Complex luffing mechanism optimization design method based on multi-target particle swarm optimization

Abstract

The invention relates to a complex luffing mechanism optimization design method based on a multi-target particle swarm algorithm, and the method comprises the steps: firstly, carrying out the deduction and building of a multi-hinge-point luffing mechanism kinematic model based on kinematic analysis in combination with a luffing mechanism structure; secondly, determining an optimization boundary condition according to a design technical requirement, optimizing a performance index according to the design requirement, and establishing a multi-objective optimization objective function on the basis; and optimizing the obtained target function by using a multi-target particle swarm optimization algorithm to finally obtain an optimized luffing mechanism and verify the optimized structure through kinematics simulation. Through an optimization algorithm, a connecting rod structure is optimized in a complex multi-hinge-point luffing mechanism design, the stress of an executing mechanism is reduced, the stability of luffing motion is improved, and the performance of the related luffing mechanism can be remarkably improved. In addition, a minimum maximum value of the ratio of the amplitude variation speed to the motion speed of the executing mechanism in the amplitude variation process is used as an optimization target, the optimization target is easy to deduce, and the optimization design efficiency can be improved.

Description

Technical Field

[0001] The invention relates to the field of structural design, and in particular to an optimization design method for a complex amplitude-changing mechanism based on a multi-objective particle swarm algorithm. Background Art

[0002] The luffing mechanism is widely used in various engineering equipment such as aerial work vehicles, folding arm cranes, and transfer devices to change the boom angle and expand the working range of the equipment. Among them, the multi-hinge luffing mechanism can achieve a larger luffing angle through a smaller actuator motion stroke, which can reduce the actuator mass and significantly improve the flexibility of the equipment. Therefore, it is increasingly widely used. However, due to the complex structure of the multi-hinge luffing mechanism and the difficulty of hinge arrangement, the performance of the luffing mechanism obtained by the conventional design method is difficult to guarantee, which easily leads to a large force on the actuator and even a dead zone problem during the luffing process. In order to solve this problem, it is necessary to propose an optimization design method for a multi-hinge luffing mechanism.

[0003] At present, the optimization design method of the luffing mechanism is usually to derive the force equation of the actuator, and then take the minimum maximum force of the actuator during the luffing process as the optimization goal, and use the optimization algorithm to optimize and solve the position of each hinge. However, in the design of the luffing mechanism, in addition to considering the force problem of the actuator, it is also necessary to consider various issues such as the stability of the luffing process and the stroke of the actuator, so it is often necessary to solve multi-objective optimization problems. Summary of the invention

[0004] Aiming at the existing problems in the optimization design of luffing mechanism, an optimization design method of complex luffing mechanism based on multi-objective particle swarm algorithm is proposed. It is suitable for equipment with luffing mechanism, such as aerial work vehicles, folding arm cranes, and barge devices, and can significantly improve the design efficiency and reliability of luffing mechanism.

[0005] The technical solution of the present invention is: a complex luffing mechanism optimization design method based on a multi-objective particle swarm algorithm. First, based on kinematic analysis and combined with the luffing mechanism structure, a kinematic model of a multi-hinge luffing mechanism is derived and established; then, the optimization boundary conditions are determined according to the design technical requirements, and the performance indicators are optimized according to the design requirements. On this basis, a multi-objective optimization objective function is established; and then the multi-objective particle swarm optimization algorithm is used to optimize the obtained objective function, and finally an optimized luffing mechanism is obtained and the optimized structure is verified by kinematic simulation.

[0006] Furthermore, the multi-objective optimization objective function: objective one is to minimize the hydraulic cylinder stroke, and objective two is to minimize the maximum absolute value of the ratio of the amplitude variation speed and the actuator speed during the amplitude variation process, which is used to optimize the connecting rod parameters in the amplitude variation mechanism structure.

[0007] Further, the multi-objective optimization objective function is established as follows:

[0008]

[0009] Where L is the distance between the two hinge points of the hydraulic cylinder; L 1 ,L 2 ,...L n is the adjustable connecting rod length in the luffing mechanism structure; t is the time parameter in the luffing process; L 0 It is the installation distance of the cylinder when the boom is in the initial position; is the boom luffing angular velocity curve, obtained by differentiating the given luffing angle curve; It is the hydraulic cylinder amplitude change speed curve, which can be obtained by calculating the cylinder displacement curve L(t) through the L calculation formula and then differentiating the curve.

[0010] Furthermore, in the multi-objective particle swarm optimization algorithm, each particle represents a potential connecting rod parameter combination scheme in the variable amplitude mechanism structure, and the initialization population is obtained by randomly generating connecting rod parameter combination schemes in the parameter space allowed by the mechanical structure. The particle position represents the specific connecting rod parameter combination of the particle in the parameter space, and the particle velocity affects the iteration direction of the connecting rod parameter represented by the particle.

[0011] The beneficial effects of the present invention are as follows: the present invention is based on a complex luffing mechanism optimization design method of a multi-objective particle swarm algorithm. Through the optimization algorithm, the connecting rod structure is optimized in the design of a complex multi-hinge luffing mechanism, the force on the actuator is reduced and the stability of the luffing motion is increased, which can significantly improve the performance of the luffing mechanism involved. In addition, a method is proposed that takes the maximum value of the ratio of the luffing speed and the actuator motion speed during the luffing process as the minimum as the optimization target. The derivation of the optimization target is simple and can improve the efficiency of the optimization design. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a flow chart of the optimization design of a complex variable amplitude mechanism based on a multi-objective particle swarm algorithm of the present invention;

[0013] Figure 2 This is a schematic diagram of the six-hinge-point luffing mechanism of the present invention;

[0014] Figure 3 It is a multi-objective particle swarm optimization flow chart of the present invention;

[0015] Figure 4A This is a force curve diagram of the actuator before optimization of the present invention;

[0016] Figure 4B This is a force curve diagram of the actuator after optimization of the present invention. DETAILED DESCRIPTION

[0017] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0018] The present invention is based on a multi-objective particle swarm algorithm to optimize the design of complex variable amplitude mechanisms. Figure 1 The optimization design process shown in the figure first derives and establishes the kinematic model of the multi-hinge luffing mechanism based on kinematic analysis and the luffing mechanism structure; then, the optimization boundary conditions are determined according to the design technical requirements, and the performance indicators are optimized according to the design requirements. On this basis, a multi-objective optimization objective function is established; the multi-objective particle swarm optimization algorithm is then used to optimize the obtained objective function, and finally the optimized luffing mechanism is obtained and the optimized structure is verified by kinematic simulation.

[0019] The complex luffing mechanism optimization design method based on a multi-objective particle swarm algorithm according to an embodiment of the present invention comprises the following steps:

[0020] As attached Figure 2 , a kinematic analysis is carried out taking the six-hinge-point luffing mechanism driven by hydraulic cylinder as an example. Figure 2 In the figure, points B, C, and D are three points on the luffing arm, points A and B are two points on the fixed arm, and point B is the rotation center of the luffing arm. Points A, E, and F are three hinge points on connecting rod 1, and points E and C are two hinge points on connecting rod 2. Points E and F are connected by a hydraulic cylinder. Figure 2 The coincidence point on different booms and connecting rods is the hinge point. With point B as the origin, the horizontal direction as the X axis, and the vertical direction as the Y axis, a plane rectangular coordinate system is established. When the boom is in the initial position, the coordinates of points A, C, D, E, and F are (a 1 ,a 2 )、(c 1 ,c 2 )、(d 1 ,d 2 )、(e 1 ,e 2 )、(f 1 ,f 2 ), AE length is set to L 1 , EC length is set to L 2 , the distance between the two hinges of the hydraulic cylinder is L. When the actuator moves, the luffing arm rotates around point B by an angle of θ. At this time, points C, D, E, and F move to points C', D', E', and F' respectively, and the kinematic model of the luffing mechanism is established:

[0021]

[0022] in,

[0023]

[0024] The optimization objectives include various issues of concern in the design, such as the minimum force on the actuator, the stable speed during the amplitude change process, and the minimum stroke of the actuator during the amplitude change process. These issues are considered according to the specific structure. In this example, the optimization objectives are determined to ensure that the hydraulic cylinder stroke is minimized, and at the same time, the maximum absolute value of the ratio of the amplitude change speed and the actuator speed during the amplitude change process is minimized to reduce the maximum force on the actuator. The two connecting rod parameters that can be used for optimization are L 1 and L 2 .

[0025] According to the proposed optimization objectives, the multi-objective optimization function is established as follows

[0026]

[0027] Where t is the time parameter in the amplitude variation process; L 0 It is the installation distance of the cylinder when the boom is in the initial position; is the boom luffing angular velocity curve, obtained by differentiating the given luffing angle curve; is the hydraulic cylinder amplitude velocity curve, which can be calculated by formula (1) and obtained by differentiating the curve.

[0028] The multi-objective particle swarm optimization algorithm is used to optimize the above multi-objective optimization function. The specific optimization process is shown in the attached Figure 3 In the process of establishing the multi-objective particle swarm optimization algorithm, each particle represents a potential combination of the optimized connecting rod parameters L1 and L2. The initial population is obtained by randomly generating connecting rod parameter combinations in the parameter space allowed by the mechanical structure. The particle position represents the specific connecting rod parameter combination of the particle in the parameter space, and the particle speed affects the iteration direction of the connecting rod parameter represented by the particle. Finally, iterative optimization is performed according to the conventional multi-objective particle swarm optimization algorithm. Finally, the optimized L 1 and L 2 Design parameters.

[0029] The optimized design parameters are brought into the simulation software for comparison. In this example, the force conditions of the actuator before and after optimization are compared. The results are detailed in the attached Figure 4A and 4B , where 4A is the simulation result before optimization, and 4B is the simulation result after optimization. It can be seen that during the amplitude change process, the maximum force of the actuator decreased from 600kN to 161kN, a decrease of 73.2%, indicating that the proposed optimization method can significantly reduce the force of the actuator. At the same time, the maximum extension of the cylinder is reduced from 400mm to 390mm, reducing the requirements for the cylinder stroke.

[0030] The above-mentioned embodiments only express the specific implementation of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the attached claims.

Claims

1. A complex variable amplitude mechanism optimization design method based on multi-objective particle swarm algorithm, characterized in that: Firstly, based on kinematic analysis and combined with the luffing mechanism structure, the kinematic model of the multi-hinge luffing mechanism is derived and established; then, the optimization boundary conditions are determined according to the design technical requirements, and the performance indicators are optimized according to the design requirements. On this basis, a multi-objective optimization objective function is established; the multi-objective particle swarm optimization algorithm is used to optimize the obtained objective function, and finally the optimized luffing mechanism is obtained and the optimized structure is verified by kinematic simulation.

2. The complex luffing mechanism optimization design method based on multi-objective particle swarm algorithm according to claim 1 is characterized in that: The multi-objective optimization objective function has the following objectives: objective one is to minimize the hydraulic cylinder stroke, and objective two is to minimize the maximum absolute value of the ratio of the amplitude variation speed to the actuator speed during the amplitude variation process, which is used to optimize the connecting rod parameters in the amplitude variation mechanism structure.

3. The complex variable amplitude mechanism optimization design method based on multi-objective particle swarm algorithm according to claim 2 is characterized in that: The multi-objective optimization objective function is established as follows: Where L is the distance between the two hinges of the hydraulic cylinder; L1, L2, ... L n is the adjustable connecting rod length in the luffing mechanism structure; t is the time parameter in the luffing process; L0 is the cylinder installation distance when the boom is in the initial position; is the boom luffing angular velocity curve, obtained by differentiating the given luffing angle curve; It is the hydraulic cylinder amplitude change speed curve, which can be obtained by calculating the cylinder displacement curve L(t) through the L calculation formula and then differentiating the curve.

4. The complex variable amplitude mechanism optimization design method based on multi-objective particle swarm algorithm according to claim 2 or 3 is characterized in that: In the multi-objective particle swarm optimization algorithm, each particle represents a potential connecting rod parameter combination scheme in the variable amplitude mechanism structure. The initialization population is obtained by randomly generating connecting rod parameter combination schemes in the parameter space allowed by the mechanical structure. The particle position represents the specific connecting rod parameter combination of the particle in the parameter space, and the particle speed affects the iteration direction of the connecting rod parameter represented by the particle.

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