Nonlinear active-disturbance-rejection control method and experimental device for permanent magnet synchronous motor of electric vehicle

Abstract

The invention discloses a nonlinear active-disturbance-rejection control method and an experimental device for a permanent magnet synchronous motor of an electric vehicle. The method comprises the following steps: establishing a state equation of total disturbance of the permanent magnet synchronous motor; constructing a tracking differentiator of the permanent magnet synchronous motor; constructing a nonlinear expansion state observer based on the tracking differentiator in combination with the state equation; constructing an initial nonlinear active-disturbance-rejection rotating speed controller of the permanent magnet synchronous motor according to the nonlinear expansion state observer; performing parameter optimization on to-be-designed parameters in the nonlinear extended state observer based on a genetic algorithm, and obtaining an optimal nonlinear active-disturbance-rejection rotating speed controller according to the initial nonlinear active-disturbance-rejection rotating speed controller; and according to the optimal nonlinear active-disturbance-rejection rotating speed controller, nonlinear active-disturbance-rejection control of the permanent magnet synchronous motor of the electric vehicle is realized. The method solves the problems that an existing method is difficult to meet the requirement of a wide speed range, the anti-interference performance is poor, real control signals and feedback signals are difficult to be not considered in an actual controller, and limitation exists in the process of converting an algorithm into the actual controller.

Description

Technical Field

[0001] This invention relates to the field of permanent magnet synchronous motor control technology for electric vehicles, and particularly to a nonlinear active disturbance rejection control method and experimental apparatus for a permanent magnet synchronous motor for electric vehicles. Background Technology

[0003] With the decreasing price of permanent magnet materials such as neodymium iron boron and the development of motor control technology, permanent magnet synchronous motors (PMSMs) have been widely used in the field of new energy vehicles due to their advantages such as low maintenance costs, high energy density, and wide speed range. However, in electric vehicles, PMSMs often need to be used in combination with other types of motors. Therefore, how to utilize complex algorithms to extend performance under high-speed conditions has become a pressing technical challenge for the electric vehicle industry.

[0004] Because permanent magnet synchronous motors (PMSMs) are typically complex systems with nonlinear and strongly coupled characteristics, both internal and external disturbances in the operating environment can affect their control performance, requiring the motor control strategy to have good anti-interference capabilities. In the electric vehicle industry, existing vector control strategies for PMSMs mostly employ PI control. Traditional PI control has advantages such as simple structure and good stability, but there is a trade-off between dynamic response speed and overshoot, making it difficult to meet the requirements of a wide speed range, and it also has poor anti-interference capabilities. Existing algorithms such as adaptive sliding mode control, fast non-singular terminal sliding mode speed control, and adaptive full-order dynamic sliding mode control have complex structures and are currently difficult to widely apply in practical controllers. Most PMSM algorithms are only verified using simulation software, without considering real control and feedback signals, which limits their application in the process of converting algorithms into actual controllers. Summary of the Invention

[0005] This invention provides a nonlinear active disturbance rejection control method and experimental apparatus for a permanent magnet synchronous motor for electric vehicles, in order to overcome the above-mentioned technical problems.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A nonlinear active disturbance rejection control method for a permanent magnet synchronous motor in an electric vehicle includes the following steps:

[0008] S1: Establish the state equation for the total disturbance of the permanent magnet synchronous motor;

[0009] S2: Construct a tracking differentiator for a permanent magnet synchronous motor;

[0010] Based on the tracking differentiator and the state equation, a nonlinear extended state observer containing the parameters to be designed is constructed; and an initial nonlinear active disturbance rejection speed controller for the permanent magnet synchronous motor is constructed based on the nonlinear extended state observer.

[0011] S3: Optimize the parameters to be designed in the nonlinear extended state observer based on a genetic algorithm, and then obtain the optimal nonlinear active disturbance rejection speed controller based on the initial nonlinear active disturbance rejection speed controller; specifically including:

[0012] S31: Define the parameters to be designed as individuals in the population of the genetic algorithm; and set the algorithm parameters of the genetic algorithm, wherein the algorithm parameters include at least the initial population size and the maximum number of iterations;

[0013] S32: Construct the optimal performance index function containing the control input square term and the overshoot suppression term of the permanent magnet synchronous motor controller, and obtain the fitness function based on the optimal performance index function;

[0014] S33: Obtain the fitness values ​​of individuals in each population group in the initial population according to the fitness function, and use the roulette wheel selection operator to select the best individual in the initial population;

[0015] S34: Obtain the genetic probability based on the fitness value; and perform a selection operation on the individuals in the initial population other than the best individual to select the individuals whose genetic probability meets the preset probability threshold as the parent individuals; perform crossover / mutation genetic operations on the parent individuals to obtain the offspring population individuals;

[0016] S35: Take the best individual and the offspring population as the new generation population, and identify the globally best individual in the new generation population; check whether the maximum number of iterations has been reached. If so, output the current best individual; otherwise, take the new generation population as the initial population and repeat steps S33 to S34.

[0017] Based on the current optimal individual, the optimal nonlinear active disturbance rejection speed controller is identified and obtained;

[0018] S4: Based on the optimal nonlinear active disturbance rejection speed controller, nonlinear active disturbance rejection control of the permanent magnet synchronous motor of electric vehicle is realized.

[0019] Furthermore, the state equation for the total disturbance of the permanent magnet synchronous motor established by S1 is as follows:

[0020]

[0021] In the formula: x1 represents the state variable; ω m J represents the mechanical angular velocity of the motor; B represents the moment of inertia; T represents the damping coefficient. Lx2 represents the load torque; f represents the extended state variable; u represents the total disturbance; and b0 represents the controller output, which is a simplified form of u(t); i represents the design constant. q p represents the q-axis current of the permanent magnet synchronous motor. n ψ represents the number of pole pairs of the motor; f Indicates the magnetic flux linkage of a permanent magnet; Represents ω m The first derivative of ; y represents the output of the state equation for the total disturbance of the permanent magnet synchronous motor.

[0022] Furthermore, S2 specifically includes the following steps:

[0023] S21: Construct a tracking differentiator for a permanent magnet synchronous motor, its expression is as follows:

[0024]

[0025]

[0026] In the formula: v1 represents the given rotational speed; v2 represents the tracking output rotational speed after the tracking differentiator transitions; r represents the tracking speed factor; Let v2 represent the first derivative; e1 represent the rotational speed error; fal(e,α,δ) represent the nonlinear function; e, α, and δ represent the error signal, nonlinear factor, and filter factor, respectively.

[0027] S22: Based on the tracking differentiator and the state equation, a state observer is constructed as follows:

[0028]

[0029] In the formula: z1 and e2 represent the estimated values ​​of the state variables and the state estimation error, respectively; l1 represents the design constant;

[0030] Based on the nonlinear feedback effect, -l1e1 is defined as the nonlinear feedback term -β1g1(e1). The state observer is then rewritten as a nonlinear state observer, with the following expression:

[0031]

[0032] Furthermore, the total disturbance f of the controller is expanded into a new state variable, and a nonlinear extended state observer containing the parameters to be designed is established based on the nonlinear state observer.

[0033] And the expression for the nonlinear extended state observer is:

[0034]

[0035] In the formula: β1, β2 represent the parameters to be designed; g1(e1), g2(e2) represent nonlinear functions and g1(e1) = fal(e1, α1, δ1); g2(e2) = fal(e2, α1, δ1); Let z1 and z2 represent the first derivatives of z2; z2 represents the total disturbance observation; α1 and δ1 represent the design parameters, i.e., the nonlinearity factor and the filtering factor.

[0036] S23: Construct the initial nonlinear active disturbance rejection speed controller for the permanent magnet synchronous motor based on the nonlinear extended state observer. Its expression is as follows:

[0037]

[0038] u0 = β k fal(e3,α2,δ2),k=1,2,

[0039]

[0040] In the formula: u0 represents the intermediate variable; β k Here, β represents the design parameters and β = 1, 2; e3 represents the error variable; α2, δ2 represent the design parameters, namely the nonlinearity factor and the filtering factor; Represents rotational speed ω m Estimated value; ω * This indicates the target rotational speed.

[0041] Furthermore, S32 constructs a control input square term u containing the permanent magnet synchronous motor controller. 2 (t) and the optimal performance index function for suppressing overshoot are expressed as follows:

[0042]

[0043] In the formula: ε(t) represents the systematic error; t u Indicates the time it takes for the system to reach a stable state; w i (i = 1, 2, 3, 4) represents the weighting value; w4|ε(t)| represents the suppression of overshoot term and w4 >> w1;

[0044] The fitness function F is expressed as: F = 1 / Q.

[0045] Furthermore, S34 specifically includes the following steps:

[0046] S341: Obtain the inheritance probability based on the fitness value, and its expression is as follows:

[0047]

[0048] In the formula: P(x if(x) represents the probability that each individual in the population will inherit the gene from the next generation; i ) represents the individual fitness value of an individual in the population; M represents the population size;

[0049] And for the individuals in the initial population other than the best individual, a selection operation is performed to select the individuals whose genetic probability meets the preset probability threshold as the parent individuals;

[0050] S342: Perform crossover / mutation genetic operations on parent individuals to obtain offspring population individuals;

[0051] And the expression for performing crossover / mutation genetic operations is:

[0052]

[0053] In the formula: x′ i ,x′ j Indicates individuals in a population after crisscross inheritance; x i ,x j Denotes the parent individual; γ represents the crossover factor and γ∈(0,1]; x′ ik Represents an individual in the population after mutation; x ik Indicates the mutation point; x represents ik The upper and lower bounds of the value; ζ represents the variation factor and ζ∈[0,1].

[0054] An experimental device for nonlinear active disturbance rejection control of a permanent magnet synchronous motor for electric vehicles includes a PC, a switch, an NI controller, a controlled object simulator, and a data interaction box.

[0055] The PC is connected to the switch via Ethernet for bidirectional communication.

[0056] The switch is bidirectionally connected to the NI controller and the controlled object simulator via Ethernet.

[0057] The PC is used to write the nonlinear active disturbance rejection control method of the permanent magnet synchronous motor of the electric vehicle into a control algorithm program and generate an executable programming file based on Simulink software; at the same time, it can also write and obtain the PMSM simulation circuit program file of the permanent magnet synchronous motor of the electric vehicle based on Simulink software.

[0058] The PC is also used to compile programming files into .dll files and download them to the NI controller based on StarSimRCP software; and to download PMSM simulation circuit program files to the controlled object simulator based on StarSimHIL software.

[0059] The NI controller is used to compile a .dll file according to the programming file, simulate and acquire the control signal of the permanent magnet synchronous motor, and transmit the SVPWM signal to the controlled object simulator through the data interaction box;

[0060] Furthermore, the control signal includes at least a current signal, an encoder signal, and an SVPWM signal.

[0061] The controlled object simulator is used to implement nonlinear active disturbance rejection control experiments on permanent magnet synchronous motors of electric vehicles based on PMSM simulation circuit program files and control signals.

[0062] Beneficial Effects: This invention provides a nonlinear active disturbance rejection control method and experimental apparatus for a permanent magnet synchronous motor (PMSM) in an electric vehicle. It constructs a tracking differentiator for the PMSM and combines it with the state equation to obtain a nonlinear extended state observer containing the parameters to be designed. The tracking differentiator softens the reference command and utilizes the transient process to coordinate the overshoot and speed of the control process, improving the robustness of the system. The constructed nonlinear extended state observer can estimate the system's state variables and real-time disturbances. The output state of the nonlinear extended state observer efficiently compensates for nonlinear state errors, improving the system's control performance. Based on a genetic algorithm, the parameters to be designed in the nonlinear extended state observer are optimized to obtain the optimal nonlinear active disturbance rejection speed controller. Since the parameters to be designed in the nonlinear extended state observer determine the system's state estimation performance, this invention uses a genetic algorithm to optimize the parameters of the nonlinear extended state observer, making the speed estimation of the nonlinear active disturbance rejection technology more accurate and precise. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart of the nonlinear active disturbance rejection control method for permanent magnet synchronous motors in electric vehicles according to the present invention;

[0065] Figure 2 This is a block diagram illustrating the control principle of the permanent magnet synchronous motor in this embodiment;

[0066] Figure 3 This is a block diagram of the nonlinear active disturbance rejection controller for the permanent magnet synchronous motor in this embodiment;

[0067] Figure 4This is a system block diagram of a hardware-in-the-loop experimental setup for a nonlinear active disturbance rejection speed controller based on a genetic algorithm for a permanent magnet synchronous motor. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] This embodiment provides a nonlinear active disturbance rejection control method for a permanent magnet synchronous motor in an electric vehicle, such as... Figure 1 As shown, it includes the following steps:

[0070] S1: Establish the state equation for the total disturbance of the permanent magnet synchronous motor, such as Figure 2 As shown, the specific steps include:

[0071] S11: Set the ideal conditions for the mathematical model of permanent magnet synchronous motor; the ideal conditions for permanent magnet synchronous motor are assumed to be: (1) ignore the magnetic reluctance of the stator and rotor cores, and disregard eddy current and hysteresis losses; (2) the excitation magnetic field generated by the permanent magnet and the armature reaction magnetic field generated by the winding are sinusoidally distributed in the air gap; (3) ignore the influence of stator winding tooth and slot asymmetry on the air gap magnetic field distribution; (4) do not consider the influence of temperature on the motor;

[0072] S12: Establish the voltage and flux linkage equations for the stator winding of the permanent magnet synchronous motor;

[0073] The voltage equation for the stator winding of a permanent magnet synchronous motor is:

[0074]

[0075] The stator winding flux linkage equation is:

[0076]

[0077] Introducing equation (1) into equation (2), the voltage equation of the stator winding of the permanent magnet synchronous motor can be rewritten as:

[0078]

[0079] In the formula: u d ,u q These represent the voltages on the d-axis and q-axis of the permanent magnet synchronous motor, respectively; i d i q ψ represents the current along the d-axis and q-axis of the permanent magnet synchronous motor, respectively; fω represents the magnetic flux linkage of a permanent magnet; e L represents electric angular velocity; d ,L q These represent the inductances of the d-axis and q-axis of the permanent magnet synchronous motor, respectively.

[0080] S13: By considering the voltage equation of the stator winding of the permanent magnet synchronous motor, the electromagnetic torque equation and mechanical motion equation of the stator winding of the permanent magnet synchronous motor are established.

[0081] The electromagnetic torque equation for a permanent magnet synchronous motor is:

[0082]

[0083] The electromagnetic torque consists of two parts: permanent magnet torque and reluctance torque.

[0084] The mechanical motion equations of a permanent magnet synchronous motor are:

[0085]

[0086] In the formula: ω m J represents the mechanical angular velocity of the motor; B represents the moment of inertia; T represents the damping coefficient. L Indicates load torque;

[0087] From equations (4) and (5), we can obtain:

[0088]

[0089] In the formula: p n Indicates the number of pole pairs of the motor;

[0090] S14: If the disturbances of the motor load, friction torque, and the system's own motor sensors are considered as the total disturbance, then equation (6) can be rewritten as the state equation for the total disturbance of the permanent magnet synchronous motor, and the expression is:

[0091]

[0092] In the formula: u represents the controller output; the total disturbance f is:

[0093]

[0094] Let x1 = ω m x2=f, and equation (7) can be written in the form of a state equation:

[0095]

[0096] In the formula: b0 represents the design constant; y represents the output of the state equation for the total disturbance of the permanent magnet synchronous motor;

[0097] S2: Construct a tracking differentiator for a permanent magnet synchronous motor;

[0098] Based on the tracking differentiator and the state equation, a nonlinear extended state observer containing the parameters to be designed is constructed; and an initial nonlinear active disturbance rejection speed controller for the permanent magnet synchronous motor is constructed based on the nonlinear extended state observer.

[0099] This embodiment proposes a nonlinear active disturbance rejection controller for permanent magnet synchronous motors, based on the design principles of simple structure and convenient parameter adjustment; such as Figure 3 As shown, the core of the proposed Active Disturbance Rejection Control (ADRC) method is to estimate the system state and disturbances by designing an extended state observer, and to achieve precise control of the system by designing a nonlinear state error feedback control law. The structural block diagram of the nonlinear ADRC controller proposed in this embodiment is shown below. Figure 3 As shown, the specific components include: a nonlinear active disturbance rejection speed controller consisting of a tracking differentiator (TD), a nonlinear extended state observer (NLESO), and a nonlinear state error feedback (NLSEF); the tracking differentiator softens the reference command and utilizes the transient process to coordinate the overshoot and speed of the control process, thereby improving the robustness of the system; the nonlinear extended state observer estimates the system's state variables and real-time disturbances; the nonlinear state error feedback utilizes nonlinear error combinations, which are efficiently compensated by the output state of the nonlinear extended state observer, thereby improving the system's control performance;

[0100] Specifically, the following steps are included:

[0101] S21: Construct a tracking differentiator for a permanent magnet synchronous motor, its expression is as follows:

[0102]

[0103] In the formula: v1 represents the given rotational speed ω ref v2 represents the tracking output speed ω* after the transition of the tracking differentiator TD; r represents the tracking speed factor, the value of which is determined according to the speed of the transition process and the performance of the system itself. The larger the value of r, the faster the tracking speed. Let v2 represent the first derivative; e1 represent the rotational speed error; fal(e,α,δ) represent the nonlinear function; e, α, and δ represent the error signal, nonlinear factor, and filter factor, respectively.

[0104] When designing a nonlinear extended state observer, the perturbation information f in equation (9) is unknown, thus preventing its utilization during observer design. Therefore, the perturbation information is discarded when establishing the state observer, and the equation of the state observer is described as follows:

[0105]

[0106] In the formula: z1 and e2 represent the estimated values ​​of the state variables and the state estimation error, respectively; l1 represents the design constant;

[0107] Although equation (12) discards the function f, the function still plays a role in the system. To better suppress disturbances, -l1e1 is defined as the nonlinear feedback term -β1g1(e1) based on the nonlinear feedback effect. Then, equation (12) is rewritten as a nonlinear state observer, and its expression is:

[0108]

[0109] Therefore, as long as the appropriate parameter β1 is chosen, the nonlinear state observer equation (13) for the nonlinear function g1(e1) can better estimate the estimated value of the state variable in equation (12);

[0110] S22: To estimate the disturbance, the total disturbance is expanded into new state variables, thereby establishing a nonlinear extended state observer, the expression of which is:

[0111]

[0112] In the formula: β1, β2 represent the parameters to be designed; g1(e1), g2(e2) represent nonlinear functions and g1(e1) = fal(e1, α1, δ1); g2(e2) = fal(e2, α1, δ1); Let z1 and z2 represent the first derivatives of z2; z2 represents the total disturbance observation; α1 and δ1 represent the design parameters, i.e., the nonlinearity factor and the filtering factor.

[0113] S23: Construct a nonlinear active disturbance rejection speed controller based on a nonlinear extended state observer;

[0114] Furthermore, the expression for the nonlinear active disturbance rejection speed controller is:

[0115]

[0116] In the formula: z2 represents the total disturbance observation value. Substituting equation (15) into equation (7) and ignoring the estimation error of the disturbance, we can obtain the intermediate variable u0 as:

[0117]

[0118] make Then the equation for u0 can be expressed as:

[0119] u0 = β k fal(e3,α2,δ2)

[0120] In the formula: u0 represents the intermediate variable; β k Here, β represents the design parameters and β = 1, 2; e3 represents the error variable; α2, δ2 represent the design parameters, namely the nonlinearity factor and the filtering factor; Represents rotational speed ω m Estimated value; ω * This indicates the target rotational speed.

[0121] This embodiment also includes:

[0122] S101: Prove the asymptotic stability of the nonlinear extended state observer:

[0123] By combining equations (9) and (13), the state error equation is obtained as follows:

[0124]

[0125] In the formula: h(t) represents a nonlinear function;

[0126] Let e 11 =e1, Equation (17) can then be rewritten as:

[0127]

[0128] The equation for constructing the Lyapunov function V1 is as follows:

[0129]

[0130] In the formula: τ represents the time variable;

[0131] By using the mean value theorem for integrals, we know that there must exist ξ∈[0,e... 11 ]satisfy:

[0132]

[0133] In the formula: ξ represents the design parameter;

[0134] Combining equations (19) and (20), we get:

[0135]

[0136] From the expression of the fal function, we know that e 11 Since fal(ξ,α1,δ1) has the same sign and β2>0, we can obtain V1>0.

[0137] Differentiating and simplifying the Lyapunov function in equation (19), we get:

[0138]

[0139] The function fal(e) 11 The expression α1, δ1) is monotonically increasing, its derivative is greater than zero, and it is a bounded real number within a finite error range. Therefore, let K = fal′(e 11 From α1,δ1)>0, we can obtain:

[0140]

[0141] When h > 0, it satisfies or And when h < 0, or Then there is When e 22 When = 0, In summary, the nonlinear extended state observer (14) has asymptotic stability, thus completing the proof;

[0142] S102: Proof of asymptotic stability of a nonlinear active disturbance rejection speed controller:

[0143] By defining the systematic error ε=x1-v1, taking its derivative and substituting equations (9) and (15) into the equation, we can obtain:

[0144]

[0145] By using the nonlinear extended state observer to determine asymptotic stability, we can deduce z1→x1, z2→x2, and v1=v2, hence ε≈e3. Therefore, equation (24) can be rewritten as:

[0146]

[0147] The equation for the Lyapunov function is constructed based on equation (25) as follows:

[0148]

[0149] Differentiating equation (26) yields:

[0150]

[0151] From the equation of the fal function, we know that ε and fal(ε,α2,δ2) have the same sign, therefore εfal(ε,α2,δ2) is greater than zero when β k When >0, there is When ε = 0, This proves that the nonlinear active disturbance rejection speed controller (15) has asymptotic stability. Q.E.D.

[0152] S3: Based on the genetic algorithm, the parameters to be designed in the nonlinear extended state observer are optimized to obtain the optimal nonlinear active disturbance rejection speed controller;

[0153] In this embodiment, the nonlinear extended state observer has a significant impact on the control performance of the nonlinear active disturbance rejection controller. The parameters β1 and β2 of the nonlinear extended state observer determine the state estimation performance of the system and directly affect the speed and accuracy of the nonlinear active disturbance rejection technology estimation. A genetic algorithm is used to automatically optimize the parameters of the nonlinear extended state observer.

[0154] Specifically, it includes:

[0155] S31: Define the parameters to be designed as individuals in the population of the genetic algorithm; and set the algorithm parameters of the genetic algorithm, wherein the algorithm parameters include at least the initial population size and the maximum number of iterations;

[0156] Specific initial population encoding and initialization: The optimization ranges of parameters β1 and β2 are determined to be [0.1, 100] and [0.1, 2500] respectively by trial and error. When initializing the population, the population size is set to 30, the number of iterations is set to 50, and the parameter pair (β1, β2) is used as the population individual.

[0157] S32: Construct the square term of the control input u including the permanent magnet synchronous motor controller 2 (t) and the optimal performance index function for suppressing overshoot, and obtain the fitness function based on the optimal performance index function;

[0158] Specifically, the constructed optimal performance index function is as follows:

[0159]

[0160] In the formula: ε(t) represents the systematic error; t u Indicates the time it takes for the system to reach a stable state; w i (i = 1, 2, 3, 4) represents the weighting value; w4|ε(t)| represents the suppression of overshoot term and w4 >> w1;

[0161] The fitness function F is expressed as: F = 1 / Q;

[0162] S33: Obtain the fitness values ​​of individuals in each population group in the initial population according to the fitness function, and use the roulette wheel selection operator to select the best individual in the initial population;

[0163] S34: Obtain the genetic probability based on the fitness value; and perform a selection operation on the individuals in the initial population other than the best individual to select the individuals whose genetic probability meets the preset probability threshold as the parent individuals; perform crossover / mutation genetic operations on the parent individuals to obtain the offspring population individuals;

[0164] Specifically, the following steps are included:

[0165] S341: Obtain the inheritance probability based on the fitness value, and its expression is as follows:

[0166]

[0167] In the formula: P(x i f(x) represents the probability that each individual in the population will inherit the gene from the next generation; i ) represents the individual fitness value of an individual in the population; M represents the population size;

[0168] And for the individuals in the initial population other than the best individual, a selection operation is performed to select the individuals whose genetic probability meets the preset probability threshold as the parent individuals;

[0169] S342: Perform crossover / mutation genetic operations on parent individuals to obtain offspring population individuals;

[0170] And the expression for performing crossover / mutation genetic operations is:

[0171]

[0172] In the formula: x i ′,x′ j Indicates individuals in a population after crisscross inheritance; x i ,x j x′ represents the parent individual; γ represents the crossover factor and γ∈(0,1]; to improve the local search capability of the genetic algorithm and prevent local optima, uniform mutation is used for mutation operation; ik Represents an individual in the population after mutation; x ik Indicates the mutation point; x represents ik The upper and lower bounds of the value; ζ represents the variation factor and ζ∈[0,1] is a random number that conforms to a uniform distribution;

[0173] S35: Take the best individual and the offspring population as the new generation population, and identify the globally best individual in the new generation population; check whether the maximum number of iterations has been reached. If so, output the current best individual; otherwise, take the new generation population as the initial population and repeat steps S33 to S34.

[0174] Based on the current optimal individual, the optimal nonlinear active disturbance rejection speed controller is identified and obtained;

[0175] S4: Based on the optimal nonlinear active disturbance rejection speed controller, nonlinear active disturbance rejection control of the permanent magnet synchronous motor of electric vehicle is realized.

[0176] This embodiment proposes a nonlinear active disturbance rejection speed controller (GA-NLADRC) for permanent magnet synchronous motors by combining a genetic algorithm (GA) with nonlinear active disturbance rejection control (NLADRC). The control principle block diagram of the proposed permanent magnet synchronous motor is shown below. Figure 2 As shown, it specifically includes:

[0177] The speed control process of the permanent magnet synchronous motor is as follows:

[0178] S201: The speed loop adopts a nonlinear ADRC controller based on a genetic algorithm. The input of the controller is the difference between the speed setpoint ω* and the actual speed value ω, and the output is the current loop setpoint.

[0179] S202: The current feedback value i obtained by transforming the three-phase current signal fed back from the permanent magnet synchronous motor through Clarke transform and Park transform. d i q The difference between the set values ​​and feedback values ​​of the d-axis and q-axis currents is output as u by the current loop PI controller. d ,u q ;

[0180] S203:u d ,u q The Park inverse converter and SVPWM module output pulse modulation signal to the inverter, and the inverter generates three-phase current to drive the motor.

[0181] An experimental setup for nonlinear active disturbance rejection control of a permanent magnet synchronous motor in an electric vehicle, such as... Figure 4 As shown, it includes a PC, a switch, an NI controller, a controlled object simulator, and a data interaction box;

[0182] Specifically, the NI controller uses a controller from National Instruments (NI) and consists of components such as the NI PXIe-11082 baseboard, PXIe-7846R FPGA, and PXI8840 controller. The controlled object simulator consists of components such as the PXIe-11082 baseboard, PXIe-7846R FPGA, PXIe-7868R FPGA, and PXI8840 controller; the controlled object is a hardware simulation system. The advantages of this experimental setup in controller development are: the use of actual I / O signals for transmission between the controller and the controlled object simulator allows for consideration of factors such as real I / O signal transmission and real-time performance, enabling testing that would otherwise require extensive physical testing to be completed during the controller prototype development stage, significantly shortening the controller development cycle; the controller algorithm program is first built using Matlab / Simulink, and then the Matlab / Simulink program is directly converted into executable code and downloaded to the actual controller, improving the efficiency of control algorithm program development.

[0183] The PC is connected to the switch via Ethernet for bidirectional communication.

[0184] The switch is bidirectionally connected to the NI controller and the controlled object simulator via Ethernet.

[0185] The PC is used to write the nonlinear active disturbance rejection control method of the permanent magnet synchronous motor of the electric vehicle into a control algorithm program and generate an executable programming file based on Simulink software; at the same time, it can also write and obtain the PMSM simulation circuit program file of the permanent magnet synchronous motor of the electric vehicle based on Simulink software.

[0186] The PC is also used to compile programming files into .dll files and download them to the NI controller based on StarSimRCP software; and to download PMSM simulation circuit program files to the controlled object simulator based on StarSimHIL software.

[0187] The NI controller is used to compile a .dll file according to the programming file, simulate and acquire the control signal of the permanent magnet synchronous motor, and transmit the SVPWM signal to the controlled object simulator through the data interaction box;

[0188] Furthermore, the control signal includes at least a current signal, an encoder signal, and an SVPWM signal.

[0189] The controlled object simulator is used to implement nonlinear active disturbance rejection control experiments on permanent magnet synchronous motors of electric vehicles based on PMSM simulation circuit program files and control signals.

[0190] In this embodiment, the experimental device can automatically generate executable code for the control algorithm and download it to the controller. Using real I / O signals, it can realize the rapid design and development of electric vehicle magnetic synchronous motor controllers. Through this experimental platform, the nonlinear active disturbance rejection speed control of permanent magnet synchronous motors based on genetic algorithms (PMSM) and other experiments can be verified.

[0191] S524: Connect the NI controller to the controlled object simulator using a data exchange box. The NI controller transmits the SVPWM signal and load signals (such as motor speed, controller output current, and other real signals) to the controlled object simulator through the data exchange box. The controlled object simulator starts running according to the input signals and outputs the motor current signal and the encoder signal (motor speed). At the same time, the controlled object simulator transmits the current signal and the encoder signal (signal for measuring motor speed) back to the controller to facilitate subsequent experimental result verification and analysis based on expert experience.

[0192] The beneficial effects of the method and experimental apparatus described in this embodiment are as follows: By constructing a tracking differentiator for a permanent magnet synchronous motor and combining it with the state equation to obtain a nonlinear extended state observer containing the parameters to be designed; the tracking differentiator can soften the reference command, utilize the transient process to coordinate the overshoot and speed of the control process, and improve the robustness of the system; the constructed nonlinear extended state observer can estimate the system's state variables and real-time disturbances; and the output state of the nonlinear extended state observer can efficiently compensate for nonlinear state errors, improving the system's control performance; the parameters to be designed in the nonlinear extended state observer are optimized using a genetic algorithm to obtain the optimal nonlinear active disturbance rejection speed controller; since the parameters to be designed in the nonlinear extended state observer determine the system's state estimation performance, this invention optimizes the parameters of the nonlinear extended state observer using a genetic algorithm, making the speed estimation of the nonlinear active disturbance rejection technology more accurate and precise.

[0193] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A nonlinear active-disturbance-rejection control method for an electric vehicle permanent magnet synchronous motor, characterized in that, Specifically comprising the following steps: S1: establishing a state equation of total disturbance of the permanent magnet synchronous motor; S2: constructing a tracking differentiator of the permanent magnet synchronous motor; Based on the tracking differentiator combined with the state equation, a nonlinear extended state observer containing the to-be-designed parameters is constructed; and an initial nonlinear active disturbance rejection speed controller of the permanent magnet synchronous motor is constructed according to the nonlinear extended state observer; S3: parameter optimization is performed on the to-be-designed parameters in the nonlinear extended state observer based on a genetic algorithm, and then an optimal nonlinear active disturbance rejection speed controller is obtained according to the initial nonlinear active disturbance rejection speed controller; specifically comprising: S31: defining the to-be-designed parameters as population individuals of the genetic algorithm; And setting algorithm parameters of the genetic algorithm, and the algorithm parameters at least include an initial population size and a maximum iteration number; S32: constructing an optimal performance index function containing a control input square term and an overshoot suppression term of the permanent magnet synchronous motor controller, and obtaining a fitness function according to the optimal performance index function; S33: obtaining fitness values of various population individuals in the initial population according to the fitness function, and selecting an optimal individual in the initial population by using a roulette wheel selection operator; S34: obtaining a genetic probability according to the fitness value; and performing a selection operation on population individuals other than the optimal individual in the initial population to obtain parent individuals whose genetic probability meets a preset probability threshold; performing a crossover / mutation genetic operation on the parent individuals to obtain child population individuals; S35: taking the optimal individual and the child population individuals as a new generation population, and confirming a global optimal individual in the new generation population; confirming whether the maximum iteration number is reached, if yes, outputting the current optimal individual; otherwise, taking the new generation population as the initial population and repeating steps S33 to S34; According to the current optimal individual, an optimal nonlinear active disturbance rejection speed controller is confirmed and obtained; S4: realizing nonlinear active disturbance rejection control of the permanent magnet synchronous motor of the electric vehicle according to the optimal nonlinear active disturbance rejection speed controller.

2. The nonlinear active-disturbance-rejection control method of a permanent magnet synchronous motor for an electric vehicle according to claim 1, characterized by, The state equation of total disturbance of the permanent magnet synchronous motor established in S1 is where: x1 represents a state variable; ω m represents the mechanical angular velocity of the motor; J represents the moment of inertia; B represents the damping coefficient; T L represents the load torque; x2 represents an extended state variable; f represents the total disturbance; u represents the controller output and u is a short form of u(t); b0 represents a design constant; i q represents the current of the q-axis of the permanent magnet synchronous motor; p n represents the number of pole pairs of the motor; ψ f represents the flux of the permanent magnet; represents the first derivative of ω m ; y represents the output of the state equation of the total disturbance of the permanent magnet synchronous motor.

3. The nonlinear active-disturbance-rejection control method of a permanent magnet synchronous motor for an electric vehicle according to claim 2, characterized in that, The S2 specifically comprises the following steps: S21: constructing a tracking differentiator of the permanent magnet synchronous motor, and its expression is wherein: v1 represents a given rotational speed; v2 represents a rotational speed of a tracking output after a tracking differentiator transition; and r represents a tracking speed factor; represents a first order derivative of v2; e1 represents a rotational speed error; fal(e, a, d) represents a non-linear function; e, a, d represent an error signal, a non-linear factor, and a filter factor, respectively; S22: based on the tracking differentiator combined with the state equation, constructing a state observer In the formula, z1 and e2 respectively represent an estimated value of a state variable and a state estimation error; l1 represents a design constant; And based on a nonlinear feedback effect, -l1e1 is defined as a nonlinear feedback term -β1g1(e1), and the state observer is rewritten as a nonlinear state observer, and its expression is And the total disturbance f of the controller is expanded into a new state variable, and a nonlinear extended state observer containing the to-be-designed parameters is constructed according to the nonlinear state observer; And the expression of the nonlinear extended state observer is wherein: β1, β2 represent parameters to be designed; g1(e1), g2(e2) represent nonlinear functions and g1(e1) = fal(e1, α1, δ1); g2(e2) = fal(e2, α1, δ1); wherein: z1, z2 represent first derivatives of z1, z2; z2 represents total disturbance observation; α1, δ1 represent design parameters, i.e., nonlinear factor and filter factor; S23: constructing an initial nonlinear active disturbance rejection speed controller of the permanent magnet synchronous motor according to the nonlinear extended state observer, and its expression is wherein: u0 represents an intermediate variable; β k denotes a design parameter and β = 1,2; e3 represents an error variable; a2, d2 represent design parameters, i.e. a non-linear factor and a filtering factor; denotes the rotational speed ω m an estimate; ω * denotes the rotational speed target value.

4. The nonlinear active-disturbance-rejection control method of a permanent magnet synchronous motor for an electric vehicle according to claim 1, characterized in that, In S32, a control input quadratic term u including a permanent magnet synchronous motor controller is constructed 2 (t) an optimal performance index function with an overshoot suppression term, expressed as wherein ε(t) represents a system error; t u represents a time for the system to reach stability; w i (i = 1, 2, 3, 4) represents a weighting value; w4|ε(t)| represents a term for suppressing overshoot and w4»wl; The expression of the fitness function F is: F=1 / Q.

5. The nonlinear active-disturbance-rejection control method of a permanent magnet synchronous motor for an electric vehicle according to claim 1, characterized by, The S34 specifically comprises the following steps: S341: obtaining a genetic probability according to the fitness value, and its expression is In the formula: P(x i f(x) represents the probability that each individual in the population will inherit the gene from the next generation; i ) represents the individual fitness value of an individual in the population; M represents the population size; The selection operation is performed on population individuals other than the optimal individual in the initial population, and population individuals whose genetic probability meets a preset probability threshold are taken as parent individuals; S342: performing cross / mutation genetic operations on the parent individuals to obtain offspring population individuals; The expression of the cross / mutation genetic operation is where x i x′ j represents the population individual after crossover; x i x′ j represents the parent individual; γ represents the crossover factor and γ ∈ (0, 1]; x′ ik represents the population individual after mutation; x ik represents the mutation point; represents the upper and lower boundaries of the value of x ik ; ζ represents the mutation factor and ζ ∈ [0, 1].

6. An experimental device for a nonlinear active-disturbance-rejection control method of a permanent-magnet synchronous motor for an electric vehicle according to any one of claims 1 to 5, characterized by, The PC, the switch, the NI controller, the controlled object simulator and the data interaction box are included. The PC is in bidirectional communication connection with the switch through Ethernet. The switch is in bidirectional communication connection with the NI controller and the controlled object simulator through Ethernet. The PC is used to write a control algorithm program and generate an executable programming file of a nonlinear active disturbance rejection control method of the electric vehicle permanent magnet synchronous motor based on Simulink software; meanwhile, the PC is also used to write and obtain a PMSM simulation circuit program file of the electric vehicle permanent magnet synchronous motor based on Simulink software; The PC is further used to compile the programming file into a.dll file and download the.dll file into the NI controller based on StarSimRCP software, and to download the PMSM simulation circuit program file into the controlled object simulator based on StarSimHIL software; The NI controller is used to simulate and obtain a control signal of the permanent magnet synchronous motor according to the.dll file compiled from the programming file, and to transmit the SVPWM signal into the controlled object simulator through the data interaction box; The control signal at least includes a current signal, an encoder signal and an SVPWM signal, and the controlled object simulator is used to realize the nonlinear active disturbance rejection control experiment on the electric vehicle permanent magnet synchronous motor based on the PMSM simulation circuit program file according to the control signal.

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