Direct speed composite control method for SMPMSM drive system based on dynamic weight factor

Abstract

The present invention relates to a direct speed composite control method for an SMPMSM drive system based on a dynamic weight factor, comprising: establishing a super-local model of the SMPMSM drive system for speed control; deriving a sliding mode control law and an integral sliding mode control law for direct speed control based on the super-local model of the SMPMSM drive system; designing a dynamic weight factor, integrating the sliding mode control law and the integral sliding mode control law for direct speed control, and realizing direct speed composite control of the SMPMSM drive system; and performing voltage and current constraint processing. The present invention makes full use of the DC bus voltage of the inverter to realize safe and stable operation of the system under voltage and current constraint conditions. The direct speed composite control method for an SMPMSM drive system based on a dynamic weight factor can realize a smooth transition between dynamic and steady states under different operating conditions of the SMPMSM drive system, and comprehensively improve the dynamic and steady-state control performance and robustness of the system.

Description

Technical Field

[0001] The invention relates to the technical field of SMPMSM drive system control, and is a direct speed composite control method of an SMPMSM drive system based on a dynamic weight factor. Background Art

[0002] Surface-mounted permanent magnet synchronous motors (SMPMSMs) offer advantages such as high power density, high efficiency, and easy maintenance. They are widely used in industries such as intelligent manufacturing, servo systems, and household appliances, and are essential for electromechanical control and energy conversion. SMPMSM drive systems often employ field-oriented control, which not only offers excellent dynamic and steady-state control performance but also boasts advantages such as a fixed inverter switching frequency. SMPMSM drive systems based on rotor-field-oriented speed control utilize the fact that the electromechanical time constant of the SMPMSM is greater than its electrical time constant, enabling independent design of the speed outer loop and current inner loop controllers. In this cascaded dual-loop closed-loop control structure, with speed control as the outer loop and current / torque control as the inner loop, the outer speed loop controller generates current / torque commands, while the inner current / torque loop controller generates inverter command voltages. These signals are then modulated by the inverter to generate on / off signals for the inverter power switches, controlling the real-time operation of the SMPMSM.

[0003] The cascade control structure offers clear physical concepts and is easy to implement. Proportional-integral (PI) controllers are typically used to control speed and current separately. However, the SMPMSM drive system is a nonlinear, uncertain system with strong multivariable coupling. Linear PI control struggles to quickly suppress or eliminate disturbances such as parameter uncertainty, unmodeled dynamics, and unknown disturbances in the SMPMSM drive system. This not only degrades the control performance of the SMPMSM drive system but can even jeopardize its stable operation. To achieve control of nonlinear uncertain systems, the system's uncertainties are estimated, and measures are taken within the control law to eliminate or suppress them. High-performance control of the SMPMSM drive system can then be achieved using nonlinear control. Key nonlinear control methods for SMPMSM drive systems include feedback linearization, adaptive control, fuzzy control, sliding mode control, and model predictive control (MPC). However, the limited speed control bandwidth of the cascade control structure results in poor control performance for speed-controlled SMPMSM drive systems, with significant speed overshoot and current ripple.

[0004] To improve the control performance of PMSM drive systems, cascadeless control, which enables simultaneous control of motor speed and current at different time scales, has emerged. Cascadeless control based on a finite control set (FCS) approximates the optimal control solution, but the system suffers from significant current pulsation, torque, and speed fluctuations. Cascadeless control based on a continuous control set (CCS) typically uses a multi-step predictive control algorithm to account for more system dynamics and reduce speed fluctuations. However, the complexity of multi-step predictive control algorithms hinders their practical application.

[0005] Furthermore, the SMPMSM drive system is a nonlinear system with multiple variables and strong coupling. A single control method cannot effectively cope with the complex and changing system operating conditions. Selecting and integrating control methods with complementary performance to leverage their respective control advantages under different operating conditions is an effective way to improve the overall performance of the SMPMSM drive system. The system operating state is divided into transient and dynamic states, and a switching function is used to coordinate the fuzzy controller and the PI controller. The fuzzy controller dominates in transient states, while the PI controller dominates in steady states. Composite control, which integrates multiple control methods, can adapt to complex operating conditions, not only improving system dynamic performance but also reducing motor torque ripple. The key to composite controller design lies in the appropriate selection of dynamic and steady-state switching functions. However, fuzzy control rules are complex, rely on manual experience, and membership functions are difficult to select. Summary of the Invention

[0006] The purpose of the present invention is to provide a direct speed composite control method for an SMPMSM drive system based on a dynamic weight factor, which can automatically sense the system's operating status, adaptively adjust the priorities and proportions of two different control laws, namely the sliding mode control law and the integral sliding mode control law, give full play to the technical advantages of different control laws, and achieve a smooth transition between dynamic and steady states under different operating conditions of the SMPMSM drive system, so that the system enjoys the technical advantages of comprehensively improved dynamic and steady-state control performance and strong robustness.

[0007] To achieve the above object, the present invention adopts the following technical solution: a direct speed composite control method of an SMPMSM drive system based on a dynamic weight factor, the method comprising the following steps in sequence:

[0008] (1) Establish a super-local model of the SMPMSM drive system with speed control;

[0009] (2) Based on the super-local model of the SMPMSM drive system, the sliding mode control law and integral sliding mode control law for its direct speed control are derived;

[0010] (3) Design dynamic weight factors to achieve the fusion of sliding mode control law and integral sliding mode control law to generate the direct speed composite control law of SMPMSM drive system;

[0011] (4) Perform voltage and current constraint processing.

[0012] The step (1) specifically refers to: according to the dynamic equation of the SMPMSM drive system:

[0013]

[0014] Among them, ω r is the electrical angular velocity of the SMPMSM, ω r =n P Ω, n P is the number of pole pairs, Ω is the measured mechanical angular velocity of the SMPMSM rotor; ψ f is the magnetic flux of the rotor permanent magnet; R s is the three-phase stator winding resistance; L s is the stator synchronous inductance; Respectively represent the optimal command voltages of the inverter d-axis and q-axis that meet the voltage and current constraints; i d and i q They represent the d-axis and q-axis stator currents obtained after coordinate transformation of the actual stator current; J and B are the moment of inertia and viscosity coefficient of the system respectively; V d,par 、V q,par They represent the disturbance voltages of the stator d-axis and q-axis caused by the uncertainty of the motor parameters; V d,dead 、V q,dead They represent the disturbance voltages of the stator d-axis and q-axis generated by the nonlinearity of the inverter; d ω is the parameter uncertainty and unknown disturbance of the mechanical part of the SMPMSM drive system; T e is the electromagnetic torque of SMPMSM; T L is the load torque;

[0015] For V d,par 、V q,par 、V d,dead 、V q,dead and d ω To make an estimate, use F d 、F q and F ω , representing the known and unknown parts of the system dynamic equations, and are written as:

[0016]

[0017] Based on this, the super-local model of the SMPMSM drive system is established, which is expressed as:

[0018]

[0019] Where, α s , α ω is the proportional coefficient selected according to the nominal parameters of SMPMSM. For the SMPMSM drive system, α s Set to 1 / L according to the motor nominal parameters s ;

[0020] Use differential algebra to solve F d 、F q and F ω Make an estimate to Represents its estimated value, and its expression is:

[0021]

[0022] Where: They represent the optimal command voltages of the inverter d-axis and q-axis at time t that meet the voltage and current constraints respectively; t is the time variable; T F is the time window, which is set to 10 control cycles.

[0023] In step (2), the sliding mode control law specifically refers to:

[0024] The sliding surface is defined as:

[0025]

[0026] Among them, c1 is the sliding surface parameter; e ω is the speed error, is the rotor electrical angular velocity command value of SMPMSM; ω r is the electrical angular velocity of the SMPMSM, ω r =n P Ω, n P is the number of pole pairs, Ω is the measured mechanical angular velocity of the SMPMSM rotor;

[0027] Based on the super-local model of the SMPMSM drive system, the differential of equation (1) is set to zero, and the equivalent control to maintain the system state on the sliding mode surface is obtained as follows:

[0028]

[0029] Among them, α ω for i q The proportionality coefficient, i q represents the q-axis stator current obtained after coordinate transformation of the actual stator current, ψ f is the magnetic flux of the rotor permanent magnet; J is the moment of inertia of the system; ueq1 It is the equivalent control part of sliding mode control; and F q 、F ω estimated value of;

[0030] Secondly, in order to quickly switch the system from any state to the sliding surface, the switching control is selected as:

[0031]

[0032] Among them, T s is the control period; u sw1 is the switching control part of sliding mode control;

[0033] According to the super-local model of the SMPMSM drive system and the defined sliding mode surface, the sliding mode control law is obtained as follows:

[0034]

[0035] In step (2), the integral sliding mode control law specifically refers to:

[0036] The integral sliding surface is defined as:

[0037]

[0038] Among them, c2 is the coefficient of the integral term in the integral sliding surface; c1 is the sliding surface parameter; α ω is the coefficient selected according to the nominal parameters of SMPMSM; e ω is the speed error, is the rotor electrical angular velocity command value of SMPMSM; i q represents the q-axis stator current obtained after coordinate transformation of the measured stator current; ω r is the electrical angular velocity of the SMPMSM, ω r =n P Ω, n P is the number of pole pairs, Ω is the measured mechanical angular velocity of the SMPMSM rotor;

[0039] According to the super-local model of the SMPMSM drive system and the defined integral sliding surface, the equivalent control part that maintains the system state on the integral sliding surface and the switching control part that quickly switches from any state of the system to the integral sliding surface are derived, which are expressed as:

[0040]

[0041] Among them, T s is the control period; α s , α ωis the proportional coefficient selected according to the nominal parameters of SMPMSM. For the SMPMSM drive system, α s According to the nominal parameters set to 1 / L s ;L s is the stator synchronous inductance; F q estimated value of;

[0042] The control law obtained based on the super-local model of the SMPMSM drive system and the defined integral sliding mode surface is:

[0043]

[0044] The step (3) specifically refers to:

[0045] Set the dynamic weight factor β, and β∈[0,1], and then generate the inverter q-axis command voltage through composite control based on the sliding mode control law and the integral sliding mode control law, then:

[0046]

[0047] Where, α s , α ω is the proportional coefficient selected according to the nominal parameters of SMPMSM. For the SMPMSM drive system, α s According to the nominal parameters set to 1 / L s , L s is the stator synchronous inductance;

[0048] n P is the pole pair number, ψ f is the magnetic flux of the rotor permanent magnet; J is the moment of inertia of the system; T s is the control period; c2 is the coefficient of the integral term in the integral sliding surface; c1 is the sliding surface parameter; i q represents the q-axis stator current obtained by coordinate transformation of the measured stator current; and F q 、F ω Estimated value of ω is the speed error, is the rotor electrical angular velocity command value of SMPMSM, ω r is the electrical angular velocity of the SMPMSM, ω r =n P Ω, Ω is the measured mechanical angular velocity of the SMPMSM rotor; Inverter q-axis command voltage generated for direct speed compound control; is the sliding mode control law, is the integral sliding mode control law.

[0049] The step (4) specifically refers to:

[0050] When the SMPMSM drive system is running, it must simultaneously meet the motor maximum current constraint and the inverter maximum output voltage constraint. To this end, first calculate the inverter q-axis command voltage that meets the maximum current constraint, and then:

[0051]

[0052] in, is the rotor electrical angular velocity command value of SMPMSM; I max is the maximum stator current allowed for safe operation of SMPMSM, sign(·) is the sign function; α s is the proportional coefficient selected according to the nominal parameters of SMPMSM. For the SMPMSM drive system, α s According to the nominal parameters set to 1 / L s ;L s is the stator synchronous inductance; T s is the control period; i q represents the q-axis stator current obtained by coordinate transformation of the measured stator current; u qlim To meet the inverter q-axis instruction voltage of the maximum current constraint; when the SMPMSM drive system adopts i d = 0 control, so that it runs in the maximum torque current ratio mode. According to the deadbeat predictive control and taking into account the control delay, the inverter d-axis command voltage is generated, and its expression is:

[0053]

[0054] The inverter command voltage that meets the current constraint is:

[0055]

[0056] Among them, min(·) is the minimum function; Inverter d-axis command voltage generated for direct speed compound control; The inverter q-axis command voltage that satisfies the current constraint condition;

[0057] Next, we further process the voltage constraint and first define θ n is the phase angle of the inverter command voltage, expressed as:

[0058]

[0059] In order to make full use of the DC bus voltage of the inverter, the inverter hexagonal voltage vector boundary equation L is obtained i where i = 1, 2, ..., 6:

[0060] L i :h dn u d +h qn u q +h cn =0 (12)

[0061] in, n∈N + :1-6; U dc is the DC bus voltage of the inverter; sector number n is based on θ n The sector is determined; h dn 、h qn are the boundary equation d-axis and q-axis voltage coefficients, h cn is the constant term of the boundary equation;

[0062] The cost function is defined as:

[0063]

[0064] in, They represent the optimal command voltages of the inverter d and q axes that satisfy the voltage and current constraints respectively;

[0065] Then, the Lagrangian function is constructed as:

[0066]

[0067] Where λ is the Lagrange multiplier;

[0068] according to and The optimal command voltage of the inverter that satisfies both current and voltage constraints is obtained by finding the extreme value and using the optimization method:

[0069]

[0070] The dynamic weight factor β is designed based on the speed error to realize the perception of the system operation status. Its expression is:

[0071]

[0072] Among them, e ω is the speed error, δ is the design parameter;

[0073] When the integral sliding mode control law accounts for 90% in the direct speed composite control law, the parameter δ is determined, and its calculation formula is:

[0074]

[0075] Wherein, Δ is the maximum speed fluctuation range determined according to the speed steady-state performance requirements.

[0076] It can be seen from the above technical solution that the beneficial effects of the present invention are: First, based on the super-local model of the SMPMSM drive system, the present invention derives the sliding mode control law and the integral sliding mode control law of direct speed control, which can not only enable the system state to quickly enter the sliding mode surface, but also effectively suppress the sliding mode chattering; Second, through the innovative design of the dynamic weight factor, the two control laws are integrated to generate a composite control law of direct speed control. By sensing the system operating state, the priority of sliding mode control and integral sliding mode control is automatically determined and the weights of different control laws are assigned, so as to achieve a smooth transition between dynamic and steady-state under different operating conditions of the system, so that the system enjoys good dynamic and steady-state control performance and strong robustness. Third, the Lyapunov function is designed to prove the stability of the system, and the basis for determining the key control parameters is given to ensure that the proposed direct speed composite control can not only operate safely and stably under the conditions of meeting the system voltage and current constraints, but also improve the DC bus voltage utilization of the inverter. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a state transition diagram;

[0078] Figure 2 Schematic diagram of the inverter hexagonal voltage vector and boundary equations;

[0079] Figure 3 It is a flow chart for constraint processing;

[0080] Figure 4 Schematic diagram of the overall control structure of the present invention;

[0081] Figure 5 Schematic diagram of steady-state speed, current and phase A current THD under different control conditions;

[0082] Figure 6 Dynamic diagram of speed and current when the speed command steps;

[0083] Figure 7 Schematic diagram of the speed and current dynamics during sudden unloading at rated speed. DETAILED DESCRIPTION

[0084] like Figure 4 As shown, a direct speed compound control method of an SMPMSM drive system based on dynamic weights includes the following steps in sequence:

[0085] (1) Establish a super-local model of the SMPMSM drive system with speed control;

[0086] (2) Based on the super-local model of the SMPMSM drive system, the sliding mode control law and integral sliding mode control law for its direct speed control are derived;

[0087] (3) Design dynamic weight factors to achieve the fusion of sliding mode control law and integral sliding mode control law to generate the direct speed composite control law of SMPMSM drive system;

[0088] (4) Perform voltage and current constraint processing.

[0089] The step (1) specifically refers to: according to the dynamic equation of the SMPMSM drive system:

[0090]

[0091] Among them, ω r is the electrical angular velocity of the SMPMSM, ω r =n P Ω, n P is the number of pole pairs, Ω is the measured mechanical angular velocity of the SMPMSM rotor; ψ f is the magnetic flux of the rotor permanent magnet; R s is the three-phase stator winding resistance; L s is the stator synchronous inductance; Respectively represent the optimal command voltages of the inverter d-axis and q-axis that meet the voltage and current constraints; i d and i q They represent the d-axis and q-axis stator currents obtained after coordinate transformation of the actual stator current; J and B are the moment of inertia and viscosity coefficient of the system respectively; V d,par 、V q,par They represent the disturbance voltages of the stator d-axis and q-axis caused by the uncertainty of the motor parameters; V d,dead 、V q,dead They represent the disturbance voltages of the stator d-axis and q-axis generated by the nonlinearity of the inverter; d ω is the parameter uncertainty and unknown disturbance of the mechanical part of the SMPMSM drive system; T e is the electromagnetic torque of SMPMSM; T L is the load torque;

[0092] For V d,par 、V q,par 、V d,dead 、V q,dead and d ω To make an estimate, use F d 、F q and F ω , representing the known and unknown parts of the system dynamic equations, and are written as:

[0093]

[0094] Based on this, the super-local model of the SMPMSM drive system is established, which is expressed as:

[0095]

[0096] Where, α s , α ω are proportional coefficients selected according to the nominal parameters of SMPMSM. For the SMPMSM drive system, α s Set to 1 / L according to the motor nominal parameters s ;

[0097] Use differential algebra to solve F d 、F q and F ω Make an estimate to Represents its estimated value, and its expression is:

[0098]

[0099] Where: They represent the optimal command voltages of the inverter d-axis and q-axis at time t that meet the voltage and current constraints respectively; t is the time variable; T F is the time window, which is set to 10 control cycles.

[0100] In step (2), the sliding mode control law specifically refers to:

[0101] The sliding surface is defined as:

[0102]

[0103] Among them, c1 is the sliding surface parameter; e ω is the speed error, is the rotor electrical angular velocity command value of SMPMSM; ω r is the electrical angular velocity of the SMPMSM, ω r =n P Ω, n P is the number of pole pairs, Ω is the measured mechanical angular velocity of the SMPMSM rotor;

[0104] Based on the super-local model of the SMPMSM drive system, the differential of equation (1) is set to zero, and the equivalent control to maintain the system state on the sliding mode surface is obtained as follows:

[0105]

[0106] Among them, α ω for i q The proportionality coefficient, iq represents the q-axis stator current obtained after coordinate transformation of the actual stator current, ψ f is the magnetic flux of the rotor permanent magnet; J is the moment of inertia of the system; u eq1 It is the equivalent control part of sliding mode control; and F q 、F ω estimated value of;

[0107] Secondly, in order to quickly switch the system from any state to the sliding surface, the switching control is selected as:

[0108]

[0109] Among them, T s is the control period; u sw1 is the switching control part of sliding mode control;

[0110] According to the super-local model of the SMPMSM drive system and the defined sliding mode surface, the sliding mode control law is obtained as follows:

[0111]

[0112] In step (2), the integral sliding mode control law specifically refers to:

[0113] The integral sliding surface is defined as:

[0114]

[0115] Among them, c2 is the coefficient of the integral term in the integral sliding surface; c1 is the sliding surface parameter; α ω is the coefficient selected according to the nominal parameters of SMPMSM; e ω is the speed error, is the rotor electrical angular velocity command value of SMPMSM; i q represents the q-axis stator current obtained after coordinate transformation of the measured stator current; ω r is the electrical angular velocity of the SMPMSM, ω r =n P Ω, n P is the number of pole pairs, Ω is the measured mechanical angular velocity of the SMPMSM rotor;

[0116] According to the super-local model of the SMPMSM drive system and the defined integral sliding surface, the equivalent control part that maintains the system state on the integral sliding surface and the switching control part that quickly switches from any state of the system to the integral sliding surface are derived, which are expressed as:

[0117]

[0118]

[0119] Among them, T s is the control period; α s , α ω is the proportional coefficient selected according to the nominal parameters of SMPMSM. For the SMPMSM drive system, α s According to the nominal parameters set to 1 / L s ;L s is the stator synchronous inductance; F q estimated value of;

[0120] The control law obtained based on the super-local model of the SMPMSM drive system and the defined integral sliding mode surface is:

[0121]

[0122] in, F q estimated value.

[0123] The step (3) specifically refers to:

[0124] Set the dynamic weight factor β, and β∈[0,1], and then generate the inverter q-axis command voltage through composite control based on the sliding mode control law and the integral sliding mode control law, then:

[0125]

[0126] Where, α s , α ω is the proportional coefficient selected according to the nominal parameters of SMPMSM. For the SMPMSM drive system, α s According to the nominal parameters set to 1 / L s , L s is the stator synchronous inductance; n P is the pole pair number, ψ f is the magnetic flux of the rotor permanent magnet; J is the moment of inertia of the system; T s is the control period; c2 is the coefficient of the integral term in the integral sliding surface; c1 is the sliding surface parameter; i q represents the q-axis stator current obtained by coordinate transformation of the measured stator current; and F q 、F ω Estimated value of ω is the speed error, is the rotor electrical angular velocity command value of SMPMSM, ω r is the electrical angular velocity of the SMPMSM, ω r =n P Ω, Ω is the measured mechanical angular velocity of the SMPMSM rotor; Inverter q-axis command voltage generated for direct speed compound control; is the sliding mode control law, is the integral sliding mode control law.

[0127] The step (4) specifically refers to:

[0128] When the SMPMSM drive system is running, it must simultaneously meet the motor maximum current constraint and the inverter maximum output voltage constraint. To this end, first calculate the inverter q-axis command voltage that meets the maximum current constraint, and then:

[0129]

[0130] in, is the rotor electrical angular velocity command value of SMPMSM; I max is the maximum stator current allowed for safe operation of SMPMSM, sign(·) is the sign function; α s is the proportional coefficient selected according to the nominal parameters of SMPMSM. For the SMPMSM drive system, α s According to the nominal parameters set to 1 / L s ;L s is the stator synchronous inductance; T s is the control period; i q represents the q-axis stator current obtained by coordinate transformation of the measured stator current; u qlim To meet the inverter q-axis instruction voltage of the maximum current constraint; when the SMPMSM drive system adopts i d = 0 control, so that it runs in the maximum torque current ratio mode. According to the deadbeat predictive control and taking into account the control delay, the inverter d-axis command voltage is generated, and its expression is:

[0131]

[0132] The inverter command voltage that meets the current constraint is:

[0133]

[0134] Among them, min(·) is the minimum function; Inverter d-axis command voltage generated for direct speed compound control; The inverter q-axis command voltage that satisfies the current constraint condition;

[0135] Next, we further process the voltage constraint and first define θ n is the phase angle of the inverter command voltage, expressed as:

[0136]

[0137] In order to make full use of the DC bus voltage of the inverter, the inverter hexagonal voltage vector boundary equation L is obtained i where i = 1, 2, ..., 6:

[0138] L i :h dn u d +h qn u q +h cn =0 (12)

[0139] in, n∈N + :1-6; U dc is the DC bus voltage of the inverter; sector number n is based on θ n The sector is determined; h dn 、h qn are the boundary equation d-axis and q-axis voltage coefficients, h cn is the constant term of the boundary equation;

[0140] The cost function is defined as:

[0141]

[0142] in, They represent the optimal command voltages of the inverter d and q axes that satisfy the voltage and current constraints respectively;

[0143] Then, the Lagrangian function is constructed as:

[0144]

[0145] Where λ is the Lagrange multiplier;

[0146] according to and The optimal command voltage of the inverter that satisfies both current and voltage constraints is obtained by finding the extreme value and using the optimization method:

[0147]

[0148] The dynamic weight factor β is designed based on the speed error to realize the perception of the system operation status. Its expression is:

[0149]

[0150] Among them, e ω is the speed error, δ is the design parameter;

[0151] When the integral sliding mode control law accounts for 90% in the direct speed composite control law, the parameter δ is determined, and its calculation formula is:

[0152]

[0153] Wherein, Δ is the maximum speed fluctuation range determined according to the speed steady-state performance requirements.

[0154] The value of β determines the proportion of different control laws in the system control law. Therefore, the reasonable design of the dynamic weight factor based on the speed error can sense the system operation state and automatically match the system operation conditions. When the speed error gradually decreases, the designed β can automatically sense the system operation from dynamic to steady state, and β adaptively tends to 1 in [0,1]. When the system is subject to external disturbances or the speed reference value changes, causing the speed error to increase, β can sense the system operation state and automatically switch to dynamic operation, and (1-β) tends to 1 in [0,1]. Figure 1 shown.

[0155] like Figure 2 As shown, with the dq rotating coordinate system as the reference, the inverter command voltage that meets the current constraint condition is The angle between the α axis and the αβ stationary coordinate system is θ. The linear equation of the boundary line L1-L6 of the voltage vector hexagon can be constructed using the vector angle θ, as shown in Equation (12).

[0156] In order to ensure that the generated inverter command voltage satisfies the voltage and current constraints, the proposed composite control first predicts the stator current based on the system super-local model, and then determines whether the stator current exceeds the limit. If the stator current exceeds the limit, the inverter command voltage that satisfies the current constraint is obtained according to formula (10). Subsequently, it is determined whether the inverter command voltage is within the voltage hexagon range of the inverter. If it exceeds the voltage hexagon range, the optimal control voltage that satisfies both the voltage and current constraints is calculated. The constraint processing flow chart is shown in the following figure. Figure 3 shown.

[0157] like Figure 4 As shown, the constraint processing is the inverter reference voltage that meets the current and voltage constraints; to avoid the generation of algebraic loops, F d 、F q and F ω The input of the estimation module is delayed by one control cycle; S abc It is the switch driving signal of the three-phase inverter.

[0158] If c1 is too small, the current transition is smooth and the pulsation is small, but the speed dynamic response is slow, especially when the load is suddenly added, and the speed adjustment time is longer. If c1 is too large, the speed rise time is shortened, but the speed overshoot and oscillation increase, and the current pulsation is aggravated. Therefore, it is crucial to reasonably determine the value of c1 to improve the system control performance. The calculation formula of c1 can be expressed as:

[0159]

[0160] Among them, ω sc For bandwidth.

[0161] Under the experimental conditions of rated speed 500 rpm and rated load torque 10 N·m, the steady-state speed, current waveform and phase current THD under the proposed control law are as follows: Figure 5 As shown in the figure, the THD of the phase A current is 4.3385%, which makes the current smoother and improves the current control performance.

[0162] Speed ​​tracking capability and resistance to external load disturbances are important indicators for measuring the robustness of control systems. To verify the dynamic performance of the system, combined with the characteristics of the experimental platform, experiments were conducted under no-load conditions with speed instructions jumping from 0 to rated speed and suddenly unloading the rated load at rated speed. The proposed composite control results in a small speed overshoot. The reason for this is that the designed dynamic weight factor integrates automatic perception of the system's operating status, achieving adaptive adjustment of the priorities and proportions of the two control laws. Figure 6 As shown in the figure, the SMPMSM drive system with direct speed compound control has good control performance.

[0163] The speed and current dynamics when suddenly unloading at rated speed are as follows: Figure 7 As shown, from Figure 7 It can be seen that when the load is suddenly unloaded, the dynamic weight factor β decreases rapidly, realizing the adaptive adjustment of the priority and proportion of the two control laws, and improving the dynamic control performance of the system.

[0164] The present invention first establishes a super-local model of the SMPMSM drive system, designs the sliding mode surface and the integral sliding mode surface respectively, and derives the sliding mode control law and the integral sliding mode control law for direct speed control. Then, an innovative dynamic weighting factor is designed, and the sliding mode control law and the integral sliding mode control law are integrated by the designed dynamic weighting factor to generate a direct speed composite control law for the SMPMSM drive system. The obtained inverter command voltage is then corrected according to the current constraint and the voltage constraint to generate the optimal inverter command voltage that meets the voltage and current constraints. This improves the inverter DC bus voltage utilization rate while controlling the safe and stable operation of the SMPMSM drive system.

[0165] The present invention fully utilizes the DC bus voltage of the inverter to achieve safe and stable operation of the system under voltage and current constraints; it can achieve smooth transition between dynamic and steady states under different operating conditions of the SMPMSM drive system, and comprehensively improve the dynamic and steady-state control performance and robustness of the system.

[0166] In summary, this invention demonstrates that the designed composite control can achieve stable system operation, provides a basis for determining key control parameters, and constructs an SMPMSM drive system with direct speed composite control that satisfies voltage and current constraints. Systematic experimental studies confirm that the proposed control does not rely on accurate modeling of the SMPMSM drive system. Furthermore, through the innovative design of dynamic weighting factors, it can automatically sense the system's operating state, automatically determine the priority of sliding mode control and integral sliding mode control, and allocate the weights of different control laws. This achieves dynamic and steady-state adaptive control under different system operating conditions, while also enjoying the technical advantages of excellent dynamic and steady-state control performance and strong robustness.

Claims

1. A direct speed compound control method for an SMPMSM drive system based on a dynamic weight factor, characterized by: The method comprises the following steps in sequence: (1) Establish a super-local model of the SMPMSM drive system with speed control; (2) Based on the super-local model of the SMPMSM drive system, the sliding mode control law and integral sliding mode control law for its direct speed control are derived; (3) Design dynamic weight factors to achieve the fusion of sliding mode control law and integral sliding mode control law to generate the direct speed composite control law of SMPMSM drive system; (4) Perform voltage and current constraint processing; The step (3) specifically refers to: Set the dynamic weight factor β, and β∈[0,1], and then generate the inverter q-axis command voltage through composite control based on the sliding mode control law and the integral sliding mode control law, then: Where, α s , α ω is the proportional coefficient selected according to the nominal parameters of SMPMSM. For the SMPMSM drive system, α s According to the nominal parameters set to 1 / L s , L s is the stator synchronous inductance; n P is the pole pair number, ψ f is the magnetic flux of the rotor permanent magnet; J is the moment of inertia of the system; T s is the control period; c2 is the coefficient of the integral term in the integral sliding surface; c1 is the sliding surface parameter; i q represents the q-axis stator current obtained by coordinate transformation of the measured stator current; and F q 、F ω Estimated value of ω is the speed error, is the rotor electrical angular velocity command value of SMPMSM, ω r is the electrical angular velocity of the SMPMSM, ω r =n P Ω, Ω is the measured mechanical angular velocity of the SMPMSM rotor; Inverter q-axis command voltage generated for direct speed compound control; is the sliding mode control law, is the integral sliding mode control law; The dynamic weight factor β is designed based on the speed error to realize the perception of the system operation status. Its expression is: Among them, e ω is the speed error, δ is the design parameter; When the integral sliding mode control law accounts for 90% in the direct speed composite control law, the parameter δ is determined, and its calculation formula is: Wherein, Δ is the maximum speed fluctuation range determined according to the speed steady-state performance requirements; The step (1) specifically refers to: according to the dynamic equation of the SMPMSM drive system: Among them, ω r is the electrical angular velocity of the SMPMSM, ω r =n P Ω, n P is the number of pole pairs, Ω is the measured mechanical angular velocity of the SMPMSM rotor; ψ f is the magnetic flux of the rotor permanent magnet; R s is the three-phase stator winding resistance; L s is the stator synchronous inductance; Respectively represent the optimal command voltages of the inverter d-axis and q-axis that meet the voltage and current constraints; i d and i q They represent the d-axis and q-axis stator currents obtained after coordinate transformation of the actual stator current; J and B are the moment of inertia and viscosity coefficient of the system respectively; V d,par 、V q,par They represent the disturbance voltages of the stator d-axis and q-axis caused by the uncertainty of the motor parameters; V d,dead 、V q,dead They represent the disturbance voltages of the stator d-axis and q-axis generated by the nonlinearity of the inverter; d ω is the parameter uncertainty and unknown disturbance of the mechanical part of the SMPMSM drive system; T e is the electromagnetic torque of SMPMSM; T L is the load torque; For V d,par 、V q,par 、V d,dead 、V q,dead and d ω To make an estimate, use F d 、F q and F ω , representing the known and unknown parts of the system dynamic equations, and are written as: Based on this, the super-local model of the SMPMSM drive system is established, which is expressed as: Where, α s , α ω is the proportional coefficient selected according to the nominal parameters of SMPMSM. For the SMPMSM drive system, α s Set to 1 / L according to the motor nominal parameters s ; Use differential algebra to solve F d 、F q and F ω Make an estimate to Represents its estimated value, and its expression is: Where: They represent the optimal command voltages of the inverter d-axis and q-axis at time t that meet the voltage and current constraints respectively; t is the time variable; T F is the time window, which is set to 10 control cycles.

2. The direct speed compound control method of the SMPMSM drive system based on dynamic weight factors according to claim 1, characterized in that: In step (2), the sliding mode control law specifically refers to: The sliding surface is defined as: Among them, c1 is the sliding surface parameter; e ω is the speed error, is the rotor electrical angular velocity command value of SMPMSM; ω r is the electrical angular velocity of the SMPMSM, ω r =n P Ω, n P is the number of pole pairs, Ω is the measured mechanical angular velocity of the SMPMSM rotor; Based on the super-local model of the SMPMSM drive system, the differential of equation (1) is set to zero, and the equivalent control to maintain the system state on the sliding mode surface is obtained as follows: Among them, α ω for i q The proportionality coefficient, i q represents the q-axis stator current obtained after coordinate transformation of the actual stator current, ψ f is the magnetic flux of the rotor permanent magnet; J is the moment of inertia of the system; u eq1 It is the equivalent control part of sliding mode control; and F q 、F ω estimated value of; Secondly, in order to quickly switch the system from any state to the sliding surface, the switching control is selected as: Among them, T s is the control period; u sw1 is the switching control part of sliding mode control; According to the super-local model of the SMPMSM drive system and the defined sliding mode surface, the sliding mode control law is obtained as follows:

3. The direct speed compound control method of the SMPMSM drive system based on dynamic weight factors according to claim 1, characterized in that: In step (2), the integral sliding mode control law specifically refers to: The integral sliding surface is defined as: Among them, c2 is the coefficient of the integral term in the integral sliding surface; c1 is the sliding surface parameter; α ω is the coefficient selected according to the nominal parameters of SMPMSM; e ω is the speed error, is the rotor electrical angular velocity command value of SMPMSM; i q represents the q-axis stator current obtained after coordinate transformation of the measured stator current; ω r is the electrical angular velocity of the SMPMSM, ω r =n P Ω, n P is the number of pole pairs, Ω is the measured mechanical angular velocity of the SMPMSM rotor; According to the super-local model of the SMPMSM drive system and the defined integral sliding surface, the equivalent control part that maintains the system state on the integral sliding surface and the switching control part that quickly switches from any state of the system to the integral sliding surface are derived, which are expressed as: Among them, T s is the control period; α s , α ω is the proportional coefficient selected according to the nominal parameters of SMPMSM. For the SMPMSM drive system, α s According to the nominal parameters set to 1 / L s ;L s is the stator synchronous inductance; F q estimated value of; The control law obtained based on the super-local model of the SMPMSM drive system and the defined integral sliding mode surface is:

4. The direct speed compound control method of the SMPMSM drive system based on dynamic weight factors according to claim 1, characterized in that: The step (4) specifically refers to: When the SMPMSM drive system is running, it must simultaneously meet the motor maximum current constraint and the inverter maximum output voltage constraint. To this end, first calculate the inverter q-axis command voltage that meets the maximum current constraint, and then: in, is the rotor electrical angular velocity command value of SMPMSM; I max is the maximum stator current allowed for safe operation of SMPMSM, sign(·) is the sign function; α s is the proportional coefficient selected according to the nominal parameters of SMPMSM. For the SMPMSM drive system, α s According to the nominal parameters set to 1 / L s ;L s is the stator synchronous inductance; T s is the control period; i q represents the q-axis stator current obtained by coordinate transformation of the measured stator current; u qlim To meet the inverter q-axis instruction voltage of the maximum current constraint; when the SMPMSM drive system adopts i d = 0 control, so that it runs in the maximum torque current ratio mode. According to the deadbeat predictive control and taking into account the control delay, the inverter d-axis command voltage is generated, and its expression is: The inverter command voltage that meets the current constraint is: Among them, min(·) is the minimum function; Inverter d-axis command voltage generated for direct speed compound control; The inverter q-axis command voltage that satisfies the current constraint condition; Next, we further process the voltage constraint and first define θ n is the phase angle of the inverter command voltage, expressed as: In order to make full use of the DC bus voltage of the inverter, the inverter hexagonal voltage vector boundary equation L is obtained i where i = 1, 2, ..., 6: L i :h dn u d +h qn u q +h cn =0 (12) in, n∈N + :1-6; U dc is the DC bus voltage of the inverter; sector number n is based on θ n The sector is determined; h dn 、h qn are the voltage coefficients of the boundary equation d-axis and q-axis, respectively, and h cn is the constant term of the boundary equation; The cost function is defined as: in, They represent the optimal command voltages of the inverter d and q axes that satisfy the voltage and current constraints respectively; Then, the Lagrangian function is constructed as: Where λ is the Lagrange multiplier; according to and The optimal command voltage of the inverter that satisfies both current and voltage constraints is obtained by finding the extreme value and using the optimization method:

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