Closed-loop feedback control method and system for marine wave compensation medical care platform

Abstract

The invention relates to the technical field of ship sea wave compensation, in particular to a closed-loop feedback control method and system for a ship wave compensation medical care platform, and the method comprises the following steps: obtaining a posture signal of a lower platform of the wave compensation medical care platform, constructing a data set, and constructing a posture prediction model of the lower platform; the processor is used for acquiring attitude predicted values at future moments; based on the data set and the lower platform pose prediction model, constructing a residual prediction model to obtain a residual prediction value of pose prediction; and constructing a kinematics model of the lower platform, determining an accurate attitude value through an attitude correction algorithm in combination with an attitude predicted value and a residual predicted value, solving a rotation angle of a servo motor by using a motion inverse solution algorithm, and further realizing closed-loop feedback control of the wave compensation medical care platform. The wave compensation medical care platform can be accurately controlled in a complex environment, the wave compensation effect on the wave compensation medical care platform is improved, and then the risk of offshore medical treatment is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of ship wave compensation technology, in particular to a closed-loop feedback control method and system for a ship wave compensation medical treatment platform. BACKGROUND

[0002] During a long sea voyage, especially when encountering complex sea conditions or performing emergency rescue tasks, a stable surgical environment is crucial to ensure the success rate of medical treatment and the safety of patients. The sway of the ship not only affects the daily rest of the crew, but for the medical team performing delicate operations on the ship, any slight sway can interfere with the accuracy of the operation and increase the risk of surgery. Therefore, the prior art proposes a ship medical treatment bed with wave compensation function. In addition to including an upper platform and a lower platform, such medical treatment beds usually include adjusting devices for adjusting the attitude of the upper platform, such as servo motors, electric cylinders, connecting rods, etc.

[0003] However, although the existing wave compensation ship medical treatment bed adjusts the attitude of the upper platform through devices such as servo motors, electric cylinders, connecting rods, its control method is still relatively rough and cannot meet the high-precision surgery requirements, becoming a bottleneck restricting the quality of maritime medical treatment. Among them, some wave compensation ship medical treatment beds use an open-loop control system, which can only roughly adjust the actuator action according to the current sway data of the ship body, and the compensation accuracy is difficult to control. Others use a pure feedback closed-loop control system, which can achieve high-precision wave compensation in regular wave sea conditions, but in irregular wave sea conditions, the compensation effect may be limited due to the randomness of sea wave changes. SUMMARY

[0004] In view of the defects in the prior art, the present application provides a closed-loop feedback control method and system for a wave compensation medical treatment platform on a ship.

[0005] To achieve the above-mentioned purpose, in a first aspect, the present application provides a closed-loop feedback control method for a wave compensation medical treatment platform on a ship, comprising the following steps: acquiring the attitude signal of the lower platform in the wave compensation medical treatment platform in real time, and using the attitude signal to establish a data set, and then constructing a lower platform pose prediction model; based on the data set and the lower platform pose prediction model, constructing a residual prediction model; using the lower platform pose prediction model to obtain the attitude prediction value at the future time, and using the residual prediction model to obtain the residual prediction value of the attitude prediction; constructing the kinematics model of the lower platform, combining the attitude prediction value and the residual prediction value, and determining the attitude accurate value through an attitude correction algorithm; according to the attitude accurate value, using a motion inverse algorithm to solve the rotation angle of the servo motor, and then realizing the closed-loop feedback control of the wave compensation medical treatment platform. The present application can realize accurate control of the wave compensation medical treatment platform in complex environments, improve the wave compensation effect of the wave compensation medical treatment platform, and thus reduce the risk of maritime medical treatment.

[0006] Optionally, the real-time acquisition of the attitude signal of the lower platform in the wave-compensated medical care platform is performed, and the attitude signal is used to establish a data set, and then a lower platform pose prediction model is constructed, including the following steps: real-time acquisition of the rotation angle of the lower platform around the X-axis and the Y-axis and the vertical displacement in the Z-axis direction; the rotation angle and the vertical displacement collected at the same time are taken as a sample data, and after a certain number of sample data are collected, a data set is constructed using all the sample data; according to the data set, a plurality of lower platform pose prediction models are constructed using an autoregressive model, which are respectively used to predict the rotation angle and the vertical displacement. This method constructs a data set by collecting the attitude signal of the lower platform in the environment of the ship, and then constructs a lower platform pose prediction model using an autoregressive model, so that the model has pertinence and real-time performance.

[0007] Optionally, the recursive least squares method with a forgetting factor is used to realize online identification of the parameters in the autoregressive model, and the forgetting factor is adjusted in real time through Bayesian optimization. This method uses the recursive least squares method with a forgetting factor for online parameter identification, so that the model can continuously update its parameters using the latest data, thereby overcoming the model misalignment problem caused by changes in the dynamic characteristics of the sea waves; in addition, the forgetting factor is adjusted in real time through Bayesian optimization, realizing the intelligentization of the parameter update strategy, and improving the robustness and accuracy of the model in complex time-varying marine environments.

[0008] Optionally, based on the data set and the lower platform pose prediction model, a residual prediction model is constructed, including the following steps: based on the data set and the lower platform pose prediction model, a prediction error data set is constructed; according to the prediction error data set, a lightweight neural network is used to dynamically model error transmission to obtain a residual prediction model. This method uses a lightweight neural network to dynamically model error transmission, which is different from simple static error correction. It can learn and capture the complex and nonlinear dynamic trends of errors, and then intelligently predict the possible deviations of the predicted values at future time, thereby providing a high-precision and forward-looking error estimate value for subsequent filtering algorithms, which helps to achieve accurate wave compensation.

[0009] Optionally, the lower platform pose prediction model is used to obtain the attitude prediction value at the future time, and the residual prediction model is used to obtain the residual prediction value of the attitude prediction, including the following steps: the lower platform pose prediction model is used to obtain the attitude prediction value at the future time, the attitude prediction value including the rotation angle prediction value and the vertical displacement prediction value; the residual prediction model is used to obtain the residual prediction value of the rotation angle prediction value and the vertical displacement prediction value.

[0010] Optionally, the method further comprises the following steps of: constructing the kinematic model of the lower platform; using the extended Kalman filter to obtain the accurate attitude value according to the kinematic model, the predicted attitude value and the predicted residual value.

[0011] Optionally, the kinematic model comprises a roll model, a pitch model and a heave model.

[0012] Optionally, the method of using the extended Kalman filter to obtain the accurate attitude value according to the kinematic model, the predicted attitude value and the predicted residual value comprises the following steps of: taking the kinematic model as a state transition equation, and taking the predicted attitude value and the predicted residual value as observation inputs to generate an initial state and an observation vector of the extended Kalman filter; and outputting the accurate attitude value fused with the model prediction and the residual compensation through a prediction-update iteration process of the extended Kalman filter based on the initial state and the observation vector. This method dynamically weighs the reliability of the calculation result of the physical model and the prediction result of the data-driven model by taking the kinematic model as the state transition equation of the extended Kalman filter and taking the predicted attitude and the predicted residual into the observation vector, and finally outputs the accurate attitude value, which provides a crucial reliable input for the motion back analysis and control, and fundamentally improves the stability and compensation performance of the entire wave compensation system.

[0013] Optionally, the method of using the motion back analysis algorithm to solve the rotation angle of the servo motor according to the accurate attitude value to realize the closed-loop feedback control of the wave compensation medical platform comprises the following steps of: using the motion back analysis algorithm to solve the rotation angle of the servo motor based on the accurate attitude value; driving the servo motor to rotate using the servo driver according to the rotation angle of the servo motor; and using the encoder to collect the actual rotation angle of the servo motor in real time and feed back to the servo driver, thereby continuously adjusting the motor action to keep the upper platform of the wave compensation medical platform horizontal. This method dynamically maintains the stability of the upper platform by using the encoder to detect the actual rotation angle in real time to form a negative feedback loop, thereby achieving the ultimate goal of active wave compensation and improving the operation safety and reliability of the wave compensation medical platform in a turbulent environment.

[0014] In a second aspect, the present application provides a closed-loop feedback control system of a marine wave-compensated medical platform, which is configured to use the closed-loop feedback control method of the marine wave-compensated medical platform provided by the present application. The system comprises: a data acquisition device, which is used to acquire a posture signal of a lower platform in the wave-compensated medical platform in real time; a control center, which is used to construct a data set using the posture signal, and then construct a lower platform pose prediction model; construct a residual prediction model based on the data set and the lower platform pose prediction model; obtain a posture prediction value at a future time using the lower platform pose prediction model, and obtain a residual prediction value of the posture prediction using the residual prediction model; construct a kinematics model of the lower platform, combine the posture prediction value and the residual prediction value, and determine a posture accurate value through a posture correction algorithm; solve a servo motor rotation angle using a motion back-solving algorithm according to the posture accurate value, and then realize closed-loop feedback control of the wave-compensated medical platform; a wave compensation execution device, which comprises a servo driver and a servo motor, and is used to respond to a control instruction of the control center, and then realize wave compensation of the wave-compensated medical platform; and a control handle, which is used to control the start and stop of the wave compensation. The system can improve the practicability of the method and facilitate the popularization of the method. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0016] Figure 1 A flowchart of the closed-loop feedback control method of the marine wave-compensated medical platform of the embodiment of the present application; Figure 2 A roll random signal of the embodiment of the present application; Figure 3 A pitch random signal of the embodiment of the present application; Figure 4 Anti-rolling effect of the simulation prototype of the embodiment of the present application on roll; Figure 5 Anti-rolling effect of the simulation prototype of the embodiment of the present application on pitch; Figure 6 A frame diagram of the closed-loop feedback control system of the marine wave-compensated medical platform of the embodiment of the present application. DETAILED DESCRIPTION

[0017] The specific embodiments of the present application will now be described in detail with specific reference being made to the figures. It is noted that the embodiments described herein are only meant to be illustrative and are not meant to limit the present application. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present application. It will be apparent, however, to one ordinarily skilled in the art that the present application can be practiced without the specific details presented herein. In other instances, well-known circuits, software or methods have not been described in detail in order to avoid obscuring the present application.

[0018] Reference throughout this specification to "one embodiment", "an embodiment", "one example", or "an example" means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the application. Thus, appearances of the phrases "in one embodiment" or "in an embodiment" or "one example" or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics can be combined in any suitable

[0019] It is noted that in an alternative embodiment, the same symbols or letters appearing in all formulas mean the same thing and have the same value except where otherwise noted.

[0020] In an alternative embodiment, see Figure 1 The present application provides a closed-loop feedback control method for a wave-compensated medical treatment platform on a ship, the method comprising the following steps: S1, acquiring a posture signal of a lower platform in the wave-compensated medical treatment platform in real time, and using the posture signal to establish a data set and further to construct a lower platform pose prediction model.

[0021] The step S1 specifically comprises the following steps: S11, collecting a rotation angle of the lower platform around the X-axis and the Y-axis and a vertical displacement of the lower platform in the Z-axis direction in real time.

[0022] Specifically, in the present embodiment, the wave-compensated medical treatment platform, i.e. a medical treatment bed with wave compensation function on a ship, comprises an upper platform (bed surface) and a lower platform (bed bottom) capable of being fixed on the ship, and devices for wave compensation such as servo motors, electric cylinders, connecting rods, etc. The posture signal of the lower platform comprises a rotation angle of the lower platform around the X-axis and the Y-axis and a vertical displacement of the lower platform in the Z-axis direction.

[0023] In this embodiment, the rotation angles of the ship around the X-axis and the Y-axis and the heave displacement are obtained by using a motion reference unit (MRU) installed on the ship. Since the lower platform is rigidly connected to the ship body when the lower platform is fixed on the ship, the rotation angles of the ship around the X-axis and the Y-axis can be directly used as the rotation angles of the lower platform around the X-axis and the Y-axis, and the vertical displacement of the lower platform in the Z-axis direction is calculated according to the following relationship: wherein, is the vertical displacement of the lower platform in the Z-axis direction, is the heave displacement of the ship, is the lateral distance of the geometric center of the lower platform relative to the center of gravity of the ship, is the longitudinal distance of the geometric center of the lower platform relative to the center of gravity of the ship. The method for determining the position of the center of gravity of the ship can refer to the prior art.

[0024] In other alternative embodiments, a 2-axis tilt sensor can also be used to measure the rotation angles of the lower platform around the X-axis and the Y-axis, and an inertial measurement unit (IMU) can be used to measure the vertical displacement of the lower platform in the Z-axis direction.

[0025] S12, the rotation angle and the vertical displacement collected at the same time are taken as a sample data, and after a certain number of sample data are collected, all sample data are used to construct a data set.

[0026] Specifically, in this embodiment, the samples in the data set are arranged in chronological order in the time dimension. The number of samples in the data set needs to be determined according to the dominant wave period of the target sea area and the sampling frequency of the sensor, and the value is not less than 5 times the number of data points corresponding to the dominant wave period, preferably 10 to 20 times. For example, for a system with a dominant wave period of 10 seconds and a sampling frequency of 50 Hz, 5000 to 10000 sample data are preferred.

[0027] S13, according to the data set, a plurality of lower platform pose prediction models are constructed using an autoregressive model, respectively for predicting the rotation angles and the vertical displacement.

[0028] Specifically, in this embodiment, for the rotation angles of the lower platform around the X-axis and the Y-axis and , and the vertical displacement of the lower platform in the Z-axis direction , an autoregressive model is used to construct a corresponding lower platform pose prediction model, which can be denoted as a first model, a second model and a third model. Among them, the first model is used to predict the rotation angle of the lower platform around the X-axis, the second model is used to predict the rotation angle of the lower platform around the Y-axis, and the third model is used to predict the vertical displacement of the lower platform in the Z-axis direction.

[0029] In constructing the platform pose prediction model, the order of the autoregressive model is determined by balancing the model fitting degree and complexity by minimizing the information criterion. The autoregressive model satisfies: wherein, is the data collected by the sensor at time k, p is the order of the autoregressive model, is the i-th parameter to be identified, is the data collected by the sensor at time k-i, c is the steady-state component, is white noise.

[0030] More specifically, the recursive least squares (RLS) with a forgetting factor is used to realize online identification of the parameters in the autoregressive model, and the forgetting factor is adjusted in real time through Bayesian optimization. The specific process is as follows: 1. After determining the order of the autoregressive model, the parameter estimation and covariance matrix of RLS are initialized, and the search space of the forgetting factor is set, and a random value in the search space is selected as the initial value of the forgetting factor. The search space of the forgetting factor is [0.9, 0.999], and the initialized parameter estimation and covariance matrix satisfy the following relationship: wherein, is the initialized parameter, which is a vector composed of the parameters to be identified in the autoregressive model, is the initialized covariance matrix; is a very small positive number, ; I is the identity matrix.

[0031] 2. For , or , take k as the current time. When the sensor collects the data at the current time, RLS updates the regression vector, gain vector, parameter estimation and covariance matrix, i.e.: wherein, is the updated regression vector, i.e. the regression vector at the current time; is the data collected by the sensor at time k-i, , n is the length of the regression vector; T is the transpose; is an updated gain vector, i.e., a gain vector at a current time; is a covariance matrix at k-1 time; is a forgetting factor; is an updated parameter, i.e., a parameter estimation at a current time; is a parameter at k-1 time; is data collected by the sensor at k time, i.e., data at a current time; is an updated covariance matrix, i.e., a covariance matrix at a current time.

[0032] 3. The updated parameter is brought into the lower platform pose prediction model to calculate the predicted value at k+1 time, and the prediction error is further calculated, and for the prediction error at k+1 time , which satisfies , is the predicted value at k+1 time.

[0033] 4. The mean square error MSE of the prediction error at the last m times under the current forgetting factor is calculated, and when the MSE is greater than the mean square error threshold, the forgetting factor that minimizes the MSE is searched in the search space through Bayesian optimization with the goal of minimizing the MSE, and is recorded as the optimal forgetting factor.

[0034] 5. The optimal forgetting factor is used to replace the original forgetting factor in RLS.

[0035] The embodiment constructs a data set by collecting the attitude signal of the lower platform in the environment of the ship, and then constructs a lower platform pose prediction model using an autoregressive model, so that the model has pertinence and real-time. Among them, the method uses a recursive least square method with a forgetting factor for online parameter identification, so that the model can continuously update its parameters using the latest data, thereby overcoming the model misalignment problem caused by the change of the dynamic characteristics of the sea wave, and adjusting the forgetting factor in real time through Bayesian optimization, realizing the intelligentization of the parameter update strategy, and improving the robustness and accuracy of the model in complex time-varying marine environments.

[0036] S2, based on the data set and the lower platform pose prediction model, a residual prediction model is constructed.

[0037] Among them, step S2 specifically includes the following steps: S21, based on the data set and the lower platform pose prediction model, a prediction error data set is constructed.

[0038] Specifically, in the present embodiment, the data in the data set is taken as the true value, and the prediction value corresponding to the true value can be obtained through the first model, the second model and the third model, and then the corresponding prediction error can be calculated, and the prediction error data set can be further constructed. It should be noted that the prediction error of the first n samples in the data set is not included in the prediction error data set 、 and because the length of the regression vector in step S13 is n.

[0039] S22, according to the prediction error data set, using a lightweight neural network to dynamically model error propagation, obtaining a residual prediction model.

[0040] Specifically, in the present embodiment, the data in the prediction error data set is sorted in chronological order. The first 80% of the data in the prediction error data set is taken as the training set, and the last 20% of the data is taken as the validation set, which is used to complete the training and validation of the lightweight neural network, and the residual prediction model is obtained. The input of the lightweight neural network is the prediction error of 、 and , and the output is the prediction error of 、 and , the network structure adopts single hidden layer design, contains 16 neurons and uses ReLU activation function to enhance nonlinear fitting ability, the output layer adopts linear activation function to adapt to the characteristics of regression task, the model training adopts mean square error as the loss function to quantify the prediction error, and the Adam optimizer is used for parameter optimization to realize efficient convergence, the batch size is set to 32 during training to balance the memory efficiency and gradient stability, the early stopping mechanism is used to monitor the validation set loss to prevent overfitting, and finally the root mean square error is used as the evaluation index on the validation set to verify the ability of the model to capture the spatiotemporal evolution of the residual, thereby ensuring the effectiveness and robustness of the residual prediction model in the ship kinematics error compensation scene.

[0041] The present embodiment uses a lightweight neural network to dynamically model error propagation, which is different from simple static error correction. It can learn and capture the complex and nonlinear dynamic change trend of the error, and then intelligently predict the possible deviation of the prediction value at the future time, thereby providing a high-precision and forward-looking error estimate value for the subsequent filtering algorithm, which helps to realize accurate wave compensation.

[0042] S3, obtaining the attitude prediction value at the future time by using the lower platform pose prediction model, and obtaining the residual prediction value of the attitude prediction by using the residual prediction model.

[0043] wherein, step S3 specifically comprises the following steps: S31, acquiring the attitude prediction value of the lower platform at the future time by using the lower platform pose prediction model, the attitude prediction value including a rotation angle prediction value and a vertical displacement prediction value.

[0044] Specifically, in the embodiment, the attitude prediction value of the lower platform at the future time is acquired by using the lower platform pose prediction model. Specifically, the rotation angle prediction value of the lower platform around the X axis is acquired by using the first model , the rotation angle prediction value of the lower platform around the Y axis is acquired by using the second model , and the vertical displacement prediction value of the lower platform in the Z axis direction is acquired by using the third model .

[0045] S32, acquiring the residual prediction value of the rotation angle prediction value and the vertical displacement prediction value by using the residual prediction model.

[0046] Specifically, in the embodiment, the prediction errors of , and at the current time are input into the residual prediction model, and the residual prediction values of , and at the future time are obtained.

[0047] S4, constructing the kinematic model of the lower platform, combining the attitude prediction value and the residual prediction value, and determining the attitude accurate value by using the attitude correction algorithm.

[0048] Specifically, the step S4 includes the following steps: S41, constructing the kinematic model of the lower platform.

[0049] Specifically, in the embodiment, since the lower platform is fixed on the ship, the lower platform and the ship body can be regarded as rigidly connected. If only the roll, pitch and heave are considered and the coupling effects of other degrees of freedom are ignored, the rotation angle of the lower platform around the X axis is the roll angle of the ship, the rotation angle of the lower platform around the Y axis is the pitch angle of the ship, and the vertical displacement of the lower platform in the Z axis direction is the vertical displacement of the ship in the Z axis direction plus the additional displacement caused by the rotation motion. Therefore, the kinematic model of the lower platform can be obtained by referring to the kinematic model of the ship, including the roll model, the pitch model and the heave model.

[0050] In the embodiment, the ship angle response data is obtained by numerically solving a second-order nonlinear stochastic differential equation, and the dynamic model is constructed based on the rigid body rotation motion equation, that is, the roll model and the pitch model satisfy the following relationships in turn: wherein, Ixx is the moment of inertia about the X axis, is the roll angular acceleration, is the linear damping coefficient, is the roll angular velocity, is the roll restoring moment coefficient, is the roll angle, is the external excitation moment about the X axis, is the random disturbance moment about the X axis, Iyy is the moment of inertia about the Y axis, is the pitch angular acceleration, is the pitch damping coefficient, is the pitch angular velocity, is the pitch restoring moment coefficient, is the pitch angle, is the external excitation moment about the Y axis, is the random disturbance moment about the Y axis.

[0051] The heave model should describe the linear motion of the ship in the vertical direction, which satisfies the following relationship: where M is the mass of the ship, is the heave acceleration of the ship, is the heave damping coefficient, is the heave velocity of the ship, is the heave restoring force coefficient, is the external excitation force in the Z axis direction, is the random disturbance force in the Z axis direction.

[0052] Further, the vertical displacement of the lower platform in the Z axis direction can be represented by the relationship of step S11, the acceleration of the lower platform in the Z axis direction can be obtained by taking the derivative of the velocity of the lower platform in the Z axis direction , can be obtained by taking the derivative of .

[0053] When solving the kinematic model of the ship, it can be converted into a first-order differential equation system, and then solved using the fourth-order Runge-Kutta method. When solving, the time step is set to 0.02s, the total time is set to 500s, and the initial conditions are set according to the actual motion state of the ship, such as setting the initial condition to 0 when the ship is stationary. The random components of each time step are independently generated by the randn function to ensure statistical independence.

[0054] S42, according to the kinematic model, the attitude prediction value and the residual prediction value, using extended Kalman filtering to obtain the accurate attitude value.

[0055] wherein, the step S42 specifically comprises the following steps: S421, taking the kinematic model as a state transition equation, and taking the attitude prediction value and the residual prediction value as observation inputs, to generate an initial state and an observation vector of the extended Kalman filter.

[0056] Specifically, in the embodiment, the state variable h of the extended Kalman filter is defined as: wherein, is the first derivative of , is the first derivative of .

[0057] The observation vector r of the extended Kalman filter satisfies: wherein, , and are the residual prediction values corresponding to , and .

[0058] S422, based on the initial state and the observation vector, through the prediction-update iteration process of the extended Kalman filter, outputting an attitude accurate value fused with model prediction and residual compensation.

[0059] Specifically, in the embodiment, this step is a prior art means, which will not be described in detail here.

[0060] The embodiment dynamically weighs the reliability of the calculation result of the physical model and the prediction result of the data-driven model by taking the kinematic model as the state transition equation of the extended Kalman filter and taking the attitude and residual prediction into the observation vector, and finally outputs the attitude accurate value, which provides a crucial reliable input for motion back analysis and control, and fundamentally improves the stability and compensation performance of the entire wave compensation system.

[0061] S5, according to the attitude accurate value, using a motion back analysis algorithm to solve the rotation angle of the servo motor, and then realizing closed-loop feedback control of the wave compensation medical platform.

[0062] wherein, the step S5 specifically comprises the following steps: S51, based on the attitude accurate value, using a motion back analysis algorithm to solve the rotation angle of the servo motor.

[0063] Specifically, in the embodiment, since the purpose of wave compensation is to keep the upper platform horizontal, the pose of the upper platform is known, and therefore after obtaining the accurate value of the pose of the lower platform, the rotation angle of the servo motor is solved by a motion inverse solution algorithm. The specific motion inverse solution algorithm needs to be determined according to the structure of the wave compensation medical care platform. Taking a 3-RPS parallel mechanism as an example, the inverse calculation principle is as follows: 1. Map the hinge points of the upper platform to the base coordinate system through the homogeneous transformation matrix.

[0064] wherein, is the homogeneous transformation matrix; R is a rotation matrix, , represents a rotation matrix (i.e. unit matrix) rotating 0 radian around the Z axis, represents a rotation matrix rotating around the Y axis, , represents a rotation matrix rotating around the X axis, ; w is a translation vector, , is a fixed offset.

[0065] The coordinate transformation formula is: wherein, is the coordinate of the jth hinge point of the upper platform in the base coordinate system, is the coordinate of the jth hinge point of the upper platform in the local coordinate system. The base coordinate system is a space rectangular coordinate system with the center point of the lower platform as the coordinate origin, and the local coordinate system is a space rectangular coordinate system with the center of the upper platform as the coordinate origin.

[0066] 2. Establish a distance constraint equation for each branch.

[0067] Since the length of the branch is fixed, the distance from the upper spherical hinge center to the intermediate hinge point and the distance from the lower spherical hinge center to the intermediate hinge point are constant values, and therefore the following distance constraint equation is established for each branch: wherein, is the coordinate of the intermediate hinge point of the jth branch, is the distance from the upper spherical hinge center to the intermediate hinge point, is the distance from the lower spherical hinge center to the intermediate hinge point, is the coordinate of the jth hinge point of the lower platform. The Levenberg-Marquardt algorithm is used to solve the above nonlinear equation set to obtain the coordinate of the intermediate hinge point of the jth branch of the intermediate hinge point. ​

[0068] 3. Calculate the angle between the swing arm and the vertical direction according to the intermediate hinge point coordinates, and obtain the deviation relative to the initial angle, that is, the driving joint rotation angle.

[0069] wherein, is the angle between the jth branch chain and the vertical direction, is the vector from the coordinate origin O to is the unit vector in the Z-axis direction in the base coordinate system, is the driving joint rotation angle, is the initial angle of the swing arm.

[0070] After obtaining the driving joint rotation angle, the servo motor rotation angle can be determined according to the ratio of the servo motor rotation angle to the driving joint rotation angle, thereby realizing the control of the servo motor to complete the wave compensation function.

[0071] S52, according to the servo motor rotation angle, using a servo driver to drive the servo motor to rotate.

[0072] Specifically, in this embodiment, the servo driver adopts a PID control algorithm, which controls the servo motor to rotate according to the servo motor rotation angle after obtaining the servo motor rotation angle.

[0073] S53, using an encoder to collect the actual rotation angle of the servo motor in real time and feed back to the servo driver, and then continuously adjusting the motor action to keep the upper platform of the wave compensation medical platform horizontal.

[0074] This embodiment forms a negative feedback loop by real-time detection of the actual rotation angle by the encoder, thereby dynamically maintaining the stability of the upper platform, achieving the ultimate goal of active wave compensation, and improving the operation safety and reliability of the wave compensation medical platform in a turbulent environment.

[0075] To verify the performance of the present scheme, a simulation test of sea wave compensation is performed using a ship active compensation medical bed disclosed in patent No. CN222584855U.

[0076] A simulation machine for simulation test is established in the MATLAB environment, taking ship roll and pitch as examples to demonstrate the anti-swing ability of the simulation machine under the present scheme. When performing the simulation experiment, the corresponding random swing signals are generated according to the roll model and the pitch model, and the corresponding parameter settings are: , , , , ,​ t is time, is a Gaussian white noise with mean 0 and standard deviation 6000, , is a Gaussian white noise with mean 0 and standard deviation 6000.

[0077] The generated random roll signal is shown in FIGS. Figure 2 and Figure 3 , wherein, Figure 2 (a) in FIG. Figure 2 (b) in FIG. Figure 3 (a) in FIG. Figure 3 (b) in FIG.

[0078] The anti-rolling effect of the simulation prototype on roll is shown in FIG. Figure 4 and the anti-rolling effect on pitch is shown in FIG. Figure 5 From FIGS. Figure 4 and Figure 5 , it can be seen that the sea wave compensation starts between 100s and 120s, and before the start of the sea wave compensation, it is the data acquisition and model training stage, and the upper platform and the lower platform present the same rolling mode, and after the sea wave compensation starts, the lower platform continues to roll, while the upper platform can basically maintain balance, proving the feasibility of the present scheme.

[0079] It should be noted that in some cases, the actions described in the specification can be performed in different orders and still achieve the desired results, and in the present embodiment, the order of the steps given is only to make the embodiment look clearer and more understandable, and is not a limitation.

[0080] In an optional embodiment, see Figure 6 , in order to improve the practicability of the method and facilitate the popularization of the method, the present application also provides a closed-loop feedback control system of a ship wave compensation medical treatment platform, which comprises a data acquisition device 1, a control center 2, a wave compensation execution device 3 and a control handle 4, the data acquisition device 1, the control center 2 and the wave compensation execution device 3 are connected through data lines for signal transmission, and the control handle 4 and the control center 2 are connected through Bluetooth.

[0081] The data acquisition device 1 is used to acquire the attitude signal of the lower platform in the wave compensation medical treatment platform in real time, and the data acquisition device 1 comprises a sensor installed on the wave compensation medical treatment platform and a motion reference unit on the ship.

[0082] The control center 2 is used to construct a data set using the attitude signal, and then construct a lower platform pose prediction model; based on the data set and the lower platform pose prediction model, a residual prediction model is constructed; the lower platform pose prediction model is used to obtain an attitude prediction value at a future time, and the residual prediction model is used to obtain a residual prediction value of the attitude prediction; a kinematics model of the lower platform is constructed, the attitude prediction value and the residual prediction value are combined, and an attitude accurate value is determined through an attitude correction algorithm; according to the attitude accurate value, a motion back solution algorithm is used to solve the rotation angle of the servo motor, and then closed-loop feedback control of the wave compensation medical platform is realized.

[0083] The wave compensation execution device 3 includes a servo driver and a servo motor, and is used to realize wave compensation of the wave compensation medical platform in response to the control instruction of the control center 2.

[0084] The control handle 4 is used to control the start and stop of the sea wave compensation.

[0085] The present application has the following beneficial effects: Firstly, the present method uses a recursive least square method with a forgetting factor to realize online identification of parameters in the autoregressive model, and adjusts the forgetting factor in real time through Bayesian optimization, and then uses the autoregressive model to construct a lower platform pose prediction model, so that the model has pertinence and real-time performance, and also has good robustness and accuracy in complex time-varying marine environments. Secondly, the present method uses a lightweight neural network to dynamically model error transmission rules, intelligently predicts possible deviations of the prediction value at a future time, and then combines the prediction result of the lower platform pose prediction model and the deviation prediction result to obtain an attitude accurate value using Kalman filtering, which provides a crucial reliable input for motion back solution and control, and fundamentally improves the stability and compensation performance of the entire wave compensation system. Finally, the rotation angle of the servo motor is solved using a motion back solution algorithm according to the attitude accurate value, so that the servo driver drives the servo motor to rotate, and the actual rotation angle is detected in real time through the encoder to form a negative feedback loop, so as to dynamically maintain the stability of the upper platform, realize the final purpose of active wave compensation, improve the operation safety and reliability of the wave compensation medical platform in a turbulent environment, and reduce the risk of offshore medical treatment. In addition, the present application provides a system adapted to the method, which helps to improve the practicability of the present method and facilitate the popularization of the present method.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the description of the present application.

Claims

1. A closed loop feedback control method for a marine wave-compensated medical platform, characterized in that, The method comprises the following steps: Real-time acquisition of the attitude signal of the lower platform in the wave-compensated medical platform, and use of the attitude signal to establish a data set, and further construction of a lower platform pose prediction model; Based on the data set and the lower platform pose prediction model, a residual prediction model is constructed; Using the lower platform pose prediction model to obtain the attitude prediction value at the future time, and using the residual prediction model to obtain the residual prediction value of the attitude prediction; Constructing the kinematic model of the lower platform, combining the attitude prediction value and the residual prediction value, and determining the attitude accurate value through the attitude correction algorithm; According to the attitude accurate value, the rotation angle of the servo motor is solved using the motion back-solving algorithm, and then the closed-loop feedback control of the wave-compensated medical platform is realized.

2. The closed loop feedback control method of a wave compensated medical platform for a ship according to claim 1, characterized in that, The real-time acquisition of the attitude signal of the lower platform in the wave-compensated medical platform, and the use of the attitude signal to establish a data set, and further construction of a lower platform pose prediction model, comprises the following steps: Real-time acquisition of the rotation angle of the lower platform around the X and Y axes and the vertical displacement in the Z axis direction; The rotation angle and the vertical displacement collected at the same time are taken as a sample data, and after a certain number of sample data are collected, all sample data are used to construct a data set; According to the data set, an autoregressive model is used to construct a plurality of lower platform pose prediction models for predicting the rotation angle and the vertical displacement.

3. The closed-loop feedback control method of the ship wave-compensated medical platform according to claim 2, characterized in that: The recursive least squares method with a forgetting factor is used to realize online identification of the parameters in the autoregressive model, and the forgetting factor is adjusted in real time through Bayesian optimization.

4. The closed loop feedback control method of a wave compensated medical platform for marine use according to claim 1, characterized in that, The construction of the residual prediction model based on the data set and the lower platform pose prediction model comprises the following steps: Based on the data set and the lower platform pose prediction model, a prediction error data set is constructed; According to the prediction error data set, a lightweight neural network is used to dynamically model error transmission to obtain a residual prediction model.

5. The closed loop feedback control method of a wave compensated medical platform for marine use according to claim 1, characterized in that, The use of the lower platform pose prediction model to obtain the attitude prediction value at the future time, and the use of the residual prediction model to obtain the residual prediction value of the attitude prediction, comprises the following steps: The lower platform pose prediction model is used to obtain the attitude prediction value at the future time, and the attitude prediction value comprises a rotation angle prediction value and a vertical displacement prediction value; The residual prediction model is used to obtain the residual prediction value of the rotation angle prediction value and the vertical displacement prediction value.

6. The closed loop feedback control method of a wave compensated medical platform for marine use according to claim 1, characterized in that, The construction of the kinematic model of the lower platform, the combination of the attitude prediction value and the residual prediction value, and the determination of the attitude accurate value through the attitude correction algorithm, comprises the following steps: Construction of the kinematic model of the lower platform; According to the kinematic model, the attitude prediction value and the residual prediction value, the extended Kalman filter is used to obtain the attitude accurate value.

7. The closed-loop feedback control method of the ship wave-compensated medical platform according to claim 1, characterized in that: The kinematic model comprises a roll model, a pitch model and a heave model.

8. The closed loop feedback control method of a wave compensated medical platform for marine use according to claim 6, characterized in that, The use of the extended Kalman filter to obtain the attitude accurate value according to the kinematic model, the attitude prediction value and the residual prediction value, comprises the following steps: The kinematic model is taken as a state transition equation, and the attitude prediction value and the residual prediction value are taken as observation inputs to generate an initial state and an observation vector of an extended Kalman filter; Based on the initial state and the observation vector, an attitude accurate value is output by a prediction-update iteration process of the extended Kalman filter.

9. The closed loop feedback control method of a wave compensated medical platform for marine use according to claim 1, characterized in that, The attitude accurate value is used to solve a servo motor rotation angle by using a motion inverse algorithm, and then closed-loop feedback control of the wave compensation medical platform is realized, including the following steps: The attitude accurate value is used to solve a servo motor rotation angle by using a motion inverse algorithm; The servo motor is driven to rotate by using a servo driver according to the servo motor rotation angle; The actual rotation angle of the servo motor is collected in real time by using an encoder and is fed back to the servo driver, and then the motor action is continuously adjusted to keep the upper platform of the wave compensation medical platform horizontal.

10. A closed-loop feedback control system for a ship-based wave-compensated medical platform, the closed-loop feedback control system being configured to use the closed-loop feedback control method of any one of claims 1-9. The method comprises the following steps: A data acquisition device is used to acquire an attitude signal of a lower platform of the wave compensation medical platform in real time; A control center is used to construct a data set by using the attitude signal, and then a lower platform pose prediction model is constructed; a residual prediction model is constructed based on the data set and the lower platform pose prediction model; an attitude prediction value at a future time is acquired by using the lower platform pose prediction model, and a residual prediction value of the attitude prediction is acquired by using the residual prediction model; a kinematic model of the lower platform is constructed, and an attitude accurate value is determined by using a pose correction algorithm in combination with the attitude prediction value and the residual prediction value; the attitude accurate value is used to solve a servo motor rotation angle by using a motion inverse algorithm, and then closed-loop feedback control of the wave compensation medical platform is realized; A wave compensation execution device comprises a servo driver and a servo motor, and is used to realize wave compensation of the wave compensation medical platform in response to a control instruction of the control center; A control handle is used to control starting and stopping of the wave compensation.

Images