A multi-axis motion control method based on a six-axis hydraulic mechanical arm

Abstract

The application relates to the technical field of mechanical arm control, and particularly discloses a multi-axis motion control method based on a six-axis hydraulic mechanical arm, which comprises the following steps: a dynamics model and a hydraulic system model of the six-axis hydraulic mechanical arm are established, a motion trajectory is generated in Cartesian space or joint space based on preset task requirements, and the trajectory smoothness is optimized through an S-shaped acceleration and deceleration algorithm; a deviation coupling and synchronization control strategy is adopted to collect position, speed and pressure signals of each hydraulic cylinder in real time. The multi-axis motion control method based on the six-axis hydraulic mechanical arm of the embodiment of the application solves core problems such as synchronization error, nonlinear interference and energy consumption in multi-axis collaborative control of the hydraulic mechanical arm through multi-level modeling, multi-algorithm fusion and intelligent optimization, realizes high-precision, high-robustness and low-energy-consumption industrial-grade motion control, and is suitable for complex scenes such as heavy-load carrying and precise assembly.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mechanical arm control, and particularly relates to a multi-axis motion control method based on a six-axis hydraulic mechanical arm. BACKGROUND

[0002] In the industrial heavy load carrying scene, the multi-axis cooperative driving device (such as a double crane, a multi-axis mechanical arm, a synchronous lifting platform, etc.) generally faces the following technical problems:

[0003] The power source (such as a hydraulic cylinder) driven by the multi-axis mechanical arm is prone to synchronization errors due to problems such as dynamic response difference, uneven load distribution, or high control coupling degree. And the existing synchronization control method (such as master-slave control, speed synchronization) relies on a single reference signal and does not fully consider the dynamic coupling effect between axes, resulting in error accumulation, which eventually manifests as end trajectory deviation or vibration. SUMMARY

[0004] The present application aims to at least solve one of the technical problems in the related art. To this end, the purpose of the present application is to propose a multi-axis motion control method based on a six-axis hydraulic mechanical arm and an electronic device to reduce multi-axis coupling effects.

[0005] To achieve the above-mentioned purpose, the first aspect of the present application proposes a multi-axis motion control method based on a six-axis hydraulic mechanical arm, the method comprising the following steps:

[0006] S1, a dynamics model and a hydraulic system model of the six-axis hydraulic mechanical arm are established, the dynamics model is based on Newton-Euler equation or Lagrange equation, and contains the coupling effects of mechanical arm inertia, gravity, friction and external load, and the hydraulic system model includes servo valve flow-pressure characteristics, cylinder dynamic response and pump-valve cooperative control relationship;

[0007] S2, based on the preset task demand, a motion trajectory is generated in the Cartesian space or the joint space, and the trajectory smoothness is optimized through an S-shaped acceleration-deceleration algorithm, the acceleration-deceleration section, the uniform acceleration section, the deceleration section, the uniform speed section, the acceleration-deceleration section, the uniform deceleration section and the deceleration-deceleration section are divided, and the velocity, acceleration and displacement functions of each stage are generated;

[0008] S3, a deviation coupling synchronization control strategy is adopted, the position, velocity and pressure signals of each hydraulic cylinder are collected in real time, the motion deviation and the deviation change rate between axes are calculated, and the control command is dynamically adjusted to realize multi-axis cooperative motion;

[0009] S4, based on the dynamic load disturbance and the external environment force feedback, the adaptive robust controller is used to compensate the nonlinear effects, including the parameter drift caused by the hydraulic valve dead zone, the oil compressibility and the aging of the sealing element;

[0010] S5, in combination with the vibration suppression algorithm, estimating the vibration state of the robot arm end through the Kalman filter, and generating a reverse compensation torque based on vibration spectrum analysis;

[0011] S6, using digital twin technology to construct a virtual control model, optimizing control parameters in real time and predicting system stability, and outputting final control instructions to the hydraulic servo system.

[0012] In some embodiments of the present application, the specific construction method of the hydraulic system model in the S1 step includes:

[0013] Through the flow equation of the servo valve Establish the dynamic relationship of the valve-controlled cylinder, where C d is the flow coefficient, A is the valve port area, P s is the oil supply pressure, P L is the load pressure, and p is the hydraulic oil density;

[0014] The LuGre friction model is used to describe the nonlinear friction force of the hydraulic cylinder, and its expression is Where F f is the friction force, z is the internal state variable of friction, is the state variable change rate, v is the piston movement speed, σ0 is the stiffness coefficient, σ1 is the damping coefficient, and σ2 is the viscous friction coefficient.

[0015] In some embodiments of the present application, the specific implementation steps of the S-type acceleration and deceleration algorithm in the S2 step include:

[0016] According to the maximum acceleration a max and the maximum speed v max , the duration T1-T7 and the transition point displacement s0-s7 are calculated;

[0017] Based on the interval in which the current displacement value s is located, the corresponding acceleration function a(t), speed function v(t) and displacement function s(t) are selected to realize the non-impact speed control.

[0018] In some embodiments of the present application, the deviation coupling synchronization control strategy in the S3 step is specifically:

[0019] The position deviation e i between the axes is constructed ref x i -x and the speed deviation where x ref is the reference trajectory position, x i is the actual position of the i-th axis, is the reference trajectory speed,

[0020] The proportional gain K, the integral gain K and the differential gain K of the fuzzy PID controller are dynamically adjusted p . i . d . The gain parameters are determined according to the deviation e(t) and the deviation change rate ec(t) by referring to a fuzzy rule table.

[0021] In some embodiments of the present application, the adaptive robust controller in the S4 step adopts the following structure:

[0022] The adaptive law is designed to estimate the hydraulic system parameter uncertainty, and the expression is Where Γ is the adaptive gain matrix, φ is the regression vector, and e is the tracking error.

[0023] The robust term is introduced to suppress unmodeled dynamics and external disturbances, and the control input u is ad +u rb , wherein u ad is the adaptive term, and u rb is the robust compensation term.

[0024] In some embodiments of the present application, the vibration suppression algorithm in the S5 step specifically includes:

[0025] The end vibration signal is collected by an acceleration sensor, and the main vibration frequency is analyzed by using fast Fourier transform (FFT);

[0026] A notch filter is designed to attenuate the resonance frequency, and a phase-opposite reverse compensation torque is generated and superimposed on the control command.

[0027] In some embodiments of the present application, the multi-axis coordinated motion further includes a force-position hybrid control mode, specifically:

[0028] In the contact operation scene, the environmental force is fed back by a six-dimensional force sensor, and the impedance control mode is switched to;

[0029] The impedance parameters M d , B d , K d are adjusted to realize compliant interaction, wherein M d is the virtual mass, B d is the virtual damping, and K d is the virtual stiffness.

[0030] In some embodiments of the present application, the control command generation method of the hydraulic servo system includes:

[0031] The speed command of the planned trajectory is converted into a PWM signal, which is transmitted to the servo valve driver through the CAN bus;

[0032] Feedforward compensation is used to eliminate valve response delay, and the feedforward quantity is obtained by fitting experimental data of the valve's step response.

[0033] In some embodiments of the present invention, an energy-saving control module is further included, specifically implemented as follows:

[0034] The hydraulic pump speed is dynamically adjusted according to load requirements, and the motor output power is controlled by a frequency converter.

[0035] Energy storage is activated during periods of low load and released during peak load periods to reduce energy consumption.

[0036] To achieve the above objectives, a second aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein when the computer program is executed by the processor, it implements the above-described multi-axis motion control method based on a six-axis hydraulic robotic arm.

[0037] The multi-axis motion control method based on a six-axis hydraulic robotic arm in this invention solves the core problems of synchronization error, nonlinear interference, and high energy consumption in the multi-axis collaborative control of hydraulic robotic arms through multi-level modeling, multi-algorithm fusion and intelligent optimization. It achieves high-precision, high-robustness, and low-energy industrial-grade motion control, and is suitable for complex scenarios such as heavy-duty handling and precision assembly. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the architecture of a multi-axis motion control system based on a six-axis hydraulic robotic arm according to an embodiment of the present invention;

[0039] Figure 2 This is a schematic diagram of the optimization layer of a multi-axis motion control system in one embodiment of the present invention;

[0040] Figure 3 This is a schematic diagram of the execution layer of a multi-axis motion control system in one embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of the control layer of a multi-axis motion control system in one embodiment of the present invention;

[0042] Figure 5 This is a schematic diagram of the modeling layer of a multi-axis motion control system in one embodiment of the present invention;

[0043] Figure 6 This is a flowchart illustrating a multi-axis motion control method based on a six-axis hydraulic robotic arm, according to another embodiment of the present invention.

[0044] Figure 7 This is a schematic diagram of the structure of an electronic device according to another embodiment of the present invention. Detailed Implementation

[0045] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0046] The following description, with reference to the accompanying drawings, describes a multi-axis motion control method and electronic device based on a six-axis hydraulic robotic arm, according to embodiments of the present invention.

[0047] Figure 1 This is a schematic diagram of the architecture of a multi-axis motion control system based on a six-axis hydraulic robotic arm according to an embodiment of the present invention.

[0048] The system includes the following levels:

[0049] like Figure 5 As shown, the modeling layer includes the dynamics model and the hydraulic system model;

[0050] like Figure 4 As shown, the control layer includes trajectory planning, synchronization control, compensation control, and vibration suppression modules.

[0051] like Figure 3 As shown, the execution layer includes a servo system, energy-saving control, and force-position hybrid control module.

[0052] like Figure 2 As shown, the optimization layer includes modules for digital twin, parameter optimization, and real-time prediction.

[0053] The various levels described above are connected by data flow arrows, illustrating the complete closed-loop control process from modeling to control, execution, and optimization. Each sub-module is labeled with key algorithms and technologies, such as the Newton-Euler equations, fuzzy PID, and reinforcement learning.

[0054] like Figure 6 As shown, the multi-axis motion control method based on a six-axis hydraulic robotic arm includes the following steps:

[0055] S1. Establish the dynamic model and hydraulic system model of the six-axis hydraulic manipulator. The dynamic model is based on the Newton-Euler equations or the Lagrange equations, and includes the coupling effects of manipulator inertia, gravity, friction, and external load. The hydraulic system model includes the servo valve flow-pressure characteristics, cylinder dynamic response, and pump-valve coordinated control relationship. Based on the Newton-Euler equations or the Lagrange equations, the model comprehensively considers the coupling effects of manipulator inertia, gravity, friction, and external load. By fusing these two equations and using weighting factors to dynamically adjust the contribution ratio, it adapts to the dynamic characteristics of different joints, thus more accurately describing the kinematic and dynamic behavior of the manipulator.

[0056] The hydraulic system model establishes the dynamic relationship between the valve-controlled cylinder through the servo valve flow equation, covering factors such as flow coefficient, valve port area, oil supply pressure, load pressure, and hydraulic oil density; it uses a friction model to describe the nonlinear friction force of the hydraulic cylinder, including parameters such as friction force, internal friction state variables, rate of change of state variables, piston movement speed, stiffness coefficient, damping coefficient, and viscous friction coefficient, accurately characterizing the characteristics of the hydraulic system.

[0057] As an example, the dynamic model uses a fusion of the Newton-Euler equations and the Lagrange equations, and dynamically adjusts the contribution ratio of the two models through weighting factors to adapt to the dynamic characteristics of different joints.

[0058] S2. Based on preset task requirements, a motion trajectory is generated in Cartesian space or joint space. The trajectory smoothness is optimized using an S-shaped acceleration / deceleration algorithm, dividing the motion into acceleration, uniform acceleration, deceleration, uniform velocity, acceleration / deceleration, uniform deceleration, and deceleration segments. The duration and transition point displacement of each stage are calculated based on the maximum acceleration and maximum velocity. The corresponding acceleration, velocity, and displacement functions are selected according to the current displacement value's interval, achieving shock-free speed control and improving trajectory tracking accuracy. The S-shaped acceleration / deceleration algorithm divides the motion into 7 segments, thereby eliminating mechanical shock and achieving a trajectory tracking accuracy of ±0.05mm.

[0059] As an example, when generating motion trajectories, a dynamic window algorithm (DWA) can be introduced for real-time obstacle avoidance, and the trajectory generation strategy can be adjusted in combination with task priority.

[0060] S3. A deviation coupling synchronous control strategy is adopted to collect the position, speed and pressure signals of each hydraulic cylinder in real time, calculate the amount of motion deviation between shafts and the rate of change of deviation, and dynamically adjust the control commands to achieve multi-axis coordinated motion.

[0061] As an example, a deviation coupling synchronization control strategy is adopted to construct the position and speed deviations between axes. The proportional, integral, and derivative gain parameters are determined by a fuzzy PID controller based on the deviation amount and the rate of change of the deviation by looking up a fuzzy rule table. The control command is dynamically adjusted to keep the synchronization error within a very small range and achieve multi-axis coordinated motion.

[0062] It should be noted that the deviation coupling strategy controls the synchronization error within ±0.1mm.

[0063] S4. Based on dynamic load disturbance and external environmental force feedback, nonlinear effects are compensated by an adaptive robust controller, including parameter drift caused by hydraulic valve dead zone, oil compressibility, and seal aging.

[0064] As an example, adaptive laws are used to estimate the uncertainty of hydraulic system parameters, introduce robust terms to suppress unmodeled dynamics and external disturbances, and achieve effective compensation for nonlinear effects of the system through designed control inputs, thereby improving the robustness and stability of the control system.

[0065] S5. Combining the vibration suppression algorithm, the vibration state of the robotic arm end effector is estimated through a Kalman filter, and a reverse compensation torque is generated based on vibration spectrum analysis.

[0066] As an example, a notch filter is designed to attenuate the resonant frequency and generate a counter-compensating torque with opposite phase to be superimposed on the control command, so as to effectively suppress the vibration of the robotic arm end and improve the dynamic performance and positioning accuracy of the system.

[0067] S6. Utilize digital twin technology to construct a virtual control model, optimize control parameters in real time and predict system stability, and output the final control command to the hydraulic servo system. Through the bidirectional mapping and interaction between the virtual model and the physical entity, the system can achieve real-time monitoring, analysis and optimization of the control system, and output the final control command to the hydraulic servo system, thereby improving the overall performance and reliability of the system.

[0068] This method solves the core problems of synchronization error, nonlinear interference, and high energy consumption in the multi-axis collaborative control of hydraulic robotic arms through multi-level modeling, multi-algorithm fusion and intelligent optimization. It achieves high-precision, high-robustness, and low-energy industrial-grade motion control, which is suitable for complex scenarios such as heavy-duty handling and precision assembly.

[0069] In some embodiments of the present invention, the specific method for constructing the hydraulic system model in step S1 includes:

[0070] Flow equation through servo valve Establish the dynamic relationship of valve-controlled cylinder, where C d Where A is the flow coefficient, A is the valve orifice area, and P is the flow coefficient. s For the oil supply pressure, P L Where ρ is the load pressure and ρ is the hydraulic oil density;

[0071] The nonlinear frictional force of the hydraulic cylinder is described using the LuGre friction model, and its expression is as follows: Among them, F f Let z be the frictional force, and z be the internal state variable of the friction. σ0 is the rate of change of the state variable, v is the piston velocity, σ1 is the stiffness coefficient, σ2 is the damping coefficient, and σ2 is the viscous friction coefficient.

[0072] As an example, the aforementioned multi-axis motion control system uses a six-axis hydraulic robotic arm as the actuator, combined with an industrial-grade controller and sensor network to achieve closed-loop control. The system components include:

[0073] Hardware layer;

[0074] Robotic arm: Yaskawa MOTOMAN GP-25 six-axis robotic arm (load capacity 25kg, repeatability ±0.02mm);

[0075] Hydraulic system: Equipped with Rexroth M-4X series digital servo valves (response time ≤8ms), HBM fiber optic displacement sensors (sampling rate 10kHz, accuracy ±0.01mm) and Kistler six-dimensional force sensors (range 5000N, accuracy ±0.1%FS).

[0076] Control layer;

[0077] Controller: A real-time control platform is built based on the TI TMS320C6678 multi-core DSP (40GFLOPS computing power), and an EtherCAT bus (100μs cycle) is integrated to realize multi-axis synchronous communication.

[0078] Algorithm library: Deploys deviation coupling synchronization control, disturbance observer, and adaptive PID parameter adjustment modules.

[0079] As an example, the DH parameter method is used to establish the kinematic model of the robotic arm, and the hydraulic stiffness (K_h=5×10^7N / m) and joint flexibility (K_j=8×10^5N / m) are obtained through joint simulation using AMESim and ADAMS. Taking the grinding and loading / unloading scenario of the intermediate shaft of a new energy vehicle gear as an example (load 500kg), the measured parameters of the joint inertia matrix M(q) and Coriolis matrix C(q,) are as follows:

[0080]

[0081] In some embodiments of the present invention, the specific implementation steps of the S-shaped acceleration / deceleration algorithm in step S2 include:

[0082] Based on the maximum acceleration a max and maximum speed v max Calculate the duration of each stage T1 to T7 and the displacement of the transition point s0 to s7;

[0083] Based on the interval of the current displacement value s, the corresponding acceleration function a(t), velocity function v(t), and displacement function s(t) are selected to achieve shock-free speed control.

[0084] As an example, a fifth-order polynomial interpolation method is used to generate the Cartesian space trajectory, and the smoothness is optimized using an S-curve acceleration / deceleration algorithm. Taking a transport speed of 2 m / s as an example, the parameters are calculated as follows:

[0085] Maximum acceleration a max =5m / s 2

[0086] Timeframes for each stage:

[0087] Among them, v trans =1.5m / s;

[0088] acceleration function

[0089] In some embodiments of the present invention, the deviation coupling synchronization control strategy in step S3 is specifically as follows:

[0090] Constructing the inter-axis positional deviation e i =x ref -x i and speed deviation Where, x ref For the reference trajectory position, x i The actual position of the i-th axis. For reference trajectory velocity, The actual velocity of the i-th axis;

[0091] The proportional gain K is dynamically adjusted using a fuzzy PID controller. p Integral gain K i and differential gain K d The gain parameter is determined by looking up the fuzzy rule table based on the deviation e(t) and the rate of change of deviation ec(t).

[0092] The system collects position, speed, and pressure signals from each hydraulic cylinder in real time to construct inter-axis position and speed deviations. A fuzzy PID controller dynamically adjusts control commands based on the deviation amount and rate of change, keeping synchronization errors within a minimal range. This control strategy fully considers the dynamic coupling effect between axes, achieving multi-axis coordinated motion through real-time feedback and dynamic adjustment. This ensures consistency between the axes of the robotic arm, avoids end-effector trajectory deviation or vibration caused by synchronization errors, and improves the system's motion accuracy and stability.

[0093] In some embodiments of the present invention, the adaptive robust controller in step S4 adopts the following structure:

[0094] Design an adaptive law to estimate the uncertainty of hydraulic system parameters, the expression of which is: Where Γ is the adaptive gain matrix, φ is the regression vector, and e is the tracking error;

[0095] Introducing robust terms to suppress unmodeled dynamics and external disturbances, controlling the input u = u ad +u rb , where u ad For the adaptive term, u rb This is a robust compensation term.

[0096] Based on dynamic load disturbances and external environmental force feedback, an adaptive law is used to estimate the parameter uncertainties of the hydraulic system, and a robust term is introduced to suppress unmodeled dynamics and external disturbances. The adaptive law can automatically adjust parameters according to the real-time system state and error information to compensate for nonlinear effects caused by hydraulic valve dead zones, oil compressibility, and seal aging, while the robust term enhances the system's resistance to model uncertainties and external disturbances, enabling the control system to maintain good performance under complex operating conditions and parameter variations, thus ensuring the system's stability and reliability.

[0097] In some embodiments of the present invention, the vibration suppression algorithm in step S5 specifically includes:

[0098] The end vibration signal is acquired by an accelerometer, and the dominant frequency is analyzed by Fast Fourier Transform (FFT).

[0099] A notch filter is designed to attenuate the resonant frequency, and a counter-compensation torque with opposite phase is generated and superimposed on the control command. The end-effector vibration signal is acquired by an accelerometer, and the dominant frequency is analyzed using Fast Fourier Transform (FFT). A notch filter is then designed to attenuate the resonant frequency, and a counter-compensation torque with opposite phase is generated and superimposed on the control command. This method addresses the vibration at its source, specifically weakening the vibration energy, while simultaneously using a counter-compensation torque to offset part of the vibration. This effectively suppresses vibration at the robotic arm's end effector, improves the system's dynamic performance and positioning accuracy, reduces settling time, allows the robotic arm to reach a stable state more quickly, and increases work efficiency.

[0100] In some embodiments of the present invention, the multi-axis cooperative motion further includes a force-position hybrid control mode, specifically:

[0101] In contact operation scenarios, the system uses a six-dimensional force sensor to report environmental forces and switches to impedance control mode.

[0102] Adjusting the impedance parameter M d B d K d To achieve compliant interaction, M d For virtual quality, B d For virtual damping, K d This is virtual stiffness.

[0103] In contact-based operations, a six-dimensional force sensor provides real-time feedback on environmental forces. When the detected contact force exceeds a set threshold, the system switches to impedance control mode. By adjusting impedance parameters such as virtual mass, virtual damping, and virtual stiffness, the mechanical characteristics of the robotic arm are altered, giving it a degree of compliance. This allows it to adapt to changes in environmental contact forces and achieve compliant interaction with the environment. This control mode avoids rigid collisions between the robotic arm and the environment, protecting both the robotic arm and the workpiece. It also improves the system's adaptability and reliability in contact-based operations, expanding the application range of the robotic arm to scenarios requiring physical contact with the environment, such as precision assembly and surface treatment.

[0104] In some embodiments of the present invention, the method for generating control commands for a hydraulic servo system includes:

[0105] The speed command for the planned trajectory is converted into a PWM signal and transmitted to the servo valve driver via the CAN bus;

[0106] Feedforward compensation is used to eliminate valve response delay. The feedforward quantity is obtained by fitting experimental data of the valve's step response, ensuring that control commands can be accurately and quickly transmitted and executed, thereby improving the system's dynamic response speed and control accuracy.

[0107] In the above scheme, the speed command for the planned trajectory is converted into a PWM signal and transmitted to the servo valve driver via the CAN bus to achieve precise control of the hydraulic servo system. Simultaneously, a feedforward compensation method is used to eliminate valve response delay. The feedforward amount is obtained by fitting experimental data of the valve's step response, compensating for the delay characteristics of the servo valve in advance. This ensures that control commands can be transmitted and executed quickly and accurately, improving the dynamic response speed and control accuracy of the hydraulic servo system. This allows the robotic arm to better track the planned trajectory, improving work efficiency and control performance.

[0108] In some embodiments of the present invention, an energy-saving control module is further included, specifically implemented as follows:

[0109] The hydraulic pump speed is dynamically adjusted according to load requirements, and the motor output power is controlled by a frequency converter.

[0110] Energy storage is activated during periods of low load and released during peak load periods to reduce energy consumption.

[0111] The energy-saving control module works by dynamically adjusting the hydraulic pump speed according to load demand and controlling the motor output power via a frequency converter to match the hydraulic system's output power with the actual load requirements, thus avoiding energy waste. During low-load periods, the accumulator stores excess hydraulic energy and releases it during peak loads to assist the hydraulic pump in providing power, reducing system energy consumption. This energy-saving control strategy effectively improves the energy utilization efficiency of the hydraulic system, reduces operating costs, and minimizes mechanical wear and electrical surges caused by frequent starts and stops of hydraulic pumps and other equipment, extending equipment lifespan and improving system reliability and stability.

[0112] Corresponding to the above embodiments, the present invention also proposes an electronic device.

[0113] like Figure 7 The diagram shows a structural schematic of an electronic device according to the present invention. The electronic device 200 includes a processor 201 and a memory 203. The processor 201 and the memory 203 are connected, for example, via a bus 202. Optionally, the electronic device 200 may further include a transceiver 204. It should be noted that in practical applications, the transceiver 204 is not limited to one unit, and the structure of this electronic device 200 does not constitute a limitation on the embodiments of the present invention.

[0114] Processor 201 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 201 may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0115] Bus 202 may include a pathway for transmitting information between the aforementioned components. Bus 202 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 202 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0116] The memory 203 stores a computer program corresponding to the multi-axis motion control method based on a six-axis hydraulic robotic arm according to the above embodiments of the present invention. This computer program is controlled and executed by the processor 201. The processor 201 executes the computer program stored in the memory 203 to implement the content shown in the aforementioned method embodiments.

[0117] Among them, electronic devices 200 include, but are not limited to: mobile terminals such as laptops and PADs (tablet computers) and fixed terminals such as desktop computers. Figure 7 The electronic device 200 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0118] The electronic device 200 of this invention solves the synchronization error problem in the multi-axis collaborative control of hydraulic robotic arms through multi-level modeling, multi-algorithm fusion and intelligent optimization, controls the synchronization error within a very small range, achieves trajectory tracking accuracy of ±0.05mm, improves end-effector positioning accuracy, and meets the needs of high-precision industrial application scenarios.

[0119] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, control, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, the computer-readable medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0120] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0121] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0122] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0123] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A multi-axis motion control method based on a six-axis hydraulic robotic arm, characterized in that, Includes the following steps: S1. Establish a dynamic model and a hydraulic system model for a six-axis hydraulic manipulator. The dynamic model is based on the Newton-Euler equation or the Lagrange equation and includes the coupling effects of manipulator inertia, gravity, friction and external load. The hydraulic system model includes the servo valve flow-pressure characteristics, cylinder dynamic response and pump-valve coordinated control relationship. S2. Based on the preset task requirements, generate motion trajectories in Cartesian space or joint space, and optimize the smoothness of the trajectory through S-shaped acceleration and deceleration algorithm. Divide the trajectory into acceleration segment, uniform acceleration segment, deceleration segment, uniform speed segment, acceleration and deceleration segment, uniform deceleration segment and deceleration and deceleration segment, and generate velocity, acceleration and displacement functions for each stage. S3. A deviation coupling synchronization control strategy is adopted to collect the position, speed, and pressure signals of each hydraulic cylinder in real time, calculate the inter-axis motion deviation and the rate of change of deviation, and dynamically adjust the control commands to achieve multi-axis coordinated motion; the deviation coupling synchronization control strategy is specifically as follows: Constructing inter-axis positional deviation and speed deviation ,in, For reference trajectory position, For the first Actual position of the shaft For reference trajectory velocity, For the first Actual shaft speed; The proportional gain is dynamically adjusted using a fuzzy PID controller. Integral gain and differential gain The gain parameter is based on the deviation. and the rate of change of deviation Determine by consulting the fuzzy rule table; S4. Based on dynamic load disturbance and external environmental force feedback, nonlinear effects are compensated by an adaptive robust controller, including hydraulic valve dead zone, oil compressibility and parameter drift caused by seal aging. S5. Combining the vibration suppression algorithm, the vibration state of the robotic arm's end effector is estimated using a Kalman filter, and a reverse compensation torque is generated based on vibration spectrum analysis; the vibration suppression algorithm specifically includes: The end vibration signal is acquired by an accelerometer, and the dominant frequency is analyzed by Fast Fourier Transform (FFT). The notch filter is designed to attenuate the resonant frequency and generate a counter-compensation torque with opposite phase to be superimposed on the control command. S6. Utilize digital twin technology to construct a virtual control model, optimize control parameters in real time, predict system stability, and output the final control command to the hydraulic servo system.

2. The multi-axis motion control method based on a six-axis hydraulic robotic arm according to claim 1, characterized in that, The specific method for constructing the hydraulic system model in step S1 includes: Flow equation through servo valve Establish the dynamic relationship of valve-controlled cylinders, whereby... For flow coefficient, For valve orifice area, For oil supply pressure, For load pressure, The density of the hydraulic oil; use The friction model describes the nonlinear frictional force of a hydraulic cylinder, and its expression is: v, where, For friction, For the internal state variables of friction, Let v be the rate of change of the state variable, and v be the piston velocity. This is the stiffness coefficient. The damping coefficient is... is the coefficient of viscous friction.

3. The multi-axis motion control method based on a six-axis hydraulic robotic arm according to claim 1, characterized in that, The specific implementation steps of the S-shaped acceleration / deceleration algorithm in step S2 include: Based on maximum acceleration and maximum speed Calculate the duration of each stage. ~ and transition point displacement ~ ; Based on the interval in which the current displacement value s is located, select the corresponding acceleration function. velocity function and displacement function This achieves shock-free speed control.

4. The multi-axis motion control method based on a six-axis hydraulic robotic arm according to claim 1, characterized in that, The adaptive robust controller in step S4 adopts the following structure: Design an adaptive law to estimate the uncertainty of hydraulic system parameters, the expression of which is: ,in, This is the adaptive gain matrix. For the regression vector, For tracking error; Introducing robust terms to suppress unmodeled dynamics and external disturbances, controlling the input ,in, For adaptive terms, This is a robust compensation term.

5. The multi-axis motion control method based on a six-axis hydraulic robotic arm according to claim 1, characterized in that, The multi-axis cooperative motion also includes a force-position hybrid control mode, specifically: In contact operation scenarios, the system uses a six-dimensional force sensor to report environmental forces and switches to impedance control mode. Adjusting impedance parameters , , To achieve compliant interaction, among which, For virtual quality, For virtual damping, This is virtual stiffness.

6. The multi-axis motion control method based on a six-axis hydraulic robotic arm according to claim 1, characterized in that, The method for generating control commands for the hydraulic servo system includes: The speed command for the planned trajectory is converted into a PWM signal and transmitted to the servo valve driver via the CAN bus; Feedforward compensation is used to eliminate valve response delay, and the feedforward quantity is obtained by fitting experimental data of the valve's step response.

7. The multi-axis motion control method based on a six-axis hydraulic robotic arm according to claim 1, characterized in that, It also includes an energy-saving control module, which is implemented as follows: The hydraulic pump speed is dynamically adjusted according to load requirements, and the motor output power is controlled by a frequency converter. Energy storage is activated during periods of low load and released during peak load periods to reduce energy consumption.

8. An electronic device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory, wherein when the computer program is executed by the processor, it implements the multi-axis motion control method based on a six-axis hydraulic robotic arm as described in any one of claims 1-7.

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