A method and system for setting control parameters of an electric sliding door

Abstract

The application discloses a kind of electric sliding door control parameter setting method and system, method includes: S1, reads the real-time position of vehicle door and motor current;S2, according to the real-time position of vehicle door and motor current identifies vehicle door operating stage;S3, according to PID controller parameter and external load setting initial parameter search domain;S4, in initial parameter search domain, coarse search is carried out using cross-entropy algorithm, and candidate parameter search domain is obtained;BOBYQA algorithm is used in candidate parameter search domain, and the optimal PID controller parameter vector of current vehicle door operating stage is obtained;S5, whether vehicle door operating stage reaches last stage, if yes, output optimal PID controller parameter, otherwise, the initial parameter search domain of parameter search of next vehicle door operating stage is updated after the confidence region of current stage fine search end, as, return S4;S6, based on optimal PID controller parameter control motor operation, so that motor completes door opening or closing action.The application improves anti-interference ability and dynamic performance.

Description

Technical Field

[0001] This invention relates to vehicle door control technology, specifically to a method and system for tuning control parameters of an electric sliding door. Background Technology

[0002] In door control systems primarily based on position control, the opening and closing of the door essentially relies on the precise position control of the drive motor. However, during actual operation, due to factors such as changes in door load, mechanical transmission backlash, external vibration, and power fluctuations, traditional fixed-parameter control methods struggle to balance response speed and stability, easily leading to overshoot, jitter, or positioning deviations, thus affecting overall vehicle safety and passenger experience. Therefore, it is necessary to design an intelligent control algorithm capable of self-tuning door position control parameters to improve the system's anti-disturbance capability and dynamic performance. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method and system for tuning control parameters of an electric sliding door, which enables offline optimization of door motor control parameters in the absence of an accurate dynamic model, thereby improving the door's operational stability, safety, and anti-disturbance capability.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A method for tuning control parameters of an electric sliding door includes the following steps: S1. Read the real-time position of the door and the motor drive current; S2. Identify the door's operating stage based on the door's real-time position and motor drive current; S3. Construct a PID controller and set the initial parameter search domain based on the PID controller parameters and the external load; S4. The cross-entropy algorithm is used to perform a coarse search in the initial parameter search domain to randomly generate candidate PID controller parameters. The fitness is calculated according to the comprehensive evaluation function, and the top K candidate PID controller parameters with the highest fitness values ​​are selected to form the candidate parameter search domain. The trust region BOBYQA algorithm is used to perform a fine search in the candidate parameter search domain to obtain the optimal PID controller parameter vector for the current door operation stage. S5. Determine whether the door operation stage has reached the final stage. If so, output the optimal PID controller parameters. Otherwise, after the final trust domain corresponding to the end of the current stage fine search is shrunk and updated, it is used as the initial parameter search domain for the next door operation stage parameter search. Return to S4. S6. Control the motor operation based on the optimal PID controller parameters to enable the motor to complete the opening or closing action of the door.

[0005] To optimize the above technical solution, the specific measures also include: Furthermore, S1 specifically refers to: By utilizing the motor encoder resolution and motor transmission ratio, the motor encoder pulse count is converted into the real-time position of the car door. The formula is expressed as:

[0006] in, This represents the encoder pulse count. For encoder resolution; This refers to the motor transmission ratio; Simultaneously, the motor drive current is collected, and the current signal is filtered. The formula is as follows:

[0007] In the formula, For the first The filtered current at time t. For the first The motor drive current is sampled at all times. These are the filter coefficients.

[0008] Furthermore, S2 specifically refers to: Based on the maximum opening position of the car door With maximum closing position Real-time position of the car door The normalization process is performed using the following formula:

[0009] In the formula, It is the normalized real-time position of the car door; Based on motor drive current Real-time position of normalized car door Identifying the door operation phase specifically involves: The threshold values ​​for the different stages of the car door operation are set as follows: , , , The following conditions are met: ; when and At that time, it is determined that the car door is in the unlocking and starting phase; when and At that time, it is determined that the car door is in the acceleration phase; when and At that time, it is determined that the car door is in a constant speed operation phase; when and At that time, it was determined that the car door was in the deceleration approach phase. and When the door is in the clamping and locking stage, the formula is as follows: It refers to the current magnitude when the car door moves at a constant speed, and the starting current threshold. Crush current threshold , It is the rate of change of current, and the threshold for the rate of change of current. , It is the fluctuation of the motor drive current. It is the current fluctuation threshold.

[0010] Furthermore, S3 specifically refers to: The control rate of the PID controller is: In the formula, It is the control input of the PID controller, namely the motor drive current. It is proportional gain. It is integral gain. It is the differential gain. It is a positional error. It is the integral variable, and t is time. Define the PID controller parameter vector as follows The initial parameter search domain is set based on the PID controller parameters and the external load, as shown in the following formula:

[0011] In the formula, D is the search domain for the initial parameters. It is the PID controller parameter vector. For external load, The search domain adjustment coefficient is used; the expression for the external load is as follows:

[0012] In the formula, The coefficient of viscous friction is... The speed at which the car door operates.

[0013] Furthermore, in S4, the comprehensive evaluation function is specifically as follows:

[0014] In the formula, It is the parameter vector of the PID controller. fitness value, n It is the sequence number of the door operation stage. For stage weights; For the first The stage evaluation function is expressed by the following formula:

[0015] Here, ITAE is the integral error index. It is the weight of the error integral index. For peak current indicators, The weight of the peak current index, Current running time Compared with reference running time deviation, The weight of the deviation, The estimated time required to complete one action; t represents time. It is the position error, and the formula is as follows:

[0016] In the formula, The target position is the maximum open position when the input command is an open command, and the maximum closed position when the input command is a close command. It shows the real-time position of the car door.

[0017] Furthermore, in S4, the detailed search using the trust-region BOBYQA algorithm within the candidate parameter search domain specifically involves: Constructing a quadratic approximation model The comprehensive evaluation function is approximated by the following formula:

[0018] In the formula, It is the PID controller parameter vector. For constant terms; The gradient vector; It is an approximation of the Hessian matrix; Under the trust region constraint, the parameter vector of the PID controller is searched to find the parameter vector that makes the PID controller more suitable for the trust region constraint. The PID controller parameter vector with the smallest value has the following trust region constraint:

[0019] in, Indicates the first The PID controller parameter vector at the next iteration is used as the center point of the local search in this round. This represents the candidate PID controller parameter vector located in the neighborhood of the center point. The radius of the trust region; The search step size is solved within the trust region. And update the PID controller parameter vector:

[0020] In the formula, Indicates the first a The PID controller parameter vector at +1 iteration; The ratio of the actual decrease in the comprehensive evaluation function before and after the update to the predicted decrease in the quadratic approximation model is calculated to obtain the trust region adjustment coefficient. :

[0021] Based on trust region adjustment coefficient Adjust the trust region using the following formula:

[0022] in, and The threshold for determining trust region shrinkage, and the trust region expansion coefficient. Shrinkage coefficient ; When the number of iterations reaches the preset maximum number of iterations or the trust region radius is less than the minimum threshold When the current door operation phase is stopped, the fine search iteration is stopped, and the optimal PID controller parameter vector for that phase is output.

[0023] Furthermore, in S5, the shrinking update of the final trust domain corresponding to the end of the current stage fine search is specifically as follows:

[0024] In the formula, This is the initial parameter search domain for the next stage. It is the phase shrinkage coefficient. It is the final trust domain corresponding to the end of the current stage of fine search.

[0025] The present invention also proposes an electric sliding door control system, comprising: An encoder is used to count pulses of a motor's rotation. The data acquisition unit is used to convert the encoder sampling results into the real-time position of the door and to acquire the motor drive current; A multi-stage recognition unit is used to identify the operating stage of the door based on the real-time position of the door and the motor drive current. An offline self-tuning control unit is used to construct a PID controller. An initial parameter search domain is set based on the PID controller parameters and the external load. A cross-entropy algorithm is used to perform a coarse search within the initial parameter search domain for the current door operation stage, randomly generating candidate PID controller parameters. Fitness is calculated based on a comprehensive evaluation function, and the top K candidate PID controller parameters with the highest fitness values ​​are selected to form the candidate parameter search domain. Within the candidate parameter search domain, a trust-domain BOBYQA algorithm is used for a fine search to obtain the optimal PID controller parameter vector for the current door operation stage. It is then determined whether the door operation stage has reached its final stage. If so, the optimal PID controller parameters are output; otherwise, the final trust domain corresponding to the end of the current stage's fine search is shrunk and updated, serving as the initial parameter search domain for the next door operation stage. The motor drive current is obtained based on the optimal PID controller parameters. A current limiting unit is used to limit the motor's torque based on the motor's drive current. The motor is used to drive the sliding door drive chain to open or close the door.

[0026] The beneficial effects of this invention are: (1) The present invention divides the opening and closing process of the sliding door into five stages: unlocking and starting, accelerating, sliding at a constant speed, decelerating and approaching, and pressing and locking. Based on the normalized position and motor current, the stage identification is performed, which can more accurately reflect the sudden change of door load and end pressing characteristics, and improve the stability and adaptability of stage identification.

[0027] (2) In the offline self-tuning mode, the present invention adopts a coarse-fine adaptive search strategy of cross-entropy algorithm and trust domain BOBYQA algorithm, and introduces cross-stage search domain inheritance and shrinking mechanism to reduce the amount of parameter search calculation and improve tuning convergence efficiency. Attached Figure Description

[0028] Figure 1 This is a flowchart of the method for tuning the control parameters of the electric sliding door proposed in this invention.

[0029] Figure 2 This is a structural diagram of the electric sliding door control system proposed in this invention. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0031] Example 1 This invention proposes a method for tuning the control parameters of an electric sliding door, the flowchart of which is shown below. Figure 1 As shown, it includes the following steps: S1. Read the real-time position of the door and the motor drive current; specifically: By utilizing the motor encoder resolution and motor transmission ratio, the motor encoder pulse count is converted into the real-time position of the car door. The formula is expressed as:

[0032] in, This represents the encoder pulse count. For encoder resolution; This refers to the motor transmission ratio; Simultaneously, the motor drive current is collected, and the current signal is filtered. The formula is as follows:

[0033] In the formula, For the first The filtered current at time t. For the first The motor drive current is sampled at all times. These are the filter coefficients.

[0034] S2. Identify the door's operating stage based on the door's real-time position and motor drive current; specifically: Based on the maximum opening position of the car door With maximum closing position Real-time position of the car door The normalization process is performed using the following formula:

[0035] In the formula, It is the normalized real-time position of the car door; Based on motor drive current Real-time position of normalized car door Identifying the door operation phase specifically involves: The threshold values ​​for the different stages of the car door operation are set as follows: , , , The following conditions are met: ; when and At that time, it is determined that the car door is in the unlocking and starting phase; when and At that time, it is determined that the car door is in the acceleration phase; when and At that time, it is determined that the car door is in a constant speed operation phase; when and At that time, it was determined that the car door was in the deceleration approach phase. and When the door is in the clamping and locking stage, the formula is as follows: It refers to the current magnitude when the car door moves at a constant speed, and the starting current threshold. Crush current threshold , It is the rate of change of current, and the threshold for the rate of change of current. , It is the fluctuation of the motor drive current. It is the current fluctuation threshold.

[0036] S3. Construct a PID controller and set the initial parameter search domain based on the PID controller parameters and the external load; specifically: The control rate of the PID controller is: In the formula, It is the control input of the PID controller, namely the motor drive current. It is proportional gain. It is integral gain. It is the differential gain. It is a positional error. It is the integral variable, and t is time. Define the PID controller parameter vector as follows The initial parameter search domain is set based on the PID controller parameters and the external load, as shown in the following formula:

[0037] In the formula, D is the search domain for the initial parameters. It is the PID controller parameter vector. For external load, The search domain adjustment coefficient is used; the expression for the external load is as follows:

[0038] In the formula, The coefficient of viscous friction is... The speed at which the car door operates.

[0039] S4. The cross-entropy algorithm (CEM) is used to perform a coarse search in the initial parameter search domain to randomly generate N candidate PID controller parameters. The candidate PID controller parameters follow a normal distribution. , The mean of the parameters, It is the covariance matrix; The fitness is calculated based on the comprehensive evaluation function, and the top K candidate PID controller parameters with the highest fitness values ​​are selected to form the candidate parameter search domain. The comprehensive evaluation function is as follows:

[0040] In the formula, It is the parameter vector of the PID controller. fitness value, n It is the sequence number of the door operation stage. For stage weights; For the first The stage evaluation function is expressed by the following formula:

[0041] Here, ITAE is the integral error index. It is the weight of the error integral index. For peak current indicators, The weight of the peak current index, Current running time Compared with reference running time deviation, The weight of the deviation, The estimated time required to complete one action; t represents time. It is the position error, and the formula is as follows:

[0042] In the formula, The target position is the maximum open position when the input command is an open command, and the maximum closed position when the input command is a close command. It shows the real-time position of the car door.

[0043] The probability distribution is then updated, expressed by the formula:

[0044] In the formula, This is the updated mean. It is the updated covariance matrix. These are the parameters of the k-th candidate PID controller.

[0045] Within the candidate parameter search domain, a fine-grained search is performed using the trust-region BOBYQA algorithm to obtain the optimal PID controller parameter vector for the current door operation phase; specifically: Constructing a quadratic approximation model The comprehensive evaluation function is approximated by the following formula:

[0046] In the formula, It is the PID controller parameter vector. For constant terms; The gradient vector; It is an approximation of the Hessian matrix; Under the trust region constraint, the parameter vector of the PID controller is searched to find the parameter vector that makes the PID controller more suitable for the trust region constraint. The PID controller parameter vector with the smallest value has the following trust region constraint:

[0047] in, Indicates the first The PID controller parameter vector at the next iteration is used as the center point of the local search in this round. This represents the candidate PID controller parameter vector located in the neighborhood of the center point. The radius of the trust region; The search step size is solved within the trust region. And update the PID controller parameter vector:

[0048] In the formula, Indicates the first a The PID controller parameter vector at +1 iteration; The ratio of the actual decrease in the comprehensive evaluation function before and after the update to the predicted decrease in the quadratic approximation model is calculated to obtain the trust region adjustment coefficient. :

[0049] Based on trust region adjustment coefficient Adjust the trust region using the following formula:

[0050] in, and The threshold for determining trust region shrinkage, and the trust region expansion coefficient. Shrinkage coefficient ; When the number of iterations reaches the preset maximum number of iterations or the trust region radius is less than the minimum threshold When the current door operation phase is stopped, the fine search iteration is stopped, and the optimal PID controller parameter vector for that phase is output.

[0051] S5. Determine whether the door operation stage has reached the final stage. If so, output the optimal PID controller parameters. Otherwise, after the final trust domain corresponding to the end of the current stage fine search is shrunk and updated, it is used as the initial parameter search domain for the next door operation stage parameter search. Return to S4. The shrinking update is specifically as follows:

[0052] In the formula, This is the initial parameter search domain for the next stage. It is the phase shrinkage coefficient. , It is the final trust domain corresponding to the end of the current stage of fine search.

[0053] S6. Control the motor operation based on the optimal PID controller parameters to enable the motor to complete the opening or closing action of the door.

[0054] Example 2 This invention proposes an electric sliding door control system corresponding to the method in Embodiment 1, comprising: An encoder is used to count pulses of a motor's rotation. The data acquisition unit is used to convert the encoder sampling results into the real-time position of the door and to acquire the motor drive current; A multi-stage recognition unit is used to identify the operating stage of the door based on the real-time position of the door and the motor drive current. An offline self-tuning control unit is used to construct a PID controller. An initial parameter search domain is set based on the PID controller parameters and the external load. A cross-entropy algorithm is used to perform a coarse search within the initial parameter search domain for the current door operation stage, randomly generating candidate PID controller parameters. Fitness is calculated based on a comprehensive evaluation function, and the top K candidate PID controller parameters with the highest fitness values ​​are selected to form the candidate parameter search domain. Within the candidate parameter search domain, a trust-domain BOBYQA algorithm is used for a fine search to obtain the optimal PID controller parameter vector for the current door operation stage. It is then determined whether the door operation stage has reached its final stage. If so, the optimal PID controller parameters are output; otherwise, the final trust domain corresponding to the end of the current stage's fine search is shrunk and updated, serving as the initial parameter search domain for the next door operation stage. The motor drive current is obtained based on the optimal PID controller parameters. A current limiting unit is used to limit the motor's torque based on the motor's drive current. The motor is used to drive the sliding door drive chain to open or close the door.

[0055] The implementation methods of each module and its function in the system are completely consistent with the steps of the method in Implementation Example 1, so they will not be repeated here.

[0056] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0057] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for tuning control parameters of an electric sliding door, characterized in that, Includes the following steps: S1. Read the real-time position of the door and the motor drive current; S2. Identify the door's operating stage based on the door's real-time position and motor drive current; S3. Construct a PID controller and set the initial parameter search domain based on the PID controller parameters and the external load; S4. Use the cross-entropy algorithm to perform a coarse search in the initial parameter search domain, randomly generate candidate PID controller parameters, calculate the fitness according to the comprehensive evaluation function, and select the top K candidate PID controller parameters by fitness value to form the candidate parameter search domain. Within the candidate parameter search domain, the trust-region BOBYQA algorithm is used for fine-grained search to obtain the optimal PID controller parameter vector for the current door operation stage. S5. Determine whether the door operation stage has reached the final stage. If so, output the optimal PID controller parameters. Otherwise, after the final trust domain corresponding to the end of the current stage fine search is shrunk and updated, it is used as the initial parameter search domain for the next door operation stage parameter search. Return to S4. S6. Control the motor operation based on the optimal PID controller parameters to enable the motor to complete the opening or closing action of the door.

2. The method for tuning control parameters of an electric sliding door as described in claim 1, characterized in that, S1 specifically refers to: By utilizing the motor encoder resolution and motor transmission ratio, the motor encoder pulse count is converted into the real-time position of the car door. The formula is expressed as: in, This represents the encoder pulse count. For encoder resolution; This refers to the motor transmission ratio; Simultaneously, the motor drive current is collected, and the current signal is filtered. The formula is as follows: In the formula, For the first The filtered current at time t. For the first The motor drive current is sampled at all times. These are the filter coefficients.

3. The method for tuning control parameters of an electric sliding door as described in claim 1, characterized in that, S2 specifically refers to: Based on the maximum opening position of the car door With maximum closing position Real-time position of the car door The normalization process is performed using the following formula: In the formula, It is the normalized real-time position of the car door; Based on motor drive current Real-time position of normalized car door Identifying the door operation phase specifically involves: The threshold values ​​for the different stages of the car door operation are set as follows: , , , The following conditions are met: ; when and At that time, it is determined that the car door is in the unlocking and starting phase; when and At that time, it is determined that the car door is in the acceleration phase; when and At that time, it is determined that the car door is in a constant speed operation phase; when and At that time, it was determined that the car door was in the deceleration approach phase. and When the door is in the clamping and locking stage, the formula is as follows: It refers to the current magnitude when the car door moves at a constant speed, and the starting current threshold. Crush current threshold , It is the rate of change of current, and the threshold for the rate of change of current. , It is the fluctuation of the motor drive current. It is the current fluctuation threshold.

4. The method for tuning control parameters of an electric sliding door as described in claim 1, characterized in that, S3 specifically refers to: The control rate of the PID controller is: In the formula, It is the control input of the PID controller, namely the motor drive current. It is proportional gain. It is integral gain. It is the differential gain. It is a positional error. It is an integral variable. t It is a moment; Define the PID controller parameter vector as follows The initial parameter search domain is set based on the PID controller parameters and the external load, as shown in the following formula: In the formula, D is the search domain for the initial parameters. It is the PID controller parameter vector. For external load, The search domain adjustment coefficient is used; the expression for the external load is as follows: In the formula, The coefficient of viscous friction is... The speed at which the car door operates.

5. The method for tuning the control parameters of an electric sliding door as described in claim 1, characterized in that, In S4, the comprehensive evaluation function is specifically as follows: In the formula, It is the parameter vector of the PID controller. fitness value, n It is the sequence number of the door operation stage. For stage weights; For the first The stage evaluation function is expressed by the following formula: Here, ITAE is the integral error index. It is the weight of the error integral index. For peak current indicators, The weight of the peak current index, Current running time Compared with reference running time deviation, The weight of the deviation, The estimated time required to complete one action; t represents time. It is the position error, and the formula is as follows: In the formula, The target position is the maximum open position when the input command is an open command, and the maximum closed position when the input command is a close command. It shows the real-time position of the car door.

6. The method for tuning the control parameters of an electric sliding door as described in claim 5, characterized in that, In S4, the detailed search using the trust-region BOBYQA algorithm within the candidate parameter search domain specifically refers to: Constructing a quadratic approximation model The comprehensive evaluation function is approximated by the following formula: In the formula, It is the PID controller parameter vector. For constant terms; The gradient vector; It is an approximation of the Hessian matrix; Under the trust region constraint, the parameter vector of the PID controller is searched to find the parameter vector that makes the PID controller more suitable for the trust region constraint. The PID controller parameter vector with the smallest value has the following trust region constraint: in, Indicates the first The PID controller parameter vector at the next iteration is used as the center point of the local search in this round. This represents the candidate PID controller parameter vector located in the neighborhood of the center point. The radius of the trust region; The search step size is solved within the trust region. And update the PID controller parameter vector: In the formula, Indicates the first a The PID controller parameter vector at +1 iteration; The ratio of the actual decrease in the comprehensive evaluation function before and after the update to the predicted decrease in the quadratic approximation model is calculated to obtain the trust region adjustment coefficient. : Based on trust region adjustment coefficient Adjust the trust region using the following formula: in, and The threshold for determining trust region shrinkage, and the trust region expansion coefficient. Shrinkage coefficient ; When the number of iterations reaches the preset maximum number of iterations or the trust region radius is less than the minimum threshold When the current door operation phase is stopped, the fine search iteration is stopped, and the optimal PID controller parameter vector for that phase is output.

7. The method for tuning the control parameters of an electric sliding door as described in claim 1, characterized in that, In S5, the shrinking update of the final trust domain corresponding to the end of the current stage fine search is specifically as follows: In the formula, This is the initial parameter search domain for the next stage. It is the phase shrinkage coefficient. It is the final trust domain corresponding to the end of the current stage of fine search.

8. An electric sliding door control system, characterized in that, include: An encoder is used to count pulses of a motor's rotation. The data acquisition unit is used to convert the encoder sampling results into the real-time position of the door and to acquire the motor drive current; A multi-stage recognition unit is used to identify the operating stage of the door based on the real-time position of the door and the motor drive current. An offline self-tuning control unit is used to construct a PID controller and set the initial parameter search domain based on the PID controller parameters and the external load. The cross-entropy algorithm is used to perform a coarse search in the initial parameter search domain during the current door operation phase, randomly generating candidate PID controller parameters. The fitness is calculated based on the comprehensive evaluation function, and the top K candidate PID controller parameters with the highest fitness values ​​are selected to form the candidate parameter search domain. Within the candidate parameter search domain, the trust domain BOBYQA algorithm is used for fine search to obtain the optimal PID controller parameter vector for the current door operation stage. It is then determined whether the door operation stage has reached the final stage. If so, the optimal PID controller parameters are output. Otherwise, the final trust domain corresponding to the end of the fine search of the current stage is shrunk and updated, and used as the initial parameter search domain for the next door operation stage. The motor drive current is obtained based on the optimal PID controller parameters; A current limiting unit is used to limit the motor's torque based on the motor's drive current. The motor is used to drive the sliding door drive chain to open or close the door.

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