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

Abstract

The invention discloses a parallel six-degree-of-freedom platform pose control method based on a PSO-GA method, and the method comprises the following steps: S1, simplifying a six-degree-of-freedom platform model, and facilitating the mathematical modeling; s2, constructing a target function according to the six-degree-of-freedom platform structure parameters; s3, solving an objective function by using a PSO-GA method; s4, according to the solution of the objective function and the geometrical relationship of the six-degree-of-freedom platform, the platform pose is obtained; and S5, a position servo controller is designed, the difference between the pose obtained through the positive solution and the given pose is obtained and input to the position controller, and the rod length of each rod of the six-degree-of-freedom platform is controlled. The invention provides a kinematics algorithm capable of quickly solving the platform position and realizing stable control so as to realize quick and stable control on the parallel platform.

Description

Technical Field

[0001] The present invention relates to the technical field of parallel six-degree-of-freedom platform control, and in particular to a parallel six-degree-of-freedom platform posture control method based on a PSO-GA method. Background Art

[0002] The parallel six-degree-of-freedom platform is a new type of manufacturing mechanism that has been booming worldwide in recent years. It includes one or more parallel mechanisms. Compared with the series mechanism, the parallel mechanism has special advantages such as good rigidity, high precision, high speed and acceleration, and has broader application prospects in the fields of shipbuilding, aerospace, automobiles, and medical treatment.

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

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

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

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

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

[0008] S2: construct the 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: According to the solution of the objective function and the geometric relationship of the six-degree-of-freedom platform, the platform posture is obtained;

[0011] S5: Design a position servo controller, make a difference between the posture obtained by the positive solution and the given posture, and give it 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 invention, 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 ball joint, and the other end of the electric cylinder is movably connected to the fixed platform through a Hooke's joint.

[0014] The rotation center of the hinge connected to the moving platform is regarded as on the moving platform, the rotation center of the hinge connected to the static platform is regarded as on the static platform, the electric cylinder is regarded as a slender rod with a particle at the center, and the three long sides of the upper platform of the hexagon are extended until they intersect each other, then the hexagon is simplified to an equilateral triangle.

[0015] In a preferred embodiment of the present invention, in step S2, the six-degree-of-freedom platform structural parameters include the upper platform radius R a , the circumscribed circle R of the lower platform b , the distance H between the planes where the hinge centers of the upper and lower platforms are located, as well as the arc angle a0 corresponding to the short side of the upper platform and the arc angle b0 corresponding to the short side of the lower platform. The coordinates of the three vertices of the simplified triangle represent the coordinates of the original six vertices of the moving platform. The pose equation is constructed based on the relationship between the vertex coordinates of the moving platform and the vertex coordinates of the static platform and the length of the rod, and three geometric constraint equations are added to ensure that the objective function obtains a unique solution.

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

[0017] S301: Initialize PSO and GA parameters, initialize particle positions and velocities, initialize individual optimal positions and global optimal positions;

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

[0019] S303: Determine whether the value of the objective function f has entered the local optimum. If so, use the GA algorithm to reinitialize some particles and remove the original particles. If not, determine whether the termination condition is met. If so, proceed to step S305. If not, return to step S301.

[0020] S304: In the genetic algorithm part, selection operation, crossover operation, and mutation operation are performed in sequence to generate new particles to replace the particles that have entered the local optimal part;

[0021] S305: Output the global optimal solution and draw a graph showing how the objective function value changes with the number of iterations.

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

[0023]

[0024] Among them, α is a balance factor between 0 and 1; w(t) is the inertia weight at time t, which changes adaptively; f(p i (t)) corresponds to the optimal position p found by particle i in its history i f(g(t)) corresponds to the value of the objective function f corresponding to the global optimal position g(t) currently found among 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 particle i to the system of equations;

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

[0027] The position update formula is as follows:

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

[0029] Furthermore, in step S303, the local optimum is when the change in the objective function value each iteration is less than a certain threshold, but the objective function value has not reached the ideal value, and it is considered that the objective function value has entered the local optimum.

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

[0031] The platform center coordinates are the average values ​​of the three vertex coordinates of the triangle obtained after the model is simplified, that is:

[0032]

[0033] The platform coordinate system z-axis, y-axis, and x-axis rotation angles γ, β, and α of the platform coordinate system established around the platform center are the vertex Q′ of the rotated triangle i The line connecting the center point P′0 and the triangle vertex Q when the platform is at zero position i The angle formed by the projection of the line connecting the center point P0 on the XY, XZ, and YZ planes of the moving platform when the platform is at zero position is:

[0034]

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

[0036] The beneficial effects of the present invention are: the present invention proposes a kinematic algorithm that can quickly solve the platform position and achieve stable control, so as to achieve rapid and stable control of the parallel platform; the specific effects are:

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

[0038] (2) Use GA algorithm to enhance the globality of search;

[0039] (3) The designed controller does not require sensors to collect data as feedback, but uses a forward solution algorithm instead, which improves accuracy and reduces costs;

[0040] (4) The structural parameters used are common to all general platforms, so the algorithm is universal. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of the posture control method of the parallel six-degree-of-freedom platform based on the PSO-GA method of the present invention;

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

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

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

[0045] Figure 5 It is the specific steps of step S3;

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

[0047] Figure 7 It is a graphical diagram of the objective function value and the number of iterations;

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

[0049] Fig. 9 It is the workspace control scheme diagram;

[0050] Fig.10 Schematic diagram of the principle of the position servo controller.

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

[0052] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more definite definition of the protection scope of the present invention.

[0053] See also Figure 1 , the embodiment of the present invention includes:

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

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

[0056] In this example, a parallel six-degree-of-freedom platform is used to conduct experiments, collect the displacement of the electric cylinder and the position of the dynamic platform, and build a Figure 2 The parallel six-degree-of-freedom platform shown includes a motor 4, a driver, six electric cylinders 3 and telescopic rods, 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 rod; 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 rod branches, one end of each branch is connected to the moving platform 1 through a ball joint 5, and the other end is connected to the fixed platform 2 through a Hooke's joint 6, and there is a moving cylindrical pair in the middle. Each branch is equipped with a servo motor to drive the upper end of the branch connecting rod to move relatively, change the length of each branch connecting rod, so as to realize the three-dimensional translation and three-dimensional rotation of the moving platform 1 relative to the fixed platform 2.

[0057] Combination Figure 3 and Figure 4, perform kinematic analysis on the platform, establish a platform coordinate system at the center of the moving platform and the static platform, and regard the center point of each hinge point as being on the platform plane. Specifically: regard the rotation center of the hinge connected to the moving platform as being on the moving platform, regard the rotation center of the hinge connected to the static platform as being on the static platform, and regard the electric cylinder as a slender rod with the particle at the center. The vertices of the hexagon of the static platform are B1~B6, and the vertices of the hexagon of the moving platform are P1~P6. Extend the three long sides of the upper platform of the hexagon until they intersect each other, and the intersection point is Q. Then the hexagon is simplified to an equilateral triangle, and the side length of the triangle is set to l. In general, five parameters are required to describe the Stewart platform structure, such as Figure 3 As shown, the upper platform radius R a , the circumscribed circle R of the lower platform b , the distance H between the planes where the hinge centers of the upper and lower platforms are located, and the angle a0 of the short side of the upper platform and the angle b0 of the short side of the lower platform. These five parameters determine the shape of the parallel platform and the coefficients in the subsequent objective function.

[0058] S2: construct the objective function according to the structural parameters of the six-degree-of-freedom platform;

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

[0060]

[0061] The center of each hinge point of the lower platform B i =(X i Y i Z i) T (i=1,2,…6) in the static coordinate system O B -X B Y B Z B The coordinates in the coordinate system can be determined based on the structural parameters of the static platform. i (i=1,2,…6) and Q i and B i The relationship is shown in the following formula, and the rod length L i (i=1,2,…6) are known quantities in the forward solution operation, which are obtained by the sensor in the servo electric cylinder. The forward solution problem is transformed into the problem of solving a set of nine quadratic equations, where 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, h1 is the length of the long side of the hexagon, h2 is the length of the short side of the hexagon, Use matrix B to represent the center of each hinge point of the lower platform i =(X i Y i Z i ) T (i=1, 2, ...6) is:

[0069]

[0070] in

[0071] Since there are nine unknowns and only six equations, three additional geometric constraints are required:

[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] In the formula, l is the simplified side length of the triangle, that is, l=h1+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: Use the PSO-GA method to solve the objective function, where the PSO algorithm is a particle swarm algorithm and the GA algorithm is a genetic algorithm. Figure 5 , the specific steps include:

[0078] S30 1: Initialize PSO and GA parameters, initialize particle positions and velocities, initialize individual optimal positions and global optimal positions;

[0079] S302: Dynamically adjust the inertia weight, enter the PSO iteration part and update the speed, update the position, calculate the current value, update the individual best position, and finally update the global best position;

[0080] Initialize PSO and GA parameters, set the number of particles to 200, the particle dimension to nine dimensions, corresponding to the nine variables in the objective function: x1, x2, x3, y1, y2, y3, z1, z2, z3, the number of iterations to 1000, calculate the fitness value of each particle and each individual, and according to the current speed of the particle and the individual best position (pBest i ) and the global best position (gBest) and the objective function value f(p i (t)), f(g(t)) is used to update the particle's velocity.

[0081] The speed update formula is as follows:

[0082]

[0083] Where: α is a balancing factor between 0 and 1.

[0084] w(t) is the inertia weight at time t and can change adaptively.

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

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

[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 system of equations:

[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, that is, the best position found by particle i so far.

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

[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 the acceleration constant of the local search.

[0095] l(t) is the local optimal position, i.e., the best position that particle i can find in its local neighborhood.

[0096] Update the particle's position 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: Determine whether the value of the objective function f has entered the local optimum. If so, use the GA algorithm to reinitialize some particles and remove the original particles. The local optimum is when the change in the objective function value per iteration is less than a certain threshold, but the objective function value has not reached the ideal value, and it can be considered to have entered the local optimum.

[0099] If not, determine whether the termination condition is met, such as reaching the maximum number of iterations or the fitness value converges to a certain threshold. If the termination condition is met, proceed to step S305. If not, continue to iterate and return to step S301.

[0100] S304: In the genetic algorithm part, selection operation, crossover operation, and mutation operation are performed in sequence to generate new particles to replace the particles that have entered the local optimal part;

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

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

[0103] Crossover operation: select two particles q i Intersect with qj to generate a 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 intensity.

[0108] N(0,1) is a normally distributed random number.

[0109] S305: Output the global optimal solution and draw a graph of the objective function value changing with the number of iterations, such as Figure 7 shown.

[0110] S4: According to the solution of the objective function and the geometric relationship of the six-degree-of-freedom platform, the platform posture is obtained;

[0111] After obtaining the coordinates of the three vertices of the triangle obtained after the model is simplified, the coordinates of the platform midpoint can be obtained. Connect each vertex with the platform midpoint, project the connecting line onto the XY, XZ, YZ plane of the moving platform when the platform is in zero position, and then calculate the angle between the projection line and the line connecting the platform vertex and the midpoint to obtain the platform rotation angles γ, β, α around the z-axis, y-axis, and x-axis, as shown in Figure 8. Specifically, the platform center coordinates are the average of the three vertex coordinates, that is:

[0112]

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

[0114]

[0115] Write a MATLAB program to perform a simulation experiment of kinematics. Finally, encapsulate the written program into a function block and add it to the position servo controller. The specific control scheme is as follows: Fig. 9 shown.

[0116] S5: Design a position servo controller, such as Fig.10 As shown, the position obtained by the positive solution is subtracted from the given position and given as input to the position controller. The position controller adopts PID control, and the parameters adjusted by PID are input to the kinematic inverse solution algorithm. The inverse solution obtains the lengths of the six rods, which are used as control variables to control the lengths of each rod.

[0117] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A parallel six-degree-of-freedom platform posture control method based on PSO-GA method, characterized in that: The following steps are involved: S1: Simplify the six-degree-of-freedom platform model to facilitate mathematical modeling; S2: construct the objective function according to the structural parameters of the six-degree-of-freedom platform; S3: Solve the objective function using the PSO-GA method; S4: According to the solution of the objective function and the geometric relationship of the six-degree-of-freedom platform, the platform posture is obtained; S5: Design a position servo controller, make a difference between the posture obtained by the positive solution and the given posture, and give it as input to the position controller to control the length of each rod of the six-degree-of-freedom platform.

2. The parallel six-degree-of-freedom platform posture control method based on the PSO-GA method according to claim 1 is characterized in that: The specific steps of step S1 include: 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 ball joint, and the other end of the electric cylinder is movably connected to the fixed platform through a Hooke's joint. The rotation center of the hinge connected to the moving platform is regarded as on the moving platform, the rotation center of the hinge connected to the static platform is regarded as on the static platform, the electric cylinder is regarded as a slender rod with a particle at the center, and the three long sides of the upper platform of the hexagon are extended until they intersect each other, then the hexagon is simplified to an equilateral triangle.

3. The parallel six-degree-of-freedom platform posture control method based on the PSO-GA method according to claim 1 is characterized in that: In step S2, the six-degree-of-freedom platform structural parameters include the upper platform radius R a , the circumscribed circle R of the lower platform b , the distance H between the planes where the hinge centers of the upper and lower platforms are located, as well as the arc angle a0 corresponding to the short side of the upper platform and the arc angle b0 corresponding to the short side of the lower platform. The coordinates of the three vertices of the simplified triangle represent the coordinates of the original six vertices of the moving platform. The pose equation is constructed based on the relationship between the vertex coordinates of the moving platform and the vertex coordinates of the static platform and the length of the rod, and three geometric constraint equations are added to ensure that the objective function obtains a unique solution.

4. The parallel six-degree-of-freedom platform posture control method based on the PSO-GA method according to claim 1 is characterized in that: The specific steps of step S3 include: S301: Initialize PSO and GA parameters, initialize particle positions and velocities, initialize individual optimal positions and global optimal positions; S302: Dynamically adjust the inertia weight, enter the PSO iteration part and update the speed, update the position, calculate the current value, update the individual best position, and finally update the global best position; S303: Determine whether the value of the objective function has entered the local optimum. If so, use the GA algorithm to reinitialize some particles and remove the original particles. If not, determine whether the termination condition is met. If so, proceed to step S305. If not, return to step S301. S304: In the genetic algorithm part, selection operation, crossover operation, and mutation operation are performed in sequence to generate new particles to replace the particles that have entered the local optimal part; S305: Output the global optimal solution and draw a graph showing how the objective function value changes with the number of iterations.

5. The parallel six-degree-of-freedom platform posture control method based on the PSO-GA method according to claim 4 is characterized in that: In step S302, the particle velocity update method is: Among them, α is a balance factor between 0 and 1; w(t) is the inertia weight at time t, which changes adaptively; f(p i (t)) corresponds to the optimal position p found by particle i in its history i f(g(t)) corresponds to the value of the objective function f corresponding to the global optimal position g(t) currently found among 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 particle i to the system of equations; 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)] pBest i is the individual best position; gBest is the global best position; c1 and c2 are learning factors; r1, r2 and r3 are random numbers between 0 and 1; c3 is the acceleration constant of local search; l(t) is the local best position. The position update formula is as follows: q i (t+1)=q i (t)+v i (t+1) 6. The method for controlling the position and posture of a parallel six-degree-of-freedom platform based on the PSO-GA method according to claim 4, characterized in that: In step S303, the local optimum is when the change in the objective function value each iteration is less than a certain threshold, but the objective function value has not reached the ideal value, and it is considered that the objective function value has entered the local optimum.

7. The parallel six-degree-of-freedom platform posture control method based on the PSO-GA method according to claim 1 is characterized in that: The specific steps of step S4 include: The platform center coordinates are the average values ​​of the three vertex coordinates of the triangle obtained after the model is simplified, that is: The platform coordinate system z-axis, y-axis, and x-axis rotation angles γ, β, and α of the platform coordinate system established around the platform center are the vertex Q′ of the rotated triangle i The line connecting the center point P0′ and the triangle vertex Q when the platform is at zero position i The angle formed by the projection of the line connecting the center point P0 on the XY, XZ, and YZ planes of the moving platform when the platform is at zero position is:

8. The method for controlling the position and posture of a parallel six-degree-of-freedom platform based on the PSO-GA method according to claim 1, characterized in that: In step S5, the position servo controller adopts PID control, and inputs the parameters adjusted by PID into the kinematic inverse solution algorithm, and the inverse solution obtains the rod lengths of the six rods, which are used as control variables to control the lengths of the rods.

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