Motion control method and system for troweling robot

Abstract

The invention discloses a troweling robot motion control method and system, and belongs to the technical field of robot control. According to the method, the movement route of the troweling robot is obtained in combination with path planning and an obstacle avoidance algorithm, and continuity and safety of the construction process of the troweling robot are ensured; a real-time control technology is combined with a model prediction controller (MPC) to predict future system behaviors of the troweling robot according to kinetic parameter information and ground environment parameter information and adjust a control instruction according to the future system behaviors, so that fine adjustment of the troweling robot on the action of a troweling mechanism (a troweling device) is realized; meanwhile, by monitoring the contact force between the troweling robot and the ground in real time and applying proper contact force according to different ground characteristics, the troweling effect can be further improved, and meanwhile the damage risk to the concrete surface is reduced.

Description

Technical Field

[0001] The present invention relates to the field of robot control technology, and more particularly to a motion control method and system for a trowel robot. Background Art

[0002] With the rapid development of the construction industry and the continuous advancement of construction technology, concrete floor treatment, as an important part of construction, has increasingly higher requirements for floor flatness and finish. Traditional concrete floor finishing operations mainly rely on manual handheld finishing tools, which is not only labor-intensive and inefficient, but also difficult to ensure the consistency and stability of construction quality. With the advancement of science and technology and the popularization of automation and intelligent technology, the construction industry has gradually begun to explore the use of finishing robots to replace traditional manual finishing operations.

[0003] When trowel robots operate in complex construction environments, they need to achieve high-precision motion control to ensure uniformity and consistency of the trowel effect. However, due to the complexity and uncertainty of the construction environment, the accuracy and smoothness of the trowel robot's motion control for trowel operations still face challenges, making it difficult to quickly adapt to and flexibly adjust to different construction environments.

[0004] Therefore, how to propose a motion control method and system for a trowel robot to improve the accuracy and smoothness of the motion control of the trowel robot is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0005] In view of this, the present invention provides a motion control method and system for a trowel robot, which can improve the accuracy and smoothness of the motion control of the trowel robot.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] In one aspect, the present invention provides a motion control method for a trowel robot, comprising the following steps:

[0008] Perform path planning for the trowel robot and generate the trowel robot's motion route by combining the obstacle avoidance algorithm;

[0009] During the movement of the trowel robot along the movement route, dynamic parameter information of the trowel robot, ground environment parameter information and contact force with the ground are obtained in real time;

[0010] The dynamic parameter information and the ground environment parameter information are used as inputs of a model predictive controller to obtain tilt angle adjustment information;

[0011] adjusting a wiping plate mechanism of the trowel robot according to the inclination adjustment information;

[0012] A contact force optimization objective function is constructed according to the contact force, the contact force optimization objective function is solved in combination with constraint conditions, and the contact force is adjusted according to the solution result.

[0013] Preferably, path planning is performed on the trowel robot, and a motion route of the trowel robot is generated by combining an obstacle avoidance algorithm, including:

[0014] Modeling the working environment of the trowel robot, and using the A* algorithm to generate an optimal path from a starting position to a target position;

[0015] Determine the best movement strategy based on the obstacle avoidance algorithm;

[0016] Based on the optimal motion strategy and optimal path, the motion route of the smoothing robot is generated by the interpolation method.

[0017] Preferably, determining the best motion strategy according to the obstacle avoidance algorithm includes:

[0018] Using sensors to obtain information about a trowel robot and information about an obstacle, and determining an obstacle distance according to the obstacle information, wherein the obstacle distance is the distance from the trowel robot to the obstacle;

[0019] generating a plurality of data sets according to the dynamic characteristics of the trowel robot, each data set including a speed and a corresponding steering angle;

[0020] For each data set, simulating the motion trajectory of the trowel robot within a given time;

[0021] Calculate the distance between each motion trajectory and the obstacle and the target position respectively;

[0022] The data set corresponding to the motion trajectory with the minimum obstacle distance and the minimum target distance is selected as the optimal motion strategy.

[0023] Preferably, the dynamic parameter information and the ground environment parameter information are used as inputs of a model predictive controller to obtain tilt angle adjustment information, including:

[0024] Build predictive models;

[0025] Establishing a first optimization objective function based on the output result of the prediction model combined with the control input constraint;

[0026] The tilt angle adjustment information is solved for the first optimization objective function within the prediction range.

[0027] Preferably, the prediction model formula is as follows:

[0028] x(k+1)=A·x(k)+B·u(k);

[0029] Where x(k+1) is the trajectory state at the k+1th time step, x(k) is the trajectory state at the kth time step, u(k) is the control input at the kth time step, and A and B are system matrices;

[0030] The formula of the first optimization objective function L is as follows:

[0031]

[0032] Where x(k+i) is the predicted trajectory state at the k+i time step, Q and R are weight matrices, 1-N is the prediction range, which represents the state of each time step at the last moment of the prediction range starting from the current moment, and x ref (k+i) is the desired trajectory state at time k+i, and u(k+i) is the control input;

[0033] According to the prediction model formula, the first optimization objective function is minimized.

[0034] Preferably, constructing a contact force optimization objective function according to the contact force, solving the contact force optimization objective function in combination with constraint conditions, and adjusting the contact force according to the solution result, including:

[0035] The contact force optimization objective function J is constructed based on the tracking error and control input smoothness, and the formula is as follows:

[0036]

[0037] Among them, F desired is the expected contact force, F(i) is the actual contact force at the i-th prediction step, Δu(i) is the change in the i-th control input, Q and R are weight matrices, and 1-N is the prediction range.

[0038] Preferably, the constraints include system dynamic constraints and operational constraints;

[0039] The system dynamic constraints are as follows:

[0040] x(k+1)=A·x(k)+B·u(k)

[0041] where x(k) is the trajectory state at the kth time step; u(k) is the control input at the kth time step, and f(·) is the system model function;

[0042] The operational constraints include:

[0043] Control input constraints: u min ≤u(k)≤u max ;

[0044] System output constraints: x min≤x(k)≤x max ;

[0045] In the formula, u min Indicates the minimum value of the control input, u max Indicates the maximum value of the control input, x min Represents the minimum value of the system state, x max Indicates the maximum value of the system status.

[0046] On the other hand, the present invention further discloses a trowel robot motion control system, which is used to implement the above trowel robot motion control method, comprising:

[0047] The motion route generation module is used to plan the path of the trowel robot and generate the motion route of the trowel robot in combination with the obstacle avoidance algorithm;

[0048] A parameter acquisition module, used for acquiring the dynamic parameter information, ground environment parameter information and contact force with the ground of the trowel robot in real time during the movement of the trowel robot along the movement route;

[0049] A tilt angle adjustment module, used to use the dynamic parameter information and ground environment parameter information as inputs of a model predictive controller to obtain tilt angle adjustment information;

[0050] A wiping plate mechanism adjustment module, used for adjusting the wiping plate mechanism of the trowel robot according to the inclination adjustment information;

[0051] The contact force adjustment module is used to construct a contact force optimization objective function according to the contact force, solve the contact force optimization objective function in combination with constraint conditions, and adjust the contact force according to the solution result.

[0052] Preferably, the movement route generation module includes:

[0053] An optimal path generation unit, used for modeling the working environment of the trowel robot and generating an optimal path from a starting position to a target position using an A* algorithm;

[0054] An optimal motion strategy generating unit, used for determining an optimal motion strategy according to an obstacle avoidance algorithm;

[0055] The motion route generating unit is used to generate the motion route of the smoothing robot through an interpolation method based on the optimal motion strategy and the optimal path.

[0056] Preferably, the tilt angle adjustment module comprises:

[0057] A model building unit, used to build a prediction model;

[0058] An objective function building unit, used to build a first optimization objective function based on the output result of the prediction model combined with the control input constraint;

[0059] The tilt angle adjustment information acquisition unit is used to solve the tilt angle adjustment information for the first optimization objective function within the prediction range.

[0060] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a motion control method and system for a trowel robot, which combines path planning and obstacle avoidance algorithms to obtain the motion route of the trowel robot, thereby ensuring the continuity and safety of the construction process of the trowel robot; the real-time control technology combined with the model predictive controller (MPC) can predict the future system behavior of the trowel robot according to the dynamic parameter information and the ground environment parameter information, and adjust the control instructions accordingly, so as to achieve fine-tuning of the action of the trowel robot on the trowel mechanism (trowel device); at the same time, the present invention can further improve the trowel effect and reduce the risk of damage to the concrete surface by real-time monitoring the contact force between the trowel robot and the ground, and applying appropriate contact force according to different ground characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0062] Figure 1 A flow chart of the method provided by the present invention;

[0063] Figure 2 This is a system architecture diagram provided by the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] The trowel device is connected to the trowel robot by mechanical connection. The trowel disc body can be installed on the chassis of the trowel robot and is rotatably connected to the trowel through the lifting and pressing assembly to realize the rotary troweling operation on the concrete floor. The control system of the trowel robot needs to be able to accurately control the movement of the trowel device, including high-speed rotation, angle adjustment, trowel shaping and other actions. In order to achieve this purpose, the embodiment of the present invention proposes a trowel robot motion control method and system.

[0066] On the one hand, the embodiment of the present invention provides a motion control method of a trowel robot, referring to Figure 1 , the method comprises the following steps:

[0067] S1. Perform path planning for the polishing robot and generate the motion route of the polishing robot by combining the obstacle avoidance algorithm, including:

[0068] S11. Model the working environment of the trowel robot and use the A* algorithm to generate an optimal path from the starting position to the target position;

[0069] S12. Determine the best motion strategy based on the obstacle avoidance algorithm, including:

[0070] S121. Use sensors to obtain information about the trowel robot and obstacle information, and determine the obstacle distance based on the obstacle information, where the obstacle distance is the distance from the trowel robot to the obstacle;

[0071] The trowel robot information includes the current position and speed information of the trowel robot. According to the acquired obstacle information of the surrounding environment, the precise data such as the distance and position between the trowel robot and the obstacle can be calculated.

[0072] S122. Generate multiple data sets according to the dynamic characteristics of the trowel robot, each data set including a speed and a corresponding steering angle;

[0073] S123. For each data set, simulate the motion trajectory of the trowel robot within a given time;

[0074] S124. Calculate the distance between each motion trajectory and the obstacle and the target position respectively;

[0075] S125. Select the data group corresponding to the motion trajectory with the minimum obstacle distance and the minimum target distance as the optimal motion strategy.

[0076] S13. Based on the optimal motion strategy and the optimal path, the points on the path are connected by an interpolation method (cubic spline interpolation) to form a smooth trajectory and generate a motion route for the smoothing robot.

[0077] S2. While the trowel robot is moving along the motion route, the dynamic parameter information of the trowel robot, the ground environment parameter information and the contact force with the ground are obtained in real time.

[0078] Ground environmental parameters include the flatness of the ground during polishing and the temperature / humidity of the environment. Sensor technology is generally used to obtain environmental parameters.

[0079] The dynamic parameter information includes the motion state, speed, acceleration and other data of the smoothing robot.

[0080] The sensors in the trowel robot are used to collect the motion parameters of the trowel robot. The collected data are preprocessed, including filtering, denoising, data fusion, etc. Then, numerical methods or optimization algorithms can be used for parameter identification. The least squares method is used to calculate the calculated values ​​of the known dynamic parameters, and the model parameters in S3 are optimized by comparing the calculated values ​​with the actual values.

[0081] S3. Using the dynamic parameter information and the ground environment parameter information as inputs of the model predictive controller to obtain the tilt angle adjustment information, including:

[0082] S31. Establish a prediction model.

[0083] A prediction model is established based on the dynamic characteristics of the trowel robot. In this embodiment, a state space expression is used to describe the dynamic behavior of the system, and the following state space model (i.e., prediction model) is established:

[0084] x(k+1)=A·x(k)+B·u(k);

[0085] The next state x(k+1) is determined by the current state x(k) and the control input u(k). A and B are system matrices.

[0086] S32. Establish a first optimization objective function based on the output result of the prediction model combined with the control input constraint.

[0087] An optimization problem is established, the goal is to minimize the error between the predicted trajectory and the desired trajectory, while considering the constraints of the control input, and the first optimization objective function L is obtained as follows:

[0088]

[0089] Where x(k+i) is the predicted trajectory state at the k+i time step, Q and R are weight matrices, 1-N is the prediction range, which represents the state of each time step at the last moment of the prediction range starting from the current moment, and x ref (k+i) is the desired trajectory state at time k+i, and u(k+i) is the control input.

[0090] According to the prediction model formula, the first optimization objective function is minimized and a series of control inputs u(k) and u(k+i) are found to make the value of L as small as possible.

[0091] S33. Solve the tilt angle adjustment information for the first optimization objective function within the prediction range.

[0092] By predicting the optimization problem in the future behavior of the system, the optimal control input sequence u* can be calculated, then u(k)=u*(0); the optimal control input sequence will make the predicted trajectory as close as possible to the expected trajectory, thereby obtaining the adjustment information of the tilt angle.

[0093] S4. Adjust the wiping disc mechanism of the trowel robot according to the inclination adjustment information.

[0094] According to the inclination adjustment information provided by the model predictive controller, the control system will adjust the wiping disc mechanism of the trowel robot to achieve the left or right movement of the robot, etc., to ensure the uniformity and accuracy of the trowel effect. Specifically including:

[0095] The control system first receives the tilt adjustment information from the MPC, which includes the target tilt angle.

[0096] The control system analyzes the tilt adjustment information provided by the MPC and determines the angle value that needs to be adjusted.

[0097] According to the target position or angle, the angle difference between the driving wiper disc mechanism and the driven wiper disc mechanism relative to the ground is calculated.

[0098] Set the next state position predicted by MPC to the target position (x target ,y target ), the current position of the trowel robot is (x current ,y current ).

[0099] Calculate the displacement vector between the target position and the current position

[0100]

[0101] Use the inverse tangent function to calculate the angle θ between the displacement vector and the horizontal axis:

[0102]

[0103] Calculate the angle difference Δθ, that is, the difference between the target angle and the current angle, using the following formula:

[0104] Δθ=θ target -θ current

[0105] where θ targetis the calculated angle, θ current is the current angle of the wiper mechanism.

[0106] The calculated angle difference is used as input to adjust the angle settings of the driving wiper mechanism and the driven wiper mechanism through the control system.

[0107] After adjusting the angle, observe the actual movement of the robot to ensure that it moves in the expected direction. If deviation is found, the angle setting can be further adjusted.

[0108] S5. Construct a contact force optimization objective function based on the contact force, solve the contact force optimization objective function in combination with the constraint conditions, and adjust the contact force size according to the solution result.

[0109] The contact force optimization objective function J is constructed based on the tracking error and control input smoothness, and the formula is as follows:

[0110]

[0111] Among them, F desired is the expected contact force, F(i) is the actual contact force at the i-th prediction step, Δu(i) is the change in the i-th control input; Q and R are weight matrices used to adjust the relative importance of tracking error and control input smoothness; 1-N is the prediction range.

[0112] F(i) can be calculated by the following formula:

[0113]

[0114] Where: t is time, M is the mass matrix, and b is the design variable vector.

[0115] z is the displacement vector. k n is the structural stiffness matrix, and the subscript n indicates that the structure has nonlinear characteristics.

[0116] f is the external load vector, i.e. F(i).

[0117] Preferably, the constraints include system dynamic constraints and operational constraints;

[0118] The system dynamic constraints are as follows:

[0119] x(k+1)=A·x(k)+B·u(k)

[0120] where x(k) is the trajectory state at the kth time step; u(k) is the control input at the kth time step, and f(·) is the system model function;

[0121] Operational constraints include:

[0122] Control input constraints: umin ≤u(k)≤u max ;

[0123] System output constraints: x min ≤x(k)≤x max ;

[0124] In the formula, u min Indicates the minimum value of the control input, u max Indicates the maximum value of the control input, x min represents the minimum value of the system state, x max Indicates the maximum value of the system status.

[0125] Under the constraints, an appropriate numerical method quadratic programming is used to solve the contact force optimization objective function, and the optimal control input obtained is applied to the actuator of the trowel robot to adjust the pressure of the trowel tool on the ground.

[0126] In this embodiment, in each control cycle, MPC uses the latest sensor data to update the estimate of the system state.

[0127] On the other hand, the embodiment of the present invention also discloses a motion control system of a trowel robot, such as Figure 2 As shown, the method for realizing the motion control of the above-mentioned trowel robot includes:

[0128] The motion route generation module is used to plan the path of the trowel robot and generate the motion route of the trowel robot in combination with the obstacle avoidance algorithm;

[0129] The parameter acquisition module is used to obtain the dynamic parameter information of the trowel robot, the ground environment parameter information and the contact force with the ground in real time when the trowel robot moves along the motion route;

[0130] The tilt angle adjustment module is used to use the dynamic parameter information and the ground environment parameter information as the input of the model prediction controller to obtain the tilt angle adjustment information;

[0131] A wiping plate mechanism adjustment module, used for adjusting the wiping plate mechanism of the trowel robot according to the inclination adjustment information;

[0132] The contact force adjustment module is used to construct a contact force optimization objective function based on the contact force, solve the contact force optimization objective function in combination with constraint conditions, and adjust the contact force size according to the solution result.

[0133] Preferably, the movement route generation module includes:

[0134] The optimal path generation unit is used to model the working environment of the trowel robot and generate an optimal path from the starting position to the target position using the A* algorithm;

[0135] An optimal motion strategy generating unit, used for determining an optimal motion strategy according to an obstacle avoidance algorithm;

[0136] The motion route generating unit is used to generate the motion route of the smoothing robot through an interpolation method based on the optimal motion strategy and the optimal path.

[0137] Preferably, the tilt angle adjustment module comprises:

[0138] A model building unit, used to build a prediction model;

[0139] An objective function building unit, used to build a first optimization objective function based on the output result of the prediction model combined with the control input constraint;

[0140] The tilt angle adjustment information acquisition unit is used to solve the tilt angle adjustment information for the first optimization objective function within the prediction range.

[0141] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0142] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A motion control method for a trowel robot, characterized in that: The following steps are involved: Perform path planning for the trowel robot and generate the trowel robot's motion route by combining the obstacle avoidance algorithm; During the movement of the trowel robot along the movement route, dynamic parameter information of the trowel robot, ground environment parameter information and contact force with the ground are obtained in real time; The dynamic parameter information and the ground environment parameter information are used as inputs of a model predictive controller to obtain tilt angle adjustment information; adjusting a wiping plate mechanism of the trowel robot according to the inclination adjustment information; A contact force optimization objective function is constructed according to the contact force, the contact force optimization objective function is solved in combination with constraint conditions, and the contact force is adjusted according to the solution result.

2. A trowel robot motion control method according to claim 1, characterized in that: Perform path planning for the polishing robot and generate the motion route of the polishing robot by combining the obstacle avoidance algorithm, including: Modeling the working environment of the trowel robot, and using the A* algorithm to generate an optimal path from a starting position to a target position; Determine the best movement strategy based on the obstacle avoidance algorithm; Based on the optimal motion strategy and optimal path, the motion route of the smoothing robot is generated by the interpolation method.

3. A trowel robot motion control method according to claim 2, characterized in that: Determine the best motion strategy based on the obstacle avoidance algorithm, including: Using sensors to obtain information about a trowel robot and information about an obstacle, and determining an obstacle distance according to the obstacle information, wherein the obstacle distance is the distance from the trowel robot to the obstacle; generating a plurality of data sets according to the dynamic characteristics of the trowel robot, each data set including a speed and a corresponding steering angle; For each data set, simulating the motion trajectory of the trowel robot within a given time; Calculate the distance between each motion trajectory and the obstacle and the target position respectively; The data set corresponding to the motion trajectory with the minimum obstacle distance and the minimum target distance is selected as the optimal motion strategy.

4. A trowel robot motion control method according to claim 1, characterized in that: The dynamic parameter information and the ground environment parameter information are used as inputs of the model predictive controller to obtain the tilt angle adjustment information, including: Build predictive models; Establishing a first optimization objective function based on the output result of the prediction model combined with the control input constraint; The tilt angle adjustment information is solved for the first optimization objective function within the prediction range.

5. A trowel robot motion control method according to claim 4, characterized in that: The prediction model formula is as follows: x(k+1)=A·x(k)+B·u(k); Where x(k+1) is the trajectory state at the k+1th time step, x(k) is the trajectory state at the kth time step, u(k) is the control input at the kth time step, and A and B are system matrices; The formula of the first optimization objective function L is as follows: Where x(k+i) is the predicted trajectory state at the k+i time step, Q and R are weight matrices, 1-N is the prediction range, which represents the state of each time step at the last moment of the prediction range starting from the current moment, and x ref (k+i) is the desired trajectory state at time k+i, and u(k+i) is the control input; According to the prediction model formula, the first optimization objective function is minimized.

6. A trowel robot motion control method according to claim 1, characterized in that: Constructing a contact force optimization objective function according to the contact force, solving the contact force optimization objective function in combination with constraint conditions, and adjusting the contact force according to the solution result, including: The contact force optimization objective function J is constructed based on the tracking error and control input smoothness, and the formula is as follows: Among them, F desired is the expected contact force, F(i) is the actual contact force at the i-th prediction step, Δu(i) is the change in the i-th control input, Q and R are weight matrices used to balance the relative importance of tracking error and control input change; 1-N is the prediction range.

7. A trowel robot motion control method according to claim 6, characterized in that: The constraints include system dynamic constraints and operation constraints; The system dynamic constraints are as follows: x(k+1)=A·x(k)+B·u(k) where x(k) is the trajectory state at the kth time step; u(k) is the control input at the kth time step, and f(·) is the system model function; The operational constraints include: Control input constraints: u min ≤u(k)≤u max ; System output constraints: x min ≤x(k)≤x max ; In the formula, u min Indicates the minimum value of the control input, u max Indicates the maximum value of the control input, x min represents the minimum value of the system state, x max Indicates the maximum value of the system status.

8. A motion control system for a trowel robot, characterized in that: include: The motion route generation module is used to plan the path of the trowel robot and generate the motion route of the trowel robot in combination with the obstacle avoidance algorithm; A parameter acquisition module, used for acquiring the dynamic parameter information, ground environment parameter information and contact force with the ground of the trowel robot in real time during the movement of the trowel robot along the movement route; A tilt angle adjustment module, used to use the dynamic parameter information and ground environment parameter information as inputs of a model predictive controller to obtain tilt angle adjustment information; A wiping plate mechanism adjustment module, used for adjusting the wiping plate mechanism of the trowel robot according to the inclination adjustment information; The contact force adjustment module is used to construct a contact force optimization objective function according to the contact force, solve the contact force optimization objective function in combination with constraint conditions, and adjust the contact force according to the solution result.

9. A trowel robot motion control system according to claim 8, characterized in that: The motion route generation module comprises: An optimal path generation unit, used for modeling the working environment of the trowel robot and generating an optimal path from a starting position to a target position using an A* algorithm; An optimal motion strategy generating unit, used for determining an optimal motion strategy according to an obstacle avoidance algorithm; The motion route generating unit is used to generate the motion route of the smoothing robot through an interpolation method based on the optimal motion strategy and the optimal path.

10. A trowel robot motion control system according to claim 8, characterized in that: The tilt adjustment module comprises: A model building unit, used to build a prediction model; An objective function building unit, used to build a first optimization objective function based on the output result of the prediction model combined with the control input constraint; The tilt angle adjustment information acquisition unit is used to solve the tilt angle adjustment information for the first optimization objective function within the prediction range.

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