Power buoy self-positioning control method based on MPC algorithm and power buoy

Abstract

The invention relates to the technical field of power buoys, in particular to a power buoy self-positioning control method based on an MPC algorithm and a power buoy. The method comprises the following steps: firstly, establishing a kinematic model of a buoy, converting the kinematic model into a continuous state space model, secondly, establishing a continuous time system equation according to the kinematic model, and then, discretizing the continuous state space model to obtain a continuous time system equation; discretizing a state transition matrix and an input influence matrix in the continuous state space model according to a continuous time system equation, then establishing an objective function, constraining control parameters in a kinematic model, and finally solving an optimal solution of the objective function in a constraint range by adopting a linear quadratic programming function, so as to obtain a continuous state space model. Optimal control parameters are obtained, and optimal positioning and navigation control are achieved. A positioning control system and a power propelling system are arranged, the posture and position of the buoy can be rapidly and automatically adjusted, and the adaptability and flexibility of the buoy are greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of powered buoys, and in particular to a powered buoy self-positioning control method based on an MPC algorithm and a powered buoy. Background Art

[0002] As a navigation mark floating on the water surface, buoys are widely used and are usually set up in places where it is difficult or inconvenient to set up fixed navigation marks. Their function is to mark shallows in the waterway or obstacles that endanger navigation safety. Buoys equipped with lights are called light buoys, which are used as signal buoys to aid navigation in waters that are navigable day and night.

[0003] However, traditional buoys are fixed buoys, that is, they are anchored at a specified position and cannot be adjusted according to user needs. After the buoy is installed, the secondary movement process is cumbersome and the cost of secondary movement is high. It is not suitable for dynamic environments or situations where frequent position adjustment is required, and its flexibility is poor. At the same time, most traditional buoys rely on anchor chains for fixing, and their positioning accuracy is easily affected by natural conditions such as water flow and wind. Especially in deep water or strong current areas, the fixed position of the buoy will be offset and the positioning accuracy is low. Therefore, in order to solve the above problems, it is urgent to design a powered buoy that can realize the self-positioning function, as well as a control method for the self-positioning of a powered buoy. Summary of the invention

[0004] The purpose of the present invention is to provide a powered buoy self-positioning control method and a powered buoy based on an MPC algorithm to address the deficiencies in the prior art, so as to solve the problems that traditional buoys in the prior art cannot flexibly adjust their positions according to user needs, are not suitable for use in dynamic environments or situations where frequent position adjustments are required, have poor flexibility, and the positioning accuracy of the buoy is easily affected by natural conditions such as water flow and wind, resulting in low positioning accuracy.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for controlling a powered buoy's self-positioning based on an MPC algorithm comprises the following steps:

[0007] Step 1: Establish the kinematic model of the buoy and transform it into a continuous state space model.

[0008] Step 2: Establish the continuous-time system equation based on the kinematic model.

[0009] Step 3: Discretize the continuous state space model.

[0010] Step 4: Discretize the state transfer matrix and input influence matrix in the continuous state space model according to the continuous-time system equation.

[0011] Step 5: Establish the objective function and constrain the control parameters in the kinematic model.

[0012] Step 6: Use the linear quadratic programming function to solve the optimal solution of the objective function within the constraints, obtain the optimal control parameters, and achieve optimal positioning and navigation control.

[0013] Furthermore, in step 1, the kinematic model of the buoy is:

[0014]

[0015] The kinematic model of the buoy is converted into a continuous state space model as follows:

[0016]

[0017] Its state space is:

[0018]

[0019] Where X is the longitude of the buoy, Y is the latitude of the buoy, and ψ is the heading of the buoy. is the celestial angular velocity of the buoy, is the celestial angular acceleration of the buoy, is the buoy's vertical speed, is the swell acceleration of the buoy, is the sway velocity of the buoy, is the sway acceleration of the buoy.

[0020] The input variables are:

[0021] TF=[TF1 TF2 TF3 TF4] T

[0022] Among them, TF i is the thrust of the ith thruster.

[0023] Furthermore, in step 2, the continuous time system equation is established as:

[0024] Furthermore, in step 3, the discrete form of the continuous state space model is: k+1 =F X X k +F U U k

[0025] Among them, F x is the state transfer matrix, F u is the input influence matrix.

[0026] Furthermore, in step 4, the state transfer matrix F x The discretization equation is:

[0027] Input influence matrix F u The discretization equation is:

[0028]

[0029] Furthermore, in step 5, the objective function is:

[0030]

[0031] in,

[0032]

[0033] Among them, Q is the state error weight matrix, and R is the control input weight matrix.

[0034] The constraints are established as follows:

[0035]

[0036] Among them, X min is the minimum longitude, X max is the maximum longitude, Y min is the minimum latitude, Y max is the maximum latitude, is the minimum sloshing velocity, is the maximum sloshing velocity, is the minimum sway velocity, is the maximum sway velocity, is the minimum angular velocity in the celestial direction, is the maximum angular velocity in the celestial direction, TF min is the minimum thrust of the propeller, TF min is the maximum thrust of the propeller.

[0037] A powered buoy controlled by the above control method comprises a main buoy, an auxiliary buoy and an autopilot for controlling the dynamic positioning and planned navigation of the buoy, four thrusters are fixedly arranged at the bottom of the main buoy at intervals of 90° along the central circumference of the buoy, and the thrusters are electrically connected to the autopilot;

[0038] The autopilot includes a buoy for measuring longitude X, latitude Y, heading ψ, and surge speed. and sway speed Dual-antenna GNSS attitude measurement system for measuring the angular velocity of the buoy Surge acceleration and sway acceleration MEMS inertial measurement components, communication modules for data transmission between the buoy and the host computer, and lithium iron phosphate battery packs.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention is provided with an autopilot and four thrusters spaced at the bottom of the buoy. Through the dual-antenna GNSS attitude measurement system and the MEMS inertial measurement component, the position and attitude of the buoy itself are acquired in real time. At the same time, the thrust and direction of the thrusters can be automatically adjusted according to the acquired real-time data to ensure the stability and positioning accuracy of the buoy itself. The present invention is equipped with a positioning control system and a power propulsion system. By adjusting the thrust and direction of each thruster, the buoy can be precisely controlled in different directions and achieve translational motion in any direction. Under complex external conditions, the buoy can quickly respond to changes in the environment and adjust its own attitude and position, which greatly improves the adaptability and flexibility of the buoy. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of the overall structure of a powered buoy provided by the present invention;

[0042] Figure 2 A bottom view of a powered buoy provided by the present invention.

[0043] Wherein, the accompanying drawings are marked as follows:

[0044] 1. Main float; 2. Auxiliary float; 3. Autopilot; 4. Thruster. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] It should be noted that when an element is referred to as being "fixed to" or "disposed on" another element, it can be directly on the other element or indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element.

[0047] It should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0048] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features.

[0049] For easier understanding, see Figure 1 to Figure 2 This embodiment provides a method for controlling a powered buoy's self-positioning based on an MPC algorithm and a powered buoy, including a main buoy 1, an auxiliary buoy 2, and an autopilot 3, wherein the main buoy 1 is an annular hollow structure, used to provide buoyancy for the buoy as a whole to support the buoy as a whole to float above the sea surface; the auxiliary buoy 2 is located above the main buoy 1 and is fixedly connected to the main buoy 1, and the auxiliary buoy 2 is a spherical hollow structure, used to assist the main buoy 1 in supporting the buoy as a whole to float above the sea surface, and the spherical structure of the auxiliary buoy 2 facilitates the staff to determine the position of the buoy; the autopilot 3 is located in the middle of the main buoy 1 and is fixedly connected to the main buoy 1. The autopilot 3 can be regarded as the controller of the buoy as a whole, and the powered buoy's self-positioning control method based on the MPC algorithm is used to control the dynamic positioning and planned navigation of the buoy, specifically including a dual-antenna GNSS attitude measurement system, a MEMS inertial measurement component, a communication module, and a lithium iron phosphate battery pack, wherein the dual-antenna GNSS attitude measurement system is used to measure the longitude X, latitude Y, heading ψ, and sway speed of the buoy and sway speed MEMS inertial measurement unit is used to measure the angular velocity of the buoy Surge acceleration and sway acceleration The communication module is used to receive control instructions from the host computer and to feed back the buoy's own status information to the host computer, so as to realize the data transmission function between the buoy and the host computer; the lithium iron phosphate battery pack is used to provide power for the electronic components installed on the buoy, and the lithium iron phosphate battery pack includes lithium iron phosphate rechargeable batteries and solar panels to support the long-term autonomous power supply and autonomous operation of the buoy. The volume of the auxiliary float is larger than that of the main float, so that the buoy's center of buoyance is higher than the center of gravity, so as to realize the function of automatic reset after the buoy is tipped over. Four DC brushless thrusters 4 are fixed at the bottom of the main float 1 at 90° intervals along the center circumference of the buoy. The four thrusters 4 are marked as T1, T2, T3, and T4 respectively, and the thrusters 4 are electrically connected to the autopilot 3. Furthermore, the dual-antenna GNSS attitude measurement system includes a spiral satellite antenna, which is particularly suitable for the application scenario of dual-antenna attitude measurement at sea due to its special polarization mode and high sensitivity.

[0050] The self-positioning control method of the powered buoy based on the MPC algorithm includes:

[0051] Step 1: Establish the kinematic model of the buoy and transform it into a continuous state space model.

[0052] The kinematic model of the buoy is established as:

[0053]

[0054] Where m is the mass of the buoy, r is the position vector of the buoy, k is the drag coefficient, and TF i is the thrust of the i-th propeller, I is the moment of inertia of the buoy, and d is the diameter of the buoy (i.e., the distance between the two propellers 4 located at diagonally opposite positions).

[0055] The kinematic model of the buoy is converted into a continuous state space model. The continuous state space model of the buoy is:

[0056]

[0057] Its state space is:

[0058]

[0059] Where X is the longitude of the buoy, Y is the latitude of the buoy, and ψ is the heading of the buoy. is the celestial angular velocity of the buoy, is the celestial angular acceleration of the buoy, is the buoy's vertical speed, is the swell acceleration of the buoy, is the sway velocity of the buoy, is the sway acceleration of the buoy.

[0060] The input variable of the system is the thrust of four thrusters 4 fixedly installed at the bottom of the buoy, that is, the input variable is:

[0061] TF=[TF1 TF2 TF3 TF4] T (4)

[0062] Under the condition of dynamic positioning of the buoy, the relationship between the thrust of the propeller 4 and the rotation speed of the propeller of the propeller 4 can be linearly approximated by a coefficient, that is, TF=k1U.

[0063] but

[0064] u=[U1 U2 U3 U4] T (5)

[0065] Where u is the input control vector, U n is the propeller speed of the nth propeller.

[0066] Step 2: Establish the continuous-time system equation based on the kinematic model.

[0067] The continuous time system equation is established according to the kinematic model:

[0068]

[0069] Step 3: Discretize the continuous state space model.

[0070] After discretizing the continuous state space model, we get:

[0071] X k+1 =F X X k +F U U k (7)

[0072] Among them, F x is the state transfer matrix, F u is the input influence matrix.

[0073] Step 4: Discretize the state transfer matrix and input influence matrix according to the continuous-time system equation.

[0074] According to the continuous-time system equation, the state transfer matrix F is calculated by the zero-order holder method. x After discretization, we get:

[0075]

[0076] Similarly, the input influence matrix F is calculated by the zero-order holder method according to the continuous-time system equation u After discretization, we get:

[0077]

[0078] Among them, θ is the installation angle of the propeller, a is the distance between the left and right propellers, and b is the distance between the front and rear propellers.

[0079] Step 5: Establish the objective function and constrain the parameters of the buoy in the kinematic model.

[0080] Based on the control objectives of optimal track, heading error and minimum energy consumption, the control objective function is established as:

[0081]

[0082] in,

[0083]

[0084] Among them, Q is the state error weight matrix, and R is the control input weight matrix.

[0085] The longitude, latitude, surge velocity, sway velocity, celestial angular velocity, and thruster thrust of the buoy in the kinematic model are constrained, that is, the constraint conditions are established as follows:

[0086]

[0087] Among them, X min is the minimum longitude, X max is the maximum longitude, Y min is the minimum latitude, Y max is the maximum latitude, is the minimum sloshing velocity, is the maximum sloshing velocity, is the minimum sway velocity, is the maximum sway velocity, is the minimum angular velocity in the celestial direction, is the maximum angular velocity in the celestial direction, TF min is the minimum thrust of the propeller, TF min is the maximum thrust of the propeller.

[0088] Step 6: Use the linear quadratic programming function to solve the optimal solution of the objective function within the constraints, obtain the optimal control parameters, and achieve optimal positioning and navigation control.

[0089] The objective function J is a quadratic function, and the constraints are linear constraints. The linear quadratic programming (LQP) function is used to solve the optimal solution Δu of the objective function J within the constraints of the above constraints. That is, the state vector, the target value, and the sequence of the control quantity are brought into the objective function J, and the minimum value is obtained to obtain the thrust TF of each thruster 4. i , which is converted into the speed U of the propeller corresponding to each propeller 4 n , and feedback outputs the control quantity to adjust the speed of the corresponding propellers of the four thrusters T1, T2, T3, and T4 to achieve optimal positioning and navigation control.

[0090] By repeating the above process, the in-situ dynamic positioning function of the buoy can be realized.

[0091] The present invention can be used in the following aspects:

[0092] 1. Water sports:

[0093] Track marking: In water competitions such as sailing, rowing, and kayaking, powered buoys can be used to mark the race route. Because the buoys can automatically maintain their predetermined positions under the action of water flow and wind, the accuracy and stability of the track markings are ensured, effectively avoiding the deviation problem that is prone to occur with traditional anchored buoys.

[0094] Flexible adjustment: During the race, if the track needs to be adjusted according to weather or hydrological conditions, the dynamic positioning buoy can receive the signal transmitted by the host computer through the autopilot and quickly move to the new position, simplifying the workflow of track adjustment.

[0095] 2. Marine Research:

[0096] Ocean observation: The powered buoy can carry special payloads, receive signals transmitted by the host computer through the autopilot, move to different locations, and conduct observations as planned.

[0097] Buoy array: Multiple powered buoys based on propulsion technology can form a precise observation array to realize an observation network such as an acoustic long baseline array.

[0098] 3. Navigation and safety:

[0099] Channel marking: used to mark channels or dangerous areas, provide high-precision location marking, and ensure the safety of ship navigation.

[0100] Search and rescue: In an emergency, the powered buoy can receive signals transmitted by the host computer through the autopilot and quickly reach the designated location to provide rescue support or environmental monitoring.

[0101] 4. Prevent drowning in public baths

[0102] Powered buoys can use their precise pointing and ability to maneuver at any time to install payloads such as smart cameras, searchlights, shouting equipment, sound and light alarm equipment, and automatic throwing equipment. They can cruise in public bathing waters, automatically or manually assist in identifying people who have fallen into the water or are drowning, and provide buoyancy support or call for rescue on site.

[0103] The powered buoy equipped with the MPC self-positioning control algorithm provides high maneuverability, high position accuracy, high pointing accuracy and high stability through advanced propulsion and control systems, giving it broad application potential in ocean observation, water sports, drowning prevention, environmental monitoring, emergency response, channel indication, navigation safety and other fields.

[0104] Although the present invention has been described using the above preferred embodiments, it is not intended to limit the scope of protection of the present invention. Any person skilled in the art who makes various changes and modifications to the above embodiments without departing from the spirit and scope of the present invention still fall within the scope of protection of the present invention.

Claims

1. A self-positioning control method for a powered buoy based on an MPC algorithm, characterized in that: The following steps are involved: Step 1: Establish the kinematic model of the buoy and transform it into a continuous state space model; Step 2: Establish the continuous time system equation according to the kinematic model; Step 3: Discretize the continuous state space model; Step 4: Discretize the state transfer matrix and input influence matrix in the continuous state space model according to the continuous time system equation; Step 5: Establish the objective function and constrain the control parameters in the kinematic model; Step 6: Use the linear quadratic programming function to solve the optimal solution of the objective function within the constraints, obtain the optimal control parameters, and achieve optimal positioning and navigation control.

2. The method for controlling the self-positioning of a powered buoy based on the MPC algorithm according to claim 1, characterized in that: In step 1, the kinematic model of the buoy is: Where m is the mass of the buoy, r is the position vector of the buoy, k is the drag coefficient, TF i is the thrust of the i-th thruster, I is the moment of inertia of the buoy, and d is the diameter of the buoy. The kinematic model of the buoy is converted into a continuous state space model as follows: Its state space is: Where X is the longitude of the buoy, Y is the latitude of the buoy, and ψ is the heading of the buoy. is the celestial angular velocity of the buoy, is the celestial angular acceleration of the buoy, is the buoy's vertical speed, is the swell acceleration of the buoy, is the sway velocity of the buoy, is the sway acceleration of the buoy. The input variables are: <h2 style=";text-align:left;direction:ltr">TF=[TF1 TF2 TF3 TF4]<h2 style=";text-align:left;direction:ltr"> T <h2 style=";text-align:left;direction:ltr"> 。 3. The method for controlling the self-positioning of a powered buoy based on the MPC algorithm according to claim 2 is characterized in that: In the step 2, the continuous time system equation is established as:

4. The method for controlling the self-positioning of a powered buoy based on the MPC algorithm according to claim 3 is characterized in that: In step 3, the discrete form of the continuous state space model is: X k+1 =F X X k +F U U k Among them, F x is the state transfer matrix, F u is the input influence matrix.

5. The method for controlling the self-positioning of a powered buoy based on the MPC algorithm according to claim 4 is characterized in that: In step 4, the state transfer matrix F x The discretization equation is: Input influence matrix F u The discretization equation is: Among them, θ is the installation angle of the propeller, a is the distance between the left and right propellers, and b is the distance between the front and rear propellers.

6. The method for controlling the self-positioning of a powered buoy based on the MPC algorithm according to claim 5, characterized in that: In step 5, the objective function is: in, Among them, Q is the state error weight matrix, and R is the control input weight matrix. The constraints are established as follows: Among them, X min is the minimum longitude, X max is the maximum longitude, Y min is the minimum latitude, Y max is the maximum latitude, is the minimum sloshing velocity, is the maximum sloshing velocity, is the minimum sway velocity, is the maximum sway velocity, is the minimum angular velocity in the celestial direction, is the maximum angular velocity in the celestial direction, TF min is the minimum thrust of the propeller, TF min is the maximum thrust of the propeller.

7. A powered buoy controlled by the control method of claim 1, characterized in that: It includes a main buoy, an auxiliary buoy and an autopilot for controlling the dynamic positioning and planned navigation of the buoy. Four thrusters are fixedly arranged at the bottom of the main buoy at intervals of 90° along the central circumference of the buoy. The thrusters are electrically connected to the autopilot. The autopilot includes a buoy for measuring longitude X, latitude Y, heading ψ, and surge speed. and sway speed Dual-antenna GNSS attitude measurement system for measuring the angular velocity of the buoy Surge acceleration and sway acceleration MEMS inertial measurement components, communication modules for data transmission between the buoy and the host computer, and lithium iron phosphate battery packs.

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