A method for evaluating torque, speed and angle parameters of humanoid robot joint motors

Abstract

The present invention discloses a method for evaluating the torque, speed and angle parameters of a humanoid robot joint motor, and relates to the field of electric drive control equipment. First, by simulating the characteristics of seagulls, the method for evaluating the torque, speed and angle parameters of a humanoid robot joint motor is divided into two actions: migration and attack. A seagull is randomly selected from a group of seagulls, and a migration search strategy and an attack strategy are applied. Based on a multi-target seagull algorithm involving torque, speed and angle, the flight endpoint of each seagull is obtained, thereby completing the parameter evaluation of the corresponding joint motor torque, speed and angle. The performance evaluation results of the humanoid robot joint motor torque, speed and angle show that the multi-target seagull algorithm can improve the quality of target tracking, and its tracking also makes it easier to optimize the performance.

Description

Technical Field

[0001] The invention relates to the field of electric drive control equipment, in particular to a method for evaluating torque, speed and angle parameters of a joint motor of a humanoid robot. Background Art

[0002] The joint motor of a humanoid robot is the core component of an exoskeleton robot, and has high requirements for volume, efficiency, power density, temperature rise, etc. As the industrial field presents diversified requirements for the driving methods of the joint motors of humanoid robots, the performance requirements are also getting higher and higher. The joint motor of a robot has the functions of deceleration, transmission, and torque enhancement. Among the various performances of a robot, high mobility is the basis and key, and running speed is a commonly used indicator to test mobility. According to the characteristics of the joint motor of a robot, evaluating the precise parameters of the torque, speed, and angle of the joint motor is crucial to improving the running speed performance of the robot.

[0003] The control aspects of robot joint motors that have emerged mainly include the use of improved exponential convergence law to introduce terminal sliding mode to control the system position-speed loop, and the use of DSP to realize absolute position measurement of industrial robot joint motors and model-free adaptive terminal sliding mode position control methods. Through invention patent retrieval, it was found that the methods generally used for evaluating the torque, speed, and angle parameters of humanoid robot joint motors include, for example, the patent application with application number 202211362654.X and titled "Method and device for speed control of motors in robot joint modules", and the patent application with application number 202311552908.9 and titled "Online detection method and system for torque fluctuation coefficient of industrial robot joint motors". According to the correlation model between electrical signals and torque fluctuation coefficients, the torque fluctuation coefficient trend is predicted based on machine learning, and the prediction result of the torque fluctuation coefficient is obtained.

[0004] The seagull optimization algorithm mainly simulates the migration and attack behaviors of seagulls. Its advantages are simple parameter settings and architecture, global optimization, few parameter settings, and good robustness. A multi-objective algorithm is an algorithm that can optimize multiple objective functions at the same time. In traditional optimization problems, usually only one objective function needs to be optimized. There are many types of multi-objective algorithms, among which the most commonly used are multi-objective genetic algorithms and multi-objective particle swarm algorithms. The effective combination of multi-objective algorithms and seagull algorithms can ensure the rapid convergence of evaluation indicators and the high accuracy of optimization ability. Therefore, this method does not need to accurately calculate the upper bound of the disturbance and the disturbance estimation error in advance. Compared with traditional artificial intelligence methods such as the seagull algorithm and metaheuristic algorithm, it can better meet the requirements of rapid maneuverability of joint motors when humanoid robots run at high speed.

[0005] After searching relevant patents and literature at home and abroad, although there are many methods for evaluating the torque, speed, and angle parameters of humanoid robot joint motors, no multi-objective Seagull algorithm was found for comprehensive performance diagnosis of joint motors. Summary of the invention

[0006] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a method for evaluating the torque, speed and angle parameters of the joint motor of a humanoid robot. The present invention can improve the quality of target tracking, and its tracking also makes it easier to optimize the performance.

[0007] The present invention adopts the following technical solutions to solve the above technical problems:

[0008] According to a method for evaluating the torque, speed and angle parameters of a humanoid robot joint motor proposed in the present invention, firstly, by simulating the characteristics of seagulls, the method for evaluating the torque, speed and angle parameters of a humanoid robot joint motor is divided into two actions: migration and attack. A seagull is randomly selected from a group of seagulls, and a migration search strategy and an attack strategy are applied. Based on a multi-target seagull algorithm involving torque, speed and angle, the flight endpoint of each seagull is obtained, thereby completing the parameter evaluation of the corresponding joint motor torque, speed and angle.

[0009] As a further optimization scheme of the method for evaluating the torque, speed and angle parameters of the joint motor of a humanoid robot described in the present invention, the specific scheme is as follows:

[0010] For the torque, speed, and angle performance parameters of the joint motor, a seagull is randomly selected from the seagull population and the following migration search strategy is applied:

[0011] A=i q ×f c -(t×(f c / Max t )) (1)

[0012] B=Δi q ×A 2 ×μ d ×γ B (t) / ψ i (2)

[0013] C=L×p+R×i d -ω e ×L×i q (3)

[0014] T(t)=[B×(γ B (t)-ψ i ×τ)] (4)

[0015] σ(t)=|ω r×C+T(t)× ω e ×ω t |×A (5)

[0016] Among them, t is the current iteration number, Max t represents the maximum number of iterations, γ B (t) represents the optimal position of the seagull at the tth iteration, T(t) represents the attack direction of the seagull at the tth iteration, μ d is a random number between [0,Π], σ(t) represents the new position of the seagull in the search process at the tth iteration, A, B and C are the torque, speed and angle of the joint motor respectively, A, B and C are used to update the new position of the seagull, i q and Δi q Indicates the feedback current and feedback current increment of the q axis, i d is the current of the d axis, f c is the control variable. When the voltage is saturated, the joint motor voltage is controlled within the minimum voltage range. e is the flight parameter of the seagull, τ is the total energy consumption of the joint motor, ω r is the electrical angular velocity of the motor, ω t is the mechanical angular velocity of the motor, p, R, L, ψ i They are the number of pole pairs, resistance, inductance and flux linkage of the motor respectively;

[0017] The attack strategy of the seagull is as shown in formulas (6)-(9);

[0018] A h =r×sink×Π a (6)

[0019] B h =ε×cosk× Π β (7)

[0020] C h =ε×k (8)

[0021] r = α × e λ ×v (9)

[0022] Among them, A h , B h , C h Represent the torque, speed, and angle distribution matrices of the joint motors, respectively. r is the attack direction, λ is the torque index, ε represents the attack radius of the seagull, α and ν are used to control the flight distance and flight radius, k is a random value in [0,2Π], π a represents the optimal Seagull sequence, π β represents the sequence of non-optimal seagulls, e is the natural base;

[0023] According to formulas (1)-(3) and using the offensive strategy to discretize A, B, and C, the discretization indexes of joint motor torque, speed, and angle are obtained. and

[0024] The two actions of comprehensive migration strategy formula (1)-(5) and attack strategy formula (6)-(9) are used to obtain the flight endpoint of each seagull according to the multi-target seagull algorithm. Each seagull flight endpoint includes and

[0025]

[0026] in, is the torque evaluation of the joint motor at the tth iteration, is the velocity evaluation of the joint motor at the tth iteration, is the angle evaluation of the joint motor at the tth iteration, is the torque evaluation of the joint motor at the t+1th iteration, is the velocity evaluation of the joint motor at the t+1th iteration, is the angle evaluation of the joint motor at the t+1th iteration;

[0027] The movement of the attack position is based on the attack strategy formulas (6)-(9), according to the obtained Equations (10)-(12) are used to update the attack position of the seagull, thereby completing the evaluation of the torque, speed, and angle of the joint motor.

[0028] As a further optimization scheme of the method for evaluating the torque, speed and angle parameters of the joint motor of a humanoid robot described in the present invention, p, R, L, ψ i Regardless of operating conditions.

[0029] As a further optimization scheme for the method for evaluating the torque, speed and angle parameters of the joint motor of a humanoid robot described in the present invention, formulas (1)-(3) are substituted into the following formulas and the offensive strategy is discretized to obtain: and

[0030]

[0031] in, It is the performance index of joint motor.

[0032] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0033] (1) Aiming at the shortcomings of the seagull optimization algorithm, such as poor global search capability and slow convergence speed, a multi-objective strategy is introduced to effectively divide the initial population of seagulls, and multi-objective optimal calculation is performed with the joint motor torque, speed and angle as the objective function. The method of the present invention is used to solve the optimal performance problem of the joint motor target parameters, which has the advantages of high search accuracy, fast convergence speed and strong robustness compared with the existing technology.

[0034] (2) The present invention is used to solve the evaluation of the output characteristics of the humanoid robot under high-speed running conditions on the torque, speed and angle of the joint motor. In the present invention, the seagull characteristics are first simulated and divided into two actions: migration and attack. Then, taking the torque, speed and angle performance parameters of the joint motor as an example, a seagull is randomly selected from the seagull group, and the migration search strategy and the attack strategy are applied. The migration measurement strategy and the attack strategy are integrated, and the flight endpoint of each seagull is obtained according to the multi-target seagull algorithm involving torque, speed and angle, thereby completing the parameter evaluation of the corresponding joint motor torque, speed and angle. The performance evaluation results of the humanoid robot joint motor torque, speed and angle show that the multi-target seagull algorithm can improve the quality of target tracking, and its tracking also makes it easier to achieve the optimal performance. The advantage of the multi-target seagull algorithm over the seagull algorithm is that the multi-target seagull algorithm belongs to local search, and the seagull algorithm focuses on global search. Although the seagull algorithm can obtain an approximate solution to the target parameters, the convergence speed is slow and the tracking effect is not ideal, while the multi-target seagull algorithm can improve the quality of target tracking, and its tracking also makes it easier to achieve the optimal performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the process of the present invention.

[0036] Figure 2 is the output torque at different current densities. 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 is used to solve the evaluation of the output characteristics of humanoid robots under high-speed running conditions on the torque, speed and angle of joint motors. In the present invention, the seagull characteristics are first simulated and divided into two actions: migration and attack. Then, taking the torque, speed and angle performance parameters of the joint motor as an example, a seagull is randomly selected from the seagull group, and the migration search strategy and the attack strategy are applied. The migration measurement strategy and the attack strategy are integrated, and the flight endpoint of each seagull is obtained according to the multi-target seagull algorithm involving torque, speed and angle, thereby completing the parameter evaluation of the corresponding joint motor torque, speed and angle. The performance evaluation results of the humanoid robot joint motor torque, speed and angle show that the multi-target seagull algorithm can improve the quality of target tracking, and its tracking also makes it easier to optimize the performance.

[0039] like Figure 1 As shown, a method for evaluating the torque, speed and angle parameters of joint motors of a humanoid robot using a multi-objective seagull algorithm comprises the following steps:

[0040] First, the invention proposes a multi-target seagull tracking optimization method for evaluating the torque, speed, and angle of the joint motor of a humanoid robot under high-speed running conditions. By simulating the characteristics of seagulls, it is divided into two actions: migration and attack. Taking the torque, speed, and angle performance parameters of the joint motor as an example, a seagull is randomly selected from the seagull group and the following migration search strategy is applied.

[0041] A=i q ×f c -(t×(f c / Max t )) (1)

[0042] B=Δi q ×A 2 ×μ d ×γ B (t) / ψ i (2)

[0043] C=L×p+R×i d -ω e ×L×i q (3)

[0044] T(t)=[B×(γ B (t)-ψ i ×τ)] (4)

[0045] σ(t)=|ω r ×C+T(t)×ω e ×ω t |×A (5)

[0046] Among them, t is the current iteration number, Maxt represents the maximum number of iterations, γ B (t) represents the optimal position of the seagull at the tth iteration, T(t) represents the attack direction of the seagull at the tth iteration, μ d is a random number between [0,Π], σ(t) represents the new position of the seagull in the search process at the tth iteration, A, B and C are the torque, speed and angle of the joint motor respectively, A, B and C are used to update the new position of the seagull, i q and Δi q Indicates the feedback current and feedback current increment of the q axis, i d is the current of the d axis, f c is the control variable. When the voltage is saturated, the joint motor voltage is controlled within the minimum voltage range. e is the flight parameter of the seagull, τ is the total energy consumption of the joint motor, ω r is the electrical angular velocity of the motor, ω t is the mechanical angular velocity of the motor, p, R, L, ψ i are the number of pole pairs, resistance, inductance and flux of the motor, p, R, L, j i It has nothing to do with the operating conditions;

[0047] The attack strategy of the seagull is as shown in formulas (6)-(9);

[0048] A h =r×sink×Π a (6)

[0049] B h =ε×cosk×Π β (7)

[0050] C h =ε×k (8)

[0051] r = α × e λ×v (9)

[0052] Among them, A h , B h , C h Represent the torque, speed, and angle distribution matrices of the joint motors, respectively. r is the attack direction, λ is the torque index, ε represents the attack radius of the seagull, α and v are used to control the flight distance and flight radius, k is a random value in [0,2∏], and π a represents the optimal Seagull sequence, π β Represents a sequence of non-optimal seagulls, e is the natural base.

[0053] According to formulas (1)-(3) and using the offensive strategy to discretize A, B, and C, the discretization indexes of joint motor torque, speed, and angle are obtained. and The details are as follows: Substitute formula (1)-(3) into the following formula and discretize the offensive strategy to obtain and

[0054]

[0055] in, It is the performance index of joint motor.

[0056] The two actions of comprehensive migration strategy formula (1)-(5) and attack strategy formula (6)-(9) are used to obtain the flight endpoint of each seagull according to the multi-target seagull algorithm. Each seagull flight endpoint includes and

[0057]

[0058] in, is the torque evaluation of the joint motor at the tth iteration, is the velocity evaluation of the joint motor at the tth iteration, is the angle evaluation of the joint motor at the tth iteration, is the torque evaluation of the joint motor at the t+1th iteration, is the velocity evaluation of the joint motor at the t+1th iteration, is the angle evaluation of the joint motor at the t+1th iteration;

[0059] The movement of the attack position is based on the attack strategy formulas (6)-(9), according to the obtained Equations (10)-(12) are used to update the attack position of the seagull, thereby completing the evaluation of the torque, speed, and angle of the joint motor.

[0060] The output torque of the joint motor increases approximately linearly with the increase of current, and is almost consistent with the analysis results, which verifies the correctness of the theoretical analysis. The output torque of the joint under different current conditions is shown in Figure 2 shown.

[0061] The advantage of the multi-target Seagull algorithm over the Seagull algorithm is that the multi-target Seagull algorithm belongs to local search, while the Seagull algorithm focuses on global search. Although the Seagull algorithm can obtain an approximate solution to the target parameters, its convergence speed is slow and the tracking effect is not ideal. The multi-target Seagull algorithm can improve the quality of target tracking, and its tracking also makes it easier to optimize the performance.

[0062] Aiming at the shortcomings of the Seagull optimization algorithm, such as poor global search capability and slow convergence speed, a multi-objective strategy is introduced to effectively divide the initial population of Seagulls, and multi-objective optimal calculation is performed with the torque, speed and angle of the joint motor as the objective function. The algorithm is used to solve the optimal performance problem of the joint motor target parameters. Compared with the traditional Seagull algorithm, it has the advantages of high search accuracy, fast convergence speed and strong robustness.

[0063] 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 method for evaluating the torque, speed and angle parameters of a humanoid robot joint motor, characterized in that: Firstly, by simulating the characteristics of seagulls, the parameter evaluation method of the torque, speed and angle of the joint motor of the humanoid robot is divided into two actions: migration and attack. A seagull is randomly selected from the seagull group, and the migration search strategy and the attack strategy are applied. Based on the multi-target seagull algorithm involving torque, speed and angle, the flight endpoint of each seagull is obtained, thereby completing the parameter evaluation of the torque, speed and angle of the corresponding joint motor. The details are as follows: For the torque, speed, and angle performance parameters of the joint motor, a seagull is randomly selected from the seagull population and the following migration search strategy is applied: A=i q ×f c -(t×(f c / Max t )) (1) B=Δi q ×A 2 ×μ d ×γ B (t) / ψ i (2) C=L×p+R×i d -oh e ×L×i q (3) T(t)=[B×(γ B (t)-ψ i ×t)] (4) σ(t)=|ω r ×C+T(t)×ω e ×ω t |×A (5) Among them, t is the current iteration number, Max t represents the maximum number of iterations, γ B (t) represents the optimal position of the seagull at the tth iteration, T(t) represents the attack direction of the seagull at the tth iteration, μ d is a random number between [0,Π], σ(t) represents the new position of the seagull in the search process at the tth iteration, A, B and C are the torque, speed and angle of the joint motor respectively, A, B and C are used to update the new position of the seagull, i q and Δi q Indicates the feedback current and feedback current increment of the q axis, i d is the current of the d axis, f c is the control variable. When the voltage is saturated, the joint motor voltage is controlled within the minimum voltage range. e is the flight parameter of the seagull, τ is the total energy consumption of the joint motor, ω r is the electrical angular velocity of the motor, ω t is the mechanical angular velocity of the motor, p, R, L, ψ i They are the number of pole pairs, resistance, inductance and flux linkage of the motor respectively; The attack strategy of the seagull is as shown in formulas (6)-(9); A h =r×sink×Π a (6) B h =ε×cosk×Π β (7) C h =ε×k (8) r＝α×e λ×v (9) Among them, A h , B h , C h Represent the torque, speed, and angle distribution matrices of the joint motors, respectively. r is the attack direction. λ is the torque index. ε represents the attack radius of the seagull. α and v are used to control the flight distance and radius. k is a random value in [0,2Π]. a represents the optimal Seagull sequence, Π β represents the sequence of non-optimal seagulls, e is the natural base; According to formulas (1)-(3) and using the offensive strategy to discretize A, B, and C, the discretization indexes of joint motor torque, speed, and angle are obtained. and The two actions of comprehensive migration strategy formula (1)-(5) and attack strategy formula (6)-(9) are used to obtain the flight endpoint of each seagull according to the multi-target seagull algorithm. Each seagull flight endpoint includes and in, is the torque evaluation of the joint motor at the tth iteration, is the velocity evaluation of the joint motor at the tth iteration, is the angle evaluation of the joint motor at the tth iteration, is the torque evaluation of the joint motor at the t+1th iteration, is the velocity evaluation of the joint motor at the t+1th iteration, is the angle evaluation of the joint motor at the t+1th iteration; The movement of the attack position is based on the attack strategy formulas (6)-(9), according to the obtained Equations (10)-(12) are used to update the attack position of the seagull, thereby completing the evaluation of the torque, speed, and angle of the joint motor.

2. A method for evaluating torque, speed and angle parameters of a humanoid robot joint motor according to claim 1, characterized in that: p, R, L, ψ i Regardless of operating conditions.

3. A method for evaluating torque, speed and angle parameters of a humanoid robot joint motor according to claim 1, characterized in that: Substituting formula (1)-(3) into the following formula and discretizing the offensive strategy, we can obtain and in, It is the performance index of joint motor.

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