High-precision rotation angle execution servo control method for intelligent driving active steering system

Abstract

The invention discloses a high-precision rotation angle execution servo control method for an intelligent driving active steering system, and the method comprises the steps: obtaining motor drive electrical parameters of a steering execution motor according to a drive current conversion mathematical model when a steering execution assembly of the active steering system controls the rotation of steering wheels through the steering execution motor; a driving electric signal of a steering execution motor is obtained through the motor driving electric parameters, and finally the steering execution motor is driven to rotate according to the driving electric signal, so that rotation control over steering wheels is achieved. Due to the fact that the driving current conversion mathematical model used for steering control over the steering execution motor is established firstly, the steering-by-wire system for intelligent driving of the vehicle is higher in steering precision and higher in robustness.

Description

Technical Field

[0001] This application relates to the field of automotive steering system technology, specifically to a high-precision steering angle execution servo control method for an intelligent driving active steering system. Background Technology

[0002] An active steering system consists of three main parts: the steering wheel assembly, the steering actuator assembly, and the main controller (ECU), as well as auxiliary systems such as a fault diagnosis system and a power supply. The steering wheel assembly includes the steering wheel, steering wheel angle sensor, torque sensor, and steering wheel return motor. Its main function is to convert the driver's steering intention (by measuring the steering wheel angle) into a digital signal and transmit it to the main controller; simultaneously, it receives torque signals from the main controller and generates steering wheel return torque to provide the driver with corresponding road feel information. The steering actuator assembly includes front wheel angle sensors, steering actuator motors, steering motor controllers, and front wheel steering components. Its function is to receive commands from the main controller and, through the steering motor controller, control the rotation of the steering wheels to achieve the driver's steering intention. The main controller analyzes and processes the collected signals, determines the vehicle's motion state, and sends commands to the steering wheel return motor and steering motor to control their operation, ensuring ideal vehicle response under various conditions. This reduces the driver's workload by compensating for changes in steering characteristics with vehicle speed. Simultaneously, the controller can also recognize the driver's operating commands and determine whether the driver's steering operation is reasonable in the current state. When the car is in an unstable state or the driver issues an incorrect command, the active steering system will shield the driver's incorrect steering operation and automatically perform stabilization control to restore the car to a stable state as quickly as possible. The steering actuation assembly is the core component for achieving fast and precise steering; therefore, improving the angular accuracy during steering is its main research and development direction. Summary of the Invention

[0003] The main technical problem addressed in this application is how to improve the accuracy of steering angle control when the steering actuator performs steering.

[0004] According to the first aspect, a high-precision steering angle execution servo control method for an intelligent driving active steering system includes:

[0005] The active steering system includes a steering actuation assembly, which controls the rotation of the steering wheels via a steering actuation motor to achieve steering control of the vehicle; wherein the motor rotation angle of the steering actuation motor is positively correlated with the rotation angle of the steering wheels;

[0006] The high-precision rotation servo control method includes:

[0007] Obtain the desired motor rotation angle θ of the steering actuator motor.m * ;

[0008] The desired motor rotation angle θ m * Input a preset driving current conversion mathematical model and obtain the motor driving electrical parameters output by the driving current conversion mathematical model; wherein, the driving current conversion mathematical model includes a permanent magnet synchronous motor mathematical model established in the dq coordinate system;

[0009] The drive electrical signal of the steering actuator motor is obtained based on the motor drive electrical parameters, and the steering actuator motor is driven to rotate according to the drive electrical signal to achieve rotation control of the steering wheel.

[0010] In one embodiment, the mathematical model of the permanent magnet synchronous motor includes:

[0011] u d =R s i d +L s (di d / dt)-ω e L s i q ;

[0012] u q =R s i q +L s (di q / dt) +ω e L s i q +ω e Ψ f ;

[0013] m =ω m ;

[0014] m =(1.5n p Ψ f i q -B m ω m -τ l )÷J m ;

[0015] Among them, u d The stator voltage on the d-axis, u q L is the stator voltage on the q-axis. s (di d / dt) represents the inductor voltage drop caused by the dynamic change of the d-axis current, Ls For stator inductance, L s (di q / dt) is the inductor voltage drop caused by the dynamic change of the q-axis current, i d Let i be the stator current on the d-axis. q R is the stator current on the q-axis. s For stator resistance, Ψ f For permanent magnet flux linkage, θ m For the mechanical angle of the motor, ω m J is the mechanical angular velocity of the motor. m B is the moment of inertia of the motor. m τ is the motor damping coefficient. l For the transmission torque, ω e Let n be the electric angular velocity of the motor. p n is the extreme logarithm p The motor drive electrical parameters include the stator voltage u on the d-axis. d and the stator voltage u on the q-axis q .

[0016] In one embodiment, the driving current conversion mathematical model further includes a deadbeat current prediction control model, wherein the deadbeat current prediction control model is:

[0017] u d (t+1)=(R s -L s / T s )×i d (t+1)- ω e (t)×L s ×i q (t+1)+ (L s / T s )×i d * (t);

[0018] u q (t+1)=(R s -L s / T s )×i q (t+1)+ ω e (t)×L s ×i d (t+1)+ (L s / T s )×i q * (t)+ω e (t)Ψ f ;

[0019] Among them, u d(t+1) represents the predicted stator voltage on the d-axis at time t+1, u q (t+1) represents the predicted stator voltage on the q-axis at time t+1, i d * (t) represents the reference stator current on the d-axis at time t, i q * (t) represents the reference stator current on the q-axis at time t, ω e (t) is the electric angular velocity at time t, T s The sampling period is t, and t+1 is the next unit of time after t.

[0020] In one embodiment, the high-precision rotation angle execution servo control method further includes:

[0021] Based on historical data of the motor angle of the steering actuator motor when the steering actuator eliminates backlash in the mechanical structure, the drive current of the steering actuator motor is compensated to compensate for the backlash effect when the steering actuator performs vehicle steering.

[0022] In one embodiment, compensating the drive current of the steering motor based on historical data of the motor angle of the steering motor when the steering assembly eliminates backlash in the mechanical structure includes:

[0023] Each time the steering assembly performs vehicle steering, a preset backlash dead zone model is used to calculate the drive current compensation value of the steering motor when performing backlash elimination compensation; wherein, a preset particle swarm optimization algorithm is used to identify the parameters in the backlash dead zone model.

[0024] In one embodiment, the high-precision rotation angle execution servo control method further includes:

[0025] A preset mathematical model of steering disturbance is applied to obtain the disturbance error value of the steering motor caused by the system disturbance of the steering actuator assembly; the system disturbance includes transmission torque estimation error, current loop tracking error and / or friction torque disturbance;

[0026] The drive current of the steering actuator motor is compensated based on the disturbance error value.

[0027] In one embodiment, the high-precision rotation angle execution servo control method further includes:

[0028] A preset sliding mode controller is used to track the motor rotation angle of the steering actuator motor in order to maintain the rotation angle of the steering wheels when the active steering system performs vehicle steering.

[0029] In one embodiment, the high-precision rotation angle execution servo control method further includes:

[0030] Real-time monitoring of the motor angle and / or drive current value of the steering actuator motor;

[0031] The drive current of the steering actuator motor is compensated based on the changes in the motor angle value and / or drive current value obtained from monitoring, so as to supplement the steering force of the steering actuator assembly.

[0032] According to a second aspect, one embodiment provides a computer-readable storage medium storing a program that can be executed by a processor to implement the high-precision cornering servo control method as described in the first aspect.

[0033] According to a third aspect, one embodiment provides a computer program product including a computer program and / or instructions, which, when executed by a processor, implement the high-precision cornering servo control method as described in the first aspect.

[0034] The angle servo control device according to the above embodiments makes the angle servo of the steering actuator more accurate, the dynamic performance stronger, and the robustness higher, which can greatly eliminate steering vibration. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the active steering system in one embodiment;

[0036] Figure 2 This is a flowchart illustrating a high-precision cornering servo control method in one embodiment;

[0037] Figure 3 This is a schematic diagram illustrating the implementation of corner servo control in one embodiment;

[0038] Figure 4 This is a schematic diagram of the structural connection of the steering execution assembly in one embodiment;

[0039] Figure 5 This is a schematic diagram illustrating the implementation of a tooth gap model parameter identification method in one embodiment;

[0040] Figure 6 This is a schematic diagram illustrating the control of the motor rotation angle of the steering actuator motor in one embodiment. Detailed Implementation

[0041] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of this application. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to this application are not shown or described in the specification. This is to avoid obscuring the core parts of this application with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0042] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.

[0043] The serial numbers assigned to components in this document, such as "first" and "second," are used only to distinguish the described objects and have no sequential or technical meaning. The terms "connection" and "linkage" used in this application, unless otherwise specified, include both direct and indirect connections (linkages).

[0044] Compared to other steering systems, active steering systems eliminate the mechanical connection between the steering wheel and the steering wheels, transmitting driver control commands via electrical signals. For future vehicles to achieve fully autonomous driving, steering servo control can be achieved silently through the steering wheel, making it the most ideal active steering solution. To meet the high safety requirements of L3 and higher levels of autonomous driving for lateral control, precise control of the steering angle is essential. For example, if the steering angle is not precise or uncontrollable when the vehicle is traveling along a pre-planned path, it will inevitably increase the difficulty of controlling the autonomous driving system.

[0045] In this embodiment, the motor drive electrical parameters of the steering actuator motor are obtained by a pre-established mathematical model of drive current conversion, and the drive electrical signal of the steering actuator motor is obtained based on the motor drive electrical parameters, so that the steering accuracy of the active steering system used for intelligent vehicle driving is higher and the robustness is stronger.

[0046] Example 1:

[0047] Please refer to Figure 1This is a schematic diagram of an active steering system in one embodiment. The active steering system mainly includes a steering wheel assembly, a power supply, a VCU (vehicle control unit), a CCU (central control unit), an OBD (on-board diagnostics) system, and a steering actuator. The steering wheel assembly mainly includes a steering wheel, a steering column, an ECU (electrical control unit), a torque angle sensor, and a road feel analog motor. The steering actuator includes a steering actuator motor, an ECU, a torque angle sensor, a rack and pinion steering gear, and a synchronous belt ball screw reduction mechanism. The steering wheel assembly, steering actuator, and CCU communicate via CANFD, with the CCU controlling the steering wheel assembly and steering actuator. The VCU and CCU communicate via a CAN bus, with the CCU receiving vehicle communication information via CAN. In the steering wheel assembly, the torque angle sensor reads the corresponding torque angle information and sends it to the road feel motor ECU. In the steering actuator, the current sensor, motor position sensor, and torque angle sensor read the corresponding information and send it to the ECU corresponding to the actuator motor. The steering actuation assembly in the active steering system controls the rotation of the steering wheels via the steering actuation motor, thereby achieving vehicle steering control. The motor rotation angle of the steering actuation motor is directly proportional to the rotation angle of the steering wheels. The active steering system power supply consists of two power sources, powered by the vehicle's high-voltage battery via a DC / DC converter. One power source supplies power to the road feel simulation motor ECU and VCU, while the other supplies power to the actuation motor ECU. The CCU is powered by both power sources simultaneously to prevent malfunction in the event of a single power source failure. The OBD and steering system's CAN bus are connected to CANFD, receiving all communication information from the steering system and ensuring its normal operation.

[0048] Please refer to Figure 2 This is a flowchart illustrating a high-precision cornering servo control method in one embodiment. The high-precision cornering servo control method includes:

[0049] Step 101: Obtain the desired motor rotation angle.

[0050] Obtain the desired motor rotation angle θ of the steering actuator motor m * Desired motor rotation angle θ m * It corresponds to both the steering wheel angle and the wheel rotation angle. In one embodiment, the torque angle is obtained through a steering wheel torque angle sensor or preset directly by intelligent driving based on the planned driving path.

[0051] Step 102: Obtain the motor drive electrical parameters.

[0052] Desired motor rotation angle θ m * A preset driving current conversion mathematical model is input, and the motor driving electrical parameters output by the driving current conversion mathematical model are obtained. The driving current conversion mathematical model includes a permanent magnet synchronous motor mathematical model established in the dq coordinate system. In one embodiment, the permanent magnet synchronous motor mathematical model includes:

[0053] u d =R s i d +L s (di d / dt)-ω e L s i q ;

[0054] u q =R s i q +L s (di q / dt) +ω e L s i q +ω e Ψ f ;

[0055] m =ω m ;

[0056] m =(1.5n p Ψ f i q -B m ω m -τ l )÷J m ;

[0057] Among them, u d The stator voltage on the d-axis, u q L is the stator voltage on the q-axis. s (di d / dt) represents the inductor voltage drop caused by the dynamic change of the d-axis current, L s For stator inductance, L s (di q / dt) is the inductor voltage drop caused by the dynamic change of the q-axis current, i d Let i be the stator current on the d-axis. q R is the stator current on the q-axis. s For stator resistance, Ψ f For permanent magnet flux linkage, θ mFor the mechanical angle of the motor, ω m J is the mechanical angular velocity of the motor. m B is the moment of inertia of the motor. m τ is the motor damping coefficient. l For the transmission torque, ω e Let n be the electric angular velocity of the motor. p n is the extreme logarithm p The motor drive electrical parameters include the stator voltage u on the d-axis. d and the stator voltage u on the q-axis q .

[0058] In one embodiment, the driving current conversion mathematical model further includes a deadbeat current prediction control model, which is as follows:

[0059] u d (t+1)=(R s -L s / T s )×i d (t+1)- ω e (t)×L s ×i q (t+1)+ (L s / T s )×i d * (t);

[0060] u q (t+1)=(R s -L s / T s )×i q (t+1)+ ω e (t)×L s ×i d (t+1)+ (L s / T s )×i q * (t)+ω e (t)Ψ f ;

[0061] Among them, u d (t+1) represents the predicted stator voltage on the d-axis at time t+1, u q (t+1) represents the predicted stator voltage on the q-axis at time t+1, i d * (t) represents the reference stator current on the d-axis at time t, i q * (t) represents the reference stator current on the q-axis at time t, ω e (t) is the electric angular velocity at time t, Ts The sampling period is t, and t+1 is the next unit of time after t.

[0062] The mathematical model of the permanent magnet synchronous motor and the deadbeat current prediction model constructed in this embodiment are used to establish a deadbeat current prediction control system for the steering actuator motor under vector control.

[0063] Step 103: Obtain the drive electrical signal to drive the steering actuator motor.

[0064] The drive electrical signal of the steering actuator motor is obtained based on the motor drive electrical parameters, and the steering actuator motor is driven to rotate according to the drive electrical signal to realize the rotation control of the steering wheel.

[0065] Please refer to Figure 3 This is a schematic diagram illustrating the implementation of a high-precision steering angle servo control method in one embodiment. In one embodiment, the steering angle servo control system of the active steering system includes position loop closed-loop control and current loop closed-loop control. The three-phase current i of the steering actuator motor is measured by a current sensor. a i b and i c The current components i in the α-β two-phase stationary coordinate system are obtained after Clark transformation. α and current component i β Then, after Park transformation, the current components i in the dq two-phase rotating coordinate system are obtained. d and current component i q Desired motor rotation angle θ m * The desired front wheel steering angle δ provided to the host computer w * Multiplying the value by the transmission ratio, the desired rotation angle θ is then calculated. m * The actual rotation angle θ measured by the sensor m The lumped disturbance estimated by the disturbance observer is input to the position loop controller, and the position loop controller output current i q1 * With backlash compensation current i q2 * Add them together to obtain the desired q-axis current i q * d-axis desired current i d * =0, actual current i d and i q With the desired current i d * and i q * Input current loop controller, current loop controller output voltage u d and output voltage u qOutput voltage u d and output voltage u q The voltage signal u is obtained after inverse Park transform. α and voltage signal u β voltage signal u α and voltage signal u β A PWM signal (drive electrical signal) is obtained through a space vector modulation algorithm. The PWM signal drives the inverter to work, and the inverter converts the DC voltage U... dc The PWM wave is applied to the motor to drive the steering motor, which in turn drives the steering wheel through the transmission mechanism.

[0066] Step 104: Perform backlash compensation.

[0067] Based on historical data of the motor angle of the steering actuator motor when eliminating backlash in the mechanical structure during steering assembly, the drive current of the steering actuator motor is compensated to compensate for backlash elimination when the steering assembly performs vehicle steering.

[0068] Please refer to Figure 4 This is a schematic diagram of the structural connection of a steering actuator assembly in one embodiment. The steering actuator includes a steering motor, a worm gear reducer, and a rack and pinion steering mechanism. Its function is to receive steering commands transmitted by the ECU and execute steering. During the transmission process of the worm gear reducer and the rack and pinion steering mechanism, there is a backlash of 2α. Therefore, backlash compensation is required for the motor rotation angle of the steering motor during steering control. In one embodiment, each time the steering actuator performs vehicle steering, a preset backlash dead zone model is used to calculate the drive current compensation value for backlash elimination compensation of the steering motor. A preset particle swarm optimization algorithm is used to identify the parameters in the backlash dead zone model.

[0069] In one embodiment, a smooth and differentiable backlash dead zone model is established to address the backlash present in the transmission process of the worm gear reduction mechanism and rack and pinion steering mechanism in the steering actuator. The backlash effect is compensated by adjusting the reference current in the controller. Please refer to [reference needed]. Figure 5 This is a schematic diagram illustrating the implementation of a tooth gap model parameter identification method based on particle swarm optimization algorithm in one embodiment. Considering that the parameters in the dead zone model are unknown, it is necessary to identify the model parameters. In one embodiment, the particle swarm optimization algorithm is used to identify the parameter k in the dead zone model. m Estimate α, specifically including:

[0070] First, a smooth and differentiable backlash dead zone model is established. This backlash dead zone model is as follows:

[0071] τ l =k m θ-(km ÷2÷h)ln[(e h(θ-α) +e -h(θ-α) )÷(e h(θ+α) +e -h(θ+α) )];

[0072] θ = θ m -N1x r / r p ;

[0073] Where θ is the relative displacement, x r For rack displacement, k m α is the stiffness coefficient, N1 is the backlash width, and r is the reduction ratio of the worm gear reducer. p denoted as the equivalent radius of the meshing of the pinion and rack, and h is a parameter characterizing the smoothness of the model, with a value greater than 0.

[0074] Then, the particle swarm optimization algorithm is used to identify the parameter k in the dead zone model. m , α.

[0075] Initialize the particles and set the parameters to be identified (k) m If α is defined as x, then x is:

[0076] x i =(k mi α i );

[0077] Where i = 1, 2, ..., Size, and Size is the population size.

[0078] x for each particle i Initialize to a random number, satisfying the following boundary conditions:

[0079] k m min ≤x[1]≤k m max α min ≤x[2]≤α max ;

[0080] Where x[1] and x[2] are the vector values ​​of the particles; .k m min and k m max It is the range limit of x[1], α min and α max It is the range limit of x[2].

[0081] The fitness function of the particle swarm optimization algorithm is defined as follows:

[0082] J = 0.5 × ∑ qj=1 [τ l [j]- l [j] 2 ;

[0083] Where q is the number of samples. l [j] represents the j-th predicted transmission torque, τ l [j] represents the j-th sampled transmission torque.

[0084] Calculate the fitness value of each particle, and obtain the p-value of the particle swarm after each iteration. i k and g k :

[0085] p i k =min{J(x i 1 ), J(x i 2 ), …, J(x i M )}, g k =min{p1 k p2 k , ..., p Sine k};

[0086] Where, p i k The optimal value of the i-th particle in the k-th iteration; g k Let g be the global optimum of the particle swarm in the k-th iteration; updating g to the historical optimum of the particle swarm in the k-th iteration, we have:

[0087] When J(p) i k )≤J(p i k-1 When p i = p i k ;

[0088] When J(p) i k )>J(p i k-1 When p i = p i k-1 ;

[0089] When J(g) k )≤J(g k-1 When ), g = g k ;

[0090] When J(g) k )>J(g k-1 When ), g = g k-1 ;

[0091] Where, p i Let g be the individual optimal value of the i-th particle, and g be the global optimal value of the particle swarm.

[0092] The velocity of each particle is updated as follows:

[0093] ν i k+1 =w k+1 ν i k +c1r1(p i k -x i k )+c2r2(g k -x i k );

[0094] Where, ν i k is the velocity of the i-th particle in the k-th iteration, w is the weight factor, c1 and c2 are learning factors, and r1 and r2 are random numbers greater than 0 and less than 1.

[0095] In one embodiment, a linearly decreasing weight update method is employed, specifically including:

[0096] w k =w max -[(w max -w min )÷M]×k;

[0097] Among them, w k w is the weight factor for the k-th iteration. max w is the maximum value of the weighting factor. min This is the minimum value of the weighting factor.

[0098] Based on the particle velocity calculated in the k-th iteration, the particle position is updated as follows:

[0099] x i k+1 = x i k +ν i k+1 ;

[0100] When the number of iterations k reaches its maximum value M, the fitness function reaches its minimum value, and the final position of the particle (k) is... m M αM If x is the identification value of the parameter, then x = ... M =(k m M α M ).

[0101] Step 105: Perform disturbance compensation.

[0102] A pre-defined mathematical model of steering disturbance is applied to obtain the disturbance error value of the steering actuator motor caused by system disturbance in the steering actuator assembly, and the drive current of the steering actuator motor is compensated based on the disturbance error value. The system disturbance includes transmission torque estimation error, current loop tracking error, and / or friction torque disturbance.

[0103] Since the steering actuator assembly also has disturbances such as friction and parameter perturbation that cannot be accurately modeled during the steering process, in order to improve the ability of the steering actuator assembly to suppress unmodeled disturbances when performing steering, in one embodiment of this application, a super-twisted extended disturbance observer is used to estimate the lumped disturbance of the steering actuator assembly (i.e., the steering disturbance mathematical model).

[0104] Based on the identified backlash model parameters, the estimated value of the transmission torque is calculated:

[0105] l = m θ-( m ÷2h)×ln[(e h(θ-à) +e -h(θ-à) )÷(e h(θ+à) +e -h(θ+à) )];

[0106] in, m α is the estimated value of the stiffness coefficient, and à is the estimated value of the backlash width.

[0107] Affected by backlash compensation:

[0108] i q * = i q1 * +(J m / 1.5n p Ψ f )× l ;

[0109] Among them, i q * For the overall control output, i q1 *The output of the sliding mode controller is used to track the motor angle of the steering actuator motor to maintain the rotation angle of the steering wheels when the active steering system performs vehicle steering. Based on system disturbances including transmission torque estimation error, current loop tracking error, and friction torque disturbance, the mathematical model of steering disturbance (lumped disturbance) is defined as follows:

[0110] d0=[( l -τ l )+ 1.5n p Ψ f (i q -i q * )-B m m ]÷J m ;

[0111] b0=1.5n p ψ f / J m ;

[0112] Therefore, the dynamic model of the steering actuator can be rewritten as:

[0113] m =ω m ;

[0114] m = b0 i q1 * +d0;

[0115] By applying the super-twisting algorithm, the extended mathematical model of the steering perturbation observer can be defined as follows:

[0116] 1= b0 i q1 * +z2-l l φ1(ε1);

[0117] φ1(ε1)=|ε1| 0.5 sign(ε1)+ε1;

[0118] 2 = -l2φ2(ε1);

[0119] φ2(ε1)=(3 / 2)×|ε1| 0.5 sign(ε1)+(1 / 2)×sign(ε1)+ε1;

[0120] Where z1 and z2 are the motor speeds ω mAnd the estimated value of the disturbance d, ε1=z1-ω m Let l1 be the rotational speed estimation error; l2 and l3 are the adjustable gain coefficients of the observer. Based on this, the disturbance estimation error is defined as:

[0121] ε2 = z2 - d0;

[0122] Therefore, the system equation for the observation error is defined as follows:

[0123] 1=ε2-l l φ1(ε1);

[0124] 2=- 0-l2φ2(ε1);

[0125] The stability of the extended steering disturbance observer described above is then proven, choosing the Lyapunov function V as:

[0126] V=ξ T ·P·ξ;

[0127] Where P is a symmetric positive definite matrix; ξ T =[ϕ1(ε1) ε2], differentiating with respect to ξ, we get:

[0128] =ε2-l l φ1(ε1)- 0÷φ1′(ε1)-l2φ1(ε1)=(Aξ+Bζ)×φ1′(ε1);

[0129] Where A = B= , ζ=- 0 / φ1′(ε1) .

[0130] Add an auxiliary function as follows:

[0131] w(ξ,ζ)= ;

[0132] Where ϑ≥0, choosing appropriate R and S can make w(ξ,ζ)≥0 hold true.

[0133] Differentiating the Lyapunov function V, we get:

[0134] =

[0135] =

[0136] ≤

[0137] =

[0138] ≤

[0139] =-k1Vφ1′(ε1)= -k1V(0.5×|ε1| 0.5 +1)≤-k1(0.5×V 0.5 λ min 0.5 {P}+V)≤0;

[0140] Where k1>0; λ{P} is the eigenvalue of P.

[0141] As can be seen from the above, the steering disturbance observer is stable.

[0142] Please refer to Figure 6 This is a schematic diagram illustrating the control implementation of the steering actuator motor's rotation angle in one embodiment. In one embodiment, a sliding mode controller monitors the rotation angle of the steering actuator motor to maintain the rotation angle of the steering wheels when the active steering system performs vehicle steering. In one embodiment, a sliding mode controller with a non-singular fast terminal sliding surface is used, and an adaptive reaching law is designed to achieve fast and stable angle tracking. The non-singular fast terminal sliding surface is defined as:

[0143] s=e+α0|e| γ sign(e) +β0| | λ sign( );

[0144] Where e=θ m * -θ m Let α0 and β0 be constants greater than 0, λ and γ be constants, and satisfy 1 < λ < 2, γ > λ, and sign(·) be the sign function.

[0145] Differentiating the sliding surface and setting it equal to 0 yields the equivalent control law:

[0146] u e =[ * m -z2+(1+α0γ|e| γ-1 ) 2-γ ÷(β0λ)];

[0147] To effectively improve the dynamic performance of sliding mode control and suppress chattering caused by switching characteristics, an adaptive reaching law is added to achieve dynamic adjustment of the reaching rate. The constructed parameter adjustment function is as follows:

[0148] f1(s) = a1·|s| P ,P=-b1·{|sat(s)|-c1};

[0149] f1(s) = a2·|s| Q , Q=-b2·{|sat(s)|-c2};

[0150] Where a1, a2, b1, b2, c1, and c2 are adjustment coefficients, and sat(·) is the saturation function, whose expression is:

[0151] When s > D, sat(s) = 1;

[0152] When |s|≤D, sat(s)=ρ*s, ρ=D -1 ;

[0153] When s < -D, sat(s) = -1;

[0154] Where D is the boundary layer, which is related to the selection of parameters c1 and c2.

[0155] Based on the parameter adjustment function, the approach law is designed as follows:

[0156] ṡ=-ε0˙a1˙|s| P ˙sat(s)-k0˙a2˙|s| Q ˙s;

[0157] Where P = -b1·{|sat(s)|-c1}, Q = -b2·{|sat(s)|-c2}, and ε0 and k0 are adjustment coefficients.

[0158] Assume that when s>0, D=1, c1=1, c2=1.

[0159] When s>1 (i.e. the system state is far from the sliding surface), then |sat(s)|-c1=0, |sat(s)|-c2=0. At this time, the approaching speed is dominated by -ε0˙a1˙s and -k0˙a2˙s, which is similar to the traditional exponential approaching law.

[0160] As s decreases to s=1, the existence of the saturation function keeps the approach law expression unchanged, reducing chattering caused by sign function switching; when s<1 (i.e., the system state approaches the sliding surface), the approach velocity changes from -ε0˙a1˙s A ˙s and -k0˙a2˙s B Let ˙s be the dominant denominator, A = -b1˙(s-1), B = -b2˙(s-1), and satisfy ε0˙a1˙s. A ˙s <ε0˙a1 and k0˙a2˙s B˙s>k0˙a2˙s, that is, compared with the traditional reaching law, the newly added parameter adjustment function makes the system reaching speed increase and the chattering decrease.

[0161] The control output of the sliding mode controller is obtained as:

[0162] i q1 * = * m -z2+(1+α0γ|e| γ-1 ) 2-γ ÷(β0λ)+ε0f1(s)sat(s)+k0f2(s)s]÷b0;

[0163] Prove the stability of the sliding mode controller, and select the Lyapunov function:

[0164] V s =0.5s 2 ;

[0165] Derive the Lyapunov function V s to get:

[0166] s = =-sβ0λ| | λ-1 [ε0f1(s)sat(s)+k0f2(s)s]

[0167] When the system parameters satisfy ε0>0, k0>0, 0<a1<1, 0<a2<1, 0<b1<1, 0<b2<1, it can make s ≤0 hold, so the above sliding mode controller is stable.

[0168] In the high-precision corner execution servo control method disclosed in the embodiments of the present application, when the steering execution assembly of the active steering system controls the steering wheel to rotate through the steering execution motor, first obtain the motor drive electrical parameters of the steering execution motor according to the drive current conversion mathematical model, then obtain the drive electrical signal of the steering execution motor from the motor drive electrical parameters, and finally drive the steering execution motor to rotate according to the drive electrical signal to achieve the rotation control of the steering wheel. Since the drive current conversion mathematical model for steering control of the steering execution motor is established first, the steering accuracy of the active steering system for vehicle intelligent driving is higher and the robustness performance is stronger.

[0169] In one embodiment, the established mathematical model for drive current conversion is based on the steering actuator motor model and deadbeat current predictive control under vector control. A particle swarm optimization algorithm is used to identify the parameters of the backlash model; the influence of backlash is compensated based on the identified backlash model parameters. In another embodiment, a super-torsional extended disturbance observer is used to estimate the lumped disturbance in the system. In yet another embodiment, a sliding mode controller with a non-singular fast terminal sliding surface is used, and an adaptive reaching law is designed to achieve fast and stable angle tracking. By suppressing the backlash phenomenon in the active steering system and estimating and compensating for the lumped disturbance, the robustness of the angle servo control system is improved.

[0170] Those skilled in the art will understand that all or part of the functions of the various methods in the above embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, which may include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to achieve the above functions. For example, the program can be stored in the memory of a device, and when the program in the memory is executed by the processor, all or part of the above functions can be achieved. In addition, when all or part of the functions in the above embodiments are implemented by computer programs, the program can also be stored in a server, another computer, disk, optical disk, flash drive, or external hard drive, etc., and can be downloaded or copied to the memory of a local device, or the system of the local device can be updated. When the program in the memory is executed by the processor, all or part of the functions in the above embodiments can be achieved.

[0171] The above examples illustrate the present invention only to aid in understanding it and are not intended to limit the scope of the invention. Those skilled in the art can make various simple deductions, modifications, or substitutions based on the principles of this invention.

Claims

1. A high-precision steering angle execution servo control method for an intelligent driving active steering system, characterized in that, The active steering system includes a steering actuation assembly, which controls the rotation of the steering wheels via a steering actuation motor to achieve steering control of the vehicle; wherein the motor rotation angle of the steering actuation motor is positively correlated with the rotation angle of the steering wheels; The high-precision rotation servo control method includes: Obtain the desired motor rotation angle θ of the steering actuator motor. m * ; The desired motor rotation angle θ m * Input a preset driving current conversion mathematical model and obtain the motor driving electrical parameters output by the driving current conversion mathematical model; wherein, the driving current conversion mathematical model includes a permanent magnet synchronous motor mathematical model established in the dq coordinate system; The drive electrical signal of the steering actuator motor is obtained based on the motor drive electrical parameters, and the steering actuator motor is driven to rotate according to the drive electrical signal to achieve rotation control of the steering wheel.

2. The high-precision rotational servo control method as described in claim 1, characterized in that, The mathematical model of the permanent magnet synchronous motor includes: the d =R s the d +L s (of d / dt)-ω e L s the q ; the q =R s the q +L s (of q / dt) +ω e L s the q +ω e Ψ f ; m =ω m ; m =(1.5n p P f I q -B m oh m -t l )÷J m ; Among them, u d The stator voltage on the d-axis, u q L is the stator voltage on the q-axis. s (di d / dt) represents the inductor voltage drop caused by the dynamic change of the d-axis current, L s For stator inductance, L s (di q / dt) is the inductor voltage drop caused by the dynamic change of the q-axis current, i d Let i be the stator current on the d-axis. q R is the stator current on the q-axis. s For stator resistance, Ψ f For permanent magnet flux linkage, θ m For the mechanical angle of the motor, ω m J is the mechanical angular velocity of the motor. m B is the moment of inertia of the motor. m τ is the motor damping coefficient. l For the transmission torque, ω e Let n be the electric angular velocity of the motor. p The number of pole pairs; the motor drive electrical parameters include the stator voltage u on the d-axis. d and the stator voltage u on the q-axis q .

3. The high-precision rotational servo control method as described in claim 2, characterized in that, The driving current conversion mathematical model also includes a deadbeat current prediction control model, which is as follows: u d (t+1)=(R s -L s / T s )×i d (t+1)- ω e (t)×L s ×i q (t+1)+ (L s / T s )×i d * (t); u q (t+1)=(R s -L s / T s )×i q (t+1)+ ω e (t)×L s ×i d (t+1)+ (L s / T s )×i q * (t)+ω e (t)Ψ f ; Among them, u d (t+1) represents the predicted stator voltage on the d-axis at time t+1, u q (t+1) represents the predicted stator voltage on the q-axis at time t+1, i d * (t) represents the reference stator current on the d-axis at time t, i q * (t) represents the reference stator current on the q-axis at time t, ω e (t) is the electric angular velocity at time t, T s The sampling period is t, and t+1 is the next unit of time after t.

4. The high-precision rotational servo control method as described in claim 3, characterized in that, Also includes: Based on historical data of the motor rotation angle of the steering actuator motor when the steering actuator eliminates backlash in the mechanical structure, the drive current of the steering actuator motor is compensated to compensate for the backlash effect when the steering actuator performs vehicle steering.

5. The high-precision rotational servo control method as described in claim 4, characterized in that, The compensation of the drive current of the steering motor based on historical data of the motor angle of the steering motor when the steering assembly eliminates backlash in the mechanical structure includes: Each time the steering assembly performs vehicle steering, a preset backlash dead zone model is used to calculate the drive current compensation value of the steering motor when performing backlash elimination compensation; wherein, a preset particle swarm optimization algorithm is used to identify the parameters in the backlash dead zone model.

6. The high-precision rotational servo control method as described in claim 1, characterized in that, Also includes: By applying a pre-defined mathematical model of steering disturbance, the disturbance error value of the steering motor caused by the disturbance of the steering actuator assembly system is obtained; The system disturbances include transmission torque estimation error, current loop tracking error and / or friction torque disturbance; The drive current of the steering actuator motor is compensated based on the disturbance error value.

7. The high-precision rotational servo control method as described in claim 1, characterized in that, Also includes: A preset sliding mode controller is used to track the motor rotation angle of the steering actuator motor in order to maintain the rotation angle of the steering wheels when the active steering system performs vehicle steering.

8. The high-precision rotational servo control method as described in claim 1, characterized in that, Also includes: Real-time monitoring of the motor angle and / or drive current value of the steering actuator motor; The drive current of the steering actuator motor is compensated based on the changes in the motor angle value and / or drive current value obtained from monitoring, so as to supplement the steering force of the steering actuator assembly.

9. A computer-readable storage medium, characterized in that, The medium stores a program that can be executed by a processor to implement the high-precision cornering servo control method as described in any one of claims 1 to 8.

10. A computer program product comprising a computer program and / or instructions, characterized in that, When the computer program and / or instructions are executed by the processor, they implement the high-precision cornering servo control method as described in any one of claims 1 to 8.

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