Parallel six-degree-of-freedom platform pose control method based on PSO-GA method

Abstract

The application discloses a parallel six-freedom-degree platform position control method based on a PSO-GA method, and comprises the following steps: S1, simplifying a six-freedom-degree platform model, facilitating mathematical modeling; S2, constructing an objective function according to six-freedom-degree platform structure parameters; S3, solving the objective function by using the PSO-GA method; S4, obtaining platform position according to the solution of the objective function and the geometric relation of the six-freedom-degree platform; and S5, designing a position servo controller, and taking the difference between the position obtained by the direct solution and the given position as input to the position controller, so as to control the length of each rod of the six-freedom-degree platform. The application proposes a kinematics algorithm capable of quickly solving platform position and realizing stable control, so as to realize quick and stable control of the parallel platform.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of parallel six-degree-of-freedom platform control, in particular to a parallel six-degree-of-freedom platform pose control method based on a PSO-GA method. BACKGROUND

[0002] Parallel six-degree-of-freedom platforms are a new type of manufacturing mechanism that has been developing rapidly worldwide in recent years, which includes one or more parallel mechanisms. Compared with serial mechanisms, parallel mechanisms have the advantages of good rigidity, high precision, high speed and acceleration, and have a wider application prospect in the fields of shipbuilding, aerospace, automobiles, medical treatment, etc.

[0003] Compared with serial robots, parallel mechanisms are very suitable for some work scenes that require high load, high dynamic response and high control precision due to their large stiffness, stable and compact structure, small cumulative error, high precision and good dynamic performance. Research on the kinematics and dynamics of parallel six-degree-of-freedom platforms helps to establish an accurate mathematical model, which is crucial for optimizing the design of the platform and improving the motion precision. A deep understanding of the kinematics and dynamics of the platform can help develop more efficient motion control algorithms and improve the control precision and stability of the platform.

[0004] Therefore, there is an urgent need to provide a new parallel six-degree-of-freedom platform pose control method based on a PSO-GA method to solve the above problems. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a parallel six-degree-of-freedom platform pose control method based on a PSO-GA method, which can quickly solve the platform position and realize rapid and stable control of the parallel platform.

[0006] To solve the above technical problems, one technical solution adopted by the present application is to provide a parallel six-degree-of-freedom platform pose control method based on a PSO-GA method, comprising the following steps:

[0007] S1: Simplify the six-degree-of-freedom platform model for mathematical modeling;

[0008] S2: Construct an objective function according to the structural parameters of the six-degree-of-freedom platform;

[0009] S3: Solve the objective function using the PSO-GA method;

[0010] S4: Obtain the platform pose according to the solution of the objective function and the geometric relationship of the six-degree-of-freedom platform;

[0011] S5: Design a position servo controller, and subtract the pose obtained by the forward solution from the given pose as input to the position controller to control the length of each rod of the six-degree-of-freedom platform.

[0012] In a preferred embodiment of the present application, the specific steps of step S1 include:

[0013] The six-degree-of-freedom platform model includes a moving platform, a fixed platform, six electric cylinders, and a motor, the motor is installed on one side of the electric cylinder, one end of the electric cylinder is movably connected to the moving platform through a spherical hinge, and the other end of the electric cylinder is movably connected to the fixed platform through a hooke hinge;

[0014] The rotation center of the hinge connected to the moving platform is regarded as being on the moving platform, the rotation center of the hinge connected to the fixed platform is regarded as being on the fixed platform, the electric cylinder is regarded as a slender rod with a mass point at the center, and the three long sides of the hexagon upper platform are extended to intersect with each other, so that the hexagon is simplified into an equilateral triangle.

[0015] In a preferred embodiment of the present application, in step S2, the six-degree-of-freedom platform structure parameters include the radius R a of the upper platform, the radius R b of the lower platform, the distance H of the planes on which the hinge center points of the upper and lower platforms are located, and the circular arc angles a0 and b0 corresponding to the short sides of the upper and lower platforms, the coordinates of the three vertices of the simplified triangle represent the coordinates of the six vertices of the original moving platform, the pose equation is constructed according to the relationship between the vertex coordinates of the moving platform and the vertex coordinates of the fixed platform and the rod length, and three geometric constraint equations are added to make the objective function have a unique solution.

[0016] In a preferred embodiment of the present application, the specific steps of step S3 include:

[0017] S301: initialize the PSO and GA parameters, initialize the particle position and velocity, initialize the individual best position and the global best position;

[0018] S302: dynamically adjust the inertia weight, enter the PSO iteration part and update the velocity, update the position, calculate the current value, update the individual best position, and finally update the global best position;

[0019] S303: judge whether the value of the objective function f enters the local optimum, if yes, re-initialize part of the particles by using the GA algorithm and eliminate the original particles; if not, judge whether the termination condition is met, if yes, enter step S305, and if not, return to step S301;

[0020] S304: genetic algorithm part, sequentially perform selection operation, crossover operation, and mutation operation to generate new particles to replace the particles in the local optimum part;

[0021] S305: output the global optimal solution and draw the image of the change of the objective function value with the iteration number.

[0022] Further, in step S302, the particle velocity updating method is:

[0023]

[0024] wherein a is a balance factor between 0 and 1; w(t) is an inertia weight at time t, which is adaptively changed; f(p i (t)) is the value of the objective function f corresponding to the optimal position p i (t) found by particle i in its history; f(g(t)) is the value of the objective function f corresponding to the global optimal position g(t) found by all particles; v i (t) is the velocity of particle i at time t; q i (t) is the position of particle i at time t, representing a set of possible solutions of the equation set by particle i;

[0025] q i (t) = [x 1i (t) x 2i (t) x 3i (t) y 1i (t) y 2i (t) y 3i (t) z 1i (t) z 2i (t) z 3i (t)

[0026] pBest i is the individual optimal position; gBest is the global optimal position; c1 and c2 are learning factors; r1, r2 and r3 are random numbers between 0 and 1; c3 is an acceleration constant of local search; and l(t) is the local optimal position.

[0027] The position updating formula is as follows:

[0028] q i (t+1) = q i (t) + v i (t+1)

[0029] Further, in step S303, the local optimum is considered to be reached when the objective function value changes less than a certain threshold value in each iteration, but the objective function value has not reached the ideal value.

[0030] In a preferred embodiment of the present application, the specific steps of step S4 include:

[0031] The platform center coordinate is the average of the three vertex coordinates of the triangle obtained after the model simplification, i.e.:

[0032]

[0033] The rotation angles of the z-axis, the y-axis and the x-axis of the platform coordinate system established by the platform around the platform center are γ, β and α i The line connecting the center point P'0 and the line connecting the center point P0 at the zero position of the platform form an angle, that is: i The line connecting the center point P'0 and the line connecting the center point P0 at the zero position of the platform form an angle, that is:

[0034]

[0035] In a preferred embodiment of the present application, in step S5, the position servo controller adopts PID control, and the parameters adjusted by PID are input to the inverse kinematics algorithm, and the lengths of the six rods obtained by inverse solution are used as control variables to control the lengths of the rods.

[0036] The present application has the advantages that: the present application provides a kinematics algorithm capable of quickly solving the position of a platform and realizing stable control, so as to realize fast and stable control of a parallel platform; and the specific effects are:

[0037] (1) The PSO algorithm introduces an adaptive inertia weight and a local search mechanism, and combines the advantages of global search and local search;

[0038] (2) The GA algorithm is used to enhance the globality of search;

[0039] (3) The designed controller does not need to collect data as feedback by using sensors, but uses a forward solution algorithm instead, so that the accuracy is improved and the cost is reduced;

[0040] (4) The structural parameters used are possessed by general platforms, so that the algorithm has universality. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 is a flowchart of the parallel six-degree-of-freedom platform pose control method based on the PSO-GA method of the present application;

[0042] Figure 2 is a structural schematic diagram of the six-degree-of-freedom platform experimental device;

[0043] Figure 3 is a parameter schematic diagram of the six-degree-of-freedom platform model;

[0044] Figure 4 is a parameter schematic diagram of the simplified six-degree-of-freedom platform model;

[0045] Figure 5 is a specific step of step S3;

[0046] Figure 6 is an operation schematic diagram of the genetic algorithm;

[0047] Figure 7 is a schematic diagram of the target function value and the number of iterations;

[0048] Figure 8 is a moving platform projection diagram;

[0049] Figure 9 is a workspace control scheme diagram;

[0050] Figure 10 is a schematic diagram of the position servo controller.

[0051] The labels of the components in the drawings are as follows: 1, moving platform, 2, static platform, 3, electric cylinder, 4, motor, 5, spherical hinge, 6, Hooke's joint. DETAILED DESCRIPTION

[0052] The preferred embodiments of the present application are described in detail below with reference to the accompanying drawings, so that the advantages and features of the present application can be more easily understood by those skilled in the art, and the scope of protection of the present application is more clearly defined.

[0053] Referring to Figure 1 , the embodiments of the present application include:

[0054] A parallel six-degree-of-freedom platform pose control method based on a PSO-GA method includes the following steps:

[0055] S1: Simplify the six-degree-of-freedom platform model for ease of mathematical modeling;

[0056] In this example, a parallel six-degree-of-freedom platform is used for experiments, and the electric cylinder displacement and the moving platform pose are collected, and a parallel six-degree-of-freedom platform is built as shown in the accompanying Figure 2 The parallel six-degree-of-freedom platform includes a motor 4, a driver, six electric cylinders 3, telescopic struts, a moving platform 1, and a fixed platform 2. The motor 4 is installed on one side of the electric cylinder 3, and the electric cylinder 3 is installed inside the telescopic strut. The upper platform is the moving platform 1, and the lower platform is the fixed platform 2, both of which are hexagonal. The upper and lower platforms are connected by six telescopic strut branches. Each branch is connected to the moving platform 1 at one end through a spherical hinge 5 and connected to the fixed platform 2 at the other end through a Hooke's joint 6, with a moving cylindrical pair in between. Each branch is equipped with a servo motor to drive the upper end of the branch link to move relatively and change the length of the branch link, thereby realizing the three-dimensional translation and three-dimensional rotation of the moving platform 1 relative to the fixed platform 2.

[0057] In combination with Figure 3 and Figure 4The kinematics of the platform is analyzed, and a platform coordinate system is established at the center of the moving platform and the static platform, and the center points of the hinge points are regarded as being on the platform plane. Specifically, the rotating center of the hinge connected with the moving platform is regarded as being on the moving platform, the rotating center of the hinge connected with the static platform is regarded as being on the static platform, and the electric cylinder is regarded as a slender rod with a mass point at the center. The vertices of the hexagon of the static platform are B1-B6, the vertices of the hexagon of the moving platform are P1-P6, the three long edges of the upper platform of the hexagon are extended to intersect with each other, and the intersection points are Q, so that the hexagon is simplified into an equilateral triangle, and the length of the side of the triangle is l. Generally, five parameters are needed to describe the structure of the Stewart platform, such as Figure 3 the radius R a of the upper platform, b the radius R a of the lower platform, b the distance H between the planes where the hinge center points of the upper and lower platforms are located, and the included angle a0 of the short edge of the upper platform and the included angle b0 of the short edge of the lower platform. The five parameters determine the shape of the parallel platform and the coefficients in the subsequent objective function.

[0058] S2: constructing an objective function according to the structure parameters of the six-degree-of-freedom platform;

[0059] The structure parameters of the platform include the radius R a of the upper platform, b the radius R a of the lower platform, b the distance H between the planes where the hinge center points of the upper and lower platforms are located, and the included angle a0 of the short edge of the upper platform and the included angle b0 of the short edge of the lower platform. The three vertices Q i (i=1, 2, 3) of the simplified triangle are in the static coordinate system O B -X B Y B Z B , and the coordinates of the three points are Q i =(x i y i z i ) T (i=1, 2, 3). According to the geometric relationship, the coordinates of the vertices of the original hexagon in the static coordinate system O B -X B Y B Z B are P i =(u i v i w i ) T (i=1, 2, …6), that is:

[0060]

[0061] And the center points B i =(X i Y i Z i) T (i = 1, 2,... 6) in the stationary coordinate system O B -X B Y B Z B The coordinates in the stationary coordinate system can be determined according to the structure parameters of the stationary platform. In this way, the relationship between the rod length L i (i = 1, 2,... 6) and Q i and B i can be expressed, that is, as shown in the following formula, and the rod length L i (i = 1, 2,... 6) is a known quantity in the positive solution operation, which is obtained by a sensor in the servo cylinder. The positive solution problem is converted into a problem of solving a nine-element quadratic equation group, in which x1, x2, x3, y1, y2, y3, z1, z2, z3 are variables.

[0062] eq1 = [(1 - k)x3 + kx1 - X1] 2 + [(1 - k)y3 + ky1 - Y1] 2 + [(1 - k)z3 + kz1 - Z1] 2 - L1 2

[0063] eq2 = [(1 - k)x2 + kx1 - X2] 2 + [(1 - k)y2 + ky1 - Y2] 2 + [(1 - k)z2 + kz1 - Z2] 2 - L2 2

[0064] eq3 = [(1 - k)x1 + kx2 - X3] 2 + [(1 - k)y1 + ky2 - Y3] 2 + [(1 - k)z1 + kz2 - Z3] 2 - L3 2

[0065] eq4 = [(1 - k)x3 + kx2 - X4] 2 + [(1 - k)y3 + ky2 - Y4] 2 + [(1 - k)z3 + kz2 - Z4] 2 - L4 2

[0066] eq5 = [(1 - k)x2 + kx3 - X5] 2 + [(1 - k)y2 + ky3 - Y5] 2 + [(1 - k)z2 + kz3 - Z5] 2 - L5 2

[0067] eq6 = [(1 - k)x1 + kx3 - X6] 2 + [(1 - k)y1 + ky3 - Y6] 2 + [(1 - k)z1 + kz3 - Z6] 2 - L6 2

[0068] where L i (i = 1, 2,... 6) is the length of the electric cylinder rod, hi is the long side length of the hexagon, h2 is the short side length of the hexagon, The matrix B represents the center of each hinge point of the lower platform B i = (X i Y i Z i ) T (i = 1, 2,... 6) is:

[0069]

[0070] where

[0071] Since there are nine unknowns and only six equations, three geometric constraints are needed to supplement them:

[0072] eq7 = (x1 - x2) 2 + (y1 - y2) 2 + (z1 - z2) 2 - l 2

[0073] eq8 = (x2 - x3) 2 + (y2 - y3) 2 + (z2 - z3) 2 - l 2

[0074] eq9 = (x3 - x1) 2 + (y3 - y1) 2 + (z3 - z1) 2 - l 2

[0075] where l is the length of the simplified triangle, i.e. l = hi + 2h2.

[0076] The objective function is constructed as: f = eq1 2 + eq2 2 + eq3 2 + eq4 2 + eq5 2 + eq6 2 + eq7 2 + eq82 +eq9 2 .

[0077] S3: solving the objective function by PSO-GA method, wherein the PSO algorithm is a particle swarm algorithm, and the GA algorithm is a genetic algorithm, refer to Figure 5 , and the specific steps include:

[0078] S301: initializing PSO and GA parameters, initializing particle position and velocity, initializing individual best position and global best position;

[0079] S302: dynamically adjusting the inertia weight, entering the PSO iteration part and updating the velocity, updating the position, calculating the current value, updating the individual best position, and finally updating the global best position;

[0080] The PSO and GA parameters are initialized, the number of particles is set to 200, the particle dimension is nine, corresponding to the nine variables in the objective function: x1, x2, x3, y1, y2, y3, z1, z2, z3, the iteration number is 1000 times, the fitness value of each particle and each individual is calculated, and the particle velocity is updated according to the current velocity of the particle, the individual best position (pBest i ) and the global best position (gBest) and the objective function value f(p i (t)) and f(g(t)) of the corresponding position.

[0081] The velocity update formula is as follows:

[0082]

[0083] Wherein: α is a balance factor, between 0 and 1.

[0084] w(t) is the inertia weight at time t, which can be self-adapted.

[0085] f(p i (t)) is the value of the objective function f corresponding to the optimal position p i (t) found by particle i in its history.

[0086] f(g(t)) is the value of the objective function f corresponding to the global optimal position g(t) found by all particles at present.

[0087] v i (t) is the velocity of particle i at time t.

[0088] q i (t) is the position of particle i at time t, which represents a set of possible solutions of particle i to the equation group:

[0089] q i (t) = [x1i (t) x 2i (t) x 3i (t)y 1i (t) y 2i (t) y 3i (t)z 1i (t) z 2i (t) z 3i (t)]

[0090] pBest i is the individual best position, i.e. the best position found so far by particle i.

[0091] gBest is the global best position, i.e. the best position found so far by all particles.

[0092] c1 and c2 are learning factors, i.e. acceleration constants.

[0093] r1, r2 and r3 are random numbers between 0 and 1.

[0094] c3 is an acceleration constant for local search.

[0095] l(t) is the local best position, i.e. the best position found by particle i in its local neighborhood.

[0096] The position of the particle is updated according to the updated velocity, the position update formula is as follows:

[0097] q i (t+1) = q i (t) + v i (t+1)

[0098] S303: judging whether the value of the objective function f enters a local optimum, if yes, re-initializing part of particles by using the GA algorithm and eliminating the original particles; the local optimum is that when the change of the value of the objective function is less than a certain threshold value each time of iteration, but the value of the objective function has not reached an ideal value, it can be considered that it enters a local optimum.

[0099] If not, judging whether a termination condition is met, such as reaching a maximum number of iterations or the fitness value converging to a certain threshold value, if yes, entering step S305, if not, continuing iteration and returning to step S301;

[0100] S304: genetic algorithm part, sequentially performing selection operation, crossover operation and mutation operation to generate new particles to replace the particles in the local optimum part;

[0101] Combining Figure 6 , the GA operation is divided into the following three parts:

[0102] Selection operation: select individuals according to fitness value, select particles with higher fitness value as selection standard, and use roulette selection. The roulette operation is as follows: count the fitness value of each individual as fi; calculate the sum of the fitness values of all individuals Calculate the cumulative probability of each individual, which is the proportion of individual fitness in total fitness. The cumulative probability Generate a random number r between 0 and 1, and the selection rule is: find the first individual i that satisfies P i ≥r, that is, the individual is selected.

[0103] Crossover operation: select two particles q i And qj to generate new particle position: q i (t+1)=βqi(t+1)+(1-β)q j tt+1)。

[0104] Where β is a random number between 0 and 1.

[0105] Mutation operation: mutate the new particle position: q i ,k(t+1)=q i ,k(t+1)+σ×N(0,1).

[0106] Where: q i ,k(t+1) is the kth dimension of particle i.

[0107] σ is the mutation strength.

[0108] N(0,1) is a random number of normal distribution.

[0109] S305: output the global optimal solution, and draw the image of the change of the target function value with the number of iterations, as shown in Figure 7 .

[0110] S4: according to the solution of the target function and the geometric relationship of the six-degree-of-freedom platform, the platform pose is obtained;

[0111] After obtaining the coordinates of the three vertices of the triangle obtained after model simplification, the coordinates of the platform midpoint can be obtained, the lines connecting each vertex and the platform midpoint are projected onto the platform zero position XY, XZ and YZ planes, and then the included angle between the projection line and the line connecting the platform vertex and the midpoint is calculated to obtain the rotation angles γ, β and α of the platform around z axis, y axis and x axis, as shown in the figure 8. Specifically, the platform center coordinates are the average value of the three vertex coordinates, that is:

[0112]

[0113] The rotation angles γ, β and α of the platform around z axis, y axis and x axis are the vertex coordinates Q′ iThe line connecting the center point P′0 and the vertex Q of the triangle at the zero position of the platform. i The angle formed by the projection of the line connecting the center point P0 to the XY,XZ,YZ plane of the static platform coordinate system, i.e.:

[0114]

[0115] A MATLAB program was written to conduct a simulation experiment of the forward kinematics solution. Finally, the written program was encapsulated into a function block and added to the position servo controller. The specific control scheme is as follows: Figure 9 As shown.

[0116] S5: Design a position servo controller, such as Figure 10 As shown, the difference between the pose obtained by the forward kinematics solution and the given pose is used as input to the position controller. The position controller uses PID control and inputs the parameters adjusted by the PID to the inverse kinematics algorithm. The inverse kinematics algorithm obtains the lengths of the six links, which are used as control variables to control the length of each link.

[0117] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A parallel six-degree-of-freedom platform pose control method based on a PSO-GA method, characterized in that, The method comprises the following steps: S1: simplifying a six-degree-of-freedom platform model and mathematically modeling; the specific steps comprise: The six-degree-of-freedom platform model comprises a moving platform, a fixed platform, six electric cylinders, and a motor; the motor is installed on one side of the electric cylinder; one end of the electric cylinder is movably connected to the moving platform through a spherical hinge; the other end of the electric cylinder is movably connected to the fixed platform through a Hooke joint; The hinge rotation center connected to the moving platform is regarded as being on the moving platform; the hinge rotation center connected to the fixed platform is regarded as being on the fixed platform; the electric cylinder is regarded as a slender rod with a mass point at the center; and three long sides of the upper platform of the hexagon are extended to intersect with each other in pairs, so that the hexagon is simplified into an equilateral triangle; S2: constructing a target function according to the structural parameters of the six-degree-of-freedom platform; S3: solving the target function by using a PSO-GA method; the specific steps comprise: S301: initializing PSO and GA parameters, initializing particle positions and velocities, initializing individual optimal positions and global optimal positions; S302: dynamically adjusting an inertia weight, entering a PSO iteration part and updating velocities, updating positions, calculating current values, updating individual optimal positions, and finally updating a global optimal position; S303: judging whether the value of the target function enters a local optimum; if yes, reinitializing some particles by using a GA algorithm and removing the original particles; if not, judging whether a termination condition is met; if yes, entering step S305; if not, returning to step S301; S304: a genetic algorithm part, sequentially performing selection operation, crossover operation, and mutation operation, generating new particles, and replacing the particles in the local optimum part; S305: outputting a global optimal solution and drawing a graph of the target function value changing with the iteration number; S4: obtaining a platform pose according to the solution of the target function and the geometric relationship of the six-degree-of-freedom platform; S5: designing a position servo controller, taking the difference between the pose obtained by the forward solution and a given pose as input to the position controller, and controlling the lengths of the rods of the six-degree-of-freedom platform.

2. The PSO-GA method-based parallel six-degree-of-freedom platform pose control method according to claim 1, characterized in that, In step S2, the six-degree-of-freedom platform structure parameters include an upper platform radius , a lower platform circumscribed circle , a distance H of a plane where upper and lower platform hinge center points are located, and a circular arc angle a0 corresponding to an upper platform short side and a circular arc angle b0 corresponding to a lower platform short side, simplified triangle three vertex coordinates represent original moving platform six vertex coordinates, a pose equation is constructed according to a relationship between the moving platform vertex coordinates and the stationary platform vertex coordinates and a rod length, and three geometric constraint equations are added so that the objective function has a unique solution. 3.The PSO-GA method based parallel 6-DOF platform pose control method of claim 1, wherein, In step S302, the particle velocity updating method is as follows: , wherein, is a balancing factor between 0 and 1 ; is the inertia weight at time t, which is adapted; the corresponding particle the best position found in its history the value of the objective function f corresponding; the global best position found so far among all particles the value of the objective function f corresponding; is the particle the velocity at time t; is the particle the position at time t, representing the particle a set of possible solutions to the system of equations; , is the individual best position; is the global best position; and is the learning factor; , and is a random number between 0 and 1 ; is the acceleration constant of the local search; is the local best position; The position updating formula is as follows: 。 4. The PSO-GA method-based parallel six-degree-of-freedom platform pose control method according to claim 1, characterized in that, In step S303, the local optimum is that the change amount of the target function value is less than a certain threshold value in each iteration, but the target function value has not reached an ideal value, and the target function value is considered to enter the local optimum.

5. The PSO-GA method-based parallel six-degree-of-freedom platform pose control method according to claim 1, characterized in that, The specific steps of step S4 comprise: The platform center coordinates are the average of the three vertex coordinates of the triangle obtained after the model is simplified, that is: , Rotation angle of z axis, y axis, x axis of platform coordinate system established by platform around platform center 、 、 Rotation angle of z axis, y axis, x axis of platform coordinate system established by platform around platform center , , , Angle formed by projection of line connecting triangle vertex and center point on platform zero position and line connecting triangle vertex and center point on platform zero position on moving platform XY, XZ, YZ plane, namely: , . 。 6. The PSO-GA method-based parallel six-degree-of-freedom platform pose control method according to claim 1, characterized in that, In step S5, the position servo controller adopts PID control; the parameters adjusted by the PID control are input to the kinematics inverse solution algorithm; the lengths of the six rods obtained by the inverse solution are taken as control variables to control the lengths of the rods.

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