of the gas at the expansion end inlet. tep c is the inlet gas temperature at the expansion end.e η is the isobaric specific heat capacity of the gas at the expansion end. ep For the expansion end efficiency, P er ρ is the target expansion ratio at the expansion end. e This represents the specific heat ratio of the gas at the expansion end.

[0073] The design of typical sliding mode controllers usually ignores the influence of load torque. However, under the dynamic operating conditions of a turbine air compressor, the compression torque and expansion torque vary with the rotational speed, making the assumption that they are constant over time inapplicable. Since the flow rate and pressure of the air compressor can be easily measured by sensors, the flow rate and pressure ratio are treated as constants and updated in real time, while the efficiency is calculated based on the inlet and outlet temperatures. Therefore, the above-mentioned parameters are proposed to lay the foundation for achieving good dynamic and steady-state performance of the air compressor under any complex operating conditions.

[0074] like Figure 3 As shown, the enhanced optimal sliding mode speed controller receives the speed observation signal from the improved harmonic suppression sliding mode speed observer. Based on the air compressor speed-torque state-space equation, it extracts the system matrix and control matrix, and establishes an extended state-space equation. Based on the speed sample data and subjective-objective entropy weight analysis data of traditional sliding mode control, combined with the compression torque index parameters at the compression end and the expansion torque index parameters at the expansion end, it establishes an enhanced optimal performance evaluation index. Combining the extended state-space equation and the enhanced performance evaluation index, it couples out the enhanced control information, and finally establishes the enhanced optimal sliding surface, receives the target speed information, and outputs the target quadrature-axis current value. The target current is applied to the PI controller in the current loop, combined with the harmonic voltage injection signal of the improved harmonic suppression sliding mode speed observer, and then combined with space vector pulse width modulation, outputting a switching pulse signal to the inverter to generate AC current and drive the motor to rotate.

[0075] Specifically, the implementation method of the enhanced optimal sliding mode speed controller includes:

[0076] 1) Using the control variable as the state input, establish the extended state equations according to the following equations:

[0077]

[0078] In the formula, X ω To expand the state vector, A ω To expand the system matrix, B ω To expand the input matrix, U ω To extend the control input vector, U is the control input vector, μ1 is a parameter greater than 0, and μ2 is a parameter greater than 0.

[0079] 2) Construct the extended state-space matrix according to the following equation:

[0080]

[0081] In the formula, X is the state vector, A is the system matrix, B is the input matrix, and I is the identity matrix.

[0082] By merging the state vector and control vector of the original state equation, a new state vector is constructed, and an extended state equation is established, taking a completely new system state into consideration, thus ensuring that the control system has a better control effect.

[0083] 3) Establish the enhanced optimal performance evaluation index G according to the following equation:

[0084]

[0085] In the formula, γ1 is the weighting coefficient obtained from the subjective and objective entropy weighting analysis data, γ2 is the weighting coefficient obtained from the subjective and objective entropy weighting analysis data, and ω ref Let x be the target mechanical angular velocity. 10 These are the linearized working point matrix elements at the current time.

[0086] This enhanced optimal performance evaluation index starts from actual working conditions and is based on the subjective and objective entropy weight analysis method. It covers the impact of controller performance on turbine air compressor output, and also includes the performance requirements of turbine air compressor speed control. It is used to evaluate the performance of the controller and is a key step to achieve good dynamic and steady-state performance under complex working conditions.

[0087] 4) Based on the enhanced optimal performance evaluation index and the extended state-space equation, the enhanced control information is obtained, and the solution to the Riccati equation of the control information is obtained according to the following equation:

[0088]

[0089] In the formula, M is a solution to the Riccati equation.

[0090] 5) Establish the enhanced optimal sliding surface S(X) according to the following equation. ω ):

[0091]

[0092] like Figure 4As shown, the improved harmonic suppression sliding mode speed observer receives the Alpha and Beta axis voltage signals in the stationary coordinate system. Based on the improved sliding mode control function, it observes the back electromotive force of the motor in the stationary coordinate system, outputs the observed values ​​of the air compressor angle and angular velocity, and adds the angle observation value to the harmonic extraction equation to extract the harmonic components of the motor current at the current moment. Using the PI control algorithm, it outputs the harmonic compensation voltage, thereby reducing the harmonics of the current while realizing the sensorless control of the air compressor and improving the accuracy of the sensorless control.

[0093] Specifically, the implementation method of the improved harmonic suppression sliding mode speed observer includes:

[0094] 1) Establish the speed observation sliding mode function Z according to the following equation. α Z β :

[0095]

[0096] In the formula, s α For the Alpha axis observation error sliding surface, s β M is the sliding surface for Beta-axis observation error. α M is the adaptive saturation function for the alpha-axis boundary layer. β R is the adaptive saturation function for the Beta-axis boundary layer. α For the Alpha axis boundary layer adaptive reaching law, R β This is the adaptive reaching law for the Beta-axis boundary layer.

[0097] 2) Define the boundary layer adaptive saturation function M according to the following equation:

[0098]

[0099] In the formula, s is the observation error sliding surface, β is a parameter greater than 0, and r is the sliding observation error.

[0100] The boundary layer adaptive saturation function uses the arctangent function to ensure that the boundary layer thickness does not increase indefinitely. When the sliding mode observation error approaches 0, the boundary layer also approaches 0, enabling the system to converge smoothly to the switching plane.

[0101] 3) Define the boundary layer adaptive reaching law R according to the following equation:

[0102]

[0103] In the formula, ε is a parameter greater than 0, η is a parameter greater than 0, and λ is a parameter greater than 0.

[0104] This boundary layer adaptive approach law automatically adjusts the approach speed when the error approaches the sliding surface; when the error moves away from the sliding surface, the approach rate also increases, but not excessively; when the observation error approaches 0, both the boundary layer and the approach rate adaptively approach 0, accelerating the system's stability and reducing system chattering caused by rotational speed observation.

[0105] 4) Substitute the observed back EMF of the motor into the arctangent function to obtain the observed angular position of the motor; based on the observed angular position of the motor, extract the harmonic components of the motor current, introduce them into the harmonic elimination PI algorithm, output the harmonic current compensation voltage value, and superimpose it with the target voltage output by the current loop PI control algorithm, and output them together to SVPWM to generate a switching pulse signal.

[0106] The implementation process of each part in the sensorless enhanced optimal sliding mode control system for ultra-high speed motors of air compressors in this embodiment will be further described in detail below.

[0107] like Figure 2 As shown, the working principle and design method of the air compressor coupling control model are as follows:

[0108] 1) Based on actual test data of the turbo air compressor, calculate the Mach number M at the compression end according to the following equation. a :

[0109]

[0110] In the formula, U c For the tip linear velocity of the pressure end impeller, d c Where N is the impeller diameter, N is the impeller speed, and ρ is the impeller diameter. c For the specific heat ratio of the gas at the compression end, R a air gas constant, T atm This refers to the atmospheric temperature.

[0111] 2) Based on actual test data of the turbo air compressor, calculate the compression end flow rate according to the following equation.

[0112]

[0113] In the formula, W cp ρ is the air mass flow rate at the pressure end outlet, and ρ is the air density.

[0114] 3) Perform the conversion of the dimensionless parameter Ψ at the compression end according to the following equation:

[0115]

[0116] In the formula, P r For pressure ratio, c c This refers to the specific heat capacity of air at constant pressure.

[0117] 4) Fit the actual experimental data of the compression end according to the following equation:

[0118]

[0119] In the formula, a4, a3, a2, a1, a0, b2, b1, b0, c5, c4, c3, c2, c1, and c0 are the fitting coefficients to be determined.

[0120] 5) Based on the fitting curve results at the compression end, with optimal efficiency as the primary condition, extract the working line with the highest efficiency from the fitting curve and use it as the output benchmark for the target flow rate and target pressure ratio at the compression end, so as to realize the transition from the target speed signal to the target flow rate, target pressure ratio, and compression efficiency signal.

[0121] 6) Based on actual test data of the turbo air compressor, calculate the Mach number at the expansion end using the following equation. e :

[0122]

[0123] In the formula, U e For the tip linear velocity of the turbine blade, d e For turbine diameter, ρ e This represents the specific heat ratio of the gas at the expansion end.

[0124] 7) Based on the actual test data of the turbo air compressor, calculate the expansion end flow rate according to the following equation.

[0125]

[0126] In the formula, W ep This refers to the inlet mass flow rate at the expansion end.

[0127] 8) Determine the dimensionless parameter Ψ at the expansion end according to the following equation. e Conversion:

[0128]

[0129] In the formula, P ep For the expansion ratio, c e The specific heat capacity at constant pressure at the expansion end, T ep This refers to the inlet gas temperature at the expansion end.

[0130] 9) Fit the experimental data of the expansion end according to the following equation.

[0131]

[0132] In the formula, k i =k i1 +k i2 Mae +k i3 Ma e 2 , i = 1 to 5 are all undetermined coefficients for fitting.

[0133] 10) Based on the results of the expansion end fitting curve, with optimal efficiency as the primary condition, extract the working line with the highest efficiency from the fitting curve and use it as the benchmark for the target flow rate and target expansion ratio output at the expansion end, so as to realize the transition from the target speed signal to the target flow rate, target expansion ratio and expansion efficiency signal.

[0134] 11) Define the optimal performance evaluation index of the air compressor and the motor drive torque index parameter λ according to the following equation. Te :

[0135]

[0136] In the formula, P n The number of pole pairs of the air compressor motor air compressor motor flux linkage, J cp This represents the rotational inertia of the air compressor.

[0137] 12) Define the compression torque parameter λ at the compression end as the optimal performance evaluation index for the enhanced air compressor according to the following equation. cp :

[0138]

[0139] In the formula, P rr For the target compression ratio at the compression end, η cp For compression efficiency.

[0140] 13) Define the expansion torque index parameter λ at the expansion end of the air compressor as the optimal performance evaluation index for the enhanced type according to the following equation. ep :

[0141]

[0142] In the formula, η ep For expansion end efficiency, T tep For the inlet gas temperature at the expansion end, P er The target expansion ratio is the expansion end.

[0143] like Figure 3 As shown, the working principle and design method of the enhanced optimal sliding mode speed controller are as follows:

[0144] 1) Based on the relationship between the electromagnetic drive torque of the motor, the compression load torque at the pressure end, and the expansion torque at the vortex end, the torque balance equation of the turbine air compressor is constructed:

[0145]

[0146] In the formula, ω m For the air compressor angular velocity, T e This refers to the electromagnetic torque of the motor.

[0147] 2) Define the state variables x1 and x2 of the turbine air compressor according to the following equations:

[0148]

[0149] In the formula, ω ref denoted as the target angular velocity of the air compressor, and u is the control input of the system.

[0150] 3) Using Taylor expansion at the system's equilibrium point, the original nonlinear system is replaced by a Taylor series, and higher-order terms are ignored to obtain an approximate linearized system:

[0151]

[0152] make f = [f 1 ,f 2 ] T At this point, the linear state equation can be expressed as:

[0153]

[0154] In the formula, X0 and U0 are the linearized operating points at the current time.

[0155] 4) Define the extended state-space equations as follows:

[0156]

[0157] In the formula, X ω To extend the state vector, A ω To expand the system matrix, B ω To expand the input matrix, U ω U is the extended control input vector, μ1 is the parameter greater than 0, and μ2 is the parameter greater than 0.

[0158] 5) Construct the extended state space matrix according to the following equation:

[0159]

[0160] In the formula, X is the state vector, A is the system matrix, B is the input matrix, and I is the identity matrix.

[0161]

[0162] 6) Construct the subjective weighted judgment matrix H of the performance evaluation index of the turbo air compressor according to the following equation:

[0163]

[0164] 7) After performing a consistency analysis on H to ensure the matrix logic is correct, normalization is performed, and the weight of each indicator is:

[0165] q j =[0.73 0.19 0.08]

[0166] In the formula, j is the index number.

[0167] 8) Based on traditional sliding mode control sample data, weights are assigned using the entropy weight method to obtain a sample data index matrix. This matrix is ​​then standardized to obtain a weight matrix. The entropy value of each index is calculated, and the weight of each index is derived using the following equation:

[0168] w j =[0.29 0.47 0.24]

[0169] 9) Calculate the combined weight γ according to the following equation. j :

[0170]

[0171] 10) Order Combined weights The subjective weighting coefficients for x1 and x2 can be obtained as γ. x1 =3.2 and γ x2 =2.1.

[0172] 11) Calculate the weighting coefficients γ1 and γ2 for x1 and x2 according to the following equation:

[0173]

[0174] In the formula, β1 and β2 are parameters greater than 0.

[0175] 12) Establish general performance evaluation indicators according to the following equations:

[0176]

[0177] 13) Based on the ordinary performance evaluation index, an extended system state-space equation is introduced, and an enhanced optimal performance evaluation index is established according to the following equation:

[0178]

[0179] 14) Based on the enhanced optimal performance evaluation index and the extended state-space equation, the enhanced control information is obtained. The solution M of the Riccati equation for the control information is obtained according to the following equation:

[0180]

[0181] 16) Establish the enhanced optimal sliding surface S(X) according to the following equation. ω ):

[0182]

[0183] 17) Choose the isokinetic approach law according to the following equation:

[0184]

[0185] In the formula, ε is a parameter greater than 0.

[0186] 18) Design the sliding mode control rate according to the following equation:

[0187]

[0188] like Figure 4 As shown, the working principle and design method of the improved harmonic suppression sliding mode speed observer are as follows:

[0189] 1) Construct the two-phase current-state-variable equations for a sliding mode observer based on back EMF using the following equations:

[0190]

[0191] In the formula, i α i β R is the current value of the current sensor in a two-phase stationary coordinate system. s L is the resistance of the motor line. s For the motor line inductance, u α u β e represents the voltage value of the voltage sensor in a two-phase stationary coordinate system. α e β This is the extended back electromotive force in a two-phase stationary coordinate system.

[0192] 2) Construct the sliding mode observer according to the following equation:

[0193]

[0194] In the formula, "^" represents observation, and Z α Z β This is the sliding mode function for observing rotational speed.

[0195] 3) Define the sliding mode observation error r according to the following equation. α rβ :

[0196]

[0197] 4) Define the sliding surface s for the sliding mode observation error according to the following equation. α s β :

[0198]

[0199] In the formula, c smo Parameters that are greater than 0.

[0200] 5) Define the speed observation sliding mode function Z according to the following equation. α Z β :

[0201]

[0202] In the formula, M α For the Alpha-axis boundary layer adaptive saturation function, M β For the Beta-axis boundary layer adaptive saturation function, R α For the Alpha axis boundary layer adaptive reaching law, R β This is the adaptive reaching law for the Beta-axis boundary layer.

[0203] 6) Define the boundary layer adaptive saturation function M according to the following equation:

[0204]

[0205] In the formula, s is the observation error sliding surface, β is a parameter greater than 0, and r is the sliding observation error.

[0206] 7) Define the boundary layer adaptive reaching law R according to the following equation:

[0207]

[0208] In the formula, ε is a parameter greater than 0, η is a parameter greater than 0, and λ is a parameter greater than 0.

[0209] 8) The rotational speed observation sliding mode function Z is equivalently considered as the extended back electromotive force e according to the following equation:

[0210]

[0211] 9) Output the extended back electromotive force observations according to the following equation:

[0212]

[0213] 10) Input the observed values ​​of the air compressor's electrical angle and electrical angular velocity according to the following equations:

[0214]

[0215] 11) Extract the fundamental current i in the natural coordinate system according to the following equation. af i bf i cf :

[0216]

[0217] In the formula, θ e For the electric angle of the motor, i dr For the direct-axis target current value, i qr This is the target current value for the quadrature axis.

[0218] 12) Calculate the harmonic current i in the natural coordinate system according to the following equation. ax i bx i cx :

[0219]

[0220] 13) Calculate the orthogonal axis harmonic currents in the 5th and 7th order rotating coordinate systems using the following equations:

[0221]

[0222] In the formula, i d5 The harmonic current in the 5th rotating coordinate system, i q5 The quadrature-axis current in a 5th-order rotating coordinate system, i d7 For the direct-axis current in the 7th selected coordinate system, i q7 The quadrature-axis current is given in a 7-fold rotating coordinate system.

[0223] 14) After passing through a low-pass filter, the corresponding filtered 5th and 7th harmonic currents i are obtained. d55 i q55 i d77 i q77 .

[0224] 15) Define the target values ​​i of the 5th and 7th harmonic currents according to the following equations. d5r i q5r i d7r i q7r :

[0225]

[0226] 16) Define the 5th and 7th harmonic current errors e according to the following equations. d5e q5 e d7 e q7 :

[0227]

[0228] 17) Define the harmonic cancellation compensation voltage U in the 5th order rotating coordinate system according to the following equation. d5 U q5 :

[0229]

[0230] In the formula, e d5PI To make e d5 The signal output after connecting to the PI controller, e q5PI To make e q5 The signal output after connecting to the PI controller, L d For direct-axis inductors, L q It is a quadrature axis inductor.

[0231] 18) Define the harmonic cancellation compensation voltage U in the 7th order rotating coordinate system according to the following equation. d7 U q7 :

[0232]

[0233] 19) Define the harmonic cancellation compensation voltage U in the rotating coordinate system according to the following equation. dc U qc The input is then superimposed and fed into the SVPWM algorithm to reduce motor voltage and current harmonics.

[0234]

[0235] in:

[0236]

[0237] As a preferred embodiment, the sensorless enhanced optimal sliding mode control system for the ultra-high speed motor of the air compressor in this embodiment adopts wide bandgap semiconductor devices represented by silicon carbide (SiC) devices, which have advantages such as low switching loss, fast switching speed, and high device withstand voltage; it adopts silicon carbide drive optocouplers, which have advantages such as high withstand voltage, high power density, and low conduction loss; and it adopts automotive-grade digital signal processor (DSP) chips, which have advantages such as strong vibration and shock resistance, high computing power, and compact design.

[0238] As a preferred embodiment, the sensorless enhanced optimal sliding mode control system for ultra-high-speed motors in air compressors adopts a power circuit design with capacitor filtering at the main power input position and a reverse connection protection scheme, which has the advantages of smooth voltage waveform, stronger power input protection, and improved system safety. It adopts a design scheme that connects the CAN output terminal to the host computer to realize real-time control of the motor and real-time observation of motor parameters and motor operating status. It adopts a non-isolated PT100 acquisition circuit. The power signal is filtered by diode reverse connection protection and bypass capacitor, then input to the inverting input terminal through resistor voltage division, and finally input to the DSP for signal processing after RC filtering.

[0239] As a preferred embodiment, the sensorless enhanced optimal sliding mode control system for the ultra-high speed motor of the air compressor in this embodiment adopts an ultrasonic gas flow sensor, which calculates the flow rate by measuring the propagation time difference of gas through the pipeline, and has the advantages of high measurement accuracy and wide range. It also adopts a semiconductor temperature sensor, which measures the temperature by measuring the resistance of metal with temperature, and has the advantages of high temperature measurement accuracy, fast response speed and small size.

[0240] As a preferred embodiment, the sensorless enhanced optimal sliding mode control system for ultra-high speed motors in air compressors in this embodiment adopts Honeywell 133Series voltage and current sensors, which are suitable for real-time monitoring of high-voltage, high-power motor systems and conform to the actual working conditions of air compressors.

[0241] Preferably, for the sensorless enhanced type of ultra-high-speed motor for air compressors in this embodiment, the motor control integrator is required to employ a DSP multi-core processor with 1MB program memory, 128KB data memory, 6KB stack memory, 150MHz main frequency, 16-bit bit width, and support for single-precision floating-point operations; a high-precision ADC with 16-bit resolution, 200kHz sampling rate, and 50kHz signal bandwidth; MOSFET power devices with a switching frequency of 50kHz, 500V power voltage, and SiC material; filters and protection circuits to reduce the impact of noise and vibration; the EtherCAT communication protocol, requiring a maximum transmission rate of 50Mbps; and current and voltage sensors with frequencies above 50kHz, providing a solid hardware foundation for the invention and ensuring the efficient, stable, and safe operation of the system.

[0242] In this embodiment, a turbine air compressor operating condition simulation is conducted with a step signal of 70,000 rpm and a simulation time of 3 seconds. The system is defined as entering a steady state when the absolute value of the speed error is less than 200 rpm and no longer exceeds 200 rpm over time. The maximum speed overshoot, stabilization time data, and analysis after system stabilization are as follows: Figure 5As shown in Table 1, the sensorless enhanced optimal sliding mode control system for ultra-high-speed motors in air compressors provided by this invention reduces the settling time by 46.16% and the maximum overshoot by 95.18% compared to the traditional optimal sliding mode control algorithm. When dealing with step speed signals, this invention reduces both the settling time and the maximum overshoot, demonstrating good adaptability to operating conditions.

[0243] Table 1. Simulation results of the turbine air compressor based on the step target speed signal.

[0244]

[0245] In this embodiment, the simulation time is 3 seconds; the target rotational speed starts at 30,000 rpm from 0.6 seconds, increases by 10,000 rpm every 0.3 seconds until it reaches 70,000 rpm, then decreases by 10,000 rpm every 0.3 seconds. The control effect is compared below. Figure 6 As shown in the figure. The sensorless enhanced optimal sliding mode control system for ultra-high-speed motors of air compressors provided by this invention, compared with traditional optimal sliding mode control, shows superior speed control performance in terms of both settling time and overshoot when facing stepped target speed signals under typical air compressor operating conditions.

[0246] The sensorless enhanced optimal sliding mode control system for ultra-high-speed motors in air compressors provided by this invention, compared to traditional optimal sliding mode speed control systems, employs a coupled control model of the air compressor, an enhanced optimal sliding mode speed controller, and an improved harmonic suppression sliding mode speed observer. By leveraging the measurable air flow and temperature at the compression and expansion ends of the turbine air compressor, it sets up gas flow sensors at the compressor compression outlet, gas flow sensors at the expansion inlet, and temperature sensors at the air inlet and expansion inlet of the compressor to monitor the air flow at the compression inlet in real time. Based on the air flow rate signal at the expansion end inlet, atmospheric temperature signal, and gas temperature signal at the expansion end inlet, an enhanced optimal performance evaluation index is established. The control quantity is input as a state quantity into the state space equation to establish an extended state space equation. The enhanced optimal performance evaluation index is combined with the extended state space equation to construct an enhanced optimal sliding surface. The harmonic voltage compensation signal of the improved harmonic suppression sliding speed observer is input to the SVPWM value to reduce the current harmonic component and improve the speed observation accuracy. At the same time, the speed observation signal is input into the enhanced optimal sliding surface, ultimately realizing rapid and accurate speed adjustment of the air compressor under sensorless conditions.

[0247] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0248] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0249] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0250] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0251] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A sensorless enhanced optimal sliding mode control system for an ultra-high-speed motor used in air compressors, characterized in that, The system includes an air compressor coupled control model, an enhanced optimal sliding mode speed controller, and an improved harmonic suppression sliding mode speed observer. The air compressor coupled control model outputs enhanced optimal performance evaluation index parameters based on curves fitted from air compressor test data. The enhanced optimal sliding mode speed controller combines the enhanced optimal performance evaluation index with the extended state-space equation to establish an enhanced optimal sliding surface and outputs the target quadrature-axis current. The improved harmonic suppression sliding mode speed observer, based on a boundary layer adaptive sliding mode control function and a voltage injection harmonic elimination algorithm, outputs the air compressor's angular velocity and angle observation values, reducing current harmonics and achieving sensorless control of the air compressor. The air compressor coupled control model incorporates the target speed signal and the actual speed observation signal into the physical curves of the air compressor compression end, the physical curve of the expansion end, and the optimal efficiency working curve of the air compressor. By setting gas flow sensors at the air compressor compression end outlet, gas flow sensors at the expansion end inlet, temperature sensors at the air compressor air inlet, and temperature sensors at the expansion end inlet, the model monitors the air flow signals at the compression end inlet, the air flow signals at the expansion end inlet, the atmospheric temperature signal, and the gas temperature signal at the expansion end inlet in real time, and finally outputs enhanced optimal performance evaluation index parameters. The implementation method of the air compressor coupling control model includes: 1) Using the Jensen & Kristensen modeling method, based on actual test data of the turbo air compressor, the flow rate, pressure ratio, speed, and power consumption curves of the compression end and expansion end are fitted with the optimal efficiency as the benchmark. By receiving the target speed signal or vehicle speed signal, and combining the sampling signals of the gas flow sensor located at the inlet of the compression end, the gas flow sensor located at the inlet of the expansion end, the gas temperature sensor located near the inlet of the compression end, and the gas temperature sensor located at the inlet of the expansion end, the air compressor parameters for control are output. 2) Calculate the motor drive torque parameter λ, the optimal performance evaluation index of the enhanced air compressor, according to the following equation. Te : In the formula, P n φ is the number of pole pairs of the air compressor motor. f air compressor motor flux linkage, J cp The moment of inertia of the air compressor; 3) Calculate the compression torque parameter λ at the compression end of the air compressor as the optimal performance evaluation index for the enhanced type according to the following equation. cp : In the formula, W cp T is the mass flow rate of the gas at the outlet of the compression end. atm For atmospheric temperature, c c P is the isobaric specific heat capacity of the gas at the compression end. rr ρ is the target compression ratio at the compression end. c η is the specific heat ratio of the gas at the compression end. cp For compression efficiency; 4) Calculate the expansion torque parameter λ at the expansion end of the air compressor as the optimal performance evaluation index for the enhanced type according to the following equation. ep : In the formula, W ep T is the mass flow rate of the gas at the expansion end inlet. tep c is the inlet gas temperature at the expansion end. e η is the isobaric specific heat capacity of the gas at the expansion end. ep For the expansion end efficiency, P er ρ is the target expansion ratio at the expansion end. e This represents the specific heat ratio of the gas at the expansion end.

2. The sensorless enhanced optimal sliding mode control system for ultra-high-speed motors in air compressors according to claim 1, characterized in that, The enhanced optimal sliding mode speed controller receives the speed observation signal from the improved harmonic suppression sliding mode speed observer. Based on the air compressor speed-torque state space equation, it extracts the system matrix and control matrix, and establishes an extended state space equation. Based on the speed sample data and subjective-objective entropy weight analysis data of traditional sliding mode control, combined with the compression torque index parameters at the compression end and the expansion torque index parameters at the expansion end, it establishes an enhanced optimal performance evaluation index. Combining the extended state space equation and the enhanced performance evaluation index, it couples out the enhanced control information, and finally establishes the enhanced optimal sliding mode surface, receives the target speed information, and outputs the target quadrature-axis current value. The target current is applied to the PI controller of the current loop, combined with the harmonic voltage injection signal of the improved harmonic suppression sliding mode speed observer, and then combined with space vector pulse width modulation to output a switching pulse signal to the inverter, generating AC current to drive the motor to rotate.

3. The sensorless enhanced optimal sliding mode control system for an ultra-high-speed motor in an air compressor according to claim 2, characterized in that, The implementation method of the enhanced optimal sliding mode speed controller includes: 1) Using the control variable as the state input, establish the extended state equations according to the following equations: In the formula, X ω To expand the state vector, A ω To expand the system matrix, B ω To expand the input matrix, U ω To extend the control input vector, U is the control input vector, μ1 is the parameter greater than 0, and μ2 is the parameter greater than 0; 2) Construct the extended state-space matrix according to the following equation: In the formula, X is the state vector, A is the system matrix, B is the input matrix, and I is the identity matrix; 3) Establish the enhanced optimal performance evaluation index G according to the following equation: In the formula, γ1 is the weighting coefficient obtained from the subjective and objective entropy weighting analysis data, γ2 is the weighting coefficient obtained from the subjective and objective entropy weighting analysis data, and ω ref Let x be the target mechanical angular velocity. 10 These are the linearized working point matrix elements at the current time. 4) Based on the enhanced optimal performance evaluation index and the extended state-space equation, the enhanced control information is obtained, and the solution to the Riccati equation of the control information is obtained according to the following equation: In the formula, M is a solution to the Riccati equation; 5) Establish the enhanced optimal sliding surface S(X) according to the following equation. ω ): 。 4. The sensorless enhanced optimal sliding mode control system for ultra-high-speed motors in air compressors according to claim 1, characterized in that, The improved harmonic suppression sliding mode speed observer receives Alpha and Beta axis voltage signals in a stationary coordinate system. Based on the improved sliding mode control function, it observes the motor back electromotive force in the stationary coordinate system, outputs the observed values ​​of the air compressor angle and angular velocity, and adds the angle observation value to the harmonic extraction equation to extract the harmonic components of the motor current at the current moment. Using a PI control algorithm, it outputs a harmonic compensation voltage, thereby reducing current harmonics and improving the accuracy of sensorless control while achieving sensorless control of the air compressor.

5. The sensorless enhanced optimal sliding mode control system for an ultra-high-speed motor in an air compressor according to claim 4, characterized in that, The implementation method of the improved harmonic suppression sliding mode speed observer includes: 1) Establish the speed observation sliding mode function Z according to the following equation. α Z β : In the formula, s α For the Alpha axis observation error sliding surface, s β M is the sliding surface for Beta-axis observation error. α M is the adaptive saturation function for the alpha-axis boundary layer. β R is the adaptive saturation function for the Beta-axis boundary layer. α For the Alpha axis boundary layer adaptive reaching law, R β For the Beta-axis boundary layer adaptive reaching law; 2) Define the boundary layer adaptive saturation function M according to the following equation: In the formula, s is the observation error sliding surface, β is a parameter greater than 0, and r is the sliding observation error; 3) Define the boundary layer adaptive reaching law R according to the following equation: In the formula, ε is a parameter greater than 0, η is a parameter greater than 0, and λ is a parameter greater than 0; 4) Substitute the observed back EMF of the motor into the arctangent function to obtain the observed angular position of the motor; based on the observed angular position of the motor, extract the harmonic components of the motor current, introduce them into the harmonic elimination PI algorithm, output the harmonic current compensation voltage value, superimpose it with the target voltage output by the current loop PI control algorithm, and output them together to SVPWM to generate a switching pulse signal.

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