CN111158238A - Force feedback equipment dynamics parameter estimation algorithm based on particle swarm optimization - Google Patents

Force feedback equipment dynamics parameter estimation algorithm based on particle swarm optimization Download PDF

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CN111158238A
CN111158238A CN202010019658.2A CN202010019658A CN111158238A CN 111158238 A CN111158238 A CN 111158238A CN 202010019658 A CN202010019658 A CN 202010019658A CN 111158238 A CN111158238 A CN 111158238A
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particle
joint
algorithm
force feedback
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CN111158238B (en
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李春泉
何永华
杨峰
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Lattice Power Jiangxi Corp
Nanchang University
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

Abstract

The invention provides a force feedback equipment dynamics parameter estimation algorithm based on a particle swarm algorithm. In particular to a dynamic parameter of a force feedback device better estimated by an improved comprehensive learning particle swarm optimization. The method mainly comprises the following steps: step 1, setting an ideal motion track of a joint angle of force feedback equipment; step 2, carrying out position tracking on the ideal motion track of the joint angle; step 3, sampling the joint angular motion track and the input torque; and 4, estimating the parameters of the force feedback equipment by using an ICLPSO algorithm. The present invention utilizes four strategies: the PSO algorithm comprises a hierarchical updating strategy, a position learning strategy, a guiding particle learning strategy and a global optimal learning strategy, and effectively solves the problems of 'two steps forward and one step backward' and the problem of precocity existing in the traditional PSO algorithm. Compared with the traditional PSO algorithm, the ICLPSO algorithm has the advantages that the convergence rate is higher, the obtained model parameters are more accurate, accurate moment estimation values can be provided for all joints, and the performance of the control algorithm is improved.

Description

Force feedback equipment dynamics parameter estimation algorithm based on particle swarm optimization
Technical Field
The invention relates to estimation of force feedback equipment parameters, in particular to a force feedback equipment dynamics parameter estimation algorithm based on a particle swarm algorithm.
Background
Since force touch has a significant position in human sense, researchers try to introduce force touch characteristics into the research fields of teleoperation robots, virtual reality and the like, and provide force touch feedback information for operators in the process of human-computer interaction. For example, when an accident occurs in a nuclear power station, tasks such as detection and maintenance need to be completed through a teleoperation robot, at the moment, video monitoring faces serious interference, and an operator can effectively improve the accuracy and reliability of operation by means of force touch feedback information. In the virtual operation, a doctor carries out real-time operation simulation training on a virtual human body, force touch feedback information is added, the training efficiency can be greatly improved, the training period is shortened, and a series of cost expenses are reduced. In addition, force haptics are also commonly used in deep sea operations, medical rehabilitation robots, minimally invasive surgery, and games and education to provide the operator with a realistic force and touch. The implementation of force haptics is dependent on the force feedback device, and therefore the performance of the force feedback device is particularly important. Generally speaking, the performance of a force feedback device is related to two factors. On one hand, the control algorithm is related to the selection of hardware such as a structure, a speed reducer, a sensor and the like, and on the other hand, the control algorithm is also a key factor influencing the performance of the control algorithm. The influence of factors such as self inertia, friction force, gravity and the like in the force feedback equipment can be eliminated through a control algorithm, and the transparency of the system and the reality of force feedback can be improved. Therefore, it is very meaningful to research the force feedback device and its related control algorithm, and it is a hot spot in research currently or in some future period.
The basic premise for studying force feedback device control algorithms is to analyze the kinematics and dynamics of the force feedback device. The accurate establishment of the dynamic model plays an important role in maintaining high-speed and high-precision operation of equipment, improving the performance of a controller and the like, and meanwhile, the design cost can be reduced, and the research time can be shortened. A joint dynamic model of the force feedback equipment is established, and dynamic parameters and friction parameters of the equipment need to be obtained, namely parameters such as joint mass, inertia tensor, mass center, coulomb friction force and viscous friction force of each joint are determined. However, in general, the force feedback device parameters are unknown or inaccurate, and the device is affected by factors such as abrasion, deformation and environment during long-term use, so that the parameters are deviated. Therefore, estimation of kinetic parameters is required. If the parameter estimation is more accurate, the accuracy of the dynamic model is higher, so that the estimation value of each joint moment is more accurate, and more accurate force feedback is provided for an operator.
The method for acquiring the force feedback equipment parameters comprises the following steps: the disintegration measurement method refers to a method for obtaining kinetic parameters of equipment by disintegrating the equipment, measuring geometrical parameters and material parameters of the equipment. The method can accurately acquire the mass, inertia tensor and mass center of the joint of the equipment. However, the operation is complex, the performability is low, the result of the parameter value is easily influenced by factors such as the shape and the material of the equipment, and the equipment can be damaged or cannot be restored in the process of disintegration; the CAD modeling method is based on computer graphics technology, and according to the structure diagram of the equipment, the parameters of the equipment are solved by using a corresponding dynamic model. Independent parameter values can be conveniently obtained, the parameter values of the force feedback equipment can be obtained in the design stage of the equipment, and then the dynamic characteristics of the robot are calculated according to the parameter values. However, the method is easily influenced by equipment manufacturing processes such as uneven material of the connecting rod and the like, so that certain errors exist in dynamic parameters; in addition to the above problems, the estimation method, whether it is a disassembly measurement method or a CAD modeling method, does not consider that the model parameters are biased due to the influence of factors such as abrasion, deformation and environment during the long-term use of the device. Honegger et al propose a method for online identification of kinetic parameters using a nonlinear Adaptive control algorithm (see: Adaptive control of the hexaglide, a 6dof parallel controllers [ C ] Proceedings of International Conference on Robotics and analysis. IEEE 1997,1: 543-. As the particle swarm algorithm has the advantages of simplicity, feasibility and rapid convergence, the kinetic parameters are estimated by the Hossein Jahandideth et al by the particle swarm algorithm (see the Use of PSO in Parameter Estimation of Robot Dynamics; Part One: No. New for Parameter Estimation [ C ]. International Conference on System theory. IEEE,2012.) in the method, each dimension of the particle represents the actual Parameter or the combination Parameter of the kinetic model, the Parameter is estimated by taking the moment error as the target function according to the assumed kinetic model, and compared with other Estimation algorithms, the PSO algorithm Estimation Parameter has the characteristic of rapid convergence speed. But the estimated moment error is larger, the parameter estimation effect is poorer, and the local optimization is easier to fall into.
Disclosure of Invention
Based on the background, the invention provides a force feedback device dynamics parameter estimation algorithm based on a particle swarm algorithm. In particular to a method for better estimating dynamic parameters of a force feedback device by using an Improved Comprehensive Learning Particle Swarm Optimization (ICLPSO) algorithm. The invention is realized by the following technical scheme.
The invention relates to a force feedback equipment dynamics parameter estimation algorithm based on a particle swarm algorithm, which comprises the following steps:
the general force feedback device's equations of dynamics are detailed below:
Figure BDA0002360254370000031
Figure BDA0002360254370000032
wherein θ is a joint angle;
Figure BDA0002360254370000033
is the joint angular velocity;
Figure BDA0002360254370000034
is the angular acceleration of the joint; d is an inertia matrix of the force feedback equipment; c is a centrifugal force and Coriolis force matrix; g is a gravity matrix; fcIs a coulomb friction coefficient matrix; fvIs a viscous friction coefficient matrix; tau is joint moment; Δ X is a compensation term introduced to account for modeling uncertainty.
The parameters to be estimated are, in addition to the parameter X, a compensation parameter Δ X, as follows:
X＝[l1,l2,l3,ma,Iaxx,Iayy,Iazz,mbe,Ibexx,Ibeyy,Ibezz,mc,Icxx,Icyy,l5,mdf,mdf
Idfxx,Idfyy,Idfzz,l6,Idf,Fc1,Fc2,Fc3,Fv1,Fv2,Fv3]
ΔX＝[a1,b1,a2,b2,a3,b3]
step 1: setting an ideal motion track of a joint angle of the force feedback equipment;
the force feedback device has N rotatable joints J1、J2…JNCorresponding N joint angles are theta1、θ2…θN. Setting ideal motion tracks of N joint angles as follows: angle theta1(t)、θ2(t)…θN(t), angular velocity
Figure BDA0002360254370000035
Figure BDA0002360254370000036
Angular acceleration
Figure BDA0002360254370000037
Step 2: carrying out position tracking on the ideal motion track of the joint angle;
using a PID control algorithm to control N joints of the force feedback equipment to track the position of the ideal track to obtain the input torque tau of the N joints1、τ2…τNAnd angle theta1(t)、θ2(t)…θN(t) of (d). Angular velocity of each joint is obtained by using nonlinear tracking-differentiator
Figure BDA0002360254370000038
And angular acceleration
Figure BDA0002360254370000039
And step 3: sampling the joint angular motion track and the input torque;
for the motion track: angle theta of articulation1(t)、θ2(t)…θN(t), angular velocity of joint
Figure BDA00023602543700000310
Angular acceleration of joint
Figure BDA00023602543700000311
And the input joint moment tau1、τ2…τNTo carry out
Figure BDA00023602543700000312
And sampling, wherein T is sampling time, and P is the number of sampling points. Obtaining a sampling track: joint angle
Figure BDA00023602543700000313
Angular velocity of joint
Figure BDA0002360254370000041
And angular acceleration of joint
Figure BDA0002360254370000042
And moment of force
Figure BDA0002360254370000043
Figure BDA0002360254370000044
And 4, step 4: the force feedback device parameters were estimated using an improved ensemble learning particle swarm (ICLPSO) algorithm.
(a) The method comprises the following steps Parameters to be estimated
Figure BDA0002360254370000045
Substituting the angular motion track of the joint into a kinetic equation, and calculating to obtain the estimated joint moment, the moment of the ith joint and the moment of the p-th sampling
Figure BDA0002360254370000046
The calculation formula is as follows:
Figure BDA0002360254370000047
in the above formula, the first and second carbon atoms are,
Figure BDA0002360254370000048
the angle of the joint obtained by sampling for the ith joint and the p th time is obtained;
Figure BDA0002360254370000049
the joint angular velocity obtained by sampling for the ith joint and the p th time;
Figure BDA00023602543700000410
for the ith joint, the p-th joint angular acceleration
(b) The method comprises the following steps Defining an error matrix as calculating the measured joint moments tau and the estimated joint moments
Figure BDA00023602543700000411
Error between
Figure BDA00023602543700000412
Figure BDA00023602543700000413
The resulting error matrix E is:
Figure BDA00023602543700000414
defining an objective function:
f＝||E||2
(c) the method comprises the following steps Parameters of the ICLPSO algorithm are set. A particle dimension D; the number N of particles; maximum number of iterations max _ iteration; maximum evaluation times max _ EFS; the maximum number of stalls max _ PST; particle search range of Xmin、Xmax；
① initializationParameters of ICLPSO algorithm, population number N, particle dimension D and search boundary [ X ]minXmax]②, evaluating the positions of the particles to obtain the fitness value of each particle, updating the individual historical optimal position pb of each particle and the optimal position gb (global optimal position) found by the population in the whole search process, and updating the iteration and the EFS;
③, updating the particle speed V and the particle position X by using a layered updating strategy, setting parameters M to be N/2 and K to be N/4, evaluating each particle to obtain a fitness value, updating pb and gb of each particle, and updating iteration and EFS;
if f (pb) of the particlei)＜f(S_pbM) The velocity of the particle is updated using the following formula
Figure BDA0002360254370000051
Otherwise, the velocity of the particle is updated using the following formula
Figure BDA0002360254370000052
Figure BDA0002360254370000053
Wherein pbiIs the historical optimal position of an individual, S _ pb is the position of pb sorted from small to large according to fitness value, g _ pb is the guide particle generated by pb, cn is the center of the first K positions in S _ pb, c1And c2Is an acceleration factor, r1And r2Are random numbers evenly distributed between 0 and 1.
④ the position of the particle X is updated by using a position learning strategy, each particle has the ability to learn other particles, the probability of learning other particles is PLX is 0.25, if the random number r of 0-13Less than PLX, then in this dimension, the particle learns another particle;
⑤ update the guide particles using a guide particle learning strategy, when the particle's stagnation number PST reaches a set maximum stagnation number max _ PST, randomly select a particle's pb to replace the individual guide particles of the stagnant particle.
⑥ use global optimal learning strategy optimization gb. to randomly select a dimension r at each iteration4Let gb directly learn a random particle in pb
Figure BDA0002360254370000054
⑦ judging whether the algorithm meets the end condition, if it meets the end condition, finding the parameter that minimizes the target function f, the parameter at this time
Figure BDA0002360254370000055
The algorithm is ended for the estimated optimal model parameters; if not, continuously jumping to the step 2 to continuously operate.
Compared with the prior art, the invention has the beneficial effects that:
the present invention utilizes four strategies: the PSO algorithm comprises a hierarchical updating strategy, a position learning strategy, a guiding particle learning strategy and a global optimal learning strategy, and effectively solves the problems of 'two steps forward and one step backward' and the problem of precocity existing in the traditional PSO algorithm. Compared with the traditional PSO algorithm, the algorithm has the advantages that the convergence speed is higher, the obtained model parameters are more accurate, accurate moment estimation values can be provided for all joints, and the performance of the control algorithm is improved.
Drawings
FIG. 1 is a flow chart of an improved Total learning particle swarm optimization (ICLPSO) algorithm.
Detailed Description
The invention will be further illustrated by the following examples.
Step 1: setting an ideal motion track of a joint angle of the force feedback equipment;
the force feedback device has 3 rotatable joints J1、J2、J3The corresponding 3 joint angles are theta1、θ2、θ3. The ideal motion trajectories for setting 3 joint angles are respectively: angle theta1(t)、θ2(t)、θ3(t), angular velocity
Figure BDA0002360254370000061
Figure BDA0002360254370000062
Angular acceleration
Figure BDA0002360254370000063
The method comprises the following specific steps:
angle: theta1＝0.5sint θ2＝0.5sint θ3＝0.2sint
Angular velocity:
Figure BDA0002360254370000064
angular acceleration:
Figure BDA0002360254370000065
step 2: carrying out position tracking on the ideal motion track of the joint angle;
using a PID control algorithm to control 3 joints of the force feedback equipment to track the position of the ideal track to obtain the input torque tau of the 3 joints1、τ2、τ3And angle theta1(t)、θ2(t)、θ3(t) of (d). Angular velocity of each joint is obtained by using nonlinear tracking-differentiator
Figure BDA0002360254370000066
And angular acceleration
Figure BDA0002360254370000067
And step 3: sampling the joint angular motion track and the input torque;
for the motion track: angle theta of articulation1(t)、θ2(t)、θ3(t), angular velocity of joint
Figure BDA0002360254370000068
Angular acceleration of joint
Figure BDA0002360254370000069
And the input joint moment tau1、τ2、τ3To carry out
Figure BDA00023602543700000610
And sampling, wherein T is 30s, and P is 200. Obtaining a sampling track: joint angle
Figure BDA00023602543700000611
Angular velocity of joint
Figure BDA00023602543700000612
Figure BDA00023602543700000613
And angular acceleration of joint
Figure BDA00023602543700000614
And moment of force
Figure BDA00023602543700000615
And 4, step 4: the force feedback device parameters were estimated using an improved ensemble learning particle swarm (ICLPSO) algorithm.
(a) The method comprises the following steps Parameters to be estimated
Figure BDA00023602543700000616
Substituting the angular motion track of the joint into a kinetic equation, and calculating to obtain the estimated joint moment, the moment of the ith joint and the moment of the p-th sampling
Figure BDA00023602543700000617
The calculation formula is as follows:
Figure BDA00023602543700000618
in the above formula, the first and second carbon atoms are,
Figure BDA00023602543700000619
the angle of the joint obtained by sampling for the ith joint and the p th time is obtained;
Figure BDA00023602543700000620
the joint angular velocity obtained by sampling for the ith joint and the p th time;
Figure BDA00023602543700000621
for the ith joint, the p-th joint angular acceleration
(b) The method comprises the following steps Defining an error matrix as calculating the measured joint moments tau and the estimated joint moments
Figure BDA0002360254370000071
Error between
Figure BDA0002360254370000072
Figure BDA0002360254370000073
The resulting error matrix E is:
Figure BDA0002360254370000074
defining an objective function:
f＝||E||2
(c) the method comprises the following steps Parameters of the ICLPSO algorithm are set. Particle dimension D-34; the number N of particles is 34; the maximum number of iterations max _ iteration is 10000; the maximum evaluation number max _ EFS is 340000; the maximum number of times of stagnation max _ PST is 7; particle search range of Xmin＝0.5X、Xmax① initializing parameters of ICLPSO algorithm, population number N, particle dimension D, search boundary [ X [ [ X ]minXmax]Iteration number iteration, evaluation number EFS, stagnation number PST of each particle, random initialization of particle position X and velocity V, ② evaluation of particle position, and obtainingUpdating the individual historical optimal position pb of each particle and the optimal position gb (global optimal position) found by the population in the whole search process, and updating iteration and EFS;
③, updating the particle speed V by using a layered learning mode, setting parameters M to be N/2 and K to be N/4, updating the particle position X, evaluating each particle to obtain a fitness value, updating pb and gb of each particle, and updating iteration and EFS;
if f (pb) of the particlei)＜f(S_pbM) The velocity of the particle is updated using the following formula
Figure BDA0002360254370000075
Otherwise, the velocity of the particle is updated using the following formula
Figure BDA0002360254370000076
Figure BDA0002360254370000077
Wherein pbiIs the historical optimal position of an individual, S _ pb is the position of pb sorted from small to large according to fitness value, g _ pb is the guide particle generated by pb, cn is the center of the first K positions in S _ pb, c1And c2Is an acceleration factor, r1And r2Are random numbers evenly distributed between 0 and 1.
④ the position of the particle X is updated by using a position learning strategy, each particle has the ability to learn other particles, the probability of learning other particles is PLX is 0.25, if the random number r of 0-13Less than PLX, then in this dimension, the particle learns another particle;
⑤ update the guide particles using a guide particle learning strategy, when the particle's stagnation number PST reaches a set maximum stagnation number max _ PST, randomly select a particle's pb to replace the individual guide particles of the stagnant particle.
⑥ use global optimal learning strategy optimization gb. to randomly select a dimension r at each iteration4Let gb directly learn a random particle in pb
Figure BDA0002360254370000081
⑦ judging whether the algorithm meets the end condition, if it meets the end condition, finding the parameter that minimizes the target function f, the parameter at this time
Figure BDA0002360254370000082
The algorithm is ended for the estimated optimal model parameters; if not, continuously jumping to the step 2 to continuously operate.
The foregoing merely represents preferred embodiments of the invention, which are described in some detail and detail, and therefore should not be construed as limiting the scope of the invention. It should be noted that, for those skilled in the art, various changes, modifications and substitutions can be made without departing from the spirit of the present invention, and these are all within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (1)

1. A force feedback device dynamics parameter estimation algorithm based on a particle swarm algorithm is characterized by comprising the following steps:
the general force feedback device's equations of dynamics are detailed below:
Figure FDA0002360254360000011
Figure FDA0002360254360000012
wherein θ is a joint angle;
Figure FDA0002360254360000013
is the joint angular velocity;
Figure FDA0002360254360000014
is the angular acceleration of the joint; d is an inertia matrix of the force feedback equipment; c is a centrifugal force and Coriolis force matrix; g is a gravity matrix; fcIs a coulomb friction coefficient matrix; fvIs a viscous friction coefficient matrix; tau is joint moment; Δ X is a compensation term introduced in view of modeling uncertainty;
the parameters to be estimated are, in addition to the parameter X, a compensation parameter Δ X, as follows:
X＝[l1，l2，l3，ma，Iaxx，Iayy，Iazz，mbe，Ibexx，Ibeyy，Ibezz，mc，Icxx，Icyy，l5，mdf，mdfIdfxx，Idfyy，Idfzz，l6，Idf，Fc1，Fc2，Fc3，Fv1，Fv2，Fv3]
ΔX＝[a1，b1，a2，b2，a3，b3]
step 1: setting an ideal motion track of a joint angle of the force feedback equipment;
the force feedback device has N rotatable joints J1、J2…JNCorresponding N joint angles are theta1、θ2…θN(ii) a Setting ideal motion tracks of N joint angles as follows: angle theta1(t)、θ2(t)…θN(t), angular velocity
Figure FDA0002360254360000015
Figure FDA0002360254360000016
Angular acceleration
Figure FDA0002360254360000017
Step 2: carrying out position tracking on the ideal motion track of the joint angle;
using a PID control algorithm to control N joints of the force feedback equipment to track the position of the ideal track to obtain the input torque tau of the N joints1、τ2…τNAnd angle theta1(t)、θ2(t)…θN(t) obtaining angular velocity of each joint by using a nonlinear tracking-differentiator
Figure FDA0002360254360000018
And angular acceleration
Figure FDA0002360254360000019
And step 3: sampling the joint angular motion track and the input torque;
for the motion track: angle theta of articulation1(t)、θ2(t)…θN(t), angular velocity of joint
Figure FDA00023602543600000110
Angular acceleration of joint
Figure FDA00023602543600000111
And the input joint moment tau1、τ2…τNTo carry out
Figure FDA00023602543600000112
Sampling, wherein T is sampling time, and P is the number of sampling points; obtaining a sampling track: joint angle
Figure FDA0002360254360000021
Angular velocity of joint
Figure FDA0002360254360000022
And angular acceleration of joint
Figure FDA0002360254360000023
And moment of force
Figure FDA0002360254360000024
Figure FDA0002360254360000025
And 4, step 4: estimating force feedback device parameters with an improved ensemble learning particle swarm (ICLPSO) algorithm;
(a) the method comprises the following steps Parameters to be estimated
Figure FDA0002360254360000026
Substituting the angular motion track of the joint into a kinetic equation, and calculating to obtain the estimated joint moment, the moment of the ith joint and the moment of the p-th sampling
Figure FDA0002360254360000027
The calculation formula is as follows:
Figure FDA0002360254360000028
in the above formula, the first and second carbon atoms are,
Figure FDA0002360254360000029
the angle of the joint obtained by sampling for the ith joint and the p th time is obtained;
Figure FDA00023602543600000210
the joint angular velocity obtained by sampling for the ith joint and the p th time;
Figure FDA00023602543600000211
the joint angular acceleration obtained by sampling for the ith joint and the p th time;
(b) the method comprises the following steps Defining an error matrix as calculating the measured joint moments tau and the estimated joint moments
Figure FDA00023602543600000215
Error between
Figure FDA00023602543600000212
Figure FDA00023602543600000213
The resulting error matrix E is:
Figure FDA00023602543600000214
defining an objective function:
f＝||E||2
(c) the method comprises the following steps Setting parameters of an ICLPSO algorithm: a particle dimension D; the number N of particles; maximum number of iterations max _ iteration; maximum evaluation times max _ EFS; the maximum number of stalls max _ PST; particle search range of Xmin、Xmax；
① initializing parameters of ICLPSO algorithm, population number N, particle dimension D, search boundary [ X [ ]minXmax]Iteration times iteration, evaluation times EFS, stagnation times PST of each particle, and random initialization of particle position X and velocity V;
②, evaluating the positions of the particles to obtain the fitness value of each particle, updating the individual historical optimal position pb of each particle and the optimal position gb found by the population in the whole search process, and updating iteration and EFS;
③, updating the particle speed V and the particle position X by using a layered updating strategy, setting the parameter M to be N/2 and the parameter K to be N/4, evaluating each particle to obtain a fitness value, updating pb and gb of each particle, and updating iteration and EFS;
if f (pb) of the particlei)＜f(S_pbM) The velocity of the particle is updated using the following formula
Figure FDA0002360254360000031
Otherwise, the velocity of the particle is updated using the following formula
Figure FDA0002360254360000032
Figure FDA0002360254360000033
Wherein pbiIs the historical optimal position of an individual, S _ pb is the position of pb sorted from small to large according to fitness value, g _ pb is the guide particle generated by pb, cn is the center of the first K positions in S _ pb, c1And c2Is an acceleration factor, r1And r2Is a random number uniformly distributed between 0 and 1;
④ updating the position X of the particle by using a position learning strategy, wherein each particle has the ability to learn other particles, the probability of learning other particles is PLX is 0.25, if the random number r of 0-13Less than PLX, then in this dimension, the particle learns another particle;
⑤, updating the guide particles by using a guide particle learning strategy, randomly selecting pb of one particle to replace the individual guide particles of the stagnation particle when the stagnation times PST of the particles reach the set maximum stagnation times max _ PST, and in addition, in order to prevent the particles from gathering together too early and losing the diversity of the population, when the pb of the particles is updated, g _ pb of the guide particles is equal to pb;
⑥ use a global optimum learning strategy to optimize gb, randomly selecting a dimension r at each iteration4Let gb directly learn a random particle in pb
Figure FDA0002360254360000034
⑦ judging whether the algorithm meets the end condition, if it meets the end condition, finding the parameter that minimizes the target function f, the parameter at this time
Figure FDA0002360254360000035
The algorithm is ended for the estimated optimal model parameters; if not, continuously jumping to the step 2 to continuously operate.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5155423A (en) * 1986-02-18 1992-10-13 Robotics Research Corporation Industrial robot with servo
CN104570736A (en) * 2014-02-28 2015-04-29 中国科学院力学研究所 Kinetic parameter on-orbit identification method and device of satellite-arm coupling system
CN107498562A (en) * 2017-04-21 2017-12-22 浙江工业大学 Sixdegree-of-freedom simulation kinetic model discrimination method
CN107685343A (en) * 2017-08-28 2018-02-13 北京邮电大学 A kind of Mechanical transmission test parameter calibration configuration optimization method
CN110471274A (en) * 2019-08-12 2019-11-19 余姚市浙江大学机器人研究中心 Based on the machine components process line dispatching method for improving unified particle swarm algorithm

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5155423A (en) * 1986-02-18 1992-10-13 Robotics Research Corporation Industrial robot with servo
CN104570736A (en) * 2014-02-28 2015-04-29 中国科学院力学研究所 Kinetic parameter on-orbit identification method and device of satellite-arm coupling system
CN107498562A (en) * 2017-04-21 2017-12-22 浙江工业大学 Sixdegree-of-freedom simulation kinetic model discrimination method
CN107685343A (en) * 2017-08-28 2018-02-13 北京邮电大学 A kind of Mechanical transmission test parameter calibration configuration optimization method
CN110471274A (en) * 2019-08-12 2019-11-19 余姚市浙江大学机器人研究中心 Based on the machine components process line dispatching method for improving unified particle swarm algorithm

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
JING LIU，等: "Comprehensive Learning Particle Swarm Optimisation with Limited Local Search for UAV Path Planning", 《2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI)》 *
YULIAN GAO，等: "Comprehensive Learning Particle Swarm Optimization Algorithm With Local Search for Multimodal Functions", 《IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION》 *
代逍遥，等: "力反馈设备运动学分析及轨迹规划", 《机床与液压》 *
周晓东，等: "基于粒子群算法的阻抗控制在机械臂柔顺控制中的应用", 《空间控制技术与应用》 *
赵娜，等: "多目标全面学习粒子群算法在分布式电源选址定容中的应用与改进", 《陕西电力》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112506054A (en) * 2020-11-27 2021-03-16 沈阳工业大学 Rehabilitation robot random finite time stable control based on SCN observation active thrust

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