Method for optimizing steering idle speed control of oil pump motor of counterbalance forklift truck

Abstract

The invention discloses a method for optimizing steering idle speed control of an oil pump motor of a counterbalance forklift truck, relates to the technical field of motor control, and solves the problems that the rotating speed of a motor is obviously lower than a set idle speed and steering is delayed or shaken due to sudden load increase of a hydraulic system at the moment of steering. The steering idle speed control optimization method comprises the steps that the steering feedback idle speed and the steering oil pressure are collected, the steering idle speed falling difference value is calculated according to the preset steering idle speed, and a load dynamic model is established; judging the duration time of the steering idle speed drop difference value, and obtaining the control torque of an oil pump motor by utilizing a composite proportional-integral controller in combination with the steering oil pressure; a torque conversion coefficient is calculated through a third-order nonlinear observer and a temperature compensation coefficient according to steering oil pressure collected by a hydraulic sensor, and then offset torque is obtained; and the control torque and the offset torque of the oil pump motor are calculated according to an idling dynamic weight distribution strategy and then input into the heavy forklift to control the oil pump motor to complete steering idling optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and particularly to a method for optimizing the steering idle speed control of an oil pump motor of a counterbalanced forklift. Background Art

[0002] In an electric counterbalanced forklift, since the steering control of the oil pump does not have a switch signal input like tilt, side shift, attachments, and lifting, the steering idle speed maintains a fixed low speed operation; when turning the steering wheel, the steering oil pressure comes out and the load increases, resulting in a drop in speed. To improve this situation, the usual method is to increase the PI, but the PI cannot be increased continuously. When it reaches a certain level, it is easy to cause jitter in the speed loop; the idle speed is usually relatively low. If the idle speed drops, it is difficult to ensure the smooth operation of the engine and the normal operation of various accessories. Advanced control methods and control strategies must be adopted to solve the problem of speed drop during steering idle speed.

[0003] Patent No. CN201811480365.3 discloses an idle driving control system, control method and vehicle applicable to multiple road conditions. The control system includes an idle target vehicle speed parameter calculation module for selecting the smaller one of the initial idle vehicle speed parameter and the idle vehicle speed parameter during steering as the idle target vehicle speed parameter; a road slope calculation module for calculating the road slope; a compensation torque calculation module during steering for calculating the compensation torque during steering; a slope compensation torque calculation module for retrieving the slope compensation torque; and an idle driving torque calculation module for retrieving the idle driving torque and sending it to the motor control unit to control the output torque of the motor. The above invention can accurately calculate the idle driving torque under various road conditions, which is beneficial to improving the safety of idle driving.

[0004] Patent No. CN201610331648.6 discloses an electric power steering assist control system, method and vehicle based on vehicle idle start-stop. The system includes a detection component, a vehicle speed sensor, an engine controller, a steering wheel torque detector, an assist motor, a motor controller and a steering control unit. The detection component includes a first detector and a second detector. The steering control unit sends a motor control signal to the motor controller when receiving a vehicle idle start-stop opening signal, an ignition switch opening signal, the vehicle speed is zero, the engine is in a stopped state and the torque value applied to the steering wheel is greater than a preset value, so as to control the assist motor to output a steering torque through the motor controller. Thus, the above system can control the assist motor to output a steering torque when the vehicle speed is zero and the engine is in a stopped state, so that the user can easily turn the steering wheel, thereby improving driving comfort and user experience.

[0005] Although the above patent uses a traditional control method to control the steering idle speed through speed feedback, the load of the hydraulic system suddenly increases during the steering moment, resulting in the motor speed being significantly lower than the set idle speed, causing steering hysteresis or jitter; the fixed PI parameters cannot meet the requirements of multiple working conditions, and the continuous high torque output leads to motor overheating and energy consumption waste; the sudden change of the hydraulic system pressure is likely to cause pipeline impact and reduce the component life. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for optimizing the steering idle speed control of the oil pump motor of a counterbalanced forklift, which can achieve stable steering idle speed through hydraulic pressure feedforward compensation and adaptive PI control; thereby reducing the peak value of the steering impact pressure and improving the system reliability.

[0007] The present invention utilizes the following technical solutions: A method for optimizing the steering idle speed control of the oil pump motor of a counterbalanced forklift sequentially includes the following steps; S1: When the steering wheel is rotated, collect the steering feedback idle speed and the steering oil pressure, and calculate the steering idle speed drop difference according to the preset steering idle speed, and establish a load dynamic model; S2: Judge the duration of the steering idle speed drop difference, and use a compound proportional-integral controller combined with the steering oil pressure to obtain the control torque of the oil pump motor; S3: When the steering wheel is rotated, use a third-order nonlinear observer combined with a temperature compensation coefficient to calculate the torque conversion coefficient from the steering oil pressure collected by the hydraulic sensor, and then obtain the offset torque; S4: After operating the control torque and the offset torque of the oil pump motor according to the idle speed dynamic weight distribution strategy, input them into the torque control system of the counterbalanced forklift to control the oil pump motor to complete the optimization of the steering idle speed.

[0008] Preferably, step S1 includes the following steps: S11: Extract the steering feedback idle speed, oil pump displacement, steering oil pressure, motor torque, motor speed, valve port flow rate, and valve port pressure difference from the central controller of the counterbalanced forklift; S12: Calculate the difference between the steering feedback idle speed and the preset steering idle speed to obtain the steering idle speed drop difference, and at the same time perform a fitting operation on the oil pump displacement and the steering oil pressure to obtain a displacement-pressure characteristic curve; S13: Perform a fitting operation on the motor torque and the motor speed to obtain a torque-speed response curve; at the same time, perform a fitting operation on the valve port flow rate and the valve port pressure difference to obtain a flow-pressure difference characteristic curve and a flow-pressure difference mapping table; S14: Calculate the dynamic load sensitivity coefficient according to the steering idle speed drop difference and the steering oil pressure combined with the oil compressibility correction index; S15: Construct a load dynamic model based on the displacement-pressure characteristic curve, torque-speed response curve, flow-differential pressure characteristic curve, and the dynamic load sensitivity coefficient.

[0009] Preferably, in step S2, if the duration of the steering idle drop difference is greater than the preset time threshold, start the integral separation algorithm to convert the compound proportional-integral controller into a dynamic proportional controller; if the duration of the steering idle drop difference is less than or equal to the preset time threshold, first generate the dynamic proportional parameter of the compound proportional-integral controller by combining the steering idle drop difference and the initial proportional parameter with the dynamic load sensitivity coefficient; at the same time, use the steering idle drop difference and the initial integral parameter combined with the oil compressibility correction index to obtain the dynamic integral parameter of the compound proportional-integral controller; then generate the feedforward torque component according to the steering oil pressure in combination with the flow-differential pressure mapping table; at the same time, input the steering idle drop difference into the compound proportional-integral controller to obtain the original torque component; finally, after the operation of the feedforward torque component and the original torque component in combination with the feedforward gain coefficient, obtain the control torque of the oil pump motor.

[0010] Preferably, step S3 includes the following steps: S31: Filter and denoise the steering oil pressure signal collected by the hydraulic sensor using a third-order Butterworth low-pass filter, and perform analog-to-digital conversion using an AD converter to obtain the steering oil pressure; S32: Collect the steering column temperature, ambient temperature, and motor winding temperature using a temperature sensor, and establish a temperature compensation matrix; S33: Establish a steering assist oil viscosity-temperature model based on non-Newtonian fluid characteristics, and use a polynomial to fit and generate a temperature compensation coefficient according to the temperature compensation matrix; S34: Establish a state space equation based on the equivalent torque, torque change rate, and motor disturbance term in combination with the road surface impact disturbance function, and perform numerical solution using the fourth-order Runge-Kutta method to obtain a third-order nonlinear observer; S35: Define an objective function and use the conjugate gradient method to solve the torque conversion coefficient according to the preset convergence threshold, and at the same time iteratively update the parameter matrix of the third-order nonlinear observer; S36: Perform a comprehensive operation on the steering oil pressure and torque conversion coefficient in combination with the temperature compensation matrix, time accumulation coefficient, and hysteresis compensation coefficient, and introduce an angular velocity correction factor to obtain the offset torque.

[0011] Preferably, the working process of the idle dynamic weight distribution strategy is as follows: A: Extract the steering angular velocity, oil pressure change rate, and motor speed from the central processing unit; B: Divide the steering angular velocity into five fuzzy elements: large negative, small negative, zero, small positive, and large positive using a Gaussian distribution function; at the same time, divide the oil pressure change rate into five fuzzy elements: rapid decrease, slow decrease, stable, slow increase, and rapid increase using a triangular function; at the same time, divide the motor speed into three fuzzy elements: extremely low, low, and critical using a trapezoidal function; C: Generate a number of fuzzy rules using the fuzzy elements of the steering angular velocity, oil pressure change rate, and motor speed, and calculate the rule strength of each fuzzy rule using the Mamdani fuzzy inference method; D: Analyze the torque weight using the centroid method according to the fuzzy rules, and correct it according to the steering idle drop difference in combination with the learning factor, and then obtain the optimal torque weight.

[0012] Preferably, step S4 includes the following steps: S41: Synchronously obtain the steering angular velocity, oil pressure change rate, and motor speed according to the CAN bus of the heavy forklift, eliminate the time delay difference of the above data using the timestamp alignment algorithm, and establish a real-time feature vector matrix; S42: Input the real-time feature vector matrix into the idle dynamic weight distribution strategy to obtain the optimal torque weights of the control torque and the bias torque respectively; S43: Combine the control torque and the bias torque with the corresponding optimal torque weights, combine the PID algorithm and the three-dimensional interpolation algorithm, and perform an operation with the feedforward compensation term to obtain the synthesized torque; S44: Modulate the synthesized torque using space vector PWM to generate a three-phase voltage command, and inject a third harmonic to compensate for the low-speed torque ripple, and then complete the conversion of the synthesized torque into a motor drive signal; S45: Input the motor drive signal into the torque control system of the heavy forklift to complete the optimization of the steering idle speed of the control oil pump motor.

[0013] The present invention realizes the stability of the steering idle speed through hydraulic pressure feedforward compensation and adaptive PI control; thereby reducing the peak value of the steering impact pressure and improving the reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative work.

[0015] Figure 1 It is a principle block diagram of the method for optimizing the idle speed control; Figure 2 It is a flow chart of the idle speed control optimization. Detailed implementation manners

[0016] The present invention will be described in detail below with reference to the accompanying drawings and embodiments: As Figure 1 - Figure 2 shown, a method for optimizing the steering idle speed control of the oil pump motor of a counterbalanced forklift sequentially includes the following steps; S1: When the steering wheel is rotated, collect the steering feedback idle speed and steering oil pressure, and calculate the steering idle speed drop difference according to the preset steering idle speed, and establish a load dynamic model; S2: Judge the duration of the steering idle speed drop difference, and use a compound proportional-integral controller combined with the steering oil pressure to obtain the control torque of the oil pump motor; S3: When the steering wheel is rotated, the steering oil pressure collected by the hydraulic sensor is used, and a third-order nonlinear observer is combined with the temperature compensation coefficient to calculate the torque conversion coefficient, and then the offset torque is obtained; S4: After the control torque and offset torque of the oil pump motor are calculated according to the idle speed dynamic weight distribution strategy, they are input into the torque control system of the heavy forklift to control the oil pump motor to complete the optimization of the steering idle speed.

[0017] In the present invention, step S1 includes the following steps: S11: Extract the steering feedback idle speed, oil pump displacement, steering oil pressure, motor torque, motor speed, valve port flow rate, and valve port pressure difference from the central controller of the heavy forklift; S12: Calculate the difference between the steering feedback idle speed and the preset steering idle speed to obtain the steering idle speed drop difference. At the same time, perform a fitting operation on the oil pump displacement and the steering oil pressure to obtain a displacement-pressure characteristic curve; S13: Perform a fitting operation on the motor torque and the motor speed to obtain a torque-speed response curve; at the same time, perform a fitting operation on the valve port flow rate and the valve port pressure difference to obtain a flow-pressure difference characteristic curve and a flow-pressure difference mapping table; S14: Calculate the dynamic load sensitivity coefficient according to the steering idle speed drop difference and the steering oil pressure combined with the oil compressibility correction index; S15: Construct a load dynamic model according to the displacement-pressure characteristic curve, torque-speed response curve, and flow-pressure difference characteristic curve combined with the dynamic load sensitivity coefficient.

[0018] In the present invention, in step S2, if the duration of the steering idle speed drop difference is greater than a preset time threshold, the integral separation algorithm is started to convert the compound proportional-integral controller into a dynamic proportional controller; if the duration of the steering idle speed drop difference is less than or equal to the preset time threshold, first, the dynamic proportional parameter of the compound proportional-integral controller is generated by combining the steering idle speed drop difference and the initial proportional parameter with the dynamic load sensing coefficient; at the same time, the dynamic integral parameter of the compound proportional-integral controller is obtained by combining the steering idle speed drop difference and the initial integral parameter with the oil compressibility correction index; then, the feedforward torque component is generated according to the steering oil pressure in combination with the flow-pressure difference mapping table; at the same time, the steering idle speed drop difference is input into the compound proportional-integral controller to obtain the original torque component; finally, after the operation of the feedforward torque component and the original torque component in combination with the feedforward gain coefficient, the control torque of the oil pump motor is obtained.

[0019] In the present invention, step S3 includes the following steps: S31: Filter and denoise the steering oil pressure signal collected by the hydraulic sensor by using a third-order Butterworth low-pass filter, and perform analog-to-digital conversion by using an AD converter to obtain the steering oil pressure; S32: Collect the steering column temperature, ambient temperature, and motor winding temperature by using a temperature sensor, and establish a temperature compensation matrix; S33: Establish a steering assist oil viscosity-temperature model based on the non-Newtonian fluid characteristics, and use a polynomial to fit and generate a temperature compensation coefficient according to the temperature compensation matrix; S34: Establish a state space equation according to the equivalent torque, torque change rate, and motor disturbance term in combination with the road surface impact disturbance function, and perform numerical solution by using the fourth-order Runge-Kutta method to obtain a third-order nonlinear observer; S35: Define an objective function and solve the torque conversion coefficient by using the conjugate gradient method according to a preset convergence threshold, and at the same time, iteratively update the parameter matrix of the third-order nonlinear observer; S36: Perform a comprehensive operation according to the steering oil pressure and the torque conversion coefficient in combination with the temperature compensation matrix, time accumulation coefficient, and hysteresis compensation coefficient, and introduce an angular velocity correction factor to obtain the bias torque.

[0020] In the present invention, the working process of the idle speed dynamic weight distribution strategy is as follows: A: Extract the steering angular velocity, oil pressure change rate, and motor speed from the central processing unit; B: Divide the steering angular velocity into five fuzzy elements: negative large, negative small, zero, positive small, and positive large by using a Gaussian distribution function; at the same time, divide the oil pressure change rate into five fuzzy elements: fast decline, slow decline, stable, slow rise, and fast rise by using a triangular function; at the same time, divide the motor speed into three fuzzy elements: extremely low, low, and critical by using a trapezoidal function; C: Generate a number of fuzzy rules using the fuzzy elements of the steering angular velocity, oil pressure change rate, and motor speed, and calculate the rule strength of each fuzzy rule using the Mamdani fuzzy inference method; D: Analyze the torque weight using the centroid method according to the fuzzy rules, and correct it according to the steering idle drop difference in combination with the learning factor, thereby obtaining the optimal torque weight.

[0021] In this embodiment, the steering angular velocity (dθ / dt); Domain: [-90° / s, +90° / s]; Fuzzy set: {Negative Big (NB), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Big (PB)}; Membership function: Gaussian distribution, standard deviation σ = 15° / s; Oil pressure change rate (dP / dt); Domain: [-5MPa / s, +5MPa / s]; Fuzzy set: {Drop Fast (DR), Drop Slow (DS), Stable (ST), Rise Slow (US), Rise Fast (UR)}; Membership function: Triangular function, vertex spacing 2MPa / s; Motor speed (ω_motor) Domain: [0rpm, 400rpm] (idle threshold 300rpm); Fuzzy set: {Very Low (VL), Low (L), Critical (CT)}; Membership function: Trapezoidal function, VL(0 - 150rpm), L(100 - 300rpm), CT(250 - 400rpm); The fuzzy rule base (25 core rules) is shown in Table 1: Table 1 Fuzzy rule base

[0022] In the present invention, step S4 includes the following steps: S41: Synchronously obtain the steering angular velocity, oil pressure change rate, and motor speed according to the CAN bus of the heavy forklift, and eliminate the time delay difference of the above data using the timestamp alignment algorithm to establish a real-time feature vector matrix; S42: Input the real-time feature vector matrix into the idle dynamic weight distribution strategy to obtain the optimal torque weights of the control torque and the bias torque respectively; S43: Combine the control torque and the bias torque with the corresponding optimal torque weights, combine the PID algorithm and the three-dimensional interpolation algorithm, and perform an operation with the feedforward compensation term to obtain the synthetic torque; S44: Modulate the synthesized torque using space vector PWM to generate three-phase voltage commands, and inject third-harmonic compensation for low-speed torque ripple, thereby completing the conversion of the synthesized torque into a motor drive signal; S45: Input the motor drive signal into the torque control system of the heavy forklift to complete the control of the steering idling optimization of the oil pump motor.

[0023] Embodiment: Extract the steering feedback idling speed, oil pump displacement, steering oil pressure, motor torque, motor speed, valve port flow rate, and valve port pressure difference from the central controller of the heavy forklift; perform a difference calculation on the steering feedback idling speed according to the preset steering idling speed to obtain the steering idling speed drop difference. At the same time, perform a fitting operation on the oil pump displacement and steering oil pressure to obtain the displacement-pressure characteristic curve; perform a fitting operation on the motor torque and motor speed to obtain the torque-speed response curve; at the same time, perform a fitting operation on the valve port flow rate and valve port pressure difference to obtain the flow-pressure difference characteristic curve and the flow-pressure difference mapping table; calculate the dynamic load sensitivity coefficient according to the steering idling speed drop difference and the steering oil pressure combined with the oil compressibility correction index; construct a load dynamic model according to the displacement-pressure characteristic curve, the torque-speed response curve, and the flow-pressure difference characteristic curve combined with the dynamic load sensitivity coefficient.

[0024] Judge the duration of the steering idling speed drop difference: If the duration of the steering idling speed drop difference is greater than the preset time threshold, start the integral separation algorithm to convert the composite proportional-integral controller into a dynamic proportional controller; if the duration of the steering idling speed drop difference is less than or equal to the preset time threshold, first generate the dynamic proportional parameter of the composite proportional-integral controller by combining the steering idling speed drop difference and the initial proportional parameter with the dynamic load sensitivity coefficient; at the same time, obtain the dynamic integral parameter of the composite proportional-integral controller by combining the steering idling speed drop difference and the initial integral parameter with the oil compressibility correction index; then generate the feedforward torque component according to the steering oil pressure combined with the flow-pressure difference mapping table; at the same time, input the steering idling speed drop difference into the composite proportional-integral controller to obtain the original torque component; finally, after performing an operation on the feedforward torque component and the original torque component combined with the feedforward gain coefficient, obtain the control torque of the oil pump motor.

[0025] Filter and denoise the steering oil pressure signal collected by the hydraulic sensor using a third-order Butterworth low-pass filter, and perform analog-to-digital conversion using an AD converter to obtain the steering oil pressure; collect the steering column temperature, ambient temperature, and motor winding temperature using a temperature sensor, and establish a temperature compensation matrix; establish a viscosity-temperature model of the steering assist oil based on the non-Newtonian fluid characteristics, and use a polynomial to fit and generate a temperature compensation coefficient according to the temperature compensation matrix; Based on the equivalent torque, torque change rate, and motor disturbance term, combined with the road surface impact disturbance function, a state-space equation is established, and the fourth-order Runge-Kutta method is used for numerical solution to obtain a third-order nonlinear observer. The objective function is defined, and according to the preset convergence threshold, the conjugate gradient method is used to solve the torque conversion coefficient, and at the same time, the parameter matrix of the third-order nonlinear observer is iteratively updated. According to the steering oil pressure and torque conversion coefficient, combined with the temperature compensation matrix, time accumulation coefficient, and hysteresis compensation coefficient, and introducing an angular velocity correction factor for comprehensive calculation, the offset torque is obtained.

[0026] Synchronously obtain the steering angular velocity, oil pressure change rate, and motor speed of the heavy forklift through the CAN bus, and use the timestamp alignment algorithm to eliminate the time delay difference of the above data, and establish a real-time feature vector matrix. Input the real-time feature vector matrix into the idle speed dynamic weight distribution strategy to obtain the optimal torque weights of the control torque and the offset torque respectively: Extract the steering angular velocity, oil pressure change rate, and motor speed from the central processing unit. Use the Gaussian distribution function to divide the steering angular velocity into five fuzzy elements: negative large, negative small, zero, positive small, and positive large. At the same time, use the triangular function to divide the oil pressure change rate into five fuzzy elements: fast decline, slow decline, stable, slow rise, and fast rise. At the same time, use the trapezoidal function to divide the motor speed into three fuzzy elements: extremely low, low, and critical. Use the fuzzy elements of the steering angular velocity, oil pressure change rate, and motor speed to generate several fuzzy rules, and use the Mamdani fuzzy inference method to calculate the rule strength of each fuzzy rule. According to the fuzzy rules, use the centroid method to analyze the torque weight, and combine the learning factor to correct it according to the steering idle speed drop difference, and then obtain the optimal torque weight; Multiply the control torque and the offset torque by their corresponding optimal torque weights respectively, combine the PID algorithm and the three-dimensional interpolation algorithm, and operate with the feedforward compensation term to obtain the synthetic torque. Use space vector PWM to modulate the synthetic torque to generate three-phase voltage commands, and inject third harmonic compensation for low-speed torque ripple, thereby completing the conversion of the synthetic torque into a motor drive signal. Input the motor drive signal into the torque control system of the heavy forklift to complete the optimization of the steering idle speed of the control oil pump motor.

Claims

1. A method for optimizing the steering idle speed control of the oil pump motor of a counterbalanced forklift, characterized in that: The method comprises the following steps in sequence; S1: When turning the steering wheel, collect the steering feedback idle speed and steering oil pressure, and calculate the steering idle speed drop difference according to the preset steering idle speed to establish the load dynamic model; S2: Determine the duration of the steering idle speed drop difference, and use a compound proportional-integral controller combined with the steering oil pressure to obtain the control torque of the oil pump motor; S3: When the steering wheel is turned, the steering oil pressure collected by the hydraulic sensor is used to calculate the torque conversion coefficient using a third-order nonlinear observer combined with a temperature compensation coefficient to obtain the bias torque; S4: After calculating the control torque and bias torque of the oil pump motor according to the idle dynamic weight distribution strategy, the control torque and bias torque are input into the torque control system of the heavy forklift to control the oil pump motor to complete the steering idle optimization.

2. The method for optimizing the steering idle speed control of the oil pump motor of a counterbalanced forklift according to claim 1, characterized in that: The step S1 includes the following steps: S11: Extract steering feedback idle speed, oil pump displacement, steering oil pressure, motor torque, motor speed, valve port flow and valve port pressure difference from the central controller of the heavy forklift; S12: performing a difference calculation on the steering feedback idle speed according to the preset steering idle speed to obtain a steering idle speed drop difference, and performing a fitting operation on the oil pump displacement and the steering oil pressure to obtain a displacement-pressure characteristic curve; S13: performing fitting calculation on the motor torque and the motor speed to obtain a torque-speed response curve; and performing fitting calculation on the valve port flow and the valve port pressure difference to obtain a flow-pressure difference characteristic curve and a flow-pressure difference mapping table; S14: Calculate the dynamic load sensitivity coefficient based on the steering idle drop difference and steering oil pressure combined with the oil compressibility correction index; S15: Construct a load dynamic model based on the displacement-pressure characteristic curve, the torque-speed response curve and the flow-pressure difference characteristic curve in combination with the dynamic load sensitivity coefficient.

3. The method for optimizing the steering idle speed control of the oil pump motor of a counterbalanced forklift according to claim 1, characterized in that: In the step S2, if the duration of the steering idle speed drop difference is greater than a preset time threshold, the integral separation algorithm is started to convert the compound proportional-integral controller into a dynamic proportional controller; if the duration of the steering idle speed drop difference is less than or equal to the preset time threshold, the dynamic proportional parameter of the compound proportional-integral controller is first generated by combining the steering idle speed drop difference and the initial proportional parameter with the dynamic load sensitivity coefficient; at the same time, the dynamic integral parameter of the compound proportional-integral controller is obtained by combining the steering idle speed drop difference and the initial integral parameter with the oil compressibility correction index; then, the feedforward torque component is generated according to the steering oil pressure combined with the flow-pressure difference mapping table; At the same time, the steering idle drop difference is input into the compound proportional-integral controller to obtain the original torque component; Finally, the control torque of the oil pump motor is obtained by calculating the feedforward torque component and the original torque component in combination with the feedforward gain coefficient.

4. The method for optimizing the steering idle speed control of the oil pump motor of a counterbalanced forklift according to claim 1, characterized in that: The step S3 comprises the following steps: S31: filtering and denoising the steering oil pressure signal collected by the hydraulic sensor using a third-order Butterworth low-pass filter, and performing analog-to-digital conversion using an AD converter to obtain the steering oil pressure; S32: using a temperature sensor to collect the steering column temperature, the ambient temperature and the motor winding temperature, and establishing a temperature compensation matrix; S33: Establish a viscosity-temperature model of the power steering fluid based on the characteristics of non-Newtonian fluid, and use polynomials to fit and generate temperature compensation coefficients according to the temperature compensation matrix; S34: Based on the equivalent torque, torque change rate, and motor disturbance term, combine with the road surface impact disturbance function to establish a state space equation, and use the fourth-order Runge-Kutta method for numerical solution to obtain a third-order nonlinear observer; S35: Define the objective function and according to the preset convergence threshold, use the conjugate gradient method to solve the torque conversion coefficient, and at the same time iteratively update the parameter matrix of the third-order nonlinear observer; S36: According to the steering oil pressure and torque conversion coefficient, combine with the temperature compensation matrix, time accumulation coefficient, and hysteresis compensation coefficient, and introduce an angular velocity correction factor for comprehensive operation to obtain the bias torque.

5. The method for optimizing the steering idle speed control of the oil pump motor of a counterbalanced forklift according to claim 1, characterized in that: The working process of the idle speed dynamic weight distribution strategy is as follows: A: Extract the steering angular velocity, oil pressure change rate, and motor speed from the central processor; B: Use the Gaussian distribution function to divide the steering angular velocity into five fuzzy elements: negative large, negative small, zero, positive small, and positive large; at the same time, use the triangular function to divide the oil pressure change rate into five fuzzy elements: fast decline, slow decline, stable, slow rise, and fast rise; at the same time, use the trapezoidal function to divide the motor speed into three fuzzy elements: extremely low, low, and critical; C: Use the fuzzy elements of the steering angular velocity, oil pressure change rate, and motor speed to generate several fuzzy rules, and use the Mamdani fuzzy inference method to calculate the rule strength of each fuzzy rule; D: Analyze the torque weight according to the fuzzy rules using the centroid method, and correct it according to the steering idle speed drop difference in combination with the learning factor to obtain the optimal torque weight.

6. The method for optimizing the steering idle speed control of the oil pump motor of a counterbalanced forklift according to claim 1, characterized in that: The step S4 includes the following steps: S41: Synchronously obtain the steering angular velocity, oil pressure change rate, and motor speed according to the CAN bus of the heavy forklift, and use the timestamp alignment algorithm to eliminate the time delay difference of the above data to establish a real-time feature vector matrix; S42: Input the real-time feature vector matrix into the idle speed dynamic weight distribution strategy to obtain the optimal torque weights of the control torque and the bias torque respectively; S43: Combine the control torque and the bias torque with their corresponding optimal torque weights, combine the PID algorithm and the three-dimensional interpolation algorithm, and perform an operation with the feedforward compensation term to obtain the synthetic torque; S44: Modulate the synthetic torque using space vector PWM to generate three-phase voltage commands, and inject third harmonic compensation for low-speed torque ripple, thereby completing the conversion of the synthetic torque into a motor drive signal; S45: Input the motor drive signal into the torque control system of the heavy forklift to complete the optimization of the steering idle speed of the oil pump motor.

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