A design method for a robot magnetic reduction gear joint motor

Abstract

The present invention discloses a design method for a robot magnetic reduction gear joint motor, which relates to the field of electric transmission control equipment. First, two binary feature vectors of seagull attack control parameter range and seagull migration behavior control parameter range are used to represent the robot magnetic reduction gear joint motor torque density and power density index; then, a number of seagulls are randomly generated as the initial population to obtain initial position information, and the golden sine coefficient is used to avoid position conflicts between multiple seagulls; finally, according to the relative direction, relative distance and flight speed of the seagulls, the robot magnetic reduction gear joint motor torque density and power density index corresponding to the two binary feature vectors of the attack control parameter range and the seagull migration behavior control parameter range are determined. The present invention has good convergence and optimization capabilities, and the optimized robot magnetic reduction gear joint motor has higher high torque density and high power density.

Description

Technical Field

[0001] The invention relates to the field of electric drive control equipment, in particular to a design method for a robot magnetic reduction gear joint motor. Background Art

[0002] In the research of robot joint motor transmission, output torque and power density are important indicators for performance evaluation, so the analysis and optimization of magnetic torque is particularly important. Compared with mechanical reduction gears, magnetic reduction gears have the important characteristics of non-contact, friction-free energy consumption, and smooth transmission. They are the core components for realizing the electromechanical energy conversion of robot joint motors. They are in an important position for energy conversion and play a role in joint motor shock absorption. They play a role in matching speed and transmitting torque between the prime mover and the working machine or actuator, which is of great significance to ensure the reliable operation of the robot.

[0003] After literature search, the applications of motor-related structural design combined with the Seagull algorithm include: "Structural Optimization Design Based on an Improved Seagull Algorithm" and "Research on the Control of Traction Device of Crawler Pultrusion Machine Based on Improved Seagull Optimization Algorithm". The objective function and constraint function are used to describe the nonlinear relationship between structural design parameters, and the optimal solution is obtained through the optimization technology of the Seagull optimization algorithm. After patent search, there is an invention patent for "Multi-parameter Identification Method of Permanent Magnet Synchronous Motor" with application number CN202111317818.2. The Seagull Optimization Algorithm is a new type of meta-heuristic intelligent optimization algorithm. The idea of ​​this algorithm is to simulate the migration and attack behavior of seagull populations in nature, and find the optimal solution through continuous updating and iteration between populations. The binary Seagull algorithm is more advantageous in using binary feature vectors to represent feature selection problems based on the traditional Seagull algorithm.

[0004] After searching relevant patents and literature at home and abroad, although there are a variety of artificial intelligence methods used to improve the performance of robot joint motors, no design method for magnetic reduction gear joint motors has been found, especially no Seagull method has been applied to magnetic reduction gear joint motors to evaluate the output torque and power density indicators. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a design method for a robot magnetic reduction gear joint motor. The present invention has good convergence and optimization capabilities, and the optimized robot magnetic reduction gear joint motor has higher high torque density and high power density.

[0006] The present invention adopts the following technical solutions to solve the above technical problems:

[0007] According to a design method of a robot magnetic reduction gear joint motor proposed by the present invention,

[0008] Firstly, two binary feature vectors of the seagull attack control parameter range and the seagull migration behavior control parameter range are used to represent the torque density and power density indicators of the robot's magnetic reduction gear joint motor;

[0009] Then, a number of seagulls are randomly generated as the initial group to obtain the initial position information, and the golden sine coefficient is used to avoid position conflicts between multiple seagulls;

[0010] Finally, according to the relative direction, relative distance and flight speed of the seagulls, the torque density and power density indicators of the robot's magnetic reduction gear joint motor corresponding to the two binary eigenvectors of the attack control parameter range and the seagull migration behavior control parameter range were determined.

[0011] As a further optimization scheme of the design method of a robot magnetic reduction gear joint motor described in the present invention, the specific scheme is as follows:

[0012] Step 1. Initialization:

[0013] Assume the total number of iterations of the total number of seagulls is t max , the robot magnetic reduction gear joint motor torque density, power density indicators are respectively used in the seagull attack control parameter range [X min ,X max ]、Seagull migration behavior control parameter range [Y min ,Y max ] are two binary eigenvectors X t , Y t Indicates; X min , X max Represent the minimum and maximum values ​​of the Seagull attack control parameter range, Y min , Y max represent the minimum and maximum values ​​of the control parameters of seagull migration behavior;

[0014] Step 2, generate an initial seagull group: randomly generate a number of seagulls as the initial group, and each seagull obtains initial position information through formula (1);

[0015] Q s (x)=τ×|cos(r1)|+r2×cos(r1)×|c1×X t -c2×Y t | (1)

[0016] Among them, τ is the golden section number, Q s (x) is the initial position information, c1 and c2 are the first and second golden sine coefficients, and r1 and r2 are interval random values;

[0017] Step 3: Avoid position conflicts between multiple seagulls by modifying the golden sine coefficients in equation (2); c1 = fτ, c2 = f(1-τ) (2)

[0018] Among them, f represents the fitness value;

[0019] Step 4: Determine the relative direction between the current i-th seagull and the optimal seagull using formula (3)

[0020] Among them, Y t represents the control parameter of the exploitation capability at the tth iteration, represents the direction of the i-th seagull in the t-th iteration, represents the current migration direction of the i-th elite seagull, and β is the total number of elite seagulls;

[0021] The relative distance of the current seagull is determined by formula (4):

[0022]

[0023] in, represents the relative distance between the i-th seagull and the current optimal seagull at the t-th iteration, represents the new position of the i-th seagull in the t-th iteration without position conflict with other seagulls;

[0024] Step 5: Get is the flying speed of the i-th seagull;

[0025]

[0026] Among them, d i is the minimum Euclidean distance between the non-dominated solution and the actual flight speed of the i-th seagull;

[0027] Step 6: Each seagull calculates the probability of updating its flight speed using the transfer function formula (6);

[0028]

[0029] in, represents the flying speed of the i-th seagull in the t-th iteration, Yes all i The average value of is the probability of updating the flight speed, and e is the natural base;

[0030] Step 7: Correct X t , Y t, thereby completing the torque density and power density indicators of the robot magnetic reduction gear joint motor corresponding to the two binary feature vectors of the seagull attack control parameter range and the seagull migration behavior control parameter range;

[0031]

[0032] Where t is the number of iterations, exp(·) is an exponential expression used to evaluate the composition of the attack control parameters and the migration behavior control parameters.

[0033] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0034] (1) Aiming at the performance index requirements of the robot for high torque density and high power density of the magnetic reduction gear joint motor, compared with the traditional Seagull method, the binary Seagull design method is obtained by analyzing the iterative optimization of the algorithm, which ensures the optimization efficiency of the two indicators of the magnetic reduction gear joint motor torque density and power density; secondly, the proposed algorithm's migration behavior and offensive behavior improve the algorithm's optimization ability, and can dynamically balance the algorithm's local search and global development capabilities;

[0035] (2) Compared with the traditional seagull method, the binary seagull design method performs better than other algorithms in terms of flight speed, flight direction, and flight position planning. The proposed algorithm can quickly and accurately avoid obstacles, thereby achieving rapid convergence of torque density and power density targets. It can perform dynamic adjustment planning for the attack control parameter range and migration behavior control parameters, and has good superiority, feasibility, and stability. It is suitable for solving the performance indicator problems of torque density and power density of magnetic reduction gear joint motors. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] The present invention aims at the defects of the existing robot magnetic reduction gear joint motor, such as small reduction ratio range and low torque density. Based on the performance index requirements of the electric drive joint for the motor, a binary seagull design method with high torque density and high power density is proposed. In the present invention, two binary feature vectors of the seagull attack control parameter range and the seagull migration behavior control parameter range are first used to represent the robot magnetic reduction gear joint motor torque density and power density index. Then, only seagulls are randomly generated as the initial population to obtain the initial position information, and the golden sine coefficient is used to avoid position conflicts between multiple seagulls. Finally, the robot magnetic reduction gear joint motor torque density and power density index corresponding to the two binary feature vectors of the attack control parameter range and the seagull migration behavior control parameter range are determined according to the relative direction, relative distance and flight speed of the seagull. A design method for a robot magnetic reduction gear joint motor shows that the binary seagull design method has good convergence and optimization ability, and the optimized robot magnetic reduction gear joint motor has higher high torque density and high power density.

[0039] like Figure 1 As shown, the present invention proposes a design method for a robot magnetic reduction gear joint motor.

[0040] Firstly, two binary feature vectors of the seagull attack control parameter range and the seagull migration behavior control parameter range are used to represent the torque density and power density indicators of the robot's magnetic reduction gear joint motor;

[0041] Then, a number of seagulls are randomly generated as the initial group to obtain the initial position information, and the golden sine coefficient is used to avoid position conflicts between multiple seagulls;

[0042] Finally, according to the relative direction, relative distance and flight speed of the seagulls, the torque density and power density indicators of the robot's magnetic reduction gear joint motor corresponding to the two binary eigenvectors of the attack control parameter range and the seagull migration behavior control parameter range were determined.

[0043] The specific steps are as follows:

[0044] Step 1. Initialization:

[0045] Assume the total number of iterations of the total number of seagulls is t max , the robot magnetic reduction gear joint motor torque density, power density indicators are respectively used in the seagull attack control parameter range [X min ,X max ]、Seagull migration behavior control parameter range [Y min ,Y max ] are two binary eigenvectors X t , Y t Indicates; X min , Xmax Represent the minimum and maximum values ​​of the Seagull attack control parameter range, Y min , Y max represent the minimum and maximum values ​​of the control parameters of seagull migration behavior;

[0046] Step 2, generate an initial seagull group: randomly generate a number of seagulls as the initial group, and each seagull obtains initial position information through formula (1);

[0047] Q s (x)=τ×|cos(r1)|+r2×cos(r1)×|c1×X t -c2×Y t | (1)

[0048] Among them, τ is the golden section number, Q s (x) is the initial position information, c1 and c2 are the first and second golden sine coefficients, and r1 and r2 are interval random values;

[0049] Step 3: Avoid position conflicts between multiple seagulls by modifying the golden sine coefficients in equation (2); c1 = fτ, c2 = f(1-τ) (2)

[0050] Among them, f represents the fitness value;

[0051] Step 4: Determine the relative direction between the current i-th seagull and the optimal seagull using formula (3)

[0052] Among them, Y t represents the control parameter of the exploitation capability at the tth iteration, represents the direction of the i-th seagull in the t-th iteration, represents the current migration direction of the i-th elite seagull, and β is the total number of elite seagulls;

[0053] The relative distance of the current seagull is determined by formula (4):

[0054]

[0055] in, represents the relative distance between the i-th seagull and the current optimal seagull at the t-th iteration, represents the new position of the i-th seagull in the t-th iteration without position conflict with other seagulls;

[0056] Step 5: Get is the flying speed of the i-th seagull;

[0057]

[0058] Among them, d i is the minimum Euclidean distance between the non-dominated solution and the actual flight speed of the i-th seagull;

[0059] Step 6: Each seagull calculates the probability of updating its flight speed using the transfer function formula (6);

[0060]

[0061] in, represents the flying speed of the i-th seagull in the t-th iteration, Yes all i The average value of is the probability of updating the flight speed, and e is the natural base;

[0062] Step 7: Correct X t , Y t , thereby completing the torque density and power density indicators of the robot magnetic reduction gear joint motor corresponding to the two binary feature vectors of the seagull attack control parameter range and the seagull migration behavior control parameter range;

[0063]

[0064] Where t is the number of iterations, exp(·) is an exponential expression used to evaluate the composition of the attack control parameters and the migration behavior control parameters.

[0065] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A design method for a robot magnetic reduction gear joint motor, characterized in that: Firstly, two binary feature vectors of the seagull attack control parameter range and the seagull migration behavior control parameter range are used to represent the torque density and power density indicators of the robot's magnetic reduction gear joint motor; Then, a number of seagulls are randomly generated as the initial group to obtain the initial position information, and the golden sine coefficient is used to avoid position conflicts between multiple seagulls; Finally, according to the relative direction, relative distance and flight speed of the seagulls, the torque density and power density indicators of the robot magnetic reduction gear joint motor corresponding to the two binary feature vectors of the attack control parameter range and the seagull migration behavior control parameter range are determined; The design method of the robot magnetic reduction gear joint motor is as follows: Step 1. Initialization: Assume the total number of iterations of the total number of seagulls is t max , the robot magnetic reduction gear joint motor torque density, power density indicators are respectively used in the seagull attack control parameter range [X min ,X max ]、Seagull migration behavior control parameter range [Y min ,Y max ] are two binary eigenvectors X t , Y t Indicates; X min , X max Represent the minimum and maximum values ​​of the Seagull attack control parameter range, Y min , Y max represent the minimum and maximum values ​​of the control parameters of seagull migration behavior; Step 2, generate an initial seagull group: randomly generate a number of seagulls as the initial group, and each seagull obtains initial position information through formula (1); Q s (x)=τ×|cos(r1)|+r2×cos(r1)×|c1×X t -c2×Y t | (1) Among them, τ is the golden section number, Q s (x) is the initial position information, c1 and c2 are the first and second golden sine coefficients, and r1 and r2 are interval random values; Step 3: Avoid position conflicts between multiple seagulls by modifying the golden sine coefficient in equation (2); c1=fτ,c2=f(1-τ) (2) Among them, f represents the fitness value; Step 4: Determine the relative direction between the current i-th seagull and the optimal seagull using formula (3) Among them, Y t represents the control parameter of the exploitation capability at the tth iteration, represents the direction of the i-th seagull in the t-th iteration, represents the current migration direction of the i-th elite seagull, and β is the total number of elite seagulls; The relative distance of the current seagull is determined by formula (4): in, represents the relative distance between the i-th seagull and the current optimal seagull at the t-th iteration, represents the new position of the i-th seagull in the t-th iteration without position conflict with other seagulls; Step 5: Get is the flying speed of the i-th seagull; Among them, d i is the minimum Euclidean distance between the non-dominated solution and the actual flight speed of the i-th seagull; Step 6: Each seagull calculates the probability of updating its flight speed using the transfer function formula (6); in, represents the flying speed of the i-th seagull in the t-th iteration, Yes all i The average value of is the probability of updating the flight speed, and e is the natural base; Step 7: Correct X t , Y t , thereby completing the torque density and power density indicators of the robot magnetic reduction gear joint motor corresponding to the two binary feature vectors of the seagull attack control parameter range and the seagull migration behavior control parameter range; Where t is the number of iterations, exp(·) is an exponential expression used to evaluate the composition of the attack control parameters and the migration behavior control parameters.

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