Attitude adjustment method and system of wave compensation platform based on model predictive control

Abstract

The embodiment of the invention provides an attitude adjustment method and system of a wave compensation platform based on model predictive control, and belongs to the technical field of attitude adjustment of wave compensation platforms. The method comprises the following steps: establishing a kinetic model of the six-degree-of-freedom parallel wave compensation platform; designing a model predictive control algorithm according to the dynamic model; and performing wave interference prediction and compensation according to a model prediction control algorithm to obtain attitude adjustment information. According to the embodiment of the invention, the control performance of the six-degree-of-freedom parallel wave compensation platform under complex sea conditions can be improved, and more reliable guarantee is provided for offshore activities such as offshore wind power operation.

Description

Technical Field

[0001] This application relates to the field of attitude adjustment technology for wave compensation platforms, and in particular to an attitude adjustment method and system for wave compensation platforms based on model predictive control. Background Technology

[0002] With the continuous development of offshore operations, six-degree-of-freedom parallel wave compensation platforms are becoming increasingly important in ensuring operational stability and safety. In actual marine environments, complex wave conditions can cause ships to move in six degrees of freedom, which can have a significant impact on the equipment and personnel on board, potentially leading to operational failures or even safety accidents. Among related technologies, wave compensation platforms suffer from insufficient compensation accuracy, slow response speed, and excessive reliance on models when dealing with complex and ever-changing wave interference. Most wave compensation platform control technologies employ motor-based control methods, which, while improving motor precision and thus the accuracy of the compensation system, cannot account for the coupling error of the overall attitude. Furthermore, they lack accurate wave interference prediction mechanisms, rely excessively on precise model assumptions, cannot effectively handle platform attitude coupling errors, and suffer from control lag, making it difficult to meet the growing demands for high-precision offshore operations.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose an attitude adjustment method and system for a wave compensation platform based on model predictive control, which enables the platform to more accurately counteract wave interference and improve compensation accuracy.

[0005] To achieve the above objectives, one aspect of this application proposes an attitude adjustment method for a wave compensation platform based on model predictive control, the method comprising the following steps:

[0006] A dynamic model of a six-degree-of-freedom parallel wave compensation platform is established;

[0007] Based on the dynamic model, design a model predictive control algorithm;

[0008] Wave interference is predicted and compensated based on the model predictive control algorithm to obtain attitude adjustment information.

[0009] In some embodiments, establishing the dynamic model of the six-degree-of-freedom parallel wave compensation platform includes:

[0010] Obtain the parameters of the six-degree-of-freedom parallel wave compensation platform; the parameters of the six-degree-of-freedom parallel wave compensation platform include the mass of the platform components, the velocity of the center of mass, the moment of inertia, the angular velocity, the height relative to the reference plane, the elastic coefficient of the branch, and the elongation of the branch;

[0011] Calculate the system kinetic energy and system potential energy of the six-degree-of-freedom parallel wave compensation platform based on the parameters of the six-degree-of-freedom parallel wave compensation platform;

[0012] Based on the Lagrange equation, the kinetic energy of the system, and the potential energy of the system, a dynamic model of the six-degree-of-freedom parallel wave compensation platform is obtained.

[0013] In some embodiments, the formulas used to calculate the system kinetic energy and system potential energy based on the parameters of the six-degree-of-freedom parallel wave compensation platform include:

[0014]

[0015] Where i is the ordinal number of the platform component, i = 1, 2, ..., n; n is the total number of platform components; m i Let the mass of the i-th platform component be denoted as . Let I be the velocity of the center of mass of the i-th platform component; i Let be the moment of inertia of the i-th platform component; Let be the angular velocity of the i-th platform component; T be the system kinetic energy; G ... i =m i g is the gravity of the i-th platform component; h i is the height of the i-th platform component relative to the reference plane; j is the ordinal number of the branch, with a total of 6 branches; k j Let Δl be the elastic coefficient of the j-th branch; j Let V be the elongation of the j-th branch; V is the system potential energy.

[0016] In some embodiments, the dynamic model of the six-degree-of-freedom parallel wave compensation platform, derived from the Lagrange equation, the system kinetic energy, and the system potential energy, includes the following formulas:

[0017]

[0018] Where M(q) is the inertia matrix; G(q) is the matrix of Coriolis force and centrifugal force; G(q) is the gravity matrix; τ is the friction force matrix; τ is the control input vector; τ d q is the external disturbance force vector; q is the position vector of the six-degree-of-freedom parallel wave compensation platform. The velocity vector of the six-degree-of-freedom parallel wave compensation platform; The acceleration vector is the acceleration vector of a six-degree-of-freedom parallel wave compensation platform.

[0019] In some embodiments, designing a model predictive control algorithm based on the dynamic model includes:

[0020] The dynamic model is discretized to obtain a state-space model in the discrete time domain;

[0021] Set constraint information; the constraint information includes the driving force constraint of the branch, the length constraint of the branch, the attitude angle constraint of the platform, the position constraint of the platform, and the velocity constraint of the platform.

[0022] Based on the state-space model in the discrete time domain and the constraint information, a feedback correction mechanism is introduced to obtain the model predictive control algorithm.

[0023] In some embodiments, the discretization of the dynamic model to obtain a state-space model in the discrete-time domain includes the following formulas:

[0024]

[0025] Where, x k Let x be the state vector of the k-th system; k+1 Let A(x) be the state vector of the (k+1)th system; C is the output matrix; A(x) is the output matrix. k B(x) is the first coefficient matrix; k B is the second coefficient matrix; d (x k ) is the third coefficient matrix; y k τ is the output vector; k τ is the control input vector for the kth iteration; d,k Let be the vector of the k-th external disturbance force.

[0026] In some embodiments, the step of predicting and compensating for wave disturbances according to the model predictive control algorithm to obtain attitude adjustment information includes:

[0027] Acquire wave data; the wave data includes wave height, period, and wave direction;

[0028] Based on the wave data, an analysis and processing method is used to establish a predictive model for wave interference.

[0029] The interference force is predicted based on the wave interference prediction model to obtain interference force prediction information;

[0030] Acquire platform data; the platform data includes the platform position, platform velocity, platform acceleration, and driving force of each branch of the six-degree-of-freedom parallel wave compensation platform;

[0031] The control signal is obtained by performing calculations and analyses based on the interference force prediction information, the platform data, and the model predictive control algorithm.

[0032] The control signal is sent to the servo motor through the driver to control the extension and retraction of the branch, thereby realizing wave compensation control of the platform and obtaining attitude adjustment information.

[0033] To achieve the above objectives, another aspect of this application proposes an attitude adjustment system for a wave compensation platform based on model predictive control, used to implement the attitude adjustment method for a wave compensation platform based on model predictive control as described above. The system includes:

[0034] The first module is used to establish the dynamic model of a six-degree-of-freedom parallel wave compensation platform;

[0035] The second module is used to design a model predictive control algorithm based on the dynamic model.

[0036] The third module is used to predict and compensate for wave interference based on the model predictive control algorithm to obtain attitude adjustment information.

[0037] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0038] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0039] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0040] The embodiments of this application include at least the following beneficial effects: This application provides an attitude adjustment method, system, electronic device, storage medium, and program product for a wave compensation platform based on model predictive control. This solution establishes a dynamic model of a six-degree-of-freedom parallel wave compensation platform; designs a model predictive control algorithm based on the dynamic model; and performs wave disturbance prediction and compensation based on the model predictive control algorithm to obtain attitude adjustment information. The embodiments of this application can improve the control performance of a six-degree-of-freedom parallel wave compensation platform under complex sea conditions, providing more reliable protection for offshore wind power operations and other maritime activities. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the attitude adjustment method of the wave compensation platform based on model predictive control provided in the embodiments of this application;

[0042] Figure 2This is a flowchart of the wave compensation system model predictive control provided in the embodiments of this application. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0044] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0045] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0047] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0048] 1) A six-degree-of-freedom parallel wave compensation platform is a complex mechanical device installed on a ship, consisting of two platforms connected by six movable links. It can actively adjust the length of the six links according to the ship's six degrees of freedom motion (pitch, sway, heave, roll, pitch, and bow) to achieve attitude compensation of the working area on the platform, keeping the working area stable relative to the sea level and providing a stable working platform for offshore operations. It has advantages such as compact structure, high load-bearing capacity, and high motion accuracy, but also faces challenges such as complex dynamic modeling and high control difficulty.

[0049] 2) Model Predictive Control (MMC): An advanced control strategy whose core idea is based on a predictive model of the system. Using current and past system input and output data, it predicts the system's output state over a future period. Then, according to pre-set objectives and constraints, an optimization algorithm calculates the optimal control sequence for that future period, and the control command at the current moment is applied to the system. At the next moment, the prediction, optimization, and control process is repeated, continuously adjusting the control strategy based on actual conditions to achieve precise control of the system. This control strategy is suitable for handling complex, multivariable, strongly coupled, and constrained dynamic systems.

[0050] 3) Dynamic Model: A mathematical description of the system's dynamic characteristics, reflecting the system's motion under external forces. For a six-degree-of-freedom parallel wave-compensated platform, the dynamic model needs to consider the mass, moment of inertia, joint friction, elastic forces, and the relationship between external forces (such as wave forces and inertial forces caused by ship motion) and the platform's motion. Establishing an accurate dynamic model provides a theoretical basis for control algorithms, helps predict the platform's motion response under different input conditions, and thus achieves effective control.

[0051] 4) Wave Interference: Irregular waves in the marine environment, such as wind waves and swells, have different frequencies, wavelengths, and wave heights. These waves exert periodic or non-periodic forces on ships, causing them to move in six degrees of freedom, which in turn affects the equipment and operations on board. Wave interference is a major external disturbance factor that a six-degree-of-freedom parallel wave compensation platform needs to overcome, and its complexity and uncertainty increase the difficulty of platform control.

[0052] 5) Feedback Correction: In model predictive control, due to uncertainties such as nonlinearity, model mismatch, and disturbances in the actual system, the predicted values ​​based on the model may deviate from the actual output values ​​of the system. Feedback correction is the process of comparing the actual output values ​​of the system with the model prediction values ​​to obtain the prediction error, and then using this error to correct the model prediction values. Through feedback correction, the accuracy of model predictions can be improved, enabling the control algorithm to better adapt to changes in the actual system, thereby improving the system's control performance.

[0053] 6) JONSWAP (Joint North Sea Wave Observation Project): A marine model used to describe the spectrum of wind and waves.

[0054] 7) PLC (Programmable Logic Controller): A programmable logic controller, an industrial automation control device.

[0055] 8) MATLAB: A high-level numerical computing and scientific programming language and interactive environment developed by MathWorks.

[0056] 9) Simulink: A modular dynamic system simulation tool in MATLAB.

[0057] Wind energy, as a clean and renewable energy source, has been widely used globally, and offshore wind power, as an important development direction for wind energy utilization, has seen continuous expansion in recent years. Wind turbine maintenance vessels, as key tools for the maintenance of offshore wind turbine equipment, are crucial for the safety and efficiency of their berthing operations. In actual operations, the complex and ever-changing marine environment, including wind, waves, and currents, causes six degrees of freedom motion when the maintenance vessel berths the wind turbine platform. This motion not only increases the difficulty for maintenance personnel to board the vessel but may also lead to collisions between the berthing equipment and the wind turbine platform, causing equipment damage or even personnel injuries.

[0058] To reduce the risks of operation and maintenance work, a berthing compensation platform is introduced. This platform actively compensates for the motion of the maintenance vessel in each degree of freedom, keeping the upper platform stable relative to the wind turbine, allowing maintenance personnel to smoothly board and alight from the turbine. A six-degree-of-freedom berthing compensation platform is one type of berthing compensation platform. The platform consists of upper and lower platforms; the lower platform is connected to the hull, and the upper platform is equipped with a berthing gangway. These are connected by six electric or hydraulic cylinders. Sensors measure the vessel's pitch, sway, heave, roll, and bow movements and compensate for these movements in the opposite direction, maintaining a relatively stable position and attitude between the end of the berthing gangway on the platform and the wind turbine platform.

[0059] However, current six-degree-of-freedom parallel wave compensation platforms face several pressing control challenges. Firstly, existing control algorithms have limited ability to predict and compensate for wave disturbances, resulting in insufficient compensation accuracy under complex sea conditions and failing to meet the demands of high-precision operations. Secondly, some control strategies are overly reliant on the system model; when actual system parameters change or unmodeled dynamics exist, control performance deteriorates significantly. Furthermore, data transmission delays and actuator response lags affect the real-time performance of the control algorithms, making rapid response to wave disturbances difficult.

[0060] This invention aims to develop a model predictive control method based on dynamic models to improve the control performance of a six-degree-of-freedom parallel wave compensation platform under complex sea conditions, and to provide more reliable protection for offshore wind power operations and other offshore activities.

[0061] This application applies model predictive control based on a dynamic model to a six-degree-of-freedom parallel wave compensation platform. Through precise dynamic modeling and model predictive control algorithms, accurate control of the platform is achieved, improving its compensation performance in complex wave environments and ensuring the safety and stability of offshore operations. The basic approach is to first establish a precise dynamic model of the six-degree-of-freedom parallel wave compensation platform, fully considering factors such as platform structure, mass distribution of components, and joint friction. Then, using this dynamic model combined with model predictive control algorithms, the platform's motion state over a future period is predicted, and the control input is optimized in real time based on the prediction results to counteract the ship's motion caused by waves. Simultaneously, the actual motion state of the platform is monitored in real time by sensors, and the actual values ​​are compared with the predicted values ​​to provide feedback correction to the model, further improving the accuracy of control.

[0062] The attitude adjustment method for a wave compensation platform based on model predictive control provided in this application relates to the field of information technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, or desktop computer, but is not limited thereto. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the attitude adjustment method for a wave compensation platform based on model predictive control, but is not limited to the above forms.

[0063] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0064] On one hand, embodiments of the present invention provide an attitude adjustment method for a wave compensation platform based on model predictive control, with reference to... Figure 1 The method includes the following steps:

[0065] Step S100: Establish a dynamic model of a six-degree-of-freedom parallel wave compensation platform;

[0066] Step S200: Based on the dynamic model, design a model predictive control algorithm;

[0067] Step S300: Perform wave interference prediction and compensation based on the model predictive control algorithm to obtain attitude adjustment information.

[0068] Furthermore, the combination of dynamic model and model predictive control algorithm can significantly reduce the platform pose error caused by waves, and the platform can still maintain stability under variable load and extreme sea conditions, demonstrating strong adaptability.

[0069] In some embodiments, step S100 of the present invention, which involves establishing a dynamic model of a six-degree-of-freedom parallel wave compensation platform, includes:

[0070] Step S110: Obtain the parameters of the six-degree-of-freedom parallel wave compensation platform; the parameters of the six-degree-of-freedom parallel wave compensation platform include the mass, center of mass velocity, moment of inertia, angular velocity, height relative to the reference plane, elastic coefficient of the branch, and elongation of the branch.

[0071] Step S120: Calculate the system kinetic energy and system potential energy of the six-degree-of-freedom parallel wave compensation platform based on the parameters of the six-degree-of-freedom parallel wave compensation platform;

[0072] Step S130: Based on the Lagrange equation, system kinetic energy, and system potential energy, the dynamic model of the six-degree-of-freedom parallel wave compensation platform is obtained.

[0073] In some embodiments, step S120 of the present invention, which calculates the system kinetic energy and system potential energy based on the parameters of a six-degree-of-freedom parallel wave compensation platform, uses the following formulas:

[0074]

[0075] Where i is the ordinal number of the platform component, i = 1, 2, ..., n; n is the total number of platform components; m i Let the mass of the i-th platform component be denoted as . Let I be the velocity of the center of mass of the i-th platform component; i Let be the moment of inertia of the i-th platform component; Let be the angular velocity of the i-th platform component; T be the system kinetic energy; G ... i =m i g is the gravity of the i-th platform component; h i is the height of the i-th platform component relative to the reference plane; j is the ordinal number of the branch, with a total of 6 branches; k j Let Δl be the elastic coefficient of the j-th branch; j Let V be the elongation of the j-th branch; V is the system potential energy.

[0076] In some embodiments of the present invention, step S130 derives a dynamic model of a six-degree-of-freedom parallel wave compensation platform based on the Lagrange equation, system kinetic energy, and system potential energy. The formulas used include:

[0077]

[0078] Where M(q) is the inertia matrix; G(q) is the matrix of Coriolis force and centrifugal force; G(q) is the gravity matrix; τ is the friction force matrix; τ is the control input vector; τ d q is the external disturbance force vector; q is the position vector of the six-degree-of-freedom parallel wave compensation platform. The velocity vector of the six-degree-of-freedom parallel wave compensation platform; The acceleration vector is the acceleration vector of a six-degree-of-freedom parallel wave compensation platform.

[0079] In some embodiments, step S200 of the present invention discloses designing a model predictive control algorithm based on a dynamic model, including:

[0080] Step S210: Discretize the dynamic model to obtain a state-space model in the discrete time domain;

[0081] Step S220: Set constraint information; constraint information includes driving force constraint of branch, length constraint of branch, attitude angle constraint of platform, position constraint of platform, and velocity constraint of platform.

[0082] Step S230: Based on the state-space model and constraint information in the discrete time domain, a feedback correction mechanism is introduced to obtain the model predictive control algorithm.

[0083] In some embodiments, step S210 of this invention discretizes the dynamic model to obtain a state-space model in the discrete time domain, and the formulas used include:

[0084]

[0085] Where, x k Let x be the state vector of the k-th system;k+1 Let A(x) be the state vector of the (k+1)th system; C is the output matrix; A(x) is the output matrix. k B(x) is the first coefficient matrix; k B is the second coefficient matrix; d (x k ) is the third coefficient matrix; y k τ is the output vector; k τ is the control input vector for the kth iteration; d,k Let be the vector of the k-th external disturbance force.

[0086] In some embodiments, step S300 of the present invention discloses performing wave disturbance prediction and compensation according to a model predictive control algorithm to obtain attitude adjustment information includes:

[0087] Step S310: Obtain wave data; wave data includes wave height, period, and wave direction;

[0088] Step S320: Analyze and process the wave data to establish a prediction model for wave interference;

[0089] Step S330: Based on the wave interference prediction model, predict the interference force to obtain interference force prediction information;

[0090] Step S340: Obtain platform data; the platform data includes the platform position, platform velocity, platform acceleration, and driving force of each branch of the six-degree-of-freedom parallel wave compensation platform.

[0091] Step S350: Calculate and analyze the control signal based on the interference force prediction information, platform data, and model predictive control algorithm;

[0092] Step S360: Send the control signal to the servo motor through the driver to control the extension and retraction of the branch, realize the wave compensation control of the platform, and obtain attitude adjustment information.

[0093] Furthermore, embodiments of the present invention also provide corrected predictions, which can fine-tune the prediction model of wave disturbances through historical data or real-time observations (such as radar or camera footage) to compensate for the limitations of pure algorithm models (such as the lack of modeling of extreme wave mutations).

[0094] Furthermore, embodiments of the present invention also provide a human-computer interaction interface to identify and prompt responses to sudden problems such as sensor failure and actuator saturation, thereby improving the completeness of wave interference prediction.

[0095] As an optional implementation, embodiments of the present invention include:

[0096] 1. Establishment of a dynamic model for a six-degree-of-freedom parallel wave compensation platform

[0097] Considering the structural complexity of the six-DOF parallel wave compensation platform and the force conditions during actual operation, an accurate dynamic model is established using the Lagrange equation. The Lagrange equation is L = TV, where T is the kinetic energy of the system and V is the potential energy. For the six-DOF parallel wave compensation platform, the system's kinetic energy T includes the translational and rotational kinetic energies of each component of the platform. Let the mass of each component of the platform be m. i (i = 1, 2, ..., n, where n is the total number of parts), the velocity of the center of mass is... The moment of inertia is I i angular velocity is The kinetic energy of the system can then be expressed as:

[0098]

[0099] The system's potential energy V mainly consists of gravitational potential energy and elastic potential energy. Let the gravity of each component of the platform be G. i =m i g (where g is the acceleration due to gravity), and the height relative to the reference plane is h. i The elastic modulus of the branch is k j (j = 1, 2, ..., 6, corresponding to six branches), the elongation of the branches is Δl j The potential energy of the system can then be expressed as:

[0100]

[0101] According to the Lagrange equation (k = 1, 2, ..., 6, q) k For generalized coordinates, corresponding to the platform's six degrees of freedom; Q k Considering the generalized forces (including external forces and constraint forces) and factors such as the external forces acting on the platform (e.g., wave forces, inertial forces caused by ship motion) and joint friction, the dynamic equations of the six-degree-of-freedom parallel wave-compensated platform can be obtained:

[0102]

[0103] Where M(q) is the inertia matrix, Let G(q) be the matrix of Coriolis force and centrifugal force, and G(q) be the gravity matrix. Let τ be the friction force matrix, and τ be the control input vector (i.e., the driving force of the six branches). d This represents the vector of external disturbance forces (mainly wave forces, etc.).

[0104] To improve the accuracy of the model, the parameters in the model were precisely identified. The motion response of the platform under different working conditions was tested experimentally, and relevant data were collected. The least squares method was used to estimate and optimize parameters such as mass, moment of inertia, elastic coefficient, and friction coefficient in the model, enabling the established dynamic model to more accurately reflect the actual dynamic characteristics of the platform.

[0105] 2. Design of Model Predictive Control Algorithm Based on Dynamic Model

[0106] The core of Model Predictive Control (MPC) is to predict the future behavior of the system using a predictive model, and then solve for the optimal control variables in real time by combining constraints and optimization objectives. For a six-degree-of-freedom parallel wave-compensated platform, its predictive model is based on the precise dynamic model established above. (Reference) Figure 2 .

[0107] 2.1 Prediction Model

[0108] The dynamic equation Discretization is performed to obtain a state-space model in the discrete-time domain. Discretization of the dynamic model involves converting continuous-time dynamic equations into discrete-time equations for implementation in a digital control system.

[0109] The specific steps are as follows:

[0110] 1) State variable selection: Selecting the system's state vector Where q is the platform's position vector. This is the platform's velocity vector.

[0111] 2) Derivation of the continuous state equation: Taking the derivative of the state vector, we can obtain... From the dynamic equation It can be solved Therefore, the state equation for continuous time is:

[0112] 3) Discretization: The Euler discretization method is used, with a sampling period of T. s Then there is Substituting the continuous state equation into the above equation, we obtain the discrete-time state-space model:

[0113]

[0114] Where, A(x) k B(x) k B d (x k ) is related to state x kThe relevant coefficient matrix, C is the output matrix, used to extract the output from the state vector. Utilizing the state x at the current time k... k and the control input sequence {τ} over a future period of time k ,τ k+1 ,…,τ k+N-1 (N is the prediction time domain), the prediction model can predict the system's state sequence {x} at N future times. k+1|k ,x k+2|k ,…,x k+N|k} and output matrix {y k+1|k ,y k+2|k ,…,y k+N|k}, where x k+i|k This represents the state estimate at time k for time k+i.

[0115] 2.2 Scrolling Optimization

[0116] Define a performance index function J to measure the deviation between the predicted output and the expected output, as well as the degree of change in the control input, expressed as:

[0117]

[0118] Among them, y ref,k+i Let Q be the expected output at time k+i (i.e., the platform's expected position and attitude), Q be the output error weight matrix, and R be the control input weight matrix.

[0119] In each control cycle, by solving the optimization problem Obtain the optimal control input sequence for a future period of time. In actual control, only the sequence... This process is applied to the system, and in the next control cycle, the above optimization process is repeated to achieve rolling optimization control.

[0120] Constraint handling: In the control process of a six-degree-of-freedom parallel wave compensation platform, there are various constraints that need to be considered in model predictive control.

[0121] Control input constraints: The driving force of each branch is limited by the output torque of the servo motor and the mechanical structure of the cylinder, and has a maximum and a minimum value. Let the driving force constraint of the j-th branch be τ. jmin ≤τ j ≤τ jmax (j=1,2,…,6), then the control input vector τ k τ must be satisfied min ≤τ k ≤τ max , where τ min =[τ 1,min ,τ 2,min ,…,τ6,min ] T , τ max =[τ 1,max ,τ 2,max ,…,τ 6,max ] T .

[0122] State constraints: The platform's range of motion is limited by its mechanical structure. The length of each branch cannot exceed its maximum extension, and the platform's attitude angles (roll, pitch, etc.) cannot exceed safe limits; otherwise, it may lead to structural damage or operational hazards. Let the platform's position constraint be q. min ≤q≤q max The speed constraint is Then the system state vector x k The corresponding constraints must be met.

[0123] During the rolling optimization process, these constraints are transformed into linear or nonlinear inequality constraints, which, together with the performance index function, constitute the optimization problem. By employing a constrained interior-point optimization algorithm, the optimal control sequence is solved while satisfying the constraints, ensuring the safe and stable operation of the platform.

[0124] 2.3 Feedback Correction

[0125] To reduce the deviation between the model's predicted values ​​and the system's actual output values, a feedback correction mechanism is introduced. At the end of each control cycle, the actual output y of the platform is acquired by the attitude sensors installed on the platform. k The actual output y k Compared with the predicted output y obtained based on the prediction model k|k-1 Compare and calculate the prediction error e k =y k -y k|k-1 .

[0126] Using prediction error e k The predicted value for the next time step is corrected. An error feedback gain matrix K is used. e The corrected state prediction value is expressed as Where x k+1|k The uncorrected state prediction value. This represents the corrected state prediction. Feedback correction enables the prediction model to better track the actual motion state of the system, improving the accuracy of model predictions and enhancing the robustness of the control algorithm.

[0127] 3. Wave interference prediction and compensation

[0128] To improve the platform's compensation effect against wave interference, wave interference prediction is necessary. Real-time wave data, including wave height, period, and direction, is collected by wave sensors installed on the ship. The collected wave data is analyzed and processed using the JONSWAP wave spectrum analysis method to establish a wave interference prediction model. Based on this model, the interference force τ generated by waves on the ship in the future is predicted. d,k+i|k (i = 1, 2, ..., N). The predicted disturbance force is incorporated into the prediction model and optimization process of model predictive control. When calculating the optimal control sequence, the impact of wave disturbance is considered in advance, so that the control input can effectively counteract wave disturbance and improve the platform's compensation accuracy and response speed.

[0129] Specific implementation steps:

[0130] 1) Construction of a six-degree-of-freedom parallel wave compensation platform

[0131] A six-degree-of-freedom parallel wave compensation platform was constructed. The upper and lower platforms are made of high-strength aluminum alloy to reduce platform weight while ensuring structural strength. The six branches utilize electric cylinders, and the servo motors are permanent magnet synchronous servo motors with matching drivers, enabling high-precision position and speed control.

[0132] Attitude sensors are installed on the platform to measure the platform's attitude angles (roll, pitch, and bow) and angular velocity; wave sensors are installed on the ship to collect wave data. All sensor data is transmitted to the PLC.

[0133] 2) Identification of dynamic model parameters

[0134] Parameter identification experiments were conducted on the constructed six-DOF parallel wave compensation platform. Servo motors were controlled to drive the branches along a preset trajectory, and sensors were used to collect data on the platform's position, velocity, acceleration, and driving force of each branch. The least squares method was used to process the collected data, identifying the inertia matrix M(q), Coriolis force, and centrifugal force matrix in the dynamic model. Gravity matrix G(q), friction matrix Parameters are identified and substituted into the dynamic model. The accuracy of the model is verified through simulation and experiment. If the model error exceeds the allowable range, the parameters are re-identified and optimized until the model meets the accuracy requirements.

[0135] 3) Implementation of Model Predictive Control Algorithm

[0136] A model predictive control algorithm module based on a dynamic model was built on the control computer using MATLAB / Simulink software. Based on the designed predictive model, performance index function, constraints, and feedback correction mechanism, the control algorithm program was written and downloaded to the PLC. The rolling optimization problem was solved using the interior-point method to improve optimization efficiency.

[0137] Wave data collected by wave sensors is input into the wave interference prediction module. A wave interference prediction model is established using the JONSWAP wave spectrum to predict the wave interference force within the next 5 seconds. The predicted wave interference force and platform state data collected by the sensors are input into the model predictive control algorithm module to calculate the optimal control sequence. The control signal is then sent to the servo motor via the driver to control the extension and retraction of the branches, thereby achieving wave compensation control of the platform.

[0138] 4) System debugging and performance testing

[0139] The constructed six-degree-of-freedom parallel wave compensation platform system was debugged, including sensor calibration, servo motor parameter setting, and control algorithm parameter adjustment. After debugging, performance testing experiments were conducted. Ship motion under different sea states (e.g., calm sea states, medium wave sea states, and large wave sea states) was simulated. By comparing the platform's attitude error, response time, and other performance indicators using the technology of this invention with existing technologies, the effectiveness and superiority of this invention were verified. Experimental results show that this invention can significantly improve the compensation accuracy and response speed of the six-degree-of-freedom parallel wave compensation platform, meeting the requirements of high-precision maritime operations.

[0140] Advantages of this invention:

[0141] 1. Improve compensation accuracy

[0142] Direct technical effects: An accurate dynamic model of a six-degree-of-freedom parallel wave compensation platform was established, and the model predictive control algorithm based on this model can accurately predict the future motion state of the platform and optimize the control input in real time.

[0143] Reasoning Process: The accurate dynamic model fully considers actual factors such as the mass of each component of the platform, moment of inertia, joint friction, and elastic deformation of the branches, accurately reflecting the platform's dynamic characteristics. Based on this, the model predictive control algorithm can predict future motion based on the current state and calculate the optimal control input through rolling optimization, making the control commands more closely match the actual needs of the platform. Simultaneously, the feedback correction mechanism compares the actual motion state with the predicted values, correcting model deviations and further improving control accuracy. These factors work together to enable the platform to more accurately counteract wave interference, thereby improving compensation accuracy.

[0144] 2. Enhanced response speed

[0145] Direct technical effects: The model predictive control adopts a rolling optimization strategy and introduces a wave disturbance prediction mechanism.

[0146] Reasoning Process: The rolling optimization strategy can quickly calculate the optimal control quantity based on the current system state and future prediction information in each control cycle and apply it to the system in a timely manner, reducing the delay in control decisions. Meanwhile, the wave disturbance prediction mechanism analyzes real-time wave data to predict future wave disturbance forces in advance and incorporates them into the control optimization process, enabling the platform to respond before wave disturbances have an actual impact. These two mechanisms work together to accelerate the platform's response speed to wave disturbances and enhance its overall responsiveness.

[0147] 3. Improve robustness

[0148] Direct technical effects: The model predictive control algorithm includes a feedback correction mechanism and considers various constraints during the optimization process.

[0149] Reasoning Process: The feedback correction mechanism obtains the error by comparing the actual output with the predicted output, and uses this error to correct the prediction model. This allows the model to better adapt to uncertainties such as system nonlinearity, model mismatch, and external disturbances, reducing the impact of these factors on the control effect. Simultaneously, consideration of constraints such as control input, state, and branch motion speed ensures that the platform operates within a safe range, avoiding system instability caused by excessive motion or excessive driving force. These two aspects together guarantee that the system maintains stable control performance in the complex and ever-changing marine environment, improving robustness.

[0150] 4. Reduce reliance on accurate models

[0151] Direct technical benefits: Model predictive control has rolling optimization and feedback correction mechanisms.

[0152] Reasoning Process: Rolling optimization enables the control algorithm to recalculate the optimal control input based on the actual system state in each cycle, eliminating the need to rely on a fixed, precise model to guide control over the long term. Feedback correction continuously adjusts the model based on the deviation between the actual and predicted outputs. Even if the initial model has some errors, continuous correction can gradually bring the model closer to the actual system. Therefore, this invention does not significantly affect the control performance due to small deviations in the model, reducing the dependence on a precise model.

[0153] 5. Effectively handle attitude coupling errors

[0154] Direct technical effect: The model predictive control algorithm can comprehensively consider the six degrees of freedom of the platform and adjust the control input of each degree of freedom simultaneously during the optimization process.

[0155] Reasoning Process: In a six-DOF parallel wave compensation platform, there is a strong coupling relationship among the degrees of freedom; changes in the motion of one degree of freedom will affect the others. Model predictive control considers all six degrees of freedom as a whole during optimization, taking into account the mutual influence between them when calculating the control input, ensuring that the driving force adjustments of each branch are coordinated. This holistic optimization approach effectively balances the motion of each degree of freedom, reduces attitude errors caused by coupling, and thus effectively handles attitude coupling errors.

[0156] Key points of this invention:

[0157] 1. Construction and parameter identification of a precise six-DOF parallel wave compensation platform dynamic model.

[0158] 2. Construction of wave interference model based on wave spectrum analysis method.

[0159] 3. Overall control framework of the model predictive control algorithm based on the above-mentioned accurate dynamic model and wave disturbance model.

[0160] On the other hand, embodiments of the present invention also provide an attitude adjustment system for a wave compensation platform based on model predictive control, used to implement the attitude adjustment method for a wave compensation platform based on model predictive control as described above. The system includes:

[0161] The first module is used to establish the dynamic model of a six-degree-of-freedom parallel wave compensation platform;

[0162] The second module is used to design model predictive control algorithms based on the dynamic model;

[0163] The third module is used to predict and compensate for wave interference based on the model predictive control algorithm, and obtain attitude adjustment information.

[0164] On the other hand, embodiments of the present invention also provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method.

[0165] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0166] Another embodiment of the hardware structure of the electronic device, the electronic device including:

[0167] The processor can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to achieve the technical solutions provided in the embodiments of this application.

[0168] The memory can be implemented in the form of read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory and called by the processor to execute the methods described in the embodiments of this application.

[0169] Input / output interfaces are used to implement information input and output;

[0170] The communication interface is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0171] A bus is used to transfer information between various components of a device, such as processors, memory, input / output interfaces, and communication interfaces.

[0172] The processor, memory, input / output interfaces, and communication interfaces communicate with each other within the device via a bus.

[0173] On the other hand, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0174] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0175] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0176] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0177] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0178] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0179] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0180] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0181] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0182] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0183] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0184] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0185] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0186] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0187] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0188] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. An attitude adjustment method for a wave compensation platform based on model predictive control, characterized in that, The method includes the following steps: A dynamic model of a six-degree-of-freedom parallel wave compensation platform is established; Based on the dynamic model, design a model predictive control algorithm; Wave interference is predicted and compensated based on the model predictive control algorithm to obtain attitude adjustment information.

2. The method according to claim 1, characterized in that, The dynamic model for establishing a six-degree-of-freedom parallel wave compensation platform includes: Obtain the parameters of the six-degree-of-freedom parallel wave compensation platform; the parameters of the six-degree-of-freedom parallel wave compensation platform include the mass of the platform components, the velocity of the center of mass, the moment of inertia, the angular velocity, the height relative to the reference plane, the elastic coefficient of the branch, and the elongation of the branch; Calculate the system kinetic energy and system potential energy of the six-degree-of-freedom parallel wave compensation platform based on the parameters of the six-degree-of-freedom parallel wave compensation platform; Based on the Lagrange equation, the kinetic energy of the system, and the potential energy of the system, a dynamic model of a six-degree-of-freedom parallel wave compensation platform is obtained.

3. The method according to claim 2, characterized in that, The formulas used to calculate the system kinetic energy and system potential energy based on the parameters of the six-degree-of-freedom parallel wave compensation platform include: Where i is the ordinal number of the platform component, i = 1, 2, ..., n; n is the total number of platform components; m i Let the mass of the i-th platform component be denoted as . Let I be the velocity of the center of mass of the i-th platform component; i Let be the moment of inertia of the i-th platform component; Let be the angular velocity of the i-th platform component; T be the system kinetic energy; G ... i =m i g is the gravity of the i-th platform component; h i is the height of the i-th platform component relative to the reference plane; j is the ordinal number of the branch, with a total of 6 branches; k j Let Δl be the elastic coefficient of the j-th branch; j Let V be the elongation of the j-th branch; V is the system potential energy.

4. The method according to claim 2, characterized in that, The dynamic model of the six-degree-of-freedom parallel wave compensation platform is obtained based on the Lagrange equation, the system's kinetic energy, and the system's potential energy. The formulas used include: Where M(q) is the inertia matrix; G(q) is the matrix of Coriolis force and centrifugal force; G(q) is the gravity matrix; τ is the friction force matrix; τ is the control input vector; τ d q is the external disturbance force vector; q is the position vector of the six-degree-of-freedom parallel wave compensation platform. The velocity vector of the six-degree-of-freedom parallel wave compensation platform; The acceleration vector is the acceleration vector of a six-degree-of-freedom parallel wave compensation platform.

5. The method according to claim 1, characterized in that, The step of designing a model predictive control algorithm based on the dynamic model includes: The dynamic model is discretized to obtain a state-space model in the discrete time domain; Set constraint information; the constraint information includes the driving force constraint of the branch, the length constraint of the branch, the attitude angle constraint of the platform, the position constraint of the platform, and the velocity constraint of the platform. Based on the state-space model in the discrete time domain and the constraint information, a feedback correction mechanism is introduced to obtain the model predictive control algorithm.

6. The method according to claim 5, characterized in that, The discretization of the dynamic model to obtain a discrete-time state-space model includes the following formulas: Where, x k Let x be the state vector of the k-th system; k+1 Let A(x) be the state vector of the (k+1)th system; C is the output matrix; A(x) is the output matrix. k B(x) is the first coefficient matrix; k B is the second coefficient matrix; d (x k ) is the third coefficient matrix; y k τ is the output vector; k τ is the control input vector for the kth iteration; d,k Let be the vector of the k-th external disturbance force.

7. The method according to claim 1, characterized in that, The step of predicting and compensating for wave interference based on the model predictive control algorithm to obtain attitude adjustment information includes: Acquire wave data; the wave data includes wave height, period, and wave direction; Based on the wave data, an analysis and processing method is used to establish a predictive model for wave interference. The interference force is predicted based on the wave interference prediction model to obtain interference force prediction information; Acquire platform data; the platform data includes the platform position, platform velocity, platform acceleration, and driving force of each branch of the six-degree-of-freedom parallel wave compensation platform; The control signal is obtained by performing calculations and analyses based on the interference force prediction information, the platform data, and the model predictive control algorithm. The control signal is sent to the servo motor through the driver to control the extension and retraction of the branch, thereby realizing wave compensation control of the platform and obtaining attitude adjustment information.

8. An attitude adjustment system for a wave compensation platform based on model predictive control, used to implement the attitude adjustment method for a wave compensation platform based on model predictive control as described in any one of claims 1 to 7, characterized in that, The system includes: The first module is used to establish the dynamic model of a six-degree-of-freedom parallel wave compensation platform; The second module is used to design a model predictive control algorithm based on the dynamic model. The third module is used to predict and compensate for wave interference based on the model predictive control algorithm to obtain attitude adjustment information.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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