Multi-objective optimization design method for underwater vehicle attitude adjusting mechanism

Abstract

The application discloses a kind of multi-objective optimization design methods of underwater vehicle attitude adjusting mechanism, comprising the following steps: (1) for the underwater vehicle using attitude adjusting mechanism, the attitude adjusting balance equation of underwater vehicle is established when reaching stable state under water;(2) determine multiple key design parameters of attitude adjusting mechanism;(3) based on design parameters, the adjusting performance objective function of attitude adjusting mechanism is constructed;(4) based on design parameters, the space occupation objective function of attitude adjusting mechanism is constructed;(5) the adjusting performance objective function and space occupation objective function are integrated, and genetic algorithm is used for multi-objective optimization, and the Pareto optimal solution set of two functions is balanced;(6) the design parameters corresponding to the Pareto optimal solution set are used to design and manufacture the attitude adjusting mechanism.The design parameters obtained by the application are used to optimize the attitude adjusting mechanism, which is significantly improved in terms of maximum pitch angle and space waste.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of underwater vehicles, and particularly relates to a multi-objective optimization design method of an attitude adjusting mechanism of an underwater vehicle. BACKGROUND

[0002] An underwater vehicle is an important equipment and carrier for marine monitoring, and it is of great significance to develop marine monitoring technology and marine monitoring equipment. For an underwater vehicle, it is an extremely necessary ability to stabilize and control its own attitude. Whether the underwater vehicle relies on hydrodynamic characteristics for navigation and control or uses a component capable of generating thrust for driving, adjusting its own attitude will have the most direct impact on its control results. If the attitude of the underwater vehicle cannot be accurately adjusted, the calculation of position data will be seriously deviated, and the orientation will be lost in the sea.

[0003] The Chinese patent document with the publication number CN115071930A discloses an attitude control and self-stabilizing mechanism suitable for an AUV unmanned underwater vehicle, which comprises two sets of middle flange rings, the bottom of each of which is fixedly sleeved with a ballast water tank, the two ballast water tanks are symmetrically arranged, the top of the ballast water tank is sleeved with an end flange ring, the two end flange rings and the two sets of middle flange rings are fixedly connected through copper columns, and an upper flange ring, a middle flange ring and a lower flange ring are arranged between the two sets of middle flange rings. Eccentric positions of the end flange ring, the middle flange ring, the upper flange ring, the middle flange ring and the lower flange ring are each provided with a through hole, and a same rack one is installed through the multiple through holes. By adjusting the positions of the battery pack one and the battery pack two, the attitude of the unmanned underwater vehicle is stably controlled, and the attitude of the unmanned underwater vehicle can be quickly adjusted, which is convenient for the diving and floating operation of the unmanned underwater vehicle.

[0004] The Chinese patent document with the publication number CN116960551A discloses a modular battery pack and an attitude adjusting unit of an underwater vehicle, which comprises a battery unit, a battery carrier, a guide sleeve, a containing part and a battery fixing part: the battery carrier constitutes a support structure for carrying the battery unit; the guide sleeve is installed on the battery carrier and has a guide sliding hole penetrating through both ends of the battery carrier; the containing part is a receiving space provided on the battery carrier for installing the battery unit, an end of the battery carrier is provided with an opening communicating with the containing part, and the battery unit can enter or move out of the containing part from the end of the battery carrier; and the battery fixing part connects the battery unit and the battery carrier, and is used for fixing the axial and radial positions of the battery unit in the containing part. The modular battery pack has the advantages of convenient replacement, good interchangeability, high maintenance efficiency and stable performance.

[0005] The Chinese patent document with the publication number CN113002741A discloses an underwater vehicle attitude adjusting device, which comprises a pitch adjusting mechanism and a roll adjusting mechanism, and further comprises a front support plate and a rear support plate. The pitch adjusting mechanism comprises a battery module and a pitch motor. The battery module is arranged between the front support plate and the rear support plate. The pitch motor is fixed on the battery module. A push rod of the pitch motor is fixed with the rear support plate. The battery module moves between the front support plate and the rear support plate along with the extension and retraction of the push rod. The pitch motor is fixed on the battery module. The position of the battery module is adjusted through the extension and retraction of the push rod of the pitch motor, so as to change the pitch attitude angle of the underwater vehicle. The battery module not only supplies power for the whole device, but also serves as a counterweight of the pitch adjusting mechanism. The overall structure is compact and simple.

[0006] The underwater vehicle is very sensitive to the volume and weight of the internal mechanism. The prior art provides a design example of the attitude adjusting mechanism, but does not describe the selection criteria or general rules of the design parameters. Meanwhile, the adjusting performance of the attitude mechanism is strongly related to the design parameters. Therefore, the design method considering the space occupation and the adjusting performance is of great significance. SUMMARY

[0007] The application provides a multi-objective optimization design method of an underwater vehicle attitude adjusting mechanism. After the obtained parameters are used to optimize the attitude adjusting mechanism, the system is significantly improved in terms of the maximum pitch angle and the space waste.

[0008] A multi-objective optimization design method of an underwater vehicle attitude adjusting mechanism comprises the following steps:

[0009] (1) For an underwater vehicle adopting the attitude adjusting mechanism, an attitude adjusting balance equation of the underwater vehicle when reaching a stable state under water is established;

[0010] (2) A plurality of key design parameters of the attitude adjusting mechanism are determined;

[0011] (3) Based on the design parameters, an adjusting performance objective function of the attitude adjusting mechanism is constructed;

[0012] (4) Based on the design parameters, a space occupation objective function of the attitude adjusting mechanism is constructed;

[0013] (5) The adjusting performance objective function and the space occupation objective function are comprehensively optimized by using a genetic algorithm to achieve a Pareto optimal solution set balancing the two functions;

[0014] (6) The design parameters corresponding to the Pareto optimal solution set are used to design and manufacture the attitude adjusting mechanism.

[0015] Further, in step (1), the attitude adjusting balance equation of the underwater vehicle when reaching a stable state under water is represented as:

[0016]

[0017] In the formula, the static mass of the underwater vehicle when it reaches a stable attitude underwater is defined as M s , including all masses rigidly connected to the body shell; dynamic mass is defined as M d , that is, all masses connected to the static mass of the body through non-rigid kinematic pairs; θ is the adjustment angle of the pitch attitude; the static mass M defined s , dynamic mass M d The coordinate positions of the center of mass in the motion reference system are:

[0018]

[0019] Where p s is the static mass M s The center of mass, p d is the dynamic mass M d The center of mass.

[0020] In step (2), multiple key design parameters include μ1, μ2, μ3, μ4, and μ5, which correspond to the mass proportion c of the posture adjustment mechanism. w , inner diameter of counterweight r inn , outer diameter of counterweight r ext , counterweight angle α w And axial motion space l max .

[0021] Preferably, the mass proportion of the posture adjustment mechanism is c w Controlled within the range of 5% to 20%, the angle of the counterweight is α w Control within the range of 30° to 120°.

[0022] In step (3), the adjustment performance objective function of the attitude adjustment mechanism is constructed, specifically:

[0023]

[0024] Among them, δ represents the regulation performance objective function, θ max represents the maximum adjustment angle of pitch attitude; ρ represents the average density of attitude adjustment mechanism; r eq Indicates the equivalent radius of the attitude adjustment mechanism.

[0025] In step (4), the space occupancy objective function of the posture adjustment mechanism is constructed, specifically:

[0026]

[0027] Where v represents the space occupancy objective function, l selfrepresents the length of the attitude adjustment mechanism itself, and p represents the average density of the attitude adjustment mechanism.

[0028] In step (5), the optimization constraint of the attitude adjustment mechanism is:

[0029]

[0030] In the formula, N is a scale factor of a design variable; p represents the average density of the attitude adjustment mechanism; r min represents the minimum value of the inner diameter of the counterweight, and r ,ax represents the maximum value of the outer diameter of the counterweight.

[0031] In step (5), a genetic algorithm is used for multi-objective optimization to achieve a Pareto optimal solution set balancing the two functions, and the specific process is as follows:

[0032] According to the optimization constraint formula of the attitude adjustment mechanism, the numerical solution range of the multi-objective parameters is estimated; the Pareto optimal solution set is obtained through the algorithm; and all results in the solution set are compared to select an optimal scheme that meets the actual situation of the project.

[0033] In the present application, the genetic algorithm uses an NSGA2 genetic algorithm, and the upper limit of iteration epoch max = 200, the population number n pop = 50, and the mutation rate c mutant = 5%.

[0034] Compared with the prior art, the present application has the following beneficial effects:

[0035] The present application establishes the functional relationship between the key design parameters of the attitude adjustment mechanism and the adjustment performance and the space occupation, determines the quantitative influence of the design parameters on the actual effect, comprehensively considers the coupling of the target functions, and obtains the Pareto solution set of the design parameters through the multi-objective optimization of the genetic algorithm, thereby achieving significant optimization effect. According to experiments, the average maximum pitch angle of the attitude adjustment mechanism in the reachable space is increased by 5.19%, and the average space waste is reduced by 15.72%. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 Fig. 1 is a flow chart of a multi-objective optimization design method of an underwater vehicle attitude adjustment mechanism according to an embodiment of the present application;

[0037] Figure 2 Fig. 2 is a multi-objective function optimization flow chart based on an NSGA2 genetic algorithm according to an embodiment of the present application;

[0038] Figure 3 Fig. 3 is a Pareto solution set of a deep-sea attitude adjustment mechanism according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] The present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be noted that the following examples are intended to facilitate understanding of the present invention and do not have any limiting effect on the present invention.

[0040] like Figure 1 As shown, a multi-objective optimization design method for an underwater vehicle attitude adjustment mechanism includes the following steps:

[0041] S10: Establishing the balance equation for posture adjustment

[0042] For underwater vehicles, their buoyancy center is often located on the central axis, that is, the motion reference system x b Therefore, when the underwater vehicle reaches a stable attitude underwater, its fixed mass (or static mass, defined as M s , including all masses rigidly connected to the body shell) should produce a moment at the center of mass that is equal to the moving mass (or dynamic mass, defined as M d , that is, the moments generated by all masses connected to the static mass of the body through non-rigid kinematic pairs relative to the center of buoyancy should be balanced. Here, the coordinate positions of the center of mass of the static mass and the dynamic mass in the motion reference system are defined as:

[0043]

[0044] Therefore, when the underwater vehicle using the attitude control system reaches a stable state underwater, its equilibrium equation can be expressed as:

[0045]

[0046] S20: Key design parameters of attitude adjustment mechanism

[0047] Existing methods have made certain estimates of the influence of the weight ratio and mobile position of the attitude control system on the aircraft attitude, but these studies have only optimized the design of some motion parameters or single variables separately, and cannot coordinate the optimization design of multiple parameters of the system shape design. When the optimal design values ​​are selected for multiple parameters, it is very likely to cause problems such as excessive system control redundancy, waste of internal space, and the design falling into local optimality. Therefore, it is necessary to carry out multi-objective optimization design before designing the shape of the attitude control mechanism. The parameters for multi-objective optimization determined by the present invention are shown in Table 1 below:

[0048] Table 1 Key design parameters of attitude adjustment mechanism

[0049]

[0050] Among them, c wThe mass ratio of the attitude adjustment mechanism is controlled in the range of 5% to 20%; the size constraint of the inner and outer diameters of the counterweight block is r min max Depending on the cavity size in the actual design; the included angle of the counterweight block is α w Controlled in the range of 30° to 120°; the axial movement space is l max On the premise of ensuring that the minimum size of the attitude adjustment mechanism determined by the mass is l self There is no upper limit, but it should be as small as possible.

[0051] S30: Attitude adjustment performance objective function

[0052] For the attitude adjustment target of the vehicle system, the adjustment range should be as large as possible (since the value is negative, it is as small as possible in value), so we have:

[0053]

[0054] Where l self is the length of the attitude adjustment mechanism itself, and α dmax is the maximum rotation angle of the counterweight:

[0055]

[0056] Therefore:

[0057]

[0058] S40: Space occupation optimization objective function

[0059] Considering the buoyancy balance design of the vehicle, the internal space is very limited. Therefore, the occupied space of the attitude adjustment mechanism should be optimized, and the objective function is represented as:

[0060]

[0061] S50: Multi-objective optimization based on genetic algorithm

[0062] By combining the adjustment performance and space occupation objective functions, the optimization constraints of the attitude adjustment mechanism are obtained as:

[0063]

[0064] ​Since the possibility of the optimal objective functions δ(μ1, μ2, μ3, μ4, μ5) and v(μ1, μ2, μ3, μ4, μ5) reaching the optimal solution at the same time is very small, the purpose of optimization is to reach the Pareto improvement optimal solution set of balancing the two functions. The design of this optimization can be divided into the following three steps: (1) estimating the numerical solution range of the multi-objective parameters according to the formula; (2) obtaining the Pareto optimal solution set by the algorithm; and (3) comparing all the results in the solution set and selecting the best scheme that meets the actual situation of the project.

[0065] The optimization involves a complex nonlinear objective function, and it is difficult to obtain a high-order numerical solution for fitting. Therefore, the NSGA2 genetic algorithm is used to optimize the multi-objective function. The NSGA2 genetic algorithm has good optimization effect on the nonlinear function involved in this study, and is better than the traditional linear programming method and weighted method in terms of anti-optimization failure effect. In this embodiment, the upper limit of iteration is set to epoch max = 200, the population size n pop = 50, and the mutation rate c mutant = 5%. The optimization process for the attitude adjustment structure is as shown in the specific optimization program flowchart and non-dominated sorting principle as Figure 2

[0066] S60: Obtain the target parameter optimization solution set

[0067] As can be seen from Figure 3 , the Pareto optimal solution set obtained by the NSGA2 genetic algorithm iteration optimization has a significant advantage over the single-objective linear optimization:

[0068] First, after the algorithm iteration optimization, the Pareto solution space range is larger than the conventional solution space: there are a large amount of invalid data and unattainable space in the single-objective linear optimization, while the genetic algorithm itself can filter out invalid data and bypass unattainable space to achieve a larger solution space range; the effective rate of the 50 population data filtered by the genetic algorithm reaches 100%, the solution space is increased by 43.64% in the calculation of the maximum pitch angle range, and the solution space is increased by 54.54% in the calculation of the space waste range. Second, the average quality of the solution space is better than that of the single-objective linear optimization: the average maximum pitch angle (in the reachable space) is improved by 5.19% compared with the single-objective linear optimization, and the average space waste is reduced by 15.72% compared with the single-objective linear optimization (in the reachable space).

[0069] After the attitude adjustment mechanism is optimized by the sampling method, the system is significantly improved in terms of the maximum pitch angle and space waste. The Pareto solution set parameters obtained by the optimization design are shown in Table 2.

[0070] Table 2 Pareto solution set table of the attitude adjustment mechanism ​

[0071]

[0072]

[0073] The above embodiments have described the technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the present application. Any modification, supplement and equivalent replacement made within the principle range of the present application shall be included in the protection scope of the present application.

Claims

1. A multi-objective optimization design method for an attitude adjustment mechanism of an underwater vehicle, characterized by, The method comprises the following steps: (1) for underwater vehicles with attitude adjustment mechanism, establishing the attitude adjustment balance equation of the underwater vehicle when reaching a stable state underwater; (2) determining a plurality of key design parameters of the attitude adjusting mechanism; the plurality of key design parameters include μ1, μ2, μ3, μ4, μ5, respectively corresponding to the mass proportion c of the attitude adjusting mechanism w , the inner diameter r of the counterweight inn , the outer diameter r of the counterweight ext , the included angle α of the counterweight w , and the axial motion space l max ; (3) based on the design parameters, constructing the adjustment performance objective function of the attitude adjustment mechanism; specifically: wherein δ represents the adjustment performance objective function, θ max represents the maximum adjustment angle of the pitch attitude; p represents the average density of the attitude adjustment mechanism; r eq represents the equivalent radius of the attitude adjustment mechanism; M s represents the static mass of the underwater vehicle when reaching a stable attitude underwater; p sx represents the x-coordinate position of the center of mass of the static mass M s in the motion reference frame; p sz represents the z-coordinate position of the center of mass of the static mass M s in the motion reference frame. (4) based on the design parameters, constructing the space occupation objective function of the attitude adjustment mechanism; specifically: where v represents a space occupation target function, l self represents the length of the posture adjustment mechanism itself, and p represents the average density of the posture adjustment mechanism. (5) comprehensively adjusting the adjustment performance objective function and the space occupation objective function, using the genetic algorithm for multi-objective optimization to achieve the Pareto optimal solution set balancing the two functions; (6) using the design parameters corresponding to the Pareto optimal solution set to design and manufacture the attitude adjustment mechanism.

2. The multi-objective optimization design method of the attitude adjustment mechanism of an underwater vehicle according to claim 1, characterized in that, In step (1), the attitude adjustment balance equation of the underwater vehicle when reaching a stable state underwater is expressed as: where M s is the static mass of the underwater vehicle when it reaches a steady attitude underwater, including all the masses rigidly connected to the body shell; M d is the moving mass, i.e. all the masses connected to the static mass of the body through non-rigid joints; θ is the adjustment angle of the pitch attitude; the coordinates of the center of mass of the static mass M s and the moving mass M d in the moving reference frame are respectively where p s is the center of mass of the static mass M s and p d is the center of mass of the moving mass M d .

3. The multi-objective optimization design method of the attitude adjustment mechanism of an underwater vehicle according to claim 1, characterized in that, Attitude adjustment mechanism mass ratio c w Control in the interval of 5%~20%, the angle α of the counterweight w Control in the interval of 30°~120°.

4. The multi-objective optimization design method of the attitude adjustment mechanism of an underwater vehicle according to claim 1, characterized in that, In step (5), when comprehensively adjusting the adjustment performance objective function and the space occupation objective function, the optimization constraint of the attitude adjustment mechanism is: In the formula, N is the scale factor of the design variable; p represents the average density of the attitude adjustment mechanism; r min rminrepresents the minimum value of the inner diameter of the counterweight max rmaxrepresents the maximum value of the outer diameter of the counterweight 5. The multi-objective optimization design method of the underwater vehicle attitude adjustment mechanism according to claim 4, characterized in that, In step (5), the genetic algorithm is used for multi-objective optimization to achieve the Pareto optimal solution set balancing the two functions, and the specific process is: Estimating the numerical solution range of the multi-objective parameters according to the optimization constraint formula of the attitude adjustment mechanism; obtaining the Pareto optimal solution set through the algorithm; comparing all the results in the solution set to select the best scheme that meets the actual situation of the project.

6. The multi-objective optimization design method of the attitude adjustment mechanism of an underwater vehicle according to claim 5, characterized in that, The genetic algorithm adopts NSGA2 genetic algorithm, sets the upper limit of iteration epoch max = 200, population number n pop = 50, mutation rate c mutant = 5%.

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