A nonlinear optimal control method, system and medium for a permanent magnet synchronous motor

Abstract

The present invention discloses a nonlinear optimal control method, system, and medium for a permanent magnet synchronous motor, belonging to the technical field of built-in permanent magnet synchronous motors. The method is used to solve the technical problems of existing built-in permanent magnet synchronous motors, such as certain errors in motor control speed and accuracy, poor performance during actual use, inaccurate processing of control parameters, and difficulty in nonlinear optimization. The method includes: establishing a linear dynamic control model based on an NOx control system; linearly deriving the flux linkage vector of the rotor permanent magnet in the built-in permanent magnet synchronous motor system to obtain a linearized function; performing LQR control on the NOx control system to obtain an LQR control function; performing nonlinear optimal control on the new LQR control function under the optimal control criterion to obtain a nonlinear optimal control function of the NOx control system; and combining the LQR control function and the nonlinear optimal control function to obtain an optimized NOx control system.

Description

Technical Field

[0001] The present application relates to the field of built-in permanent magnet synchronous motors, and in particular to a nonlinear optimal control method, system, and medium for a permanent magnet synchronous motor. Background Art

[0002] Interior permanent magnet synchronous motor (IPMSM) has the advantages of high power density, high efficiency, large torque-to-current ratio and compact structure. In addition, it has excellent low-speed performance, weak magnetic high-speed control and a wide speed regulation range. Therefore, it is widely used in electric vehicles, industrial robots, CNC machine tools and other fields.

[0003] Field-oriented control (FOC) is a standard for IPMSM drives manufactured and used in industry. This method uses the motor's magnetic field direction and rotor position as control variables, and the motor's stator current as a regulating variable to control the motor's torque and speed. As industrial requirements increase, the demands on motor control are also rising.

[0004] FOC achieves optimal control of the motor by minimizing the electrical loss of the motor, that is, maximum efficiency control, or by minimizing only the copper loss of the motor. The concept of static nonlinearity is introduced in the d-axis or q-axis control, and the parameters of the PI controller are adjusted iteratively through experiments or simulations. In this way, this method can provide effective control within the speed range of the motor. However, this method can only provide solutions for specific motors and cannot solve the problems of minimizing nonlinearity and inaccurate parameters. If the speed tracking signal is forced, the control will be slowed down. The motor control speed and accuracy of existing built-in permanent magnet synchronous motors have certain errors, and the effect is poor during actual use. At the same time, there are also problems such as inaccurate processing of control parameters and difficulty in nonlinear optimization.

[0005] With the development of control methods, motor control can also apply many algorithmically complex control strategies. Currently, there is a lot of research on sliding mode control strategies for AC servo systems in motor control, and some results have been achieved. However, many problems remain unresolved. The sliding surface of ordinary sliding mode control is linear. Linear sliding surfaces can ensure that the tracking error converges to zero asymptotically, but they cannot ensure that the state error converges to zero within a finite time.

[0006] Furthermore, if nonlinear functions are introduced into the sliding surface design of sliding mode control, a nonlinear sliding surface can be constructed, where the state tracking error on the sliding surface converges to zero within a finite time. This method offers higher steady-state tracking accuracy and better dynamic performance than traditional sliding mode methods, and is gaining increasing attention both domestically and internationally. However, nonlinear sliding surfaces still present the problem of chattering. Summary of the Invention

[0007] The embodiments of the present application provide a nonlinear optimal control method, system and medium for a permanent magnet synchronous motor, which are used to solve the following technical problems: the motor control speed and accuracy of the existing built-in permanent magnet synchronous motor have certain errors, and the effect is poor during actual use. At the same time, there are also problems such as inaccurate processing of control parameters and difficulty in nonlinear optimization.

[0008] The embodiments of this application adopt the following technical solutions:

[0009] On the one hand, an embodiment of the present application provides a nonlinear optimal control method for a permanent magnet synchronous motor, including: establishing a linear dynamic control model based on the NO control system based on the vector parameters of the NO control system in the built-in permanent magnet synchronous motor system; linearly deriving the rotor permanent magnet flux vector in the built-in permanent magnet synchronous motor system according to the linear dynamic control model to obtain a linearization function; performing LQR control on the NO control system based on the cost function according to the linearization function and the dynamic error time integral function to obtain an LQR control function for linear control; performing maximum gain coefficient control on the LQR control function based on the time constant to obtain a new LQR control function; performing nonlinear optimal control on the new LQR control function under the optimal control criterion based on preset optimal control parameters to obtain a nonlinear optimal control function of the NO control system; combining the LQR control function for linear control and the nonlinear optimal control function for nonlinear control to obtain an optimized NO control system for the built-in permanent magnet synchronous motor system.

[0010] The present embodiment replaces conventional PI controllers for the d-axis and q-axis current loops and speed loops with nonlinear optimal controllers. Each nonlinear optimal controller has both stable control and parameter optimization capabilities. It can provide stable control for a specific operating point and incorporates a nonlinear control component designed based on optimality criteria. When the actual operating point differs from the designed operating point, the nonlinear control component can optimize the motor's dynamic characteristics, thereby improving the robustness and accuracy of motor control.

[0011] In a feasible embodiment, based on the vector parameters of the NO control system in the built-in permanent magnet synchronous motor system, a linear dynamic control model based on the NO control system is established, specifically including: based on the mechanical speed input by the built-in permanent magnet synchronous motor system, obtaining the vector parameters of the NO control system; wherein the vector parameters include at least: mechanical angle, electromagnetic torque, d-axis stator current and q-axis stator current, d-axis stator voltage and q-axis stator voltage, circuit voltage and phase current; according to the vector parameters, and through the system state variables, control output and disturbance vector, the linear dynamic control model is established.

[0012] In a feasible embodiment, according to the linear dynamic control model, the rotor permanent magnet flux vector in the built-in permanent magnet synchronous motor system is linearly derived to obtain a linearization function, which specifically includes: based on the linear dynamic control model, the mechanical parameters, d-axis stator current and q-axis stator current in the built-in permanent magnet synchronous motor system are dynamically controlled, wherein the dynamic control includes: deviation dynamic control and integral dynamic control; through the linear dynamic control model, the rotor permanent magnet flux vector is aligned with the reference system to obtain the linear control parameters in the built-in permanent magnet synchronous motor system; wherein the linear control parameters include at least: d-axis time constant, q-axis time constant, mechanical motion time constant, stator resistance, d-axis inductance, q-axis inductance, motor moment of inertia, friction torque gain, pole pair permanent magnet flux, static friction torque and load torque; based on the linear control parameters, the linearization function based on the linear dynamic control model is generated.

[0013] In a feasible embodiment, before performing LQR control based on a cost function on the NOx control system according to the linearization function and the dynamic error time integral function to obtain the LQR control function for linear control, the method further includes: performing time integral calculations on the d-axis stator current and the q-axis stator current and the d-axis stator current reference value and the q-axis stator current reference value in the linearization function regarding the dynamic error to determine a current dynamic error function; performing time integral calculations on the mechanical speed and the mechanical speed reference value in the linearization function regarding the dynamic error to determine a mechanical speed dynamic error function; merging the current dynamic error function and the mechanical speed dynamic error function to obtain the dynamic error time integral function; performing elimination processing on the steady-state second-order minimum value on the linearization function and the dynamic error time integral function according to preset steady-state values and deviation values, and multiplying the time constant by the small variable deviation to obtain multiple model variables based on the NOx control system.

[0014] In a feasible embodiment, according to the linearization function and the dynamic error time integral function, the NO control system is subjected to LQR control based on the cost function to obtain an LQR control function for linear control, specifically including: performing consistency calculation on the linear dynamic control model under relevant variable values and reference values through the multiple model variables to obtain an optimized linear dynamic control model; generating an LQR cost function based on the optimized linear dynamic control model; and performing LQR control on the NO control system according to a preset Bellman criterion function and the LQR cost function to obtain an LQR control function for linear control of the NO control system.

[0015] In a feasible implementation, the LQR control function is subjected to maximum value control of the gain coefficient under the time constant to obtain a new LQR control function, specifically comprising: according to the maximum value of the first gain coefficient and the maximum value of the second gain coefficient in the LQR control function, replacing the time constant in the LQR control function with the minimum value to obtain new control parameter values based on each variable in the LQR control function; and optimizing the LQR control function through the new control parameter values to obtain the new LQR control function.

[0016] In a feasible embodiment, based on preset optimal control parameters, the new LQR control function is subjected to nonlinear optimal control under the optimal control criterion to obtain the nonlinear optimal control function of the NO control system, specifically including: according to the optimal control parameters and the optimal control gain under steady state and deviation, the new LQR control function is subjected to nonlinear optimal control under the optimal control criterion to obtain an optimized LQR control function; the optimized LQR control is input into the linear dynamic control model to obtain a new NO control system; through the optimal control criterion, the control variables and gain parameters in the new NO control system are subjected to cost minimization processing, and based on the positive weighted constant, the nonlinear optimal control of the Lyapunov function and the optimized LQR control function are jointly processed to obtain the nonlinear optimal control function for nonlinear control of the NO control system.

[0017] In a feasible implementation, the LQR control function for linear control and the nonlinear optimal control function for nonlinear control are combined to obtain an optimized NO control system for the built-in permanent magnet synchronous motor system, specifically including: jointly optimizing the NO control system through the LQR control function and the nonlinear optimal control function to generate the optimized NO control system based on the built-in permanent magnet synchronous motor system.

[0018] In a second aspect, an embodiment of the present application further provides a nonlinear optimal control system for a permanent magnet synchronous motor, the system comprising: a linear component design module for establishing a linear dynamic control model based on the NO control system based on the vector parameters of the NO control system in the built-in permanent magnet synchronous motor system; according to the linear dynamic control model, linearly deriving the rotor permanent magnet flux vector in the built-in permanent magnet synchronous motor system to obtain a linearization function; according to the linearization function and the dynamic error time integral function, performing LQR control on the NO control system based on the cost function to obtain an LQR control function for linear control; a nonlinear component design module for performing maximum gain coefficient control on the LQR control function based on the time constant to obtain a new LQR control function; based on preset optimal control parameters, performing nonlinear optimal control on the new LQR control function under the optimal control criterion to obtain the nonlinear optimal control function of the NO control system; a combined control module for combining the LQR control function for linear control and the nonlinear optimal control function for nonlinear control to obtain an optimized NO control system for the built-in permanent magnet synchronous motor system.

[0019] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium, characterized in that the storage medium is a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores at least one program, each of which includes instructions, and when the instructions are executed by the terminal, the terminal executes a nonlinear optimal control method for a permanent magnet synchronous motor described in any of the above embodiments.

[0020] The present application provides a nonlinear optimal control method, system, and medium for a permanent magnet synchronous motor. Compared with the prior art, the embodiments of the present application have the following beneficial technical effects:

[0021] In the embodiment of the present application, a NO controller for controlling the d-axis and q-axis current and speed is optimized in an IPMSM control system, and the NO controller is a nonlinear optimal controller. The nonlinear optimal controller includes a linear part designed as LQR control and an optimized nonlinear control part. The linear part is designed for a specific operating point, while the nonlinear part is designed using Krasovskiy's optimality criterion. It can simulate the automatic adjustment of the LQR gain according to different operating conditions. This IPMSM control method has extremely strong robustness. Compared with traditional methods, it can greatly reduce speed overshoot and torque oscillation, and will not prolong the transition time, with excellent control performance. It can: (1) design the nonlinear optimal control link of the controller using the optimization criterion, improve control performance, and shorten control time. (2) The design of the LQR control point makes the control of the motor at a certain operating point extremely robust. (3) The NO controller can scientifically introduce the concept of nonlinearity and optimize the inaccuracy of parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0023] Figure 1 A flow chart of a nonlinear optimal control method for a permanent magnet synchronous motor provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of nonlinear optimal control of a built-in permanent magnet synchronous motor system provided in an embodiment of the present application;

[0025] Figure 3 This is a schematic diagram of an optimized control system for a NO control system provided in an embodiment of the present application;

[0026] Figure 4 A schematic structural diagram of a nonlinear optimal control device for a permanent magnet synchronous motor provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the technical solutions in this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0028] It should be noted that Figure 2 A schematic diagram of nonlinear optimal control of a built-in permanent magnet synchronous motor system provided in an embodiment of the present application is shown in FIG. Figure 2 The nonlinear optimal control scheme of the IPMSM system is shown in Figure 2, where ω m is the mechanical speed, θ m is the mechanical angle measured by sensor S, T e is the electromagnetic torque, i d ,i q is the stator current of the d-axis and q-axis, i a ,i b is the current in phases a and b, v d ,v q are the stator voltages of the d-axis and q-axis, V dc is the DC circuit voltage, and all * indicates the corresponding reference value.

[0029] Based on the control loop and parameters in the control model described above, the optimized NOx controller design steps are as follows: Step 1: First, create a control model based on motor parameters such as current and speed, and then determine the LQR control equation for linear control. Step 2: Based on the LQR control equation obtained in the previous step, introduce nonlinear optimization control design to obtain the nonlinear optimal control equation for the motor, laying the foundation for the next step of building the NOx controller model. Step 3: In the simulation software MATLAB, build the NOx controller model and add the motor control parameters. Simulate the torque and speed results and compare them with a conventional controller.

[0030] The embodiment of the present application provides a nonlinear optimal control method for a permanent magnet synchronous motor, such as Figure 1 As shown, the nonlinear optimal control method of the permanent magnet synchronous motor specifically includes steps S101-S106:

[0031] S101. Based on the vector parameters of the NOx control system in the built-in permanent magnet synchronous motor system, a linear dynamic control model based on the NOx control system is established.

[0032] Specifically, the vector parameters of the NO control system are first obtained based on the mechanical speed input from the built-in permanent magnet synchronous motor system. The vector parameters include at least: mechanical angle, electromagnetic torque, d-axis stator current and q-axis stator current, d-axis stator voltage and q-axis stator voltage, circuit voltage, and phase current.

[0033] Furthermore, a linear dynamic control model is established based on the vector parameters and through the system state variables, control output and disturbance vector.

[0034] In one embodiment, Figure 2 As shown, the linear dynamic control model is first constructed: the linear part assumes that the motor is in a stable working state, that is, a specific working point, so the various parameters of the control system do not change dynamically. The control subsystem model is established as follows: Where X represents the vector of subsystem state variables, u is the corresponding control input, and F is the disturbance vector.

[0035] S102. According to the linear dynamic control model, linearly derive the flux linkage vector of the rotor permanent magnet in the built-in permanent magnet synchronous motor system to obtain a linearization function.

[0036] Specifically, based on a linear dynamic control model, mechanical parameters, d-axis stator current, and q-axis stator current in the built-in permanent magnet synchronous motor system are dynamically controlled, wherein the dynamic control includes deviation dynamic control and integral dynamic control.

[0037] Furthermore, the linear dynamic control model is used to align the rotor permanent magnet flux vector with the reference frame, thereby obtaining the linear control parameters of the internal permanent magnet synchronous motor system. These linear control parameters include at least the d-axis time constant, the q-axis time constant, the mechanical motion time constant, the stator resistance, the d-axis inductance, the q-axis inductance, the motor moment of inertia, the friction torque gain, the pole-pair number of permanent magnet flux, the static friction torque, and the load torque.

[0038] Furthermore, based on the linear control parameters, a linearization function based on the linear dynamic control model is generated.

[0039] In one embodiment, each of the above quadratic linear differential equations can describe the i d and i q and ω m Therefore, in the reference frame aligned with the rotor permanent magnet flux vector, the linearization function is derived using the dynamic model of the permanent magnet synchronous motor:

[0040]

[0041] Among them, T d =L d / R s , T q =L q / R s , T m = J / B are d-axis, q-axis and mechanical speed ω m The corresponding time constant, R s is the stator resistance, L d 、L q is the inductance of the d-axis and q-axis, J is the moment of inertia of the motor, and Bf is the friction torque gain, n p is the pole pair number, λ af is the permanent magnet flux, T f is the static friction torque, T l Load torque.

[0042] S103 , performing LQR control on the NO control system based on the cost function according to the linearization function and the dynamic error time integral function, to obtain an LQR control function for linear control.

[0043] Specifically, a time integral calculation of the dynamic error is performed on the d-axis stator current and the q-axis stator current as well as the d-axis stator current reference value and the q-axis stator current reference value in the linearization function to determine a current dynamic error function.

[0044] Furthermore, a time integral calculation of the dynamic error is performed on the mechanical speed and the mechanical speed reference value in the linearization function to determine a mechanical speed dynamic error function.

[0045] Furthermore, the current dynamic error function and the mechanical speed dynamic error function are combined to obtain a dynamic error time integral function.

[0046] Furthermore, according to the preset steady-state value and deviation value, the linearization function and the dynamic error time integral function are processed to eliminate the steady-state second-order minimum value, and the time constant is multiplied by the small variable deviation to obtain multiple model variables based on the NO control system.

[0047] In one embodiment, modeling is performed for deviations from steady-state values, so the controller design assumes that the parameters are constant, but they may vary for different specific operating points. Similarly, the motor moment of inertia J is constant for different operations, so the time constant in function (2) varies with the operation.

[0048] First, the time integration of the dynamic error is defined as the current dynamic error function i ind，q and mechanical speed w in Dynamic error function:

[0049]

[0050] Therefore, the steady-state coordinates are considered to be the sum of the steady-state values and the corresponding small deviations, so

[0051] i d，q =i d，q01 +δi d，q ,ω m =ω m01 +δω m ,iind，q =i ind，q01 +δi ind，q ,ω in =ω in01 +δω in , where “01” represents the corresponding steady-state value and δ represents the deviation.

[0052] Therefore, the following definitions can be obtained for interference and parameters:

[0053] v d,q =v d,q01 +δv d,q ,L d,q =L d,q01 +δL d,q ,as well as

[0054] T e =T e01 +δT e ,T f =T f01 +δT f

[0055] T l =T lo1 +δT l .

[0056] Substituting the above definitions into the linear equation (2) and the error equation (3), subtracting the steady-state equation, and ignoring the second-order minimum, we obtain the second-order linearized equation (Equation (1)). Since the time constant is multiplied by the deviation of the small variable, the deviation of the time constant caused by the parameter change is ignored in the final linearized function.

[0057] in this way, Figure 3 This is a schematic diagram of an optimized control of a NO control system provided in an embodiment of the present application, such as Figure 3 As shown, multiple model variables can be obtained:

[0058] and as well as,

[0059]

[0060] as well as

[0061]

[0062] In steady state, the value of the variable agrees with the reference value: so Similarly, for the mechanical speed controller: so

[0063] Furthermore, through multiple model variables, the linear dynamic control model is calculated for consistency between the relevant variable values and the reference values to obtain an optimized linear dynamic control model.

[0064] Furthermore, based on the optimized linear dynamic control model, an LQR cost function is generated.

[0065] Furthermore, LQR control is performed on the NOx control system according to a preset Bellman criterion function and an LQR cost function to obtain an LQR control function for linearly controlling the NOx control system.

[0066] In one embodiment, the cost function of LQR is: Where α1, α2 and c are all positive weight constants. According to the closed form Bellman standard equation and assuming that the Lyapunov equation is quadratic, the Riccati equation is solved to obtain the following LQR control function: u = -(b1 / c)k 12 x1-(b1 / c)k 22 x2=-k1x1-k2x2(7)where:

[0067]

[0068] At this point, the linear control link LQR linear control part of the NO controller is completed.

[0069] As a feasible implementation method, LQR control can be used for specific working points, while the working state of the motor may be variable. Therefore, when the motor works at a non-specific working point, it is necessary to adjust the gain parameters k1 and k2. This frequent adjustment is not conducive to control. Therefore, in order to solve this problem, a nonlinear optimal control method is introduced based on LQR. The nonlinear optimal control link is based on the optimal design of LQR control. The two are combined to form an optimized NO controller for the motor.

[0070] S104 , performing maximum value control of the gain coefficient based on the time constant on the LQR control function to obtain a new LQR control function.

[0071] Specifically, according to the maximum value of the first gain coefficient and the maximum value of the second gain coefficient in the LQR control function, the time constant in the LQR control function is replaced by the minimum value to obtain new control parameter values based on various variables in the LQR control function.

[0072] Furthermore, the LQR control function is optimized and calculated using the new control parameter value to obtain a new LQR control function.

[0073] In one embodiment, Figure 3The motor shown is operating at an unspecified point. To optimize the control state, assume that the gain coefficients k1 and k2 in (7) are the maximum values achievable for the time constant, so: |a1| = |a 1min |,|b1|=|b 1min |, when the time constant is minimized, a1 is replaced by a2, and b1 is replaced by b2. |a2|=|a 2max |,|b2|=|b 2max |, so the parameter change of formula (5) is, that is, the new control parameter value based on each variable in the LQR control function:

[0074]

[0075] Then we get the new control formula, which is the new LQR control function:

[0076] u'=-(b2 / c)k' 12 x1-(b2 / c)k' 22 x2=-k'1x1-k'2x2 (9)

[0077] S105 , based on the preset optimal control parameters, perform nonlinear optimal control on the new LQR control function under the optimal control criterion to obtain the nonlinear optimal control function of the NO control system.

[0078] Specifically, according to the optimal control parameters and the optimal control gain based on the steady state and the deviation, the new LQR control function is subjected to nonlinear optimal control under the optimal control criterion to obtain the optimized LQR control function.

[0079] Furthermore, the optimized LQR control is input into the linear dynamic control model to obtain a new NO control system.

[0080] Furthermore, the cost of the control variables and gain parameters in the new NO control system is minimized through the optimal control criterion, and based on the positive weighted constant, the nonlinear optimal control of the Lyapunov function and the optimized LQR control function are jointly processed to obtain the nonlinear optimal control function for the nonlinear control of the NO control system.

[0081] In one embodiment, Figure 3 As shown, the optimal control parameters are introduced when:

[0082] T d,q,m0min <T d,q,m0 <T d,q,m0max When the gain is k i and k' i Therefore, within the allowable range, for any time constant, the optimized LQR control function can be expressed as:

[0083] u' = -(k'1x1 + k'2x2) = -((k1 + δk1)x1 + (k2 + δk2)x2) (10), where δk1 and δk2 are the optimal control gains based on optimal control. Substituting equation (10) into equation (1), the arbitrary control system design without considering interference is:

[0084]

[0085] Then, according to the optimal control criterion, the cost minimization function is designed:

[0086] in: as well as Where: c1 and c2 are positive weighted constants. It can be seen that the first four terms in the cost minimization function are similar to the LQR design, and the addition of the last two terms can solve the optimal control equation. Then, (10) gives the nonlinear optimal control based on the Lyapunov function: in:

[0087] k4=(α1 / 2b2k1)

[0088] k5=(α2+2k4) / (2(b2k2-a2)). Finally, combining equations (10) and (12), we can obtain:

[0089] in:

[0090] k6=k4b2,k7=k5b2.

[0091] At this point, the design of the nonlinear optimal control link is completed, that is, the nonlinear optimal control function for nonlinear control of the NO control system is obtained.

[0092] S106 , combining the LQR control function for linear control and the nonlinear optimal control function for nonlinear control to obtain an optimized NO control system for the built-in permanent magnet synchronous motor system.

[0093] Specifically, the NOx control system is jointly optimized and controlled through the LQR control function and the nonlinear optimal control function to generate an optimized NOx control system based on the built-in permanent magnet synchronous motor system.

[0094] In one embodiment, Figure 2 As shown in the figure, the linear control part LQR and the nonlinear optimal control part of the optimized NO controller achieve the best combination, which can not only achieve the best control for a specific working point, but also achieve the optimal control for the specific working state of the motor.

[0095] It should be noted that Figure 3 This is a schematic diagram of an optimized control of a NO control system provided in an embodiment of the present application, such as Figure 3 As shown in the figure, a NO controller model is built in the simulation software MATLAB, where X2 is the control input signal, X1 is its time integral, and the remaining gains are derived from the control parameters of formula (13). A series of control results are obtained by inputting various K value parameters.

[0096] As a feasible implementation method, in the simulation software MATLAB, after comparing the generated PI control curve with the NO control curve: the IPMSM inputs a speed signal that increases constantly to 210 (rad / s) and a torque signal that changes in steps, the simulation shows that in the speed change area and the torque change area, the NO controller has stronger stability in controlling the speed and torque of the motor, and the stabilization time is shorter than that of the PI controller, and the overshoot phenomenon of the PI controller does not occur, indicating that the NO controller has stronger robustness in controlling the speed and torque of the motor than the PI control. It can be seen that by introducing and solving the nonlinear optimal control problem, the control characteristics of the IPMSM are better and more stable. The designed NO nonlinear optimal controller can not only perform stable control for a specific working point, but also has a nonlinear optimal control link, optimizes the dynamic characteristics of the motor, and accurately controls it, thereby improving the robustness and accuracy of the motor control and improving the working efficiency of the IPMSM. The NO optimal controller designed by the present invention can automatically adjust the gain under different working conditions, accurately control the parameters, and significantly improve the control effect.

[0097] In addition, the embodiment of the present application also provides a nonlinear optimal control system for a permanent magnet synchronous motor, such as Figure 4 As shown, the nonlinear optimal control system 400 of the permanent magnet synchronous motor specifically includes:

[0098] The linear component design module 410 is configured to establish a linear dynamic control model for the NOx control system based on vector parameters of the NOx control system in the internal permanent magnet synchronous motor system; linearly derive the rotor permanent magnet flux vector in the internal permanent magnet synchronous motor system based on the linear dynamic control model to obtain a linearization function; and perform LQR control of the NOx control system based on a cost function based on the linearization function and a dynamic error time integral function to obtain an LQR control function for linear control.

[0099] The nonlinear component design module 420 is configured to perform a maximum gain coefficient control on the LQR control function based on a time constant to obtain a new LQR control function; and perform a nonlinear optimal control on the new LQR control function under an optimal control criterion based on preset optimal control parameters to obtain a nonlinear optimal control function for the NO control system.

[0100] The combined control module 430 is used to combine the LQR control function for linear control and the nonlinear optimal control function for nonlinear control to obtain an optimized NO control system for the internal permanent magnet synchronous motor system.

[0101] In the embodiment of the present application, a NO controller for controlling the d-axis and q-axis current and speed is optimized in an IPMSM control system, and the NO controller is a nonlinear optimal controller. The nonlinear optimal controller includes a linear part designed as LQR control and an optimized nonlinear control part. The linear part is designed for a specific operating point, while the nonlinear part is designed using Krasovskiy's optimality criterion. It can simulate the automatic adjustment of the LQR gain according to different operating conditions. This IPMSM control method has extremely strong robustness. Compared with traditional methods, it can greatly reduce speed overshoot and torque oscillation, and will not prolong the transition time, with excellent control performance. It can: (1) design the nonlinear optimal control link of the controller using the optimization criterion, improve control performance, and shorten control time. (2) The design of the LQR control point makes the control of the motor at a certain operating point extremely robust. (3) The NO controller can scientifically introduce the concept of nonlinearity and optimize the inaccuracy of parameters.

[0102] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device and medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant portions, refer to the descriptions of the method embodiments.

[0103] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0104] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0105] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0108] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0109] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0110] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0111] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0112] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the specification of the present application.

Claims

1. A nonlinear optimal control method for a permanent magnet synchronous motor, characterized in that: The method comprises: Based on the vector parameters of the NO control system in the built-in permanent magnet synchronous motor system, a linear dynamic control model based on the NO control system is established; According to the linear dynamic control model, a linear derivation is performed on the rotor permanent magnet flux vector in the built-in permanent magnet synchronous motor system to obtain a linearization function; Performing time integral calculation of the dynamic error on the d-axis stator current and the q-axis stator current and the d-axis stator current reference value and the q-axis stator current reference value in the linearization function to determine a current dynamic error function; Performing a time integral calculation on the dynamic error of the mechanical speed and the mechanical speed reference value in the linearization function to determine a mechanical speed dynamic error function; Combining the current dynamic error function and the mechanical speed dynamic error function to obtain the dynamic error time integral function; According to the preset steady-state value and the deviation value, the linearization function and the dynamic error time integral function are subjected to a process of eliminating the steady-state second-order minimum value, and the time constant is multiplied by the small variable deviation to obtain a plurality of model variables based on the NO control system; According to the linearization function and the dynamic error time integral function, the NO control system is subjected to LQR control based on the cost function to obtain an LQR control function for linear control, which specifically includes: By using the plurality of model variables, performing consistency calculation on the linear dynamic control model under relevant variable values and reference values, to obtain an optimized linear dynamic control model; generating an LQR cost function based on the optimized linear dynamic control model; performing LQR control on the NOx control system according to a preset Bellman criterion function and the LQR cost function to obtain an LQR control function for linearly controlling the NOx control system; Performing a maximum value control of a gain coefficient based on a time constant on the LQR control function to obtain a new LQR control function; Based on the preset optimal control parameters, the new LQR control function is subjected to nonlinear optimal control under the optimal control criterion to obtain the nonlinear optimal control function of the NO control system; wherein, according to , and obtain the nonlinear optimal control function ; is the first gain coefficient; is the second gain coefficient; is the sixth gain coefficient, and ,and , 、 and are all separate representations of positive weight constants; is the seventh gain coefficient, and ,and ; and are separate representations of positive weighting constants; and Both are separate representations of system state variables; The LQR control function for linear control and the nonlinear optimal control function for nonlinear control are controlled and combined to obtain an optimized NO control system for the built-in permanent magnet synchronous motor system.

2. The nonlinear optimal control method for a permanent magnet synchronous motor according to claim 1, characterized in that: Based on the vector parameters of the NO control system in the built-in permanent magnet synchronous motor system, a linear dynamic control model based on the NO control system is established, specifically including: Based on the mechanical speed input by the built-in permanent magnet synchronous motor system, the vector parameters related to the NO control system are obtained; wherein the vector parameters include at least: mechanical angle, electromagnetic torque, d-axis stator current and q-axis stator current, d-axis stator voltage and q-axis stator voltage, circuit voltage, and phase current; The linear dynamic control model is established according to the vector parameters and through system state variables, control output and disturbance vector.

3. The nonlinear optimal control method for a permanent magnet synchronous motor according to claim 1, characterized in that: According to the linear dynamic control model, a linear derivation is performed on the rotor permanent magnet flux vector in the built-in permanent magnet synchronous motor system to obtain a linearization function, which specifically includes: Based on the linear dynamic control model, dynamically controlling the mechanical parameters, d-axis stator current, and q-axis stator current in the built-in permanent magnet synchronous motor system, wherein the dynamic control includes: deviation dynamic control and integral dynamic control; Using the linear dynamic control model, the rotor permanent magnet flux vector is aligned with the reference system to obtain linear control parameters in the built-in permanent magnet synchronous motor system; wherein the linear control parameters include at least: d-axis time constant, q-axis time constant, mechanical motion time constant, stator resistance, d-axis inductance, q-axis inductance, motor moment of inertia, friction torque gain, pole-pair number permanent magnet flux, static friction torque, and load torque; Based on the linear control parameters, the linearization function based on the linear dynamic control model is generated.

4. The nonlinear optimal control method for a permanent magnet synchronous motor according to claim 1, characterized in that: The LQR control function is subjected to a maximum gain coefficient control based on a time constant to obtain a new LQR control function, specifically comprising: replacing a time constant in the LQR control function with a minimum value according to a maximum value of a first gain coefficient and a maximum value of a second gain coefficient in the LQR control function, to obtain a new control parameter value based on each variable in the LQR control function; The LQR control function is optimized and calculated using the new control parameter value to obtain the new LQR control function.

5. The nonlinear optimal control method for a permanent magnet synchronous motor according to claim 1, characterized in that: Based on the preset optimal control parameters, the new LQR control function is subjected to nonlinear optimal control under the optimal control criterion to obtain the nonlinear optimal control function of the NO control system, specifically including: According to the optimal control parameters and the optimal control gain under steady state and deviation, the new LQR control function is subjected to nonlinear optimal control under the optimal control criterion to obtain an optimized LQR control function; Inputting the optimized LQR control into the linear dynamic control model to obtain a new NO control system; Through the optimal control criterion, the control variables and gain parameters in the new NO control system are cost minimized, and based on the positive weighted constant, the nonlinear optimal control of the Lyapunov function and the optimized LQR control function are jointly processed to obtain the nonlinear optimal control function for the nonlinear control of the NO control system.

6. The nonlinear optimal control method for a permanent magnet synchronous motor according to claim 1, characterized in that: The LQR control function for linear control and the nonlinear optimal control function for nonlinear control are combined to obtain an optimized NO control system for the built-in permanent magnet synchronous motor system, specifically including: The NOx control system is jointly optimized and controlled by the LQR control function and the nonlinear optimal control function to generate the optimized NOx control system based on the built-in permanent magnet synchronous motor system.

7. A nonlinear optimal control system for a permanent magnet synchronous motor, characterized in that: The system is capable of executing the nonlinear optimal control method for a permanent magnet synchronous motor according to any one of claims 1 to 6.

8. A non-volatile computer storage medium, characterized in that The storage medium is a non-volatile computer-readable storage medium, which stores at least one program. Each program includes instructions, and when the instructions are executed by the terminal, the terminal executes the nonlinear optimal control method of a permanent magnet synchronous motor according to any one of claims 1 to 6.

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