Screw type lifting machine multi-machine cooperation dynamic balance driving method, system and equipment

Abstract

The invention relates to a multi-machine cooperative dynamic balance driving method, system and device for a spiral lifting machine, and the method comprises the steps: carrying out the compensation of the time lag of an actuator through employing a non-linear state observation and time lag compensation combined method, and obtaining a feedforward control quantity; performing Lyapunov stability feedback processing on the vibration acceleration signal to obtain a vibration suppression control quantity; designing a multi-section S-shaped speed curve, and calculating a closed-loop feedback control quantity by adopting PID (Proportion Integration Differentiation) combined with a robust control algorithm; and according to the feedforward control quantity, the vibration suppression control quantity and the closed-loop feedback control quantity, multi-machine cooperative dynamic balance driving of the spiral lifting machine is carried out. According to the invention, a non-linear state observation and time delay compensation technology is adopted, so that the synchronization error is reduced to a millisecond level; the low-frequency vibration amplitude is reduced through dynamic feedback control designed based on the Lyapunov theory; the hydraulic impact is obviously reduced by adopting a multi-section S-shaped speed curve; external disturbance is observed in real time through Kalman filtering, feed-forward compensation is carried out, and the robustness of the system is greatly enhanced.

Description

Technical Field

[0001] This invention relates to the field of hydraulic drive and control technology, specifically to a multi-machine coordinated dynamic balance drive method, system, and equipment for a spiral lift. Background Technology

[0002] High-lift-ratio heavy-duty screw lifts play a crucial role in heavy-duty and high-precision applications, and are widely used for lifting, moving, and precisely positioning large mechanical equipment. However, with the development of heavy-duty precision equipment applications, existing technologies still face many challenges in multi-machine collaborative control, dynamic balancing, and vibration suppression under complex working conditions. First, synchronous control is difficult; multiple hydraulic actuators are prone to accumulating synchronization errors due to parameter differences, especially under time-varying loads, making it difficult to ensure consistency in multi-machine collaboration. Second, dynamic balancing and vibration resistance are insufficient; during high-load lifting, the hydraulic system is susceptible to pressure pulsations and flow fluctuations, making it difficult to effectively suppress low-frequency vibration energy and affecting equipment lifespan. Furthermore, sudden trajectory changes during lifting can easily trigger acceleration shocks, further exacerbating vibration and synchronization deviations. Simultaneously, hydraulic drives have poor anti-interference robustness; when external load disturbances or oil temperature changes occur, traditional closed-loop feedback control lags in response, resulting in decreased dynamic accuracy.

[0003] Therefore, for spiral lifts operating under complex conditions, there is an urgent need to develop a hydraulic drive system that integrates high-precision synchronous control, dynamic balancing, and vibration-resistant design to meet the high-precision requirements and stable operation needs of heavy-duty precision equipment. Summary of the Invention

[0004] This invention provides a multi-machine coordinated dynamic balancing drive method, system, and equipment for a spiral lift, in order to solve at least one of the above-mentioned technical problems.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A multi-machine coordinated dynamic balancing drive method for a spiral lift, comprising:

[0006] S1 synchronously collects displacement signals, speed signals, vibration acceleration signals, hydraulic pressure signals, and oil temperature signals of each actuator under the multi-machine cooperative working condition of the spiral lift;

[0007] S2, based on the displacement signal, the velocity signal, the vibration acceleration signal, the hydraulic pressure signal, and the oil temperature signal, the time delay of each actuator is compensated using a nonlinear state observation combined with time delay compensation method to obtain the feedforward control quantity;

[0008] S3, perform Lyapunov stability feedback processing on the vibration acceleration signal to obtain the vibration suppression control quantity;

[0009] S4. Based on the preset target displacement, maximum velocity, and maximum vibration acceleration, and combined with the actuator kinematic model, design a multi-segment S-shaped velocity curve; based on the multi-segment S-shaped velocity curve, use a PID combined with a robust control algorithm to calculate the closed-loop feedback control quantity.

[0010] S5, perform multi-machine coordinated dynamic balance drive of the spiral lift according to the feedforward control quantity, the vibration suppression control quantity and the closed-loop feedback control quantity.

[0011] Based on the above technical solution, the present invention can be further improved as follows.

[0012] Furthermore, S2 specifically includes:

[0013] S21, Construct a nonlinear state-space model of the actuator based on the displacement signal, the velocity signal, the vibration acceleration signal, and the hydraulic pressure signal;

[0014] S22, Based on the nonlinear state-space model of the actuator, a state observer is used to estimate the system state and total disturbance online to obtain the state estimate;

[0015] S23, Based on the state estimate, the oil temperature signal, and the load information, establish an extended Kalman filter disturbance estimation model, and use the extended Kalman filter disturbance estimation model to make the optimal estimate of the external disturbance to obtain the disturbance estimate;

[0016] S24, Based on the model predictive control method, the time delay of the actuator is compensated using the state estimation and the disturbance estimation to generate a pre-compensation quantity;

[0017] S25, the pre-compensation amount and the disturbance estimate are weighted and fused to obtain the feedforward control amount.

[0018] Furthermore, in S25, the formula for weighted fusion of the pre-compensation amount and the disturbance estimate is as follows:

[0019]

[0020] Among them, u ff U represents the feedforward control quantity. mpc This represents the pre-compensation amount. Let K represent the perturbation estimate, and K represent the feedforward gain matrix.

[0021] Furthermore, S3 specifically includes:

[0022] S31, perform modal decomposition on the vibration acceleration signal to extract the set of amplitude values ​​of the main vibration modes;

[0023] S32, construct the Lyapunov function based on the set of amplitude values ​​of the main vibration modes;

[0024] S33. Based on the Lyapunov function, derive the multi-input multi-output feedback control law to obtain the vibration suppression control quantity.

[0025] Furthermore, in S4, the multiple S-shaped speed curves specifically include seven S-shaped speed curves: initial acceleration, constant acceleration, deceleration, constant speed, deceleration, constant deceleration, and stopping.

[0026] Furthermore, in step S4, based on the multiple S-shaped velocity curves, a PID combined with a robust control algorithm is used to calculate the closed-loop feedback control quantity, specifically including:

[0027] S41, based on the preset target displacement, maximum velocity and maximum vibration acceleration, combined with the actuator kinematic model, and according to the multiple S-shaped velocity curves, the reference curve based on displacement-velocity-acceleration for each segment is calculated in sequence;

[0028] S42, Obtain the real-time status of the actuator, and calculate the trajectory deviation based on the real-time status and the reference curve;

[0029] S43, based on the trajectory deviation, fuzzy control or genetic algorithm is used to fine-tune the key node time and / or acceleration parameters of the reference curve to generate a corrected velocity increment;

[0030] S44, the corrected velocity increment is superimposed on the multiple S-shaped velocity curves over time to obtain an adaptive velocity curve, and the adaptive velocity curve is integrated to obtain an adaptive position curve.

[0031] S45, based on the real-time state of the actuator and combined with the adaptive speed curve and the adaptive position curve, calculate the speed error and position error;

[0032] S46, a PID control algorithm combined with a robust control algorithm based on H∞ or sliding mode is used to perform closed-loop feedback control on the position error and the velocity error to obtain the closed-loop feedback control quantity; the calculation formula for the closed-loop feedback control quantity is:

[0033]

[0034] Among them, u fb K represents the closed-loop feedback control quantity. p K i and K d The PID control parameters are represented by e(t), where e(t) represents the position error. The velocity error, u robust This indicates an anti-interference item.

[0035] Furthermore, between S1 and S2, the following is also included:

[0036] The acquired displacement signal, velocity signal and hydraulic pressure signal are subjected to a fourth-order low-pass filter to obtain the filtered displacement signal, velocity signal and hydraulic pressure signal;

[0037] The collected vibration acceleration signal is bandpass filtered to obtain the filtered vibration acceleration signal.

[0038] Furthermore, the multi-machine collaborative dynamic balance drive of the spiral lift adopts a synchronous control strategy of master and slave controllers.

[0039] Based on the above-mentioned multi-machine collaborative dynamic balancing drive method for a spiral lift, the present invention also provides a multi-machine collaborative dynamic balancing drive system for a spiral lift.

[0040] A multi-machine cooperative dynamic balancing drive system for a spiral lift, applied to the multi-machine cooperative dynamic balancing drive method for spiral lifts as described above, includes:

[0041] The signal acquisition module is used to synchronously acquire displacement signals, speed signals, vibration acceleration signals, hydraulic pressure signals, and oil temperature signals of each actuator under the multi-machine cooperative operation of the screw lift;

[0042] The feedforward control quantity calculation module is used to compensate for the time delay of each actuator based on the displacement signal, the velocity signal, the vibration acceleration signal, the hydraulic pressure signal, and the oil temperature signal, using a nonlinear state observation combined with a time delay compensation method to obtain the feedforward control quantity.

[0043] The vibration suppression control quantity calculation module is used to perform Lyapunov stability feedback processing on the vibration acceleration signal to obtain the vibration suppression control quantity.

[0044] The closed-loop feedback control quantity calculation module is used to design multiple S-shaped velocity curves based on preset target displacement, maximum velocity, and maximum vibration acceleration, combined with the actuator kinematic model; based on the multiple S-shaped velocity curves, the closed-loop feedback control quantity is calculated using a PID combined with a robust control algorithm.

[0045] The integrated control module is used to perform multi-machine coordinated dynamic balance drive of the spiral lift based on the feedforward control quantity, the vibration suppression control quantity, and the closed-loop feedback control quantity.

[0046] Based on the above-mentioned multi-machine collaborative dynamic balancing drive method for a spiral lift, the present invention also provides a multi-machine collaborative dynamic balancing drive device for a spiral lift.

[0047] A multi-machine collaborative dynamic balancing drive device for a spiral lift includes a processor, a memory, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the multi-machine collaborative dynamic balancing drive method for a spiral lift as described above.

[0048] The beneficial effects of this invention are as follows: This invention provides a multi-machine coordinated dynamic balance drive method, system, and equipment for a spiral lift. In terms of synchronization accuracy, the use of nonlinear state observation and time-delay compensation technology reduces the synchronization error between actuators to the millisecond level, significantly improving the overall coordinated accuracy of the system. Regarding vibration suppression, dynamic feedback control based on Lyapunov theory reduces low-frequency vibration amplitude by more than 60%, achieving dynamic balance under all working conditions and extending the service life of hydraulic components and mechanical parts. In terms of dynamic performance, the use of multi-segment S-shaped speed curve planning significantly reduces acceleration impact during startup, stopping, and turning, reducing hydraulic shock peak by more than 20%, effectively protecting the long-term stable operation of seals, pipelines, and proportional valves. Regarding anti-interference, by using Kalman filtering to observe external disturbances in real time and perform feedforward compensation, the system can maintain high-precision positioning and rapid response even under disturbances such as sudden load changes or oil temperature fluctuations, greatly enhancing system robustness. Attached Figure Description

[0049] Figure 1 A three-dimensional structural diagram of a high-lift-ratio heavy-duty spiral lift.

[0050] Figure 2 This is a schematic diagram of the hydraulic system of a high-lift-ratio heavy-duty spiral lift.

[0051] Figure 3 This is a flowchart of a multi-machine coordinated dynamic balancing drive method for a spiral lift according to the present invention;

[0052] Figure 4 This is a structural block diagram of a multi-machine coordinated dynamic balancing drive system for a spiral lift according to the present invention. Detailed Implementation

[0053] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0054] This embodiment is for a high lift ratio, heavy-duty spiral lift, whose three-dimensional structure is as follows: Figure 1 As shown, its hydraulic system is as follows Figure 2As shown, the hydraulic system of the high-lift-ratio heavy-duty screw lift uses a hydraulic actuator with the lifting cylinder 1 as its core, along with key components such as a gear pump 2, a proportional relief valve 3, a multi-way valve 4, a positive chamber balance valve 5, and a negative chamber balance valve 6. The gear pump 2 serves as the power source, providing stable oil pressure and flow to the system. The proportional relief valve 3 precisely regulates the system pressure, achieving accurate pressure control and overload protection; its pressure regulation accuracy can reach ±0.3% FS, effectively ensuring that output pressure fluctuations are controlled within ±0.5%. The multi-way valve 4, in conjunction with the positive chamber balance valve 5 and the negative chamber balance valve 6, precisely controls the lifting and lowering movements of the lifting cylinder 1, preventing load slippage, suppressing hydraulic shock, and ensuring the smoothness of the lifting process.

[0055] Example 1:

[0056] like Figure 3 As shown, a multi-machine coordinated dynamic balancing drive method for a spiral lift includes:

[0057] S1 synchronously collects displacement signals, speed signals, vibration acceleration signals, hydraulic pressure signals, and oil temperature signals of each actuator under the multi-machine cooperative working condition of the spiral lift;

[0058] S2, based on the displacement signal, the velocity signal, the vibration acceleration signal, the hydraulic pressure signal, and the oil temperature signal, the time delay of each actuator is compensated using a nonlinear state observation combined with time delay compensation method to obtain the feedforward control quantity;

[0059] S3, perform Lyapunov stability feedback processing on the vibration acceleration signal to obtain the vibration suppression control quantity;

[0060] S4. Based on the preset target displacement, maximum velocity, and maximum vibration acceleration, and combined with the actuator kinematic model, design a multi-segment S-shaped velocity curve; based on the multi-segment S-shaped velocity curve, use a PID combined with a robust control algorithm to calculate the closed-loop feedback control quantity.

[0061] S5, perform multi-machine coordinated dynamic balance drive of the spiral lift according to the feedforward control quantity, the vibration suppression control quantity and the closed-loop feedback control quantity.

[0062] The present invention provides a multi-machine coordinated dynamic balancing drive method for a spiral lifting machine, which solves the following problems:

[0063] 1. Synchronization error control problem: To address the parameter differences among multiple hydraulic actuators caused by manufacturing deviations and dynamic aging, a nonlinear state observation and time delay compensation mechanism is designed to ensure that each actuator maintains millisecond-level synchronization accuracy under complex working conditions.

[0064] 2. Vibration suppression and dynamic balance: Through active vibration mode identification and feedback adjustment, the low-frequency pressure pulsation and flow fluctuation of the hydraulic system are attenuated in real time, thereby maintaining dynamic balance and stability during the lifting process.

[0065] 3. Trajectory optimization and shock mitigation: To address the abrupt changes in velocity and vibration acceleration in traditional motion trajectory planning, a multi-segment S-shaped velocity curve and parameter adaptive correction algorithm are proposed to smooth the motion process and reduce the damage of hydraulic shock to key components.

[0066] 4. Insufficient robustness against interference: Design a disturbance observation and feedforward compensation strategy based on Kalman filtering to estimate and compensate for dynamic deviations caused by external load changes, oil temperature drift and other interference factors in real time, so as to ensure that the system can still operate stably under non-ideal working environment.

[0067] In some embodiments, the multi-machine cooperative dynamic balance drive method for a spiral lift of the present invention adopts a master-slave controller synchronous control strategy.

[0068] The master and slave controller synchronous control strategy adopts a distributed control system, which consists of a master controller (based on an ARM or DSP platform) and multiple slave controllers. Each controller has a built-in dedicated signal processing module and a real-time operating system (RTOS) to ensure that the control cycle does not exceed 1ms. It adopts an industrial-grade CAN or Ethernet / IP network to achieve a data transmission delay of less than 500μs between nodes and has a redundancy backup mechanism.

[0069] The main controller calculates the feedforward control quantity, vibration suppression control quantity, and closed-loop feedback control quantity. The slave controller controls the hydraulic system of the screw lift based on the feedforward control quantity, vibration suppression control quantity, and closed-loop feedback control quantity calculated by the main controller, thereby controlling the actuator movement. In addition, the actual state of the actuator and the master-slave synchronization error are fed back to the main controller.

[0070] In some embodiments, in S1, the displacement signal X, velocity signal V, vibration acceleration signal A, hydraulic pressure signal P, and oil temperature signal TM of each actuator under the multi-machine cooperative working condition of the spiral lift are synchronously collected; wherein, the sampling frequency of the displacement signal X, velocity signal V, hydraulic pressure signal P, and oil temperature signal TM is 1kHz, and the sampling frequency of the vibration acceleration signal A is 10kHz.

[0071] Specifically, this invention employs multimodal sensor fusion technology, integrating a digital pressure sensor (0.01MPa resolution) into the hydraulic circuit to monitor the pressure in the lifting cylinder's positive and negative chambers and the pump outlet in real time, acquiring hydraulic pressure signals P. A laser displacement sensor (accuracy ±0.01mm, sampling frequency ≥1kHz) is configured at the load end or piston rod of the lifting cylinder to acquire high-frequency displacement signals X. The velocity signal V is obtained by real-time differentiation of the displacement signal X acquired by the laser displacement sensor using a differential method. Simultaneously, a high-frequency accelerometer (10kHz sampling) is installed on the cylinder support or load end to sense vibration characteristics in real time, acquiring vibration acceleration signals A. Data acquisition supports high-speed sampling, possesses fault self-diagnosis and parameter self-correction functions, and utilizes multi-sensor data to construct a high-precision digital model of the system state.

[0072] Additionally, the following is included between S1 and S2:

[0073] The acquired displacement signal X, velocity signal V, and hydraulic pressure signal P are subjected to a fourth-order low-pass filter (cutoff frequency 500Hz) to obtain the filtered displacement signal X, velocity signal V, and hydraulic pressure signal P.

[0074] The collected vibration acceleration signal A is bandpass filtered (10Hz~200Hz) to obtain the filtered vibration acceleration signal A.

[0075] The fourth-order low-pass filter is used to eliminate high-frequency noise, while the band-pass filter is used to extract low-frequency vibration mode features.

[0076] In some embodiments, S2 specifically includes:

[0077] S21, Construct a nonlinear state-space model of the actuator based on the displacement signal, the velocity signal, the vibration acceleration signal, and the hydraulic pressure signal;

[0078] S22, Based on the nonlinear state-space model of the actuator, a state observer is used to estimate the system state and total disturbance online to obtain the state estimate;

[0079] S23, Based on the state estimate, the oil temperature signal, and the load information, establish an extended Kalman filter disturbance estimation model, and use the extended Kalman filter disturbance estimation model to make the optimal estimate of the external disturbance to obtain the disturbance estimate;

[0080] S24, Based on the model predictive control method, the time delay of the actuator is compensated using the state estimation and the disturbance estimation to generate a pre-compensation quantity;

[0081] S25, the pre-compensation amount and the disturbance estimate are weighted and fused to obtain the feedforward control amount.

[0082] Specifically, in step S21, the actuator nonlinear state-space model is used to describe the dynamic characteristics of the system using a hydraulic-mechanical coupling nonlinear model; the state vector of the actuator nonlinear state-space model is defined as [X,V,A,P]. T Where X, V, A, and P represent displacement signal, velocity signal, vibration acceleration signal, and hydraulic pressure signal, respectively, and T represents matrix transpose.

[0083] In S22, the state observer is specifically an extended state observer (ESO), a sliding mode observer, or an improved nonlinear extended state observer.

[0084] The update equation for the third-order ESO is: Where β1, β2, and β3 are all observation gains, u is the control input reference value, and x, These represent the actual displacement, the estimate of the actual displacement, the velocity estimate, the estimate of the actual velocity, the acceleration estimate, the estimate of the total disturbance, and the update rate of the disturbance estimate, respectively. and All are state estimates.

[0085] The update equation for the improved nonlinear extended state observer is:

[0086] in, This represents the state estimation error, and x represents the actual displacement. and Both are state estimates, and This represents an estimate of the actual displacement. This represents an estimate of the actual speed. y represents the estimate of the total disturbance; y-z1 represents the observation error, which is the difference between the output and the estimated output, used to drive the observer correction; y represents the measurement output, which is the displacement signal X collected by the laser displacement sensor. and All represent the observer bandwidth, b0 represents the control gain, and u represents the control input.

[0087] The improved nonlinear ESO is based on the third-order ESO framework, but replaces the correction term with a nonlinear function that adapts to the error, in order to achieve better convergence and robustness.

[0088] Preferably, in step S25, the formula for weighted fusion of the pre-compensation amount and the disturbance estimate is as follows:

[0089]

[0090] Among them, u ff U represents the feedforward control quantity. mpc This represents the pre-compensation amount. Let K represent the perturbation estimate, and K represent the feedforward gain matrix.

[0091] This invention designs a disturbance observer based on Kalman filtering to estimate load changes and oil temperature drift in real time, and generate feedforward control input to correct the control input. This enables the system to maintain high-precision positioning and fast response under disturbances such as sudden load changes or oil temperature fluctuations, and greatly enhances the robustness of the system.

[0092] In some embodiments, S3 specifically includes:

[0093] S31, perform modal decomposition on the vibration acceleration signal to extract the set of amplitude values ​​of the main vibration modes;

[0094] S32, construct the Lyapunov function based on the set of amplitude values ​​of the main vibration modes;

[0095] S33. Based on the Lyapunov function, derive the multi-input multi-output feedback control law to obtain the vibration suppression control quantity.

[0096] Wherein, the set of amplitude values ​​of the main vibration modes is represented as {A} k The Lyapunov function is expressed as follows: The multi-input multi-output feedback control law is expressed as follows: K v U is the vibration suppression gain matrix. vib This indicates the vibration suppression control amount.

[0097] Vibration suppression control quantity u vib By superimposing this onto the control input of the proportional valve and / or servo valve, real-time attenuation of low-frequency vibration energy can be achieved. Specifically, S3 is a scheme for achieving vibration mode suppression: a multivariable dynamic pressure feedback loop can be designed to monitor pressure pulsations in the hydraulic circuit in real time, and by adjusting the working states of the proportional valve and servo valve, the vibration energy caused by hydraulic fluctuations can be effectively attenuated.

[0098] In some embodiments, in step S4, the multiple S-shaped speed curves specifically include seven S-shaped speed curves: initial acceleration, constant acceleration, deceleration, constant speed, deceleration, constant deceleration, and stopping.

[0099] Specifically, the design concept of the seven-segment S-shaped velocity curve is based on the acceleration and velocity boundaries of each segment. The time required for each segment is calculated using the relationships between velocity and initial velocity, acceleration, and displacement and initial velocity, acceleration, and time. The seven-segment S-shaped velocity curve represents the reference velocity v. d The relationship between (t) and time t, expressed by the initial seven-segment S-shaped velocity curves, is as follows:

[0100]

[0101] Where P1(t)~P7(t) correspond to the initial acceleration segment, constant acceleration segment, deceleration segment, constant speed segment, deceleration segment, constant deceleration segment, and stopping segment, respectively, and the coefficients of the polynomials in each segment are {a k,j The values ​​of k (k = 1, 2, ..., 7, j = 0, 1, 2, 3) are determined by the boundary conditions of the segment (the velocity and acceleration values ​​at the beginning and end of the segment).

[0102] In some embodiments, in step S4, based on the multiple S-shaped velocity curves, a PID combined with a robust control algorithm is used to calculate the closed-loop feedback control quantity, specifically including:

[0103] S41, based on the preset target displacement, maximum velocity and maximum vibration acceleration, combined with the actuator kinematic model, and according to the multiple S-shaped velocity curves, a reference curve based on displacement-velocity-acceleration is generated for each segment in sequence;

[0104] S42, Obtain the real-time status of the actuator, and calculate the trajectory deviation based on the real-time status and the reference curve;

[0105] S43, based on the trajectory deviation, fuzzy control or genetic algorithm is used to fine-tune the key node time and / or acceleration parameters of the reference curve to generate a corrected velocity increment;

[0106] S44, the corrected velocity increment is superimposed on the multiple S-shaped velocity curves over time to obtain an adaptive velocity curve, and the adaptive velocity curve is integrated to obtain an adaptive position curve.

[0107] S45, based on the real-time state of the actuator and combined with the adaptive speed curve and the adaptive position curve, calculate the speed error and position error;

[0108] S46, a PID control algorithm combined with a robust control algorithm based on H∞ or sliding mode is used to perform closed-loop feedback control on the position error and the velocity error to obtain the closed-loop feedback control quantity; the calculation formula for the closed-loop feedback control quantity is:

[0109]

[0110] Among them, u fb K represents the closed-loop feedback control quantity. p K i and K d The PID control parameters are represented by e(t), where e(t) represents the position error. The velocity error, u robust This indicates an anti-interference item.

[0111] Specifically, the reference curve based on displacement-velocity-acceleration is represented as {x}. d (t),v d (t),a d (t)};where x d (t), v d (t), a d (t) represent the reference displacement, reference velocity, and reference acceleration, respectively.

[0112] In step S42, the formula for calculating the trajectory deviation is: Wherein, Δx(t) represents the trajectory deviation. This represents the real-time displacement estimate in the real-time state.

[0113] In S43, since the node time and intra-segment acceleration directly determine the shape of the reference velocity curve, fine-tuning the key node time and / or acceleration parameters of the reference curve is equivalent to making incremental corrections to the reference velocity, thereby obtaining the corrected velocity increment Δv(t).

[0114] In step S44, the corrected velocity increment Δv(t) calculated online is superimposed onto the reference velocity v over time. d On (t), the final adaptive velocity curve is formed: v final (t)=v d (t)+Δv(t); where v final (t) represents the final velocity. Integrating the adaptive velocity curve yields the adaptive position curve: Where, x final (t) represents the final displacement.

[0115] In S45, the real-time state of the actuator is specifically a real-time state estimate, which can be obtained from the state estimate in S22; the position error is expressed as... Speed ​​error is expressed as in, This represents the real-time displacement estimate in the real-time state. This represents the real-time velocity estimate in the real-time state.

[0116] Based on the actuator's kinematic model, a speed curve was designed comprising seven segments: initial acceleration, constant acceleration, deceleration, constant speed, deceleration, constant deceleration, and stopping. This ensures smooth changes in speed and acceleration, significantly reducing acceleration shock during startup, stopping, and turning, thereby minimizing hydraulic shock. Simultaneously, closed-loop feedback control ensures the following accuracy:

[0117] (1) Error dynamic compensation: When the position error and / or speed error exceeds the preset threshold, the slope or constant speed duration of the subsequent segments in the seven-segment S-shaped speed curve can be adjusted appropriately.

[0118] (2) Vibration suppression: When the vibration acceleration exceeds the standard, the vibration suppression control quantity is superimposed to temporarily reduce the acceleration reference.

[0119] (3) Parameter self-adaptation: The system friction / hysteresis model can be dynamically corrected online in the background based on the cumulative tracking error.

[0120] In some embodiments, S5 specifically refers to:

[0121] The feedforward control quantity, the vibration suppression control quantity, and the closed-loop feedback control quantity are superimposed to obtain the final control quantity u. total ; where u total =u ff +u vib +u fb ;

[0122] The final control quantity u is transmitted via real-time bus. total The hydraulic proportional valve and / or servo valve of the corresponding actuator are sent to control the corresponding actuator to perform the action.

[0123] Example 2:

[0124] Based on the above-mentioned multi-machine collaborative dynamic balancing drive method for a spiral lift, the present invention also provides a multi-machine collaborative dynamic balancing drive system for a spiral lift.

[0125] like Figure 4 As shown, a multi-machine cooperative dynamic balancing drive system for a spiral lift, applied to the multi-machine cooperative dynamic balancing drive method for spiral lifts as described above, includes:

[0126] The signal acquisition module is used to synchronously acquire displacement signals, speed signals, vibration acceleration signals, hydraulic pressure signals, and oil temperature signals of each actuator under the multi-machine cooperative operation of the screw lift;

[0127] The feedforward control quantity calculation module is used to compensate for the time delay of each actuator based on the displacement signal, the velocity signal, the vibration acceleration signal, the hydraulic pressure signal, and the oil temperature signal, using a nonlinear state observation combined with a time delay compensation method to obtain the feedforward control quantity.

[0128] The vibration suppression control quantity calculation module is used to perform Lyapunov stability feedback processing on the vibration acceleration signal to obtain the vibration suppression control quantity.

[0129] The closed-loop feedback control quantity calculation module is used to design multiple S-shaped velocity curves based on preset target displacement, maximum velocity, and maximum vibration acceleration, combined with the actuator kinematic model; based on the multiple S-shaped velocity curves, the closed-loop feedback control quantity is calculated using a PID combined with a robust control algorithm.

[0130] The integrated control module is used to perform multi-machine coordinated dynamic balance drive of the spiral lift based on the feedforward control quantity, the vibration suppression control quantity, and the closed-loop feedback control quantity.

[0131] The specific functions of each module in the multi-machine cooperative dynamic balancing drive system of the spiral lift of the present invention are described in the specific steps of the multi-machine cooperative dynamic balancing drive method of the spiral lift of the present invention, and will not be repeated here.

[0132] Furthermore, this invention allows for system integration, debugging, and verification. Wherein:

[0133] (1) System Integration and Distributed Collaborative Testing

[0134] The entire control process was modeled and simulated using a virtual simulation platform (such as MATLAB / Simulink) to verify the dynamic performance of master-slave synchronization, trajectory planning, and disturbance compensation. The hydraulic actuator, sensor module, and control unit were integrated and debugged on the actual experimental platform to test the response performance of each component under different loads, temperatures, and dynamic disturbances.

[0135] (2) Fault diagnosis and adaptive correction mechanism

[0136] The system has a built-in real-time monitoring module that uses multi-sensor data fusion algorithms to monitor key parameters and combines fuzzy logic and expert systems to predict faults. When abnormal parameters or synchronization errors exceed the threshold, the system automatically initiates redundancy switching and safety mode to ensure that basic functions are maintained in the event of a fault and to record detailed fault data for subsequent analysis and parameter optimization.

[0137] (3) Long-term data acquisition and system optimization

[0138] Big data statistical analysis is performed on the pressure, displacement, vibration and other data collected during operation to optimize controller parameters and dynamic compensation algorithms, so as to realize the system's adaptive adjustment. By combining offline data model updates with online parameter updates, the system's collaborative control accuracy and dynamic response performance are continuously improved.

[0139] Example 3:

[0140] Based on the above-mentioned multi-machine collaborative dynamic balancing drive method for a spiral lift, the present invention also provides a multi-machine collaborative dynamic balancing drive device for a spiral lift.

[0141] A multi-machine collaborative dynamic balancing drive device for a spiral lift includes a processor, a memory, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the multi-machine collaborative dynamic balancing drive method for a spiral lift as described above.

[0142] This invention discloses a multi-machine coordinated dynamic balance drive method for a spiral lift. Regarding synchronization accuracy, it employs nonlinear state observation and time-delay compensation technology to reduce the synchronization error between the main actuators to the millisecond level, significantly improving the overall system coordination accuracy. In terms of vibration suppression, dynamic feedback control based on Lyapunov theory reduces low-frequency vibration amplitude by over 60%, achieving dynamic balance under all operating conditions and extending the service life of hydraulic components and mechanical parts. Regarding dynamic performance, the use of multi-segment S-shaped speed curve planning significantly reduces acceleration impact during startup, stopping, and turning, lowering the hydraulic impact peak by over 20%, effectively protecting the long-term stable operation of seals, pipelines, and proportional valves. Regarding anti-interference, Kalman filtering is used to observe external disturbances in real time and perform feedforward compensation, enabling the system to maintain high-precision positioning and rapid response even under disturbances such as sudden load changes or oil temperature fluctuations, greatly enhancing system robustness.

[0143] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-machine coordinated dynamic balancing drive method for a spiral lifting machine, characterized in that, include: S1 synchronously collects displacement signals, speed signals, vibration acceleration signals, hydraulic pressure signals, and oil temperature signals of each actuator under the multi-machine cooperative working condition of the spiral lift; S2, based on the displacement signal, the velocity signal, the vibration acceleration signal, the hydraulic pressure signal, and the oil temperature signal, the time delay of each actuator is compensated using a nonlinear state observation combined with time delay compensation method to obtain the feedforward control quantity; S3, perform Lyapunov stability feedback processing on the vibration acceleration signal to obtain the vibration suppression control quantity; S4. Based on the preset target displacement, maximum velocity, and maximum vibration acceleration, and combined with the actuator kinematic model, design a multi-segment S-shaped velocity curve; based on the multi-segment S-shaped velocity curve, use a PID combined with a robust control algorithm to calculate the closed-loop feedback control quantity. S5, perform multi-machine coordinated dynamic balance drive of the spiral lift according to the feedforward control quantity, the vibration suppression control quantity and the closed-loop feedback control quantity.

2. The multi-machine coordinated dynamic balancing drive method for a spiral lift according to claim 1, characterized in that, S2 specifically includes: S21, Construct a nonlinear state-space model of the actuator based on the displacement signal, the velocity signal, the vibration acceleration signal, and the hydraulic pressure signal; S22, Based on the nonlinear state-space model of the actuator, a state observer is used to estimate the system state and total disturbance online to obtain the state estimate; S23, Based on the state estimate, the oil temperature signal, and the load information, establish an extended Kalman filter disturbance estimation model, and use the extended Kalman filter disturbance estimation model to make the optimal estimate of the external disturbance to obtain the disturbance estimate; S24, Based on the model predictive control method, the time delay of the actuator is compensated using the state estimation and the disturbance estimation to generate a pre-compensation quantity; S25, the pre-compensation amount and the disturbance estimate are weighted and fused to obtain the feedforward control amount.

3. The multi-machine coordinated dynamic balancing drive method for a spiral lift according to claim 2, characterized in that, In step S25, the formula for weighted fusion of the pre-compensation amount and the disturbance estimate is as follows: Among them, u ff U represents the feedforward control quantity. mpc This represents the pre-compensation amount. Let K represent the perturbation estimate, and K represent the feedforward gain matrix.

4. The multi-machine coordinated dynamic balancing drive method for a spiral lift according to claim 1, characterized in that, S3 specifically includes: S31, perform modal decomposition on the vibration acceleration signal to extract the set of amplitude values ​​of the main vibration modes; S32, construct the Lyapunov function based on the set of amplitude values ​​of the main vibration modes; S33. Based on the Lyapunov function, derive the multi-input multi-output feedback control law to obtain the vibration suppression control quantity.

5. The multi-machine coordinated dynamic balancing drive method for a spiral lift according to claim 1, characterized in that, In S4, the multiple S-shaped speed curves specifically include seven S-shaped speed curves: initial acceleration, constant acceleration, deceleration, constant speed, deceleration, constant deceleration, and stopping.

6. The multi-machine coordinated dynamic balancing drive method for a spiral lift according to claim 1, characterized in that, In step S4, based on the multiple S-shaped velocity curves, a PID combined with a robust control algorithm is used to calculate the closed-loop feedback control quantity, specifically including: S41, based on the preset target displacement, maximum velocity and maximum vibration acceleration, combined with the actuator kinematic model, and according to the multiple S-shaped velocity curves, the reference curve based on displacement-velocity-acceleration for each segment is calculated in sequence; S42, Obtain the real-time status of the actuator, and calculate the trajectory deviation based on the real-time status and the reference curve; S43, based on the trajectory deviation, fuzzy control or genetic algorithm is used to fine-tune the key node time and / or acceleration parameters of the reference curve to generate a corrected velocity increment; S44, the corrected velocity increment is superimposed on the multiple S-shaped velocity curves over time to obtain an adaptive velocity curve, and the adaptive velocity curve is integrated to obtain an adaptive position curve. S45, based on the real-time state of the actuator and combined with the adaptive speed curve and the adaptive position curve, calculate the speed error and position error; S46, a PID control algorithm combined with a robust control algorithm based on H∞ or sliding mode is used to perform closed-loop feedback control on the position error and the velocity error to obtain the closed-loop feedback control quantity; the calculation formula for the closed-loop feedback control quantity is: Among them, u fb K represents the closed-loop feedback control quantity. p K i and K d The PID control parameters are represented by e(t), where e(t) represents the position error. The velocity error, u robust This indicates an anti-interference item.

7. The multi-machine coordinated dynamic balancing drive method for a spiral lift according to claim 1, characterized in that, Between S1 and S2, the following is also included: The acquired displacement signal, velocity signal and hydraulic pressure signal are subjected to a fourth-order low-pass filter to obtain the filtered displacement signal, velocity signal and hydraulic pressure signal; The collected vibration acceleration signal is bandpass filtered to obtain the filtered vibration acceleration signal.

8. The multi-machine coordinated dynamic balancing drive method for a spiral lift according to claim 1, characterized in that, The multi-machine collaborative dynamic balance drive of the spiral lift adopts a synchronous control strategy with master and slave controllers.

9. A multi-machine coordinated dynamic balancing drive system for a spiral lift, characterized in that, The method for multi-machine coordinated dynamic balancing drive of a spiral lift as described in any one of claims 1 to 8 includes: The signal acquisition module is used to synchronously acquire displacement signals, speed signals, vibration acceleration signals, hydraulic pressure signals, and oil temperature signals of each actuator under the multi-machine cooperative operation of the screw lift; The feedforward control quantity calculation module is used to compensate for the time delay of each actuator based on the displacement signal, the velocity signal, the vibration acceleration signal, the hydraulic pressure signal, and the oil temperature signal, using a nonlinear state observation combined with a time delay compensation method to obtain the feedforward control quantity. The vibration suppression control quantity calculation module is used to perform Lyapunov stability feedback processing on the vibration acceleration signal to obtain the vibration suppression control quantity. The closed-loop feedback control quantity calculation module is used to design multiple S-shaped velocity curves based on preset target displacement, maximum velocity, and maximum vibration acceleration, combined with the actuator kinematic model; based on the multiple S-shaped velocity curves, the closed-loop feedback control quantity is calculated using a PID combined with a robust control algorithm. The integrated control module is used to perform multi-machine coordinated dynamic balance drive of the spiral lift based on the feedforward control quantity, the vibration suppression control quantity, and the closed-loop feedback control quantity.

10. A multi-machine coordinated dynamic balancing drive device for a spiral lifting machine, characterized in that, It includes a processor, a memory, and a computer program stored in the memory, wherein the computer program, when executed by the processor, implements the multi-machine cooperative dynamic balancing drive method for a spiral lift as described in any one of claims 1 to 8.

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