Stewart platform self-stabilization control method and device and electronic equipment

Abstract

The invention provides a Stewart platform self-stabilization control method and device and electronic equipment, and relates to the technical field of automatic control. The system is applied to a Stewart platform and comprises an upper platform, a lower platform, a camera, a visual calibration plate and a first inertial measurement unit, the camera and the first inertial measurement unit are installed on the upper platform, the visual calibration plate is installed on the lower platform, and the visual calibration plate and the camera are oppositely arranged. The Stewart platform self-stabilization control method comprises the following steps: acquiring a visual image of a visual calibration board through a camera, and determining a first real-time state of an upper platform according to the visual image; acquiring a second real-time state of the upper platform through the first inertial measurement unit; determining real-time state information of the upper platform according to the first real-time state and the second real-time state; and when the lower platform moves, adjusting the upper platform according to the movement information of the lower platform and the real-time state information of the upper platform. According to the invention, the real-time performance and the control precision of the Stewart platform are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automation control, in particular to a Stewart platform self-stabilization control method and device and electronic equipment. BACKGROUND

[0002] As an important parallel mechanism, the Stewart platform has a wide range of applications in simulation, precision machining, and motion control. The working principle of the existing Stewart platform is based on geometry and forward / inverse kinematics, which adjusts the position and attitude of the platform by calculating the extension amount of each driver. Specifically, inverse kinematics is used to calculate the length of the driver according to the required pose, while forward kinematics is used to verify the actual pose of the platform under the current driver length.

[0003] Due to its high precision and flexibility, the Stewart platform is widely used in some specific working environments to reduce base vibration, compensate for external disturbances, and improve system stability and precision, ensuring reliable operation of the platform in complex environments. Traditional self-stabilization control methods have many limitations in complex dynamic environments, requiring additional calculations of the length of each driver and forward kinematics to determine the platform attitude. This approach has a slow response speed in rapidly changing working conditions, making it difficult to meet the needs of high-speed motion or precise work tasks. In particular, the speed and control accuracy of the drive are difficult to meet the requirements, and small attitude deviations may cause serious result deviations. SUMMARY

[0004] The problem solved by the present application is how to efficiently implement self-stabilization control of the Stewart platform.

[0005] To solve the above problems, the present application provides a Stewart platform self-stabilization control method, device and electronic equipment.

[0006] In a first aspect, the present application provides a Stewart platform self-stabilization control method applied to a Stewart platform, the Stewart platform comprising an upper platform, a lower platform, a camera, a visual calibration board and a first inertial measurement unit, the camera and the first inertial measurement unit being installed on the upper platform, the visual calibration board being installed on the lower platform, the visual calibration board being oppositely arranged with the camera, the Stewart platform self-stabilization control method comprising: acquiring a visual image of the visual calibration board through the camera, and determining a first real-time state of the upper platform according to the visual image; acquiring a second real-time state of the upper platform through the first inertial measurement unit; determine real-time state information of the upper platform according to the first real-time state and the second real-time state; adjust the upper platform according to the motion information of the lower platform and the real-time state information of the upper platform when the lower platform moves.

[0007] Optionally, the determining the first real-time state of the upper platform according to the visual image comprises: performing image processing and visual transformation on the visual image to determine position information and attitude information of the upper platform; determining the first real-time state of the upper platform according to the position information and the attitude information of the upper platform.

[0008] Optionally, the acquiring the second real-time state of the upper platform by the first inertial measurement unit comprises: acquiring linear acceleration, angular velocity and attitude angle of the upper platform by the first inertial measurement unit; determining the second real-time state of the upper platform according to the linear acceleration, the angular velocity and the attitude angle of the upper platform.

[0009] Optionally, the determining the real-time state information of the upper platform according to the first real-time state and the second real-time state comprises: fusing the first real-time state and the second real-time state by using an extended Kalman filtering algorithm to determine the real-time state information.

[0010] Optionally, the Stewart platform further comprises a push rod connected with the upper platform, and the adjusting the upper platform according to the motion information of the lower platform and the real-time state information of the upper platform comprises: determining an expected position and an expected attitude of the lower platform according to the motion information of the lower platform; determining a desired position and a desired attitude of the upper platform according to the expected position and the expected attitude of the lower platform; determining a control instruction for the push rod according to an error between the desired position, the desired attitude of the upper platform and the real-time state information to adjust the position and the attitude of the upper platform.

[0011] Optionally, the Stewart platform further comprises a push rod encoder and a motor, and the determining the control instruction for the push rod according to the error between the desired position, the desired attitude of the upper platform and the real-time state information comprises: determining a current length of the push rod by the push rod encoder; determining an acceleration of the push rod by a rotating speed and a torque of the motor; determine the control command according to the error and the current length and the acceleration of the push rod.

[0012] Optionally, the Stewart platform further comprises a second inertial measurement unit, the second inertial measurement unit is installed on the lower platform, and the self-stabilization control method of the Stewart platform further comprises: obtaining linear acceleration, angular acceleration, linear velocity and angular velocity information of the lower platform through the second inertial measurement unit to determine the motion information of the lower platform.

[0013] In a second aspect, the present application provides a Stewart platform self-stabilization control device, which is applied to a Stewart platform, the Stewart platform comprises an upper platform, a lower platform, a camera, a visual calibration board and a first inertial measurement unit, the camera and the first inertial measurement unit are installed on the upper platform, the visual calibration board is installed on the lower platform, the visual calibration board is arranged opposite to the camera, and the Stewart platform self-stabilization control device comprises: a first module for obtaining a visual image of the visual calibration board through the camera, and determining a first real-time state of the upper platform according to the visual image; a second module for obtaining a second real-time state of the upper platform through the first inertial measurement unit; a third module for determining real-time state information of the upper platform according to the first real-time state and the second real-time state; a fourth module for adjusting the upper platform according to motion information of the lower platform and the real-time state information of the upper platform when the lower platform moves.

[0014] In a third aspect, the present application provides an electronic device, comprising a memory and a processor; the memory is used for storing a computer program; the processor is used for executing the computer program to realize the self-stabilization control method of the Stewart platform according to the first aspect.

[0015] In a fourth aspect, the present application provides a computer readable storage medium, the storage medium stores a computer program, and the computer program is executed by a processor to realize the self-stabilization control method of the Stewart platform according to the first aspect.

[0016] The Stewart platform self-stabilization control method has the beneficial effects that: by fusing the data of the camera and the first inertial measurement unit, using the multi-sensor fusion technology, the limitations of a single sensor are effectively overcome, for example, the camera provides accurate visual measurement information, and the first inertial measurement unit provides high-frequency acceleration and angular velocity data, and after fusion, high-precision measurement of the position and attitude of the upper platform is realized, which lays a solid foundation for accurate control, thereby improving the real-time performance and control precision of the Stewart platform, and even if the lower platform shakes or moves, the upper platform can be quickly adjusted and kept stable. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flowchart of the Stewart platform self-stabilization control method of the embodiment of the present application is shown in the figure. Figure 2 A structural schematic diagram of the Stewart platform of the embodiment of the present application is shown in the figure. Figure 3 A flowchart of determining the first real-time state of the embodiment of the present application is shown in the figure. Figure 4 A flowchart of determining the second real-time state of the embodiment of the present application is shown in the figure. Figure 5 A flowchart of adjusting the upper platform of the embodiment of the present application is shown in the figure. Figure 6 A flowchart of determining the control instruction of the embodiment of the present application is shown in the figure. Figure 7 A platform control framework of the embodiment of the present application is shown in the figure. Figure 8 A system architecture diagram of the Stewart platform self-stabilization control device of the embodiment of the present application is shown in the figure. Figure 9 A system architecture diagram of the electronic device of the embodiment of the present application is shown in the figure.

[0018] Reference signs are explained as follows: 1-upper platform, 2-Hooke joint, 3-push rod, 4-motor, 5-second inertial measurement unit, 6-vision calibration board, 7-lower platform, 8-camera, first inertial measurement unit. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described in detail below with reference to the drawings. Although some embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms, and should not be interpreted as being limited to the embodiments described herein, on the contrary, these embodiments are provided to make the present application more thorough and complete. It should be understood that the drawings and embodiments of the present application are only for illustrative purposes, and are not intended to limit the scope of protection of the present application.

[0020] It should be understood that each of the steps recited in the method embodiments of the present application can be performed in different orders and / or in parallel. In addition, the method embodiments can include additional steps and / or omit performing the steps shown. The scope of the present application is not limited in this respect.

[0021] The term "comprising" and variations thereof as used herein are open-ended, that is, mean "including but not limited to"; the term "based on" means "based, at least in part, on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optional" means "optional in at least some embodiments". Related terms shall be construed accordingly. It should be noted that reference to a "first", "second", etc. concept does not limit the scope of the concepts to which it refers to only a single one, but rather allows that concept to be implemented by one or more such concepts, unless the context explicitly dictates otherwise.

[0022] It should be noted that the terms "a" and "an" and "the" and similar referents used in the context of describing the application (especially in the context of claims) are to be construed to cover both singular and plural, unless otherwise indicated herein or clearly contradicted by context. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless the context indicates otherwise. Unless otherwise indicated herein, the use of

[0023] The names of the messages or information exchanged between the plurality of devices in the embodiments of the present application are used only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0024] As shown in Figure 1 The Stewart platform self-stabilization control method provided by the embodiments of the present application is applied to a Stewart platform, the Stewart platform comprises an upper platform, a lower platform, a camera, a visual calibration board and a first inertial measurement unit, the camera and the first inertial measurement unit are installed on the upper platform, the visual calibration board is installed on the lower platform, the visual calibration board is arranged opposite to the camera, and the Stewart platform self-stabilization control method comprises the following steps. S100: acquiring a visual image of the visual calibration board through the camera, and determining a first real-time state of the upper platform according to the visual image.

[0025] Specifically, in combination with Figure 2As shown, the Stewart platform includes an upper platform 1, a hook joint 2, a lower platform 7, a camera 8, a visual calibration board 6 (for example, a Charuco board), and a first inertial measurement unit 9 (for example, a 9-axis inertial measurement unit), the upper platform 1 and the lower platform 7 can be connected by a push rod 4, the camera 8 and the first inertial measurement unit 9 are installed on the upper platform 1, the visual calibration board 6 is installed on the lower platform 7, the visual calibration board 6 is arranged opposite to the camera 8, a visual image of the visual calibration board 6 is acquired through the camera 8, and a first real-time state of the upper platform 1 is determined according to the visual image.

[0026] Wherein, the camera 8 and the visual calibration board 6 need to be accurately calibrated in advance, and the first inertial measurement unit 9 needs to be calibrated, so as to reduce the initial measurement error.

[0027] S200: acquiring a second real-time state of the upper platform through the first inertial measurement unit.

[0028] Specifically, in combination with Figure 2 As shown, a first inertial measurement unit 9 (Inertial Measurement Unit, abbreviated as IMU) is installed on the upper platform 1, which is used to measure the linear acceleration, angular velocity and attitude angle of the upper platform 1, that is, to determine the second real-time state of the upper platform 1.

[0029] S300: determining real-time state information of the upper platform according to the first real-time state and the second real-time state.

[0030] Specifically, since there is a drift problem in IMU data, especially a significant cumulative error may be generated after a long time of use, therefore, an extended Kalman filter algorithm (EKF) can be used to fuse the IMU data and the industrial camera data, through fusing the data of the camera 8 and the first inertial measurement unit 9 (fusing the camera 8 and the first inertial measurement unit 9 as an external observer to replace the original forward kinematics platform attitude state estimator with slow response speed), the real-time state information of the upper platform 1 is determined according to the first real-time state and the second real-time state, the multi-sensor fusion technology is used, the limitations of a single sensor are effectively overcome, the accurate pose estimation without traditional driver length measurement is realized, for example, the camera 8 provides accurate visual measurement information, the first inertial measurement unit 9 provides high-frequency acceleration and angular velocity data, after fusion, high-precision measurement of the position and attitude of the upper platform 1 is realized, which can greatly improve the control precision and dynamic response ability of the platform, quickly adapt and stably work in a complex environment, lay a solid foundation for accurate control, effectively reduce the system cost, and at the same time ensure high-efficiency dynamic performance and accurate trajectory tracking, thereby improving the real-time performance and control precision of the Stewart platform.

[0031] Wherein, the linear velocity information of the platform can be obtained by using the IMU data and a specific calculation method (such as integration), so as to provide more rich state data for subsequent control.

[0032] S400: adjusting the upper platform according to the motion information of the lower platform and the real-time state information of the upper platform when the lower platform moves.

[0033] Specifically, when the lower platform 7 moves, the upper platform 1 is adjusted according to the motion information of the lower platform 7 and the real-time state information of the upper platform 1, so that the upper platform 1 can be quickly adjusted and kept stable even if the lower platform 7 shakes or moves, for example, the SMC algorithm is used to calculate the expected length, linear velocity or linear acceleration of each push rod connected to the upper platform 1, so as to realize self-stabilization control of the platform.

[0034] In this embodiment, by fusing the data of the camera 8 and the first inertial measurement unit 9, the multi-sensor fusion technology is used to effectively overcome the limitations of a single sensor, for example, the camera 8 provides accurate visual measurement information, and the first inertial measurement unit 9 provides high-frequency acceleration and angular velocity data, and after fusion, high-precision measurement of the position and attitude of the upper platform 1 is realized, which lays a solid foundation for accurate control, thereby improving the real-time performance and control accuracy of the Stewart platform.

[0035] Optionally, the determining the first real-time state of the upper platform according to the visual image comprises: S110: image processing and visual transformation are performed on the visual image to determine the position information and attitude information of the upper platform.

[0036] Specifically, as shown in Figure 3 After the camera 8 captures the image of the visual calibration board 6, a series of image processing and coordinate transformation are performed to accurately obtain the position coordinates and attitude angles of the upper platform 1 in the three-dimensional space, for example, the image of the visual calibration board 6 can be preprocessed by denoising, enhancing contrast, threshold segmentation and the like to improve the accuracy of subsequent feature extraction, then the features of the calibration board in the image are detected, such as the corner points of the checkerboard, and the pose (including the rotation matrix and the translation vector) of the camera 8 is calculated through the feature points in the image and the corresponding world coordinate points, so as to obtain the position of the camera 8 in the world coordinate system, and further determine the position information and attitude information of the upper platform 1.

[0037] S120: determining the first real-time state of the upper platform according to the position information and the attitude information of the upper platform.

[0038] Specifically, as shown in Figure 3 The above position coordinates and attitude angles can be taken as the first real-time state of the upper platform.

[0039] In this optional embodiment, the first real-time state of the upper platform determined according to the visual image can be fused with the second real-time state to realize high-precision measurement of the position and attitude of the upper platform.

[0040] Optionally, the acquiring, by the first inertial measurement unit, of the second real-time state of the upper platform comprises: S210: acquiring, by the first inertial measurement unit, linear acceleration, angular velocity and attitude angle of the upper platform.

[0041] Specifically, in combination with Figure 4 As shown in the figure, the linear acceleration, angular velocity and attitude angle of the upper platform 1 are measured in real time by the first inertial measurement unit 9.

[0042] S220: determining the second real-time state of the upper platform according to the linear acceleration, angular velocity and attitude angle of the upper platform.

[0043] Specifically, in combination with Figure 4 As shown in the figure, the linear acceleration, angular velocity and attitude angle of the upper platform 1 can be taken as the second real-time state of the upper platform.

[0044] In this optional embodiment, the second real-time state of the upper platform acquired by the first inertial measurement unit can be fused with the first real-time state to realize high-precision measurement of the position and attitude of the upper platform.

[0045] Optionally, the determining of the real-time state information of the upper platform according to the first real-time state and the second real-time state comprises: The first real-time state and the second real-time state are fused by using an extended Kalman filtering algorithm to determine the real-time state information.

[0046] Specifically, since the use of external sensors brings observation challenges, IMU data may drift, especially after long-term use, resulting in significant cumulative errors; while the camera can provide accurate pose information, but it may be limited by lighting conditions or occlusions in complex environments, resulting in delayed data updates or data loss, so the Kalman filtering algorithm can be used to fuse the data to obtain accurate state estimation of the platform, i.e. real-time state information. Through the Kalman filtering algorithm, the outputs from different sensors can be comprehensively evaluated according to the dynamic model of the system, noise can be filtered out, and more accurate platform linear velocity and angular velocity estimates relative to the chassis can be obtained.

[0047] wherein the angular velocity fusion is represented as: ; wherein, represents the fused angular velocity, represents the angular velocity measurement from the camera 8, represents the angular velocity measurement from the first inertial measurement unit 9, EKF represents the extended Kalman filter algorithm; wherein the linear velocity fusion is represented as: ; wherein, represents the fusion result of the linear velocity, represents the timestamp provided by the camera 8, represents the acceleration data provided by the first inertial measurement unit 9, represents the acceleration, represents the time step; wherein the full state estimation is represented as: ; wherein, represents the full state estimation including the estimation of the angular velocity and the linear velocity, represents the angular velocity measurement; wherein the effect of the platform rotation on the acceleration measurement can also be considered and the IMU acceleration data is corrected by calculating and subtracting the centrifugal force. Since the IMU is usually not located at the instantaneous velocity center of the platform, the rotation of the platform will introduce additional centrifugal acceleration, resulting in errors in the directly measured acceleration data. To solve this problem, the centrifugal force can be calculated and subtracted from the acceleration measured by the IMU, so that the true acceleration of the instantaneous rotation center (ICR) is obtained as follows: ; wherein, represents the linear acceleration measured by the IMU, represents the linear acceleration at the instantaneous rotation center (ICR), represents the angular acceleration measured by the IMU (if the IMU does not directly provide the angular acceleration, it can be obtained by differentiating the angular velocity, represents the position vector of the IMU relative to the ICR, offset represents the cross product between the angular velocity and the position vector, which describes the instantaneous linear velocity caused by the rotational motion. By correcting the filter as described above, the filter more accurately reflects the actual dynamic behavior of the platform in the state prediction stage, so that the relative motion between the platform and the chassis can be more accurately estimated when fusing the IMU and camera data.

[0048] In this optional embodiment, the first real-time state and the second real-time state are fused by using the extended Kalman filter algorithm, which ensures that the filter more accurately reflects the actual dynamic behavior of the platform in the state prediction stage, and the relative motion between the platform and the chassis can be more accurately estimated.

[0049] Optionally, the Stewart platform further comprises push rods connected with the upper platform, and the adjusting the upper platform according to the motion information of the lower platform and the real-time state information of the upper platform comprises: S410: determining an expected position and an expected attitude of the lower platform according to the motion information of the lower platform.

[0050] Specifically, in combination with Figure 2 As shown in the figure, the Stewart platform comprises push rods 3, and the main goal of the sliding mode controller is to accurately control the movement of each push rod 3 in the case of unknown disturbance, for example, the lower platform 7 shakes, so that the upper platform 1 quickly recovers and remains in the desired stable attitude and position. For example, when it is detected that the movement of the lower platform 7 causes the attitude of the upper platform 1 to change, the SMC quickly calculates the required adjustment amount of each push rod 3 to ensure that the attitude of the upper platform 1 quickly converges to zero.

[0051] S420: determining a desired position and a desired attitude of the upper platform according to the expected position and the expected attitude of the lower platform.

[0052] Specifically, in combination with Figure 5 As shown in the figure, according to the above description, the desired position and the desired attitude of the upper platform can be determined according to the expected position and the expected attitude of the lower platform. For example, first, the expected position and the attitude of the lower platform 7 are converted to the coordinate system of the upper platform 1. Due to the geometric relationship between the upper platform 1 and the lower platform 7, the inverse kinematics can be calculated, and by constructing a geometric model of the platform motion, in combination with the geometric constraints of the Stewart platform, the expected position and the attitude of the lower platform 7 can be converted to the desired position and the attitude of the upper platform 1.

[0053] S430: determining a control instruction for the push rod according to the error between the desired position and the desired attitude of the upper platform and the real-time state information, so as to adjust the position and the attitude of the upper platform.

[0054] Specifically, when the lower platform 7 is detected to shake or move, the self-stabilization controller is immediately started. Since the disturbance is random, it cannot be directly predicted. At this time, the real-time linear acceleration, angular acceleration, linear velocity and angular velocity information of the lower platform is obtained through the 9-axis IMU installed on the lower platform 7. The self-stabilization controller uses the motion information obtained from the 9-axis IMU to predict the future position and attitude of the lower platform 7, and the whole system expects the target to keep the position and attitude of the upper platform unchanged. Based on the predicted future position and attitude of the lower platform 7, the self-stabilization controller calculates the desired position and attitude of the upper platform 1 needed to keep its own position and attitude unchanged. Then the adjusted desired position and attitude of the upper platform 1 is sent as input to the SMC (sliding mode controller). The SMC controller calculates the accurate motion command of each push rod 3 according to the received desired position and attitude information of the upper platform 1, and then controls the motion of the platform push rod 3, so as to realize the accurate self-stabilization control of the upper platform 1, effectively avoiding the convergence and singularity problems inherent in forward kinematics, and significantly reducing the system cost. With the synergistic effect of Kalman filtering and SMC control, the system can quickly respond to the motion of the lower platform 7. Kalman filtering optimizes the sensor data in real time to provide accurate state estimation for SMC, and SMC quickly calculates the control command according to the information, so that the upper platform 1 can adjust the attitude and position in a short time to maintain stability, effectively improving the performance of the platform in dynamic environment. By monitoring the motion state of the lower platform 7 in real time and using the disturbance model to predict the disturbance as a feedforward value, the system can respond to potential disturbances in advance.

[0055] Wherein, in combination with Figure 7 the platform control framework shown in the figure, in the speed feedforward, the attitude is corrected by the gain obtained from the linear speed error, and the predicted speed at the next moment predicted by the disturbance model is used as the feedforward input of the platform , so that the platform can reach a self-stabilization state in a short time. For the position loop, the error between the expected value and the actual value is also used for feedforward control, but since the operator as a whole is a rigid body, when the static platform (i.e. the lower platform 7) is disturbed, the moving platform (i.e. the upper platform 1) will be affected by the perturbation and produce an additional linear velocity in the translation axis direction, and the size of the velocity can be calculated by the following formula: ; In order to realize the stability of the platform relative to the ground, the influence of the velocity needs to be subtracted from the output linear velocity. At the same time, the predicted linear velocity at the next moment predicted by the disturbance model is also needed as the linear velocity feedforward input of the platform in the translation direction .

[0056] Wherein, the SMC controller realizes the optimal control of the push rod 3 based on the accurate sensor fusion data and disturbance feedforward information, ensures that the platform always remains stable in a complex dynamic environment, and significantly improves the self-stabilization ability of the system. In the whole process, the system continuously performs data acquisition, fusion, control calculation and execution, etc. to ensure that the platform always remains in a stable state. At the same time, according to the actual operation, the control parameters are adjusted and optimized in real time to adapt to the control requirements under different working conditions.

[0057] Wherein, the sliding mode surface is designed based on the error between the actual position and attitude of the upper platform 1 and the expected position and attitude, for example, the following sliding mode surface equation is adopted: ; Wherein, is the position and attitude error vector, which covers linear displacement error, angle error, etc. is a positive definite diagonal matrix, which is used to adjust the weight of error and its derivative in the sliding mode surface. Each diagonal element of the diagonal matrix corresponds to a degree of freedom, and is finely adjusted according to the requirements of each degree of freedom on control accuracy and response speed. For example, for the degree of freedom with high attitude accuracy requirement, the corresponding value is increased, so that the system is more sensitive to error and tends to approach the sliding mode surface faster in this degree of freedom. At the same time, considering the coupling relationship between the multiple degrees of freedom of the Stewart platform, a nonlinear sliding mode surface or reasonable selection of parameters is adopted in the sliding mode surface design to ensure stable operation of each degree of freedom.

[0058] Wherein, for the equivalent control term in the control law calculation, the equivalent control term can be calculated according to the sliding mode surface design and platform kinematics model. The purpose is to keep the system state on the sliding mode surface under ideal non-interference and uncertainty conditions. The equivalent control term is obtained by solving the control input that makes the derivative of the sliding mode surface zero.

[0059] Wherein, for the switching control term in the control law calculation, the switching control term is used to overcome system uncertainty, disturbance and unmodeled dynamics. For example, the form is adopted: ; Wherein is a sign function, which provides positive and negative information for the sliding mode surface . Since the sign function in the sliding mode control may cause high-frequency switching (chattering phenomenon), a continuous function can be used to replace the sign function, such as a saturation function or a smooth approximation of the sign function; is a switching gain, and the selection of the switching gain is crucial. If the Too small will cause insufficient robustness. Adjust the value through experiment and simulation, for example, test the system response to disturbance under different values in simulation, observe the chattering situation, and then fine-tune on the actual platform.

[0060] wherein, for the final control law, it can be expressed as: ; Combine the equivalent and switching control terms to drive the system state to approach and remain on the sliding mode surface, and realize the self-stabilization control of the platform. After calculating the control law, convert it into the control speed, acceleration or position of the push rod motor through the acceleration inverse kinematics solution of the Stewart platform, and use the PID algorithm to drive the push rod movement to adjust the attitude and position of the platform.

[0061] wherein, the sliding mode surface parameters and switching gain need to be finely adjusted; according to the accuracy and response speed requirements of each degree of freedom of the system, combined with theoretical analysis and simulation experiment, observe the dynamic response, stability and chattering situation of the system under different values to adjust; under the premise of ensuring robustness, suppress chattering, and optimize through experiment test different values of platform to disturbance stability time, steady-state error and chattering amplitude and other indicators. At the same time, the SMC parameters are adjusted with the Kalman filter parameters and the motor controller PID parameters. Use hierarchical or global optimization method, first optimize each part respectively, and then optimize as a whole, to ensure the coordination of each control link. For example, the Kalman filter noise covariance matrix affects the accuracy of state estimation, and then affects the SMC control effect; the motor controller PID parameters affect the push rod response speed and accuracy, which need to be optimized with SMC parameters.

[0062] wherein, Lyapunov stability theory can be used to analyze the stability of the SMC system, construct a suitable Lyapunov function, and prove that the system is asymptotically stable under the sliding mode control law. Considering the nonlinearity, uncertainty and disturbance factors of the system, ensure the accuracy and reliability of the analysis. For example, use robust Lyapunov function analysis for systems with model uncertainty. Verify the performance of SMC through simulation and experiment.

[0063] In this optional embodiment, the control instruction to the push rod is determined according to the error between the expected position and attitude of the upper platform and the real-time state information, so as to adjust the position and attitude of the upper platform, so that the upper platform 1 can adjust the attitude and position in a short time, maintain stability, and effectively improve the performance of the platform in dynamic environment.

[0064] Optionally, the Stewart platform further comprises a push rod encoder and a motor, and the determination of the control instruction to the push rod according to the error between the expected position and attitude of the upper platform and the real-time state information comprises: S431: Determine the current length of the push rod through the push rod encoder.

[0065] Specifically, in combination with Figure 2 As shown in the figure, the Stewart platform includes push rod encoders and motors 4, each push rod 3 is driven by a direct current brushless motor with a multi-turn encoder, the encoder accurately measures the length of the push rod 3 in real time, and the acceleration of the push rod is calculated through the motor 4 rotation speed and torque circuit data, etc. to provide the control system with the motion state information of the push rod 3 itself.

[0066] S432: Determine the acceleration of the push rod through the rotation speed and torque of the motor.

[0067] Specifically, in combination with Figure 6 As shown in the figure, the motor rotation speed and torque data are used to calculate the push rod acceleration.

[0068] S433: Determine the control command according to the error and the current length and the acceleration of the push rod.

[0069] Specifically, in combination with Figure 6 As shown in the figure, when the lower platform 7 motion is detected, the self-stabilization controller adjusts the expected position and attitude of the upper platform 1 according to the lower platform 7 motion information and disturbance prediction results, and inputs the feedforward value to the SMC controller together with the expected information. The SMC controller outputs a control signal to the push rod motor driver to drive the push rod 3 to move, realizing the rapid self-stabilization control of the upper platform.

[0070] In this optional embodiment, the control command of the push rod is determined according to the error between the expected position and expected attitude of the upper platform and the real-time state information, so as to adjust the position and attitude of the upper platform, so that the upper platform 1 can adjust the attitude and position in a short time, maintain stability, and effectively improve the performance of the platform in a dynamic environment.

[0071] Optionally, the Stewart platform further comprises a second inertial measurement unit, the second inertial measurement unit is installed on the lower platform, and the Stewart platform self-stabilization control method further comprises: Obtain the linear acceleration, angular acceleration, linear velocity and angular velocity information of the lower platform through the second inertial measurement unit to determine the motion information of the lower platform.

[0072] Specifically, in combination with Figure 2 As shown in the figure, the Stewart platform further comprises a second inertial measurement unit 5, in order to comprehensively monitor the disturbance suffered by the platform during the movement, the second inertial measurement unit 5 is installed on the lower platform 7, which is used to measure the linear acceleration and angular velocity of the lower platform 7, and the angular acceleration information is obtained through differentiation. The linear acceleration, angular acceleration, linear velocity and angular velocity information can be used as the motion information of the lower platform.

[0073] In the optional embodiment, the linear acceleration and angular velocity of the lower platform 7 are measured by the second inertial measurement unit 5 to comprehensively monitor the disturbance suffered by the platform during movement.

[0074] As shown in Figure 8 The Stewart platform self-stabilization control device 800 provided by the embodiment of the present application is applied to a Stewart platform, the Stewart platform comprises an upper platform, a lower platform, a camera, a visual calibration board and a first inertial measurement unit, the camera and the first inertial measurement unit are installed on the upper platform, the visual calibration board is installed on the lower platform, the visual calibration board is arranged opposite to the camera, and the Stewart platform self-stabilization control device 800 comprises: A first module 810 is configured to acquire a visual image of the visual calibration board by the camera, and determine a first real-time state of the upper platform according to the visual image; A second module 820 is configured to acquire a second real-time state of the upper platform by the first inertial measurement unit; A third module 830 is configured to determine real-time state information of the upper platform according to the first real-time state and the second real-time state; A fourth module 840 is configured to adjust the upper platform according to movement information of the lower platform and the real-time state information of the upper platform when the lower platform moves.

[0075] As shown in Figure 9 The electronic device 900 comprises a memory 920 and a processor 910; the memory 920 is configured to store a computer program; and the processor 910 is configured to implement the Stewart platform self-stabilization control method as described above when executing the computer program.

[0076] Alternatively, the electronic device 900 comprises a memory 920 and a processor 910 coupled to the memory 920; the memory 920 is configured to store a computer program; and the processor 910 is configured to perform the following operations when executing the computer program: acquire a visual image of the visual calibration board by the camera, and determine a first real-time state of the upper platform according to the visual image; acquire a second real-time state of the upper platform by the first inertial measurement unit; determine real-time state information of the upper platform according to the first real-time state and the second real-time state; adjust the upper platform according to movement information of the lower platform and the real-time state information of the upper platform when the lower platform moves.

[0077] The embodiment of the present application provides a computer readable storage medium, and the storage medium stores a computer program.

[0078] Alternatively, a non-volatile computer readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the following operations: acquire a visual image of the visual calibration board through the camera, and determine a first real-time state of the upper platform according to the visual image; acquire a second real-time state of the upper platform through the first inertial measurement unit; determine real-time state information of the upper platform according to the first real-time state and the second real-time state; adjust the upper platform according to the motion information of the lower platform and the real-time state information of the upper platform when the lower platform moves.

[0079] An electronic device 900 that can be a server or a client of the present application will now be described, which is an example of a hardware device that can be applied to aspects of the present application. The electronic device 900 is intended to represent various forms of digital electronic computing devices such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computing devices. The electronic device 900 can also represent various forms of mobile devices such as personal digital processing, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0080] The electronic device 900 includes a computing unit that can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded into a random access memory (RAM) from a storage unit. In the RAM, various programs and data required for device operation can also be stored. The computing unit, the ROM, and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0081] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware, and the program can be stored in a computer readable storage medium. When the program is executed, the program can include the processes of the above-mentioned embodiment methods. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), a random access memory (RAM), or the like. In this application, the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present application. In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically independently, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0082] Although the present application is disclosed as above, the protection scope of the present application is not limited to this. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application, and these changes and modifications will fall within the protection scope of the present application.

Claims

1. A method for self-stabilization control of a Stewart platform, characterized in that, The application is applied to a Stewart platform, the Stewart platform comprises an upper platform, a lower platform, a camera, a visual calibration board and a first inertial measurement unit, the camera and the first inertial measurement unit are installed on the upper platform, the visual calibration board is installed on the lower platform, the visual calibration board is arranged opposite to the camera, and the Stewart platform self-stabilization control method comprises: acquiring a visual image of the visual calibration board through the camera, and determining a first real-time state of the upper platform according to the visual image; acquiring a second real-time state of the upper platform through the first inertial measurement unit; determining real-time state information of the upper platform according to the first real-time state and the second real-time state; adjusting the upper platform according to motion information of the lower platform and the real-time state information of the upper platform when the lower platform moves.

2. The method of claim 1, wherein, The determination of the first real-time state of the upper platform according to the visual image comprises: image processing and visual transformation are performed on the visual image to determine position information and attitude information of the upper platform; the first real-time state of the upper platform is determined according to the position information and the attitude information of the upper platform.

3. The method of claim 1, wherein, The acquisition of the second real-time state of the upper platform through the first inertial measurement unit comprises: linear acceleration, angular velocity and attitude angle of the upper platform are acquired through the first inertial measurement unit; the second real-time state of the upper platform is determined according to the linear acceleration, the angular velocity and the attitude angle of the upper platform.

4. The method of claim 1, wherein, The determination of the real-time state information of the upper platform according to the first real-time state and the second real-time state comprises: the first real-time state and the second real-time state are fused by using an extended Kalman filtering algorithm to determine the real-time state information.

5. The method of claim 1, wherein, The Stewart platform further comprises a push rod connected with the upper platform, and the adjustment of the upper platform according to the motion information of the lower platform and the real-time state information of the upper platform comprises: an expected position and an expected attitude of the lower platform are determined according to the motion information of the lower platform; an expected position and an expected attitude of the upper platform are determined according to the expected position and the expected attitude of the lower platform; a control instruction of the push rod is determined according to an error between the expected position, the expected attitude of the upper platform and the real-time state information, so as to adjust the position and the attitude of the upper platform.

6. The method of claim 5, wherein, The Stewart platform further comprises a push rod encoder and a motor, and the determination of the control instruction of the push rod according to the error between the expected position, the expected attitude of the upper platform and the real-time state information comprises: a current length of the push rod is determined through the push rod encoder; an acceleration of the push rod is determined through a rotating speed and a torque of the motor; the control instruction is determined according to the error, the current length and the acceleration of the push rod.

7. The method of claim 1, wherein, The Stewart platform further comprises a second inertial measurement unit installed on the lower platform, and the Stewart platform self-stabilization control method further comprises: The second inertial measurement unit is used to acquire linear acceleration, angular acceleration, linear velocity and angular velocity information of the lower platform to determine the motion information of the lower platform.

8. A self-stabilization control device for a Stewart platform, characterized by comprising: The Stewart platform comprises an upper platform, a lower platform, a camera, a visual calibration board and a first inertial measurement unit, the camera and the first inertial measurement unit are installed on the upper platform, the visual calibration board is installed on the lower platform, the visual calibration board is arranged opposite to the camera, and the Stewart platform self-stabilization control device comprises: A first module is configured to acquire a visual image of the visual calibration board by the camera, and determine a first real-time state of the upper platform according to the visual image; A second module is configured to acquire a second real-time state of the upper platform by the first inertial measurement unit; A third module is configured to determine real-time state information of the upper platform according to the first real-time state and the second real-time state; A fourth module is configured to adjust the upper platform according to motion information of the lower platform and the real-time state information of the upper platform when the lower platform moves.

9. An electronic device, comprising: comprise a memory and a processor; The memory is configured to store a computer program; The processor is configured to implement the Stewart platform self-stabilization control method according to any one of claims 1 to 7 when the computer program is executed.

10. A computer-readable storage medium, characterized in that, The storage medium has a computer program stored thereon, and the computer program, when executed by a processor, implements the Stewart platform self-stabilization control method according to any one of claims 1 to 7.

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