Surface-mounted permanent magnet synchronous motor sensorless high-performance sliding mode control method

Abstract

The invention discloses a surface-mounted permanent magnet synchronous motor sensorless high-performance sliding-mode control method, and the method comprises the steps: designing an improved self-adaptive terminal sliding-mode switching function for the problem of high-frequency buffeting of a sliding-mode observer caused by the fixed gain design of the switching function; and dynamically adjusting the gain of the switching function according to the sum of the stator current error and the integral thereof under the two-phase static coordinate system so as to suppress high-frequency buffeting and improve the estimation precision. In order to further improve the performance of the position sensorless control system, a reaching law in a sliding mode controller is optimized, a novel composite gain self-adaptive sliding mode reaching law is designed, the reaching law combines a self-adaptive gain term, a nonlinear exponential term and a linear term, the system state is accelerated to approach when being far away from a sliding mode surface, buffeting is reduced when the system state approaches, and the control precision of the position sensorless control system is improved. Therefore, the dynamic response speed and buffeting suppression capability of the control system are balanced.

Description

Technical Field

[0001] This invention belongs to the field of sensorless control of surface-mount permanent magnet synchronous motors, and specifically relates to a high-performance sliding mode control method for surface-mount permanent magnet synchronous motors without position sensors. Background Technology

[0002] In high-tech industries such as new energy vehicles and industrial robots, surface-mounted permanent magnet synchronous motors (SPMSMs) are increasingly widely used due to their miniaturized design, optimized operating efficiency, and diverse functional configurations. The core requirement for building a high-performance SPMSM control system is the accurate acquisition of rotor position and speed information. Traditional solutions rely on mechanical sensors such as optical encoders to collect data, but these devices have significant limitations: on the one hand, adding extra hardware increases system costs; on the other hand, environmental interference such as temperature fluctuations and mechanical vibrations can easily lead to measurement errors, restricting their applicability under complex operating conditions. To overcome these technical bottlenecks, sensorless control strategies for SPMSMs have become a research focus in the field of electromechanical drives in recent years.

[0003] When the SPMSM sensorless control system operates in the medium-to-high speed domain, it relies on the fundamental mathematical model of the SPMSM. Various control algorithms process this model to obtain various observers for acquiring physical quantities related to motor speed or rotor position. These observers primarily include flux linkage estimation, sliding mode observer, extended Kalman filter, model reference adaptive method, and Romberg observer method. Rotor position and speed information are then extracted from the physical quantities observed by these observers. The observed speed information is then fed back to the speed controller in the outer speed loop of the SPMSM sensorless control system. This speed controller primarily includes PI controllers, active disturbance rejection controllers, model predictive controllers, and sliding mode controllers, to achieve stable and efficient control of the SPMSM sensorless control system.

[0004] Compared to other sensorless control methods, the Sliding Mode Observer (SMO) method is widely used in various medium- and high-speed sensorless control systems due to its superior dynamic performance, low computational cost, and strong robustness to changes in motor parameters and external disturbances. However, traditional SMO, which uses a sign function to construct the error feedback of state variables, inevitably introduces chattering, causing the results to deviate from the actual values. Current technologies typically employ continuously switching functions, such as the sigmoid function, to replace the sign function and reduce system chattering, but this still does not meet the requirements of high-precision applications. Therefore, there is an urgent need to design novel sliding mode switching functions to further suppress system chattering and improve estimation accuracy.

[0005] In a sensorless PMSM control system based on a sliding mode observer, the speed controller in the outer speed loop is the core component determining the system's dynamic performance and steady-state accuracy. However, traditional PI controllers rely on the accuracy of the system model and are highly sensitive to system disturbances and parameter changes; active disturbance rejection controllers are sensitive to noise and suffer from inherent phase lag; and model predictive controllers suffer from high computational burden and high model dependence. Compared to other speed controllers, sliding mode control (SMC) has lower requirements for system model accuracy and strong robustness to internal and external disturbances, thus attracting extensive research and development. However, traditional SMC suffers from chattering due to high-frequency switching of system states within the sliding surface region. Existing improvements have achieved chattering suppression, increased convergence speed, and finite-time convergence, but these are not considered simultaneously. Therefore, there is an urgent need to design novel sliding mode controllers that can reduce system chattering while improving convergence speed. Summary of the Invention

[0006] To address the aforementioned problems in existing technologies, this invention proposes a sensorless high-performance sliding mode control method for surface-mounted permanent magnet synchronous motors. The method is rationally designed, overcomes the shortcomings of existing technologies, and exhibits excellent performance.

[0007] A sensorless high-performance sliding mode control method for surface-mounted permanent magnet synchronous motors includes the following steps:

[0008] Step 1: Design an improved adaptive terminal sliding mode switching function and construct an improved sliding mode observer;

[0009] Step 2: Perform Clark transformation on the three-phase voltage and three-phase current collected by the voltage sensor to obtain the actual voltage and current in the two-phase stationary coordinate system, and input them into the improved sliding mode observer to obtain the back electromotive force estimate.

[0010] Step 3: Filter the back EMF estimate using a low-pass filter to obtain the filtered back EMF estimate. Extract the rotor position information from the filtered back EMF estimate using the arctangent function method.

[0011] Step 4: Design a novel composite adaptive gain sliding mode reaching law, optimize the reaching law in the sliding mode controller, and obtain a novel sliding mode controller;

[0012] Step 5: Input the difference between the estimated speed and the target speed into the optimized sliding mode controller, and dynamically adjust the quadrature axis reference current to improve the speed control performance of the SPMSM system;

[0013] Step 6: The inner current loop tracks the direct-axis reference current and quadrature-axis reference current through the PI controller and outputs the voltage command in the rotating coordinate system. Then, the voltage value in the stationary coordinate system is obtained through inverse Park transformation. The PWM drive signal of the three-phase inverter is generated by the space vector pulse width modulation module SVPWM, which controls the inverter to output the equivalent voltage vector to drive the motor. Finally, the motor control system forms a closed-loop control circuit.

[0014] Further, step 1 includes the following sub-steps:

[0015] Step 1.1: Design an improved adaptive terminal sliding mode switching function F(s), with the following expression:

[0016]

[0017] In the formula, s is the sliding mode variable; t represents time; t0 is the initial integration time; k1 is the linear gain coefficient; λ is the exponential parameter of the nonlinear term; μ is the weighting coefficient of the exponential decay term; k n (t) represents the adaptive gain term; h represents the adaptive gain k. n The integral gain coefficient of (t); the damping coefficient of the adaptive gain η;

[0018] Step 1.2: Construct an improved sliding mode observer, specifically as follows:

[0019] To effectively eliminate steady-state errors caused by system uncertainties or external disturbances, the sliding mode variable s includes not only the error between the estimated current and the actual current, but also the integral of the current error, which is defined as:

[0020]

[0021] In the formula, α and β represent the α-axis and β-axis in a two-phase stationary coordinate system; This represents the error between the actual and estimated values ​​of the stator current in a two-phase stationary coordinate system. The stator current observations are in a two-phase stationary coordinate system; i α i β The actual value of the stator current in a two-phase stationary coordinate system; s α s β The variable represents the sliding mode variable in the two-phase stationary coordinate system; M is the integral term gain.

[0022] Combining formulas (1) and (2), an improved sliding mode observer is designed:

[0023]

[0024] In the formula, The stator current observation value is given in a two-phase stationary coordinate system; L SFor stator inductance; R s For stator resistance; u α u β k is the control input for the observer. α (t), k β (t) represents the adaptive gain term in the two-phase stationary coordinate system;

[0025] in,

[0026]

[0027] Furthermore, step 2 includes the following sub-steps:

[0028] Step 2.1: Perform Clark transformation on the three-phase voltage and three-phase current collected by the voltage sensor to obtain the voltage and current in a two-phase stationary coordinate system. The Clark transformation formula is as follows:

[0029]

[0030] The expressions for voltage and current when transformed from a three-phase stationary coordinate system to an α-β two-phase stationary coordinate system are:

[0031]

[0032] Among them, u A u B u C i A i B i C The three-phase voltages and currents are in a three-phase stationary coordinate system; u α u β i α i β The voltage and current are in the α-β two-phase stationary coordinate system;

[0033] Step 2.2: Input the voltage and current in the two-phase stationary coordinate system into the improved sliding mode observer to obtain the estimated value of the back electromotive force:

[0034]

[0035] Furthermore, step 3 includes the following sub-steps:

[0036] Step 3.1: Filter the back EMF estimate using a low-pass filter to obtain the filtered back EMF estimate, expressed as:

[0037]

[0038] In the formula, v α v β This is an estimate of the back electromotive force; ω is the filtered back electromotive force estimate; c It is the cutoff frequency of the low-pass filter;

[0039] Step 3.2: Extract the rotor electric angular velocity from the filtered back EMF estimate using the arctangent function method. and electric angle The expression is:

[0040]

[0041] In the formula, ψ f For SPMSM permanent magnets, the magnetic flux linkage is... The rotor position information before angle compensation is added;

[0042] The electric angular velocity of SPMSM in formula (9) The estimated rotational speed of SPMSM is obtained as follows:

[0043] Furthermore, step 4 includes the following sub-steps:

[0044] Step 4.1: Design a novel composite adaptive gain sliding mode reaching law, the expression of which is:

[0045]

[0046] In the formula, k3 is the gain coefficient of the first term, controlling the amplitude of the first term; k4 is the gain coefficient of the second term, controlling the amplitude of the second term; k5 is the gain coefficient of the linear term; λ1 is the gain adjustment parameter of the first term, adjusting the adaptive gain of the first term; ε is the gain change rate parameter of the first term, controlling the exponential function e in the first term. -ε|s| The decay rate is adjusted to regulate the degree to which the gain changes with |s|; γ is the first power parameter, which adjusts the first nonlinear intensity; σ is the second exponential coefficient, which scales the second exponential parameter σ(|sat(s)|-τ); τ is the threshold parameter in the second exponential parameter.

[0047] in,

[0048]

[0049] In the formula, Δ is the boundary layer thickness, which defines the boundary layer of the saturation function sat(s);

[0050] Step 4.2: Optimize the sliding mode controller based on the novel composite adaptive gain sliding mode reaching law, expressed as:

[0051]

[0052] Among them, i qThe q-axis current in a synchronously rotating coordinate system; D equals x1 is the SPMSM mechanical angular velocity reference value and

[0053] The difference between the estimated values; c is the gain of x1; w m The mechanical angular velocity of the SPMSM; This represents the disturbance value caused by the load torque and parameters, which is estimated by the extended disturbance observer.

[0054] The beneficial technical effects of this invention are as follows:

[0055] To address the significant high-frequency chattering phenomenon caused by the fixed-gain design of traditional sliding mode observers in sensorless SPM (Sliding Mode Controller) systems operating at medium to high speeds, this invention proposes a sliding mode observer based on an improved adaptive terminal sliding mode switching function. The gain of the new switching function is dynamically adjusted based on the sum of the stator current error and its integral in the two-phase stationary coordinate system to further suppress system chattering and improve estimation accuracy. Furthermore, to resolve the contradiction between the approach speed and chattering in traditional sliding mode reaching laws that ensure finite-time convergence, this invention proposes a novel composite-gain adaptive sliding mode reaching law. This law optimizes the sliding mode reaching law in the sliding mode controller by incorporating multiple nonlinear terms. This invention enables SPMSM to have a faster response speed and smaller overshoot, improves system robustness, and further reduces chattering. Attached Figure Description

[0056] Figure 1 This is a block diagram of a sensorless sliding mode control system for a surface-mounted permanent magnet synchronous motor based on an improved sliding mode observer, as described in this invention.

[0057] Figure 2 This is a block diagram of the improved sliding mode observer in this invention;

[0058] Figure 3 This is a block diagram of the rotor information extraction structure using the arctangent function in this invention;

[0059] Figure 4 This is a block diagram of the optimized sliding mode controller structure in this invention;

[0060] Figure 5 This is a comparison diagram of the method of the present invention and the existing method for suppressing speed chattering under no-load conditions using a sliding mode observer;

[0061] Figure 6 This is a comparison chart of the method of the present invention and existing methods for suppressing rotational chattering under sliding mode observer load;

[0062] Figure 7 This is a comparison diagram of the rotor speed at startup state of the sliding mode controller in the method of this invention and existing methods;

[0063] Figure 8 This is a comparison diagram of the anti-interference speed of the sliding mode controller between the method of this invention and existing methods;

[0064] Figure 9 This is a comparison diagram of steady-state speed chattering in the sliding mode controller of the present invention and existing methods; Detailed Implementation

[0065] The specific embodiments of the present invention will be further described below with reference to specific examples:

[0066] A sensorless, high-performance sliding mode control method for surface-mounted permanent magnet synchronous motors, such as... Figure 1 As shown, it includes the following steps:

[0067] Step 1: Design an improved adaptive terminal sliding mode switching function and construct an improved sliding mode observer, such as... Figure 2 As shown, it includes the following sub-steps:

[0068] Step 1.1: Design an improved adaptive terminal sliding mode switching function, the expression of which is:

[0069]

[0070] In the formula, s is the sliding mode variable; t represents time; t0 is the initial integration time; k1 is the linear gain coefficient; λ is the exponential parameter of the nonlinear term; μ is the weighting coefficient of the exponential decay term; k n (t) represents the adaptive gain term; h represents the adaptive gain k. n The integral gain coefficient of (t); the damping coefficient of the adaptive gain η.

[0071] When the sliding mode variable s moves away from the sliding mode surface, the nonlinear term k2(t)(|s|+μe) -|s| )|s| λ In sign(s), the higher-order term |s| λ+1 The dominant approach process ensures the system has a high and stable convergence rate. When the sliding mode variable s approaches the sliding surface, |s|≈0, |s| λ+1 Rapid decay, lower gain to suppress chattering; μe -|s| ≈μ, and |s| λ+1 Working together to avoid |s| λ+1 Too small a value leads to insufficient nonlinear gain; a balance must be struck between convergence speed and chattering suppression. Adaptive term k n (t) The gain of the nonlinear term is dynamically adjusted according to the sliding mode variable s.

[0072] Step 1.2: Construct a traditional sliding mode observer;

[0073] The mathematical expression for SPMSM in a two-phase stationary coordinate system is:

[0074]

[0075] Where α and β represent the α-axis and β-axis in the two-phase stationary coordinate system; R s For stator resistance; L s For stator inductance; ω e ω is the electric angular velocity; p is the differential operator; [u α u β ] T The stator voltage in a two-phase stationary coordinate system; [i α i β ] T The stator current is in a two-phase stationary coordinate system; [E] α E β ] T To extend the back electromotive force, it satisfies:

[0076]

[0077] Where, ψ f For the SPMSM permanent magnet flux linkage, the voltage equation in equation (2) is rewritten as the current state equation:

[0078]

[0079] The back electromotive force of each phase can be described as:

[0080]

[0081] As can be seen from formula (5), the back electromotive force contains rotor position and speed information.

[0082] In the design of traditional sliding mode observers, a sign function is used to reconstruct the mathematical model of the permanent magnet synchronous motor, and the expression is:

[0083]

[0084] in, The stator current observation value is given in a two-phase stationary coordinate system; u α u β i α i β For the control input of the observer; v α v β The back electromotive force is the observed value; k s Observer gain.

[0085] Subtracting formula (4) from formula (6) yields:

[0086]

[0087] in, And the sliding surface is designed as

[0088] when Reaching the sliding surface Afterward, the observer state will continuously switch on the sliding surface. According to the equivalence principle of sliding mode control, the control quantity at this time can be regarded as the equivalent control quantity, and we can obtain:

[0089]

[0090] From this, the estimated value of the back electromotive force [v] can be observed. α v β ] T .

[0091] Step 1.3: Construct an improved sliding mode observer, specifically as follows:

[0092] To effectively eliminate steady-state errors caused by system uncertainties or external disturbances, the sliding mode variable s includes not only the error between the estimated current and the actual current, but also the integral of the current error, which is defined as:

[0093]

[0094] In the formula, This represents the error between the actual and estimated values ​​of the stator current in a two-phase stationary coordinate system. The stator current observation value is given in a two-phase stationary coordinate system; i α i β The actual value of the stator current in a two-phase stationary coordinate system; s α s β The variable represents the sliding mode variable in the two-phase stationary coordinate system; M is the integral term gain.

[0095] Combining formulas (1) and (9), an improved sliding mode observer is designed:

[0096]

[0097] Where, k α (t), k β (t) represents the adaptive gain term in the two-phase stationary coordinate system.

[0098]

[0099] Step 2: Perform Clark transformation on the three-phase voltage and three-phase current collected by the voltage sensor to obtain the actual voltage and current in the two-phase stationary coordinate system. Input these values ​​into the improved sliding mode observer to obtain the back electromotive force estimate. This includes the following sub-steps:

[0100] Step 2.1: Perform Clark transformation on the three-phase voltage and three-phase current collected by the voltage sensor to obtain the voltage and current in a two-phase stationary coordinate system. The Clark transformation formula is as follows:

[0101]

[0102] The expressions for voltage and current when transformed from a three-phase stationary coordinate system to an α-β two-phase stationary coordinate system are:

[0103]

[0104] Among them, u A u B u C i A i B i C The three-phase voltages and currents are in a three-phase stationary coordinate system; u α u β i α i β Let α and β be the voltage and current in the stationary coordinate system of the two phases α and β.

[0105] Step 2.2: Input the voltage and current in the two-phase stationary coordinate system into the improved sliding mode observer to obtain the estimated value of the back electromotive force:

[0106]

[0107] Step 3: Filter the back EMF estimate using a low-pass filter to obtain a filtered back EMF estimate. Extract the rotor position information from the filtered back EMF estimate using the arctangent function method, including the following sub-steps:

[0108] Step 3.1: Since the actual control quantity is a discontinuous high-frequency switching signal, in order to extract the continuous extended back EMF estimate, it is necessary to filter the back EMF estimate using a low-pass filter to obtain the filtered back EMF estimate, expressed as:

[0109]

[0110] Among them, v α v β This is an estimate of the back electromotive force. ω is the filtered back electromotive force estimate. c It is the cutoff frequency of the low-pass filter;

[0111] Step 3.2: Extract the rotor electric angular velocity from the filtered back EMF estimate using the arctangent function method. and electrical angle like Figure 3 As shown;

[0112] To obtain rotor position information, it can be obtained using the arctangent function method, i.e.

[0113]

[0114] The back EMF estimate obtained through filtering in equation (15) will cause a phase delay, which will directly affect the accuracy of rotor position estimation. A smaller filter cutoff frequency will cause a larger phase delay. In practical applications, to solve this problem, an angle compensation is usually added to the rotor position calculated in equation (16) to compensate for the position angle estimation error caused by the delay effect of the low-pass filter, that is:

[0115]

[0116] For SPMSM, the expression for the speed estimate is:

[0117]

[0118] In the formula, ψ f For SPMSM permanent magnets, To incorporate rotor position information before angle compensation, the electric angular velocity of SPMSM in formula (18) is used. The estimated rotational speed of SPMSM can be obtained as follows:

[0119] Step 4: Design a novel composite adaptive gain sliding mode reaching law and optimize the reaching law in the sliding mode controller, such as... Figure 4 As shown, it includes the following sub-steps:

[0120] Step 4.1: Design a novel composite adaptive gain sliding mode reaching law, the expression of which is:

[0121]

[0122] in,

[0123]

[0124] In the formula, k3 is the gain coefficient of the first term, controlling the amplitude of the first term; k4 is the gain coefficient of the second term, controlling the amplitude of the second term; k5 is the gain coefficient of the linear term; λ1 is the gain adjustment parameter of the first term, adjusting the adaptive gain of the first term; ε is the gain change rate parameter of the first term, controlling the exponential function e in the first term. -ε|s|The decay rate is adjusted to regulate the degree of gain change with |s|; γ is the first power parameter, which adjusts the first nonlinear intensity; σ is the second exponential coefficient, which scales the second exponential parameter σ(|sat(s)|-τ); τ is the threshold parameter in the second exponential parameter; Δ is the boundary layer thickness, which defines the boundary layer of the saturation function sat(s).

[0125] When s is far from the sliding surface, |s| is relatively large, and the first gain is... In the second term, |sat(s)| = 1, and the exponent is σ(1-τ). If 0 < τ < 1, the exponent is positive. At this point, both the first and second terms have high gains, accelerating the convergence. When s approaches the sliding surface, |s| is smaller, and in the first term, e... -ε|s| ≈1, the gain is converted into the terminal sliding mode term k3|s| γ The second term gain k4|s| σ(|sat(s)|-τ) As s approaches the sliding surface, it gradually decreases. When s approaches the sliding surface, it is coordinated with the terminal sliding term for control, ensuring convergence in a finite time while suppressing high-frequency chattering.

[0126] Step 4.2: Optimize the sliding mode controller based on the novel composite adaptive gain sliding mode reaching law:

[0127] The equation of motion for PMSM is:

[0128]

[0129] Among them, i q B is the q-axis current in a synchronously rotating coordinate system; J is the coefficient of viscous friction; T is the moment of inertia; B is the coefficient of viscous friction; J is the moment of inertia; T is the q-axis current in a synchronously rotating coordinate system. L ω is the load torque. m For SPMSM mechanical angular velocity.

[0130] Considering the system parameters and torque variations, formula (21) can be expressed as:

[0131]

[0132] Where Δα1, Δα2, and Δα3 represent the parameter changes of the motor, and ξ represents the disturbance value caused by the load torque and parameters.

[0133] make:

[0134]

[0135] in, ω is the SPMSM reference mechanical angular velocity; c is the gain of x1.

[0136] Differentiating the sliding surface function and substituting equation (22) into it, we get:

[0137]

[0138] According to formulas (19) and (24), the novel sliding mode controller is constructed as follows:

[0139]

[0140] Where D is ξ represents the disturbance value caused by the load torque and parameters; it is estimated by the extended disturbance observer.

[0141] In practical motor control systems, PMSM parameter disturbances change very slowly, and the first derivative of the parameter disturbance can be approximated as 0. It can be approximated as the first derivative of the torque disturbance change. It can be estimated using an extended perturbation observer, and its mathematical expression is:

[0142]

[0143] in, These represent the estimated mechanical angular velocity and the estimated load parameter perturbation, respectively; ζ1, ζ2, and δ are positive real numbers; to achieve high gain, δ is taken to be very small. This observer can be used to estimate the perturbation term of the load torque. As a feedforward compensation for sliding mode control, it can also achieve Approaching ξ, Approaching ω m .

[0144] Step 5: Input the difference between the estimated speed and the target speed into the optimized sliding mode controller, and dynamically adjust the quadrature axis reference current (torque component) to improve the speed control performance of the SPMSM system;

[0145] Step 6: The inner current loop tracks the direct-axis reference current and quadrature-axis reference current through the PI controller and outputs the voltage command in the rotating coordinate system. Then, the voltage value in the stationary coordinate system is obtained through inverse Park transformation. The PWM drive signal of the three-phase inverter is generated by the space vector pulse width modulation module SVPWM, which controls the inverter to output the equivalent voltage vector to drive the motor. Finally, the motor control system forms a closed-loop control circuit.

[0146] The feasibility of this invention will be verified below using simulation waveforms;

[0147] Figure 5 , Figure 6 The figures show a comparison of speed chatter suppression simulations between the improved sliding mode observer designed in this invention, the traditional sliding mode observer, and the sigmoid function sliding mode observer, under no-load and 3 N·m load conditions, when the motor's given speed is 1000 r / min. Figure 5It can be seen that, under no-load conditions, the rotational speed jitter amplitude of the traditional sliding mode observer is around 20, the sigmoid function sliding mode observer is around 3, and the improved sliding mode observer designed in this invention has a rotational speed jitter amplitude of around 1. Therefore, the output speed of the SPMSM sensorless control system based on the improved sliding mode observer is more stable and has a lower jitter amplitude. Similarly, from Figure 6 It can be seen that under load conditions, the speed chattering amplitude based on the improved sliding mode observer is lower, and the control accuracy of the SPMSM sensorless control system is higher. Figure 7 The figure shows a comparison of the rotor starting speed under no-load conditions between the novel sliding mode observer and the PI controller, the traditional sliding mode controller (SMC), and the optimal sliding mode controller, when the given motor speed is 1000 r / min. Figure 7 It can be seen that the optimal sliding mode controller has a smaller speed overshoot compared to the PI controller and the traditional sliding mode controller, but its time to reach steady state is much longer. The novel sliding mode controller designed in this invention, compared to the other three speed controllers, significantly reduces speed overshoot while shortening the time to reach steady state, thus improving the dynamic response performance of the SPMSM sensorless control system. Figure 8 It can be seen that during the stable operation of SPMSM, when the control system suddenly experiences external disturbances, the novel sliding mode controller designed in this invention exhibits the strongest anti-interference capability. Figure 9 As can be seen, during the stable operation of the SPMSM sensorless control system, compared with the other three sliding mode controllers, the novel sliding mode controller designed in this invention minimizes the speed fluctuation amplitude of the system. In summary, the simulation comparison charts show that the improved sliding mode observer designed in this invention makes the output speed of the SPMSM sensorless control system more stable and reduces the fluctuation amplitude; based on this, the introduction of the novel sliding mode controller designed in this invention further suppresses speed fluctuation while improving the dynamic performance of the SPMSM sensorless control system.

[0148] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A sensorless high-performance sliding mode control method for a surface-mounted permanent magnet synchronous motor, characterized in that, Includes the following steps: Step 1: Design an improved adaptive terminal sliding mode switching function and construct an improved sliding mode observer; Step 2: Perform Clark transformation on the three-phase voltage and three-phase current collected by the voltage sensor to obtain the actual voltage and current in the two-phase stationary coordinate system, and input them into the improved sliding mode observer to obtain the back electromotive force estimate. Step 3: Filter the back EMF estimate using a low-pass filter to obtain the filtered back EMF estimate. Extract the rotor position information from the filtered back EMF estimate using the arctangent function method. Step 4: Design a novel composite adaptive gain sliding mode reaching law, optimize the reaching law in the sliding mode controller, and obtain a novel sliding mode controller; Step 5: Input the difference between the estimated speed and the target speed into the optimized sliding mode controller, and dynamically adjust the quadrature axis reference current to improve the speed control performance of the SPMSM system; Step 6: The inner current loop tracks the direct-axis reference current and quadrature-axis reference current through the PI controller and outputs the voltage command in the rotating coordinate system. Then, the voltage value in the stationary coordinate system is obtained through inverse Park transformation. The PWM drive signal of the three-phase inverter is generated by the space vector pulse width modulation module SVPWM, which controls the inverter to output the equivalent voltage vector to drive the motor. Finally, the motor control system forms a closed-loop control circuit.

2. The sensorless high-performance sliding mode control method for a surface-mounted permanent magnet synchronous motor according to claim 1, characterized in that, Step 1 includes the following sub-steps: Step 1.1: Design an improved adaptive terminal sliding mode switching function F(s), with the following expression: In the formula, s is the sliding mode variable; t represents time; t0 is the initial integration time; k1 is the linear gain coefficient; λ is the exponential parameter of the nonlinear term; μ is the weighting coefficient of the exponential decay term; k n (t) represents the adaptive gain term; h represents the adaptive gain k. n The integral gain coefficient of (t); the damping coefficient of the adaptive gain η; Step 1.2: Construct an improved sliding mode observer, specifically as follows: To effectively eliminate steady-state errors caused by system uncertainties or external disturbances, the sliding mode variable s includes not only the error between the estimated current and the actual current, but also the integral of the current error, which is defined as: In the formula, α and β represent the α-axis and β-axis in a two-phase stationary coordinate system; This represents the error between the actual and estimated values ​​of the stator current in a two-phase stationary coordinate system. These are the stator current observations in a two-phase stationary coordinate system. i α i β This represents the actual value of the stator current in a two-phase stationary coordinate system. s α s β Represents the sliding mode variable in a two-phase stationary coordinate system; M is the integral term gain; Combining formulas (1) and (2), an improved sliding mode observer is designed: In the formula, The stator current observation value is given in a two-phase stationary coordinate system; L S For stator inductance; R s For stator resistance; u α u β k is the control input for the observer. α (t), k β (t) represents the adaptive gain term in the two-phase stationary coordinate system; in, 3. The sensorless high-performance sliding mode control method for a surface-mounted permanent magnet synchronous motor according to claim 2, characterized in that, Step 2 includes the following sub-steps: Step 2.1: Perform Clark transformation on the three-phase voltage and three-phase current collected by the voltage sensor to obtain the voltage and current in a two-phase stationary coordinate system. The Clark transformation formula is as follows: The expressions for voltage and current when transformed from a three-phase stationary coordinate system to an α-β two-phase stationary coordinate system are: Among them, u A u B u C i A i B i C The three-phase voltages and currents are in a three-phase stationary coordinate system; u α u β i α i β The voltage and current are in the α-β two-phase stationary coordinate system; Step 2.2: Input the voltage and current in the two-phase stationary coordinate system into the improved sliding mode observer to obtain the estimated value of the back electromotive force:

4. The sensorless high-performance sliding mode control method for a surface-mounted permanent magnet synchronous motor according to claim 3, characterized in that, Step 3 includes the following sub-steps: Step 3.1: Filter the back EMF estimate using a low-pass filter to obtain the filtered back EMF estimate, expressed as: In the formula, v α v β This is an estimate of the back electromotive force; ω is the filtered back electromotive force estimate; c It is the cutoff frequency of the low-pass filter; Step 3.2: Extract the rotor electric angular velocity from the filtered back EMF estimate using the arctangent function method. and electrical angle The expression is: In the formula, ψ f For SPMSM permanent magnets, the magnetic flux linkage is... The rotor position information before angle compensation is added; The electric angular velocity of SPMSM in formula (9) The estimated rotational speed of SPMSM is obtained as follows:

5. The sensorless high-performance sliding mode control method for a surface-mounted permanent magnet synchronous motor according to claim 4, characterized in that, Step 4 includes the following sub-steps: Step 4.1: Design a novel composite adaptive gain sliding mode reaching law, the expression of which is: In the formula, k3 is the gain coefficient of the first term, controlling the amplitude of the first term; k4 is the gain coefficient of the second term, controlling the amplitude of the second term; k5 is the gain coefficient of the linear term; λ1 is the gain adjustment parameter of the first term, adjusting the adaptive gain of the first term; ε is the gain change rate parameter of the first term, controlling the exponential function e in the first term. -ε|s| The decay rate is adjusted to regulate the degree to which the gain changes with |s|; γ is the first power parameter, which adjusts the intensity of the first nonlinear term; σ is the coefficient of the second exponential term, and the scaling factor of the second exponential parameter term is σ(|sat(s)|-τ); τ is the threshold parameter in the second exponential parameter term; in, In the formula, Δ is the boundary layer thickness, which defines the boundary layer of the saturation function sat(s); Step 4.2: Optimize the sliding mode controller based on the novel composite adaptive gain sliding mode reaching law, expressed as: Among them, i q The q-axis current in a synchronously rotating coordinate system; D equals x1 is the difference between the reference and estimated values ​​of the SPMSM mechanical angular velocity; c is the gain of x1; w m The mechanical angular velocity of the SPMSM; This represents the disturbance value caused by the load torque and parameters, which is estimated by the extended disturbance observer.

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