Motion platform fuzzy control method based on genetic algorithm optimization, computer equipment and storage medium

Abstract

The invention discloses a motion platform fuzzy control method based on genetic algorithm optimization, computer equipment and a storage medium, relates to the field of fuzzy control, and solves the problems that existing PID parameter initial values, membership functions and fuzzy rules are tedious to determine; and a particle swarm optimization algorithm is only suitable for a continuous problem, is easy to fall into local convergence and cannot achieve a globally optimal solution. According to the technical scheme, a motion platform model is established, and the lengths of a plurality of push rods are calculated through an upper platform target attitude inverse solution; designing a cascade PID control system, wherein the cascade PID control system is formed by connecting an attitude angle fuzzy controller and a push rod cascade controller in series; a constructed attitude angle fuzzy controller is designed; genetic algorithm optimization is carried out; and constructing a genetic algorithm to optimize the fuzzy PID control system, comparing the fuzzy PID control system of which the fuzzy rule is artificially formulated with the fuzzy PID control system optimized by the genetic algorithm, and simulating a control result. The method is suitable for PID parameter setting and fuzzy rule determination of a motion platform control system.

Description

Technical Field

[0001] The invention relates to the field of fuzzy control. Background Art

[0002] When performing tasks such as laser scanning 3D reconstruction, underwater carriers will be disturbed by water flow, undergo posture changes and generate errors. In order to study the impact of posture on measurement, a motion platform is usually used to simulate the posture change of the carrier under the influence of the environment. The motion platform is usually controlled by the PID method. The PID parameter setting method is cumbersome and relies on manual experience. It is difficult to achieve the optimal control effect. After the parameters are determined, once the structural parameters of the motion platform and the mass of the carrying device change, the PID parameters need to be adjusted again. The motion platform control system is multi-degree-of-freedom and nonlinear. Its posture simulation effect will directly affect the task quality. In order to improve the effect of the motion platform posture simulation, the motion platform control method needs to be optimized.

[0003] In recent years, fuzzy PID control methods have been adopted. Through fuzzy logic reasoning, real-time adjustment of PID control parameters has been achieved, so that control parameters can adapt to system changes in real time, improve the control accuracy and stability of deep motion systems, and also improve the adaptive performance of motion platforms to changes. The patent with document number CN105955026A discloses a fuzzy PID control method, device and system, but the fuzzy PID membership function and fuzzy rules described in the patent are also obtained through experience, and the determination of PID initial value, fuzzy membership function and fuzzy rules is very cumbersome. Therefore, many methods for optimizing fuzzy PID control have been derived. The patent with document number CN109190675A discloses a fuzzy classification method and device based on particle swarm optimization algorithm. The patent introduces a fuzzy classification method based on particle swarm optimization algorithm, but the particle swarm optimization algorithm is only suitable for continuous problems, and it is easy to fall into local convergence and cannot reach the global optimal solution. Summary of the invention

[0004] The purpose of the present invention is to solve the problems that the determination of the existing PID parameter initial values, membership functions and fuzzy rules is cumbersome; the particle swarm optimization algorithm is only suitable for continuous problems, and is prone to local convergence and cannot reach the global optimal solution.

[0005] To achieve the above object, the present invention provides the following technical solution: a motion platform fuzzy control method based on genetic algorithm optimization, comprising the following steps:

[0006] S1: Establish a motion platform model and calculate the lengths of multiple push rods by inversely solving the target posture of the upper platform;

[0007] S2: Design a cascade PID control system, which is composed of an attitude angle fuzzy controller and a push rod cascade controller in series;

[0008] S3: Design the attitude angle fuzzy controller constructed in S2 above;

[0009] S4: Optimize the attitude angle fuzzy controller constructed in S3 using genetic algorithm;

[0010] S5: construct a genetic algorithm optimized fuzzy PID control system, compare the fuzzy PID control system with manually formulated fuzzy rules with the fuzzy PID control system optimized by the genetic algorithm, and simulate the control results.

[0011] Furthermore, a preferred embodiment is provided, wherein there are three push rods in S1.

[0012] Further, a preferred embodiment is provided, wherein the process of calculating the lengths of multiple push rods by inversely solving the upper platform target posture is as follows:

[0013] The motion platform model forms a dynamic platform D after movement 11 ~D 13 , D 11 ~D 13 The coordinates of the three points in the static coordinate system are:

[0014]

[0015] Where: D 1i D on the moving platform when it is in the initial state 11 ~D 13 The three-point coordinate matrix, D 1i * To update the status, move the platform D 11 ~D 13 Three-point coordinate matrix, R is the direction cosine matrix of the upper platform attitude, T is the translation matrix of the upper platform attitude;

[0016]

[0017] α, β, and γ are the rotation angles of the upper platform around the X-axis, Y-axis, and Z-axis of the coordinate system, respectively;

[0018] T=[Δx Δy Δz-h] T

[0019] Among them, h is the height difference between the upper and lower platforms in the initial state;

[0020] Then the length of each push rod is:

[0021]

[0022] D 1ix , D 1iy , D 1iz D1i The x, y, and z spatial coordinates of 0ix , D 0iy , D 0iz D 0i The x, y, and z space coordinates of .

[0023] Furthermore, a preferred implementation is provided, wherein S3 specifically comprises: placing a fuzzy controller in a PID control system in a motion platform attitude angle position loop, for tracking the target attitude by the upper platform's real-time attitude.

[0024] Furthermore, a preferred implementation is provided, wherein S3 is: placing a PID controller in the motion platform attitude angle position loop for tracking the target attitude by the upper platform in real time.

[0025] Further, a preferred implementation is provided, S3 specifically comprises:

[0026] S3.1: Set the range of system output and set the sampling period for real-time calculation of the control algorithm;

[0027] S3.2: Input variables are the system error e between the target attitude angle and the actual attitude angle and the system error change rate e c , the output variables are the three correction values ​​of the control parameters in the fuzzy controller in the PID control system;

[0028] S3.3: Adjust the parameters such as proportional gain, integral gain and differential gain of the fuzzy controller in the PID control system;

[0029] S3.4: fuzzify the input variables and output variables of the system, determine the fuzzy set and membership function, and set a fuzzy rule base, which includes fuzzy rules between input variables and output variables in the fuzzy controller in the PID control system; formulate a fuzzy rule table according to the fuzzy rules;

[0030] S3.5: Calculate the gain value of the proportional gain of the fuzzy controller in the PID control system using the fuzzy rule table, and combine it with the controller in the traditional PID control system to obtain the final control signal.

[0031] Further, a preferred implementation is provided, in which the formula for obtaining the final control signal by adjusting the PID parameters by the PID controller and the fuzzy control in S3.5 is:

[0032]

[0033]

[0034]

[0035]

[0036] Where: u(k) is the output of the control system; is the cumulative error; de(k) / dk is the error change rate; K p , K i , K d Output value for the controller; is the basic value of PID parameters; ΔK p , ΔK i , ΔK d It is the online setting value of PID parameters.

[0037] Further, a preferred implementation is provided, the genetic algorithm optimization fuzzy control process in S4 is:

[0038] S4.1: The absolute value of the error time integral performance index is used as the objective function for parameter selection, and its formula is:

[0039]

[0040] Where Q(x) is the objective function and e(t) is the system error;

[0041] S4.2: Encoding to generate the initial population;

[0042] S4.3: The fitness is selected to reflect the degree to which an individual approaches the optimal value of the group during the genetic algorithm optimization process;

[0043] S4.4: Use genetic algorithms to perform operations such as mutation, crossover, and replication to generate a new generation of individuals and achieve an evolutionary process of survival of the fittest;

[0044] S4.5: When the fitness of the best individual and the fitness of the population no longer increase, or the fitness of the best individual reaches the set threshold, or the number of iterations reaches the preset value, the algorithm terminates.

[0045] Further, a preferred implementation is provided, wherein S5 is specifically:

[0046] S5.1: Execute genetic algorithm to optimize fuzzy rules and PID controller parameters;

[0047] S5.2: Load the fuzzy control system file;

[0048] S5.3: Construct the transfer function of the motion platform control system;

[0049] S5.4: Set the system sampling cycle and control time;

[0050] S5.5: Input step / follow attitude angle setting;

[0051] S5.6: Calculate the error e and the error change rate e c ;

[0052] S5.7: Perform fuzzy logic calculation to obtain ΔK p , ΔK i , ΔK d , and calculate K p , K i , K d ;

[0053] S5.8: Execute PID controller control to obtain attitude angle control result;

[0054] S5.9: Jump to S5.6 for loop control;

[0055] S5.10: After reaching the set control time, the system pauses.

[0056] Solution 3: A computer device includes a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes any one of the methods described above.

[0057] Solution 4: A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the methods described above are implemented.

[0058] The present invention is beneficial in that:

[0059] The present invention proposes a fuzzy control method for a motion platform based on genetic algorithm optimization. It requires a fuzzy control method that can determine the appropriate PID parameter initial values, membership functions and fuzzy rules of the control system and has global convergence. The present invention uses the global convergence ability and parallel optimization ability of the genetic algorithm to perform multi-objective optimization on the quantization factor of the fuzzy PID controller and the correction coefficient of the controller. The effectiveness of this method is proved by simulation, and it can be applied to the setting of PID parameters and the determination of fuzzy rules of motion platform control systems.

[0060] The present invention utilizes the global convergence ability and parallel optimization ability of the genetic algorithm to perform multi-objective optimization on the quantization factor of the fuzzy PID controller and the correction coefficient of the controller. The simulation results show that the motion platform control system after optimizing the fuzzy PID controller by the genetic algorithm has better control effect than the fuzzy PID control system with fuzzy rules set by artificial experience. When the step response of the given attitude angle and the following change of attitude angle are given, the optimized control system responds faster, has less overshoot, and stabilizes faster.

[0061] The invention is also applicable to the setting of PID parameters and the determination of fuzzy rules of a motion platform control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a schematic diagram of the structure of a posture simulation motion platform according to the fuzzy control method of a motion platform based on genetic algorithm optimization described in Implementation Method 1.

[0063] Figure 2 This is a block diagram of a posture simulation motion platform control system based on a motion platform fuzzy control method optimized by a genetic algorithm as described in implementation mode 1.

[0064] Figure 3 This is a schematic diagram of the fuzzy PID controller process after genetic algorithm optimization of a motion platform fuzzy control method based on genetic algorithm optimization described in Implementation Method 1.

[0065] Figure 4 This is a fitness transformation curve diagram of the genetic algorithm optimized for 100 generations.

[0066] Figure 5 ΔK before and after the genetic algorithm optimization of the present invention p Comparison of membership functions. (a) is ΔK before optimization. p Membership function, (b) is the optimized ΔK p Membership function.

[0067] Figure 6 This is a comparison chart of the control effects of the fuzzy PID and genetic algorithm optimized fuzzy PID of the attitude simulation motion platform of the present invention. (a) is the pitch angle step response control effect, and (b) is the pitch angle following control effect.

[0068] In the figure, there are posture sensor 1, upper platform 2, three-dimensional reconstruction instrument 3, camera 4, universal joint 5, base 6, push rod 7, motor and encoder 8, and line laser 9. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of the implementation methods of the present application clearer, the technical solutions in the implementation methods of the present application will be clearly and completely described below in conjunction with the drawings in the implementation methods of the present application. Obviously, the described implementation methods are only part of the implementation methods of the present application, not all of the implementation methods.

[0070] Implementation method 1, see Figures 1 to 6 This embodiment provides a fuzzy control method for a motion platform based on genetic algorithm optimization, the method comprising the following steps:

[0071] S1: Establish a motion platform model and calculate the lengths of multiple push rods by inversely solving the target posture of the upper platform;

[0072] S2: Design a cascade PID control system, which is composed of an attitude angle fuzzy controller and a push rod cascade controller in series;

[0073] S3: Design the attitude angle fuzzy controller constructed in S2 above;

[0074] S4: Optimize the attitude angle fuzzy controller constructed in S3 using genetic algorithm;

[0075] S5: construct a genetic algorithm optimized fuzzy PID control system, compare the fuzzy PID control system with manually formulated fuzzy rules with the fuzzy PID control system optimized by the genetic algorithm, and simulate the control results.

[0076] See also Figure 1 This embodiment is described. In this embodiment, a posture simulation motion platform structure is used. The motion platform is composed of a base 6, three push rods 7, a universal joint 5, an upper platform 2, and a posture sensor 1. The base 6 is used to fix the push rod 7; the push rod 7 is driven to extend and retract by a DC motor 8, thereby driving the upper platform 2 to change its posture in combination; the universal joint 5 connects the push rod 7 and the upper platform 2; the posture sensor 1 is connected to the upper platform 2 for feedback of posture signals; the upper platform 2 is connected to a laser three-dimensional reconstruction instrument 3 used in the experiment, such as a translation scanning system used in the laser three-dimensional reconstruction instrument 3; and the camera 4 is used to record the extension and retraction changes of the push rod 7.

[0077] The three fulcrums of the upper platform 2 and the lower platform are D 11 D 12 , D 01 D 02 The upper platform 2 can perform translational motion along the z-axis and spatial rotational motion around the x-axis and y-axis during the movement of the three telescopic support rods, and when the lengths of the three push rods 77 are constant, the position and posture of the upper motion platform can be uniquely determined.

[0078] The motion platform posture refers to the position and posture of the upper platform 2. The position refers to the distance moved by the upper platform 2 relative to the initial state, which can be expressed by the translation matrix T. The formula is as follows:

[0079] T=[ΔxΔyΔz]

[0080] Where Δx, Δy, and Δz are the moving distances of the upper platform 2 along the X-axis, Y-axis, and Z-axis of the coordinate system respectively; the posture refers to the rotation angle of the upper platform 2 relative to the initial state, which can be expressed by the rotation matrix R, and its formula is as follows:

[0081] R=[α β γ]

[0082] Among them, α, β, and γ are the rotation angles of the upper platform 2 around the X-axis, Y-axis, and Z-axis of the coordinate system. The spatial posture relationship of the motion platform refers to the relationship between the posture of the upper platform 2 and the length of the three push rods 7. The motion platform control uses posture inverse solution, and solves the rod length by inputting the posture of the upper platform 2 and controls the extension and retraction of the push rods 7.

[0083] The purpose of the present invention is to provide a motion platform fuzzy control method based on genetic algorithm optimization, to achieve iterative adjustment of initial parameters, membership functions and fuzzy rules of the motion platform fuzzy PID controller, so as to achieve stability and accuracy of motion platform posture simulation.

[0084] Implementation method 2: This implementation method further limits the motion platform fuzzy control method based on genetic algorithm optimization described in implementation method 1. There are three push rods in S1.

[0085] Implementation method 3: This implementation method is a further limitation of the fuzzy control method of the motion platform based on genetic algorithm optimization described in implementation method 1. In S1, the process of calculating the lengths of multiple push rods by inversely solving the upper platform target posture is as follows:

[0086] The motion platform model forms a dynamic platform D after movement 11 ~D 13 , D 11 ~D 13 The coordinates of the three points in the static coordinate system are:

[0087]

[0088] Where: D 1i D on the moving platform when it is in the initial state 11 ~D 13 The three-point coordinate matrix, D 1i * To update the status, move the platform D 11 ~D 13 Three-point coordinate matrix, R is the direction cosine matrix of the upper platform attitude, T is the translation matrix of the upper platform attitude;

[0089]

[0090] α, β, and γ are the rotation angles of the upper platform around the X-axis, Y-axis, and Z-axis of the coordinate system, respectively;

[0091] T=[Δx Δy Δz-h] T

[0092] Among them, h is the height difference between the upper and lower platforms in the initial state;

[0093] Then the length of each push rod is:

[0094]

[0095] D 1ix , D 1iy , D 1iz D 1i The x, y, and z spatial coordinates of 0ix , D 0iy , D 0iz D 0i The x, y, and z space coordinates of .

[0096] Implementation method 4. This implementation method is a further limitation of the motion platform fuzzy control method based on genetic algorithm optimization described in implementation method 1. Specifically, S3 is: placing the fuzzy controller in the PID control system in the motion platform attitude angle position loop, which is used to track the target attitude of the upper platform in real time.

[0097] See also Figure 2 This embodiment is described. In this embodiment, the attitude angle fuzzy controller and the push rod 7 cascade controller are connected in series. The attitude angle fuzzy controller inputs the desired target attitude angle, and the feedback is the attitude angle calculated by the fusion of the gyroscope, accelerometer, and magnetometer in the IMU, and the output is the desired control attitude parameter; the desired control attitude parameter is calculated by the attitude inverse solution to the target position of each push rod 7, and is input to the control system of each push rod 7; the push rod 7 control system is a cascade PID controller, the outer loop is the motor position loop, the input is the target position of the push rod 7, the feedback is the position of the push rod 7 calculated by the encoder, and the output is the expected speed of the motor, the inner loop is the motor speed loop, the input is the target speed of the push rod 7, the feedback is the motor speed calculated by the encoder, and the output is the duty cycle of the motor speed control signal and the level of the motor direction control signal, thereby driving the upper platform 2 to change its attitude to the expected attitude.

[0098] Implementation method five: This implementation method is a further limitation of the motion platform fuzzy control method based on genetic algorithm optimization described in implementation method one. Specifically, S3 is: placing the PID controller in the motion platform attitude angle position loop to track the target attitude of the upper platform 2 in real time.

[0099] Implementation mode 5: This implementation mode further limits the motion platform fuzzy control method based on genetic algorithm optimization described in implementation mode 1. S3 is specifically:

[0100] S 3.1: Set the range of system output and set the sampling period for real-time calculation of the control algorithm;

[0101] S3.2: Input variables are the system error e between the target attitude angle and the actual attitude angle and the system error change rate e c , the output variables are the three correction values ​​of the control parameters in the fuzzy controller in the PID control system;

[0102] S3.3: Adjust the parameters such as proportional gain, integral gain and differential gain of the fuzzy controller in the PID control system;

[0103] S3.4: fuzzify the input variables and output variables of the system, determine the fuzzy set and membership function, and set a fuzzy rule base, which includes fuzzy rules between input variables and output variables in the fuzzy controller in the PID control system; formulate a fuzzy rule table according to the fuzzy rules;

[0104] S3.5: Calculate the gain value of the proportional gain of the fuzzy controller in the PID control system using the fuzzy rule table, and combine it with the controller in the traditional PID control system to obtain the final control signal.

[0105] In S3.1 of this embodiment, the target attitude angle range is -30°-30°, the sampling period is set to 0.01s, and the attitude angle change speed range is -0.15° / 0.01s-0.15° / 0.01s.

[0106] In this embodiment S3.2, the fuzzy domain of the system error e is taken as [-3, 3], and the error change rate e c The fuzzy domain is [-3, 3], and the output variable ΔK p The fuzzy domain is taken as [-0.3, 0.3], ΔK i The fuzzy domain is taken as [-0.06, 0.06], ΔK d The fuzzy domain is taken as [-0.3, 0.3].

[0107] In this embodiment, the proportional gain K in S3.3 p =3, integral gain K i is 0.6, differential gain K d is 3.

[0108] In this embodiment, the control language value in S3.4 is NB with large negative, NM with medium negative, NS with small negative, ZO with zero, PS with small positive, PM with medium positive, and PB with large positive. The fuzzy rule table of the fuzzy PID controller is set according to experimental experience. The membership function uses a triangular membership function.

[0109] Implementation method 6. This implementation method further limits the motion platform fuzzy control method based on genetic algorithm optimization described in implementation method 5. The formula for the final control signal obtained by adjusting the PID parameters by the PID control algorithm and fuzzy control in S3.5 is:

[0110]

[0111]

[0112]

[0113]

[0114] Where: u(k) is the output of the control system; is the cumulative error; de(k) / dk is the error change rate; K p , K i , K d Output value for the controller; is the basic value of PID parameters; ΔK p , ΔK i , ΔK d It is the online setting value of PID parameters.

[0115] Implementation method 7: This implementation method further limits the motion platform fuzzy control method based on genetic algorithm optimization described in implementation method 5. The genetic algorithm optimization fuzzy control process in S4 is:

[0116] S4.1: The absolute value of the error time integral performance index is used as the objective function for parameter selection, and its formula is:

[0117]

[0118] Where Q(x) is the objective function and e(t) is the system error;

[0119] S4.2: Encoding to generate the initial population;

[0120] S4.3: The fitness is selected to reflect the degree to which an individual approaches the optimal value of the group during the genetic algorithm optimization process;

[0121] S4.4: Use genetic algorithms to perform operations such as mutation, crossover, and replication to generate a new generation of individuals and achieve an evolutionary process of survival of the fittest;

[0122] S4.5: When the fitness of the best individual and the fitness of the population no longer increase, or the fitness of the best individual reaches the set threshold, or the number of iterations reaches the preset value, the algorithm terminates.

[0123] In the preferred embodiment S4.2, binary coding is used to treat the quantization factor and the proportional factor in the fuzzy controller as the gene in the individual. By optimizing the above parameters, the performance of the original fuzzy controller is improved. Specifically, the initial population size is set to 100, and an initial population is randomly generated in the initial stage of calculation, and the value remains unchanged during the iteration process.

[0124] The purpose of optimizing the fuzzy controller by genetic algorithm in S4.3 of this embodiment is to minimize the objective function Q(x) of the system after parameter optimization. Therefore, the fitness function takes the inverse of the objective function, and its formula is:

[0125]

[0126] In this implementation, S4.4 selection is to pass on individuals with higher fitness to the next generation population; crossover refers to exchanging parts of the structure of two parent individuals to produce new individuals; mutation refers to replacing certain gene values ​​in the individual encoding string with other gene values ​​to form a new individual. This iteration is repeated for 100 generations, so that the scope of optimization is gradually narrowed, and individuals are close to the optimal solution. Specifically, the selection probability is defined as 0.9, the crossover probability is 0.8, and the mutation probability is 0.02.

[0127] In S4.5 of this implementation mode, specifically, the genetic algorithm program is set to terminate spontaneously when the number of iterations reaches 100, and output the optimal solution.

[0128] Implementation 8: This implementation is a further limitation on the motion platform fuzzy control method based on genetic algorithm optimization described in Implementation 5. S5 is specifically:

[0129] S5.1: Execute genetic algorithm to optimize fuzzy rules and PID controller parameters;

[0130] S5.2: Load the fuzzy control system file;

[0131] S5.3: Construct the transfer function of the motion platform control system;

[0132] S5.4: Set the system sampling cycle and control time;

[0133] S5.5: Input step / follow attitude angle setting;

[0134] S5.6: Calculate the error e and the error change rate e c ;

[0135] S5.7: Perform fuzzy logic calculation to obtain ΔK p , ΔK i , ΔK d , and calculate K p , K i , K d ;

[0136] S5.8: Execute PID control to obtain attitude angle control result;

[0137] S5.9: Jump to S5.6 for loop control;

[0138] See also Figure 4-Figure 6 To explain this embodiment, Figure 4 After the simulation, the genetic algorithm optimizes the fitness transformation curve for 100 generations. The fuzzy rules before and after optimization are expressed as ΔK p For example, see Figure 5 shown.

[0139] The simulation output control results compare the control effects of the fuzzy PID based on the artificial fuzzy rules and the fuzzy PID optimized by the genetic algorithm. Figure 6 As shown, (a) is the pitch angle step response control effect, and (b) is the pitch angle following control effect.

[0140] The rise time of the fuzzy PID in the pitch angle step response is 5.66s, and it is 4.97s after genetic algorithm optimization, a year-on-year decrease of 1.24%; the adjustment time is 11.01s, and it is 7.42s after genetic algorithm optimization, a year-on-year decrease of 32.67%; the steady-state error is 0.14°, and it is 0.03° after genetic algorithm optimization, a year-on-year decrease of 78.57%; the overshoot is 2.49°, and it is 0.61° after algorithm optimization, a year-on-year decrease of 75.50%. The cumulative error Q(x) of the fuzzy PID in the pitch angle following is 21.170, and it is 9.816 after algorithm optimization, a year-on-year decrease of 53.6%; the overshoot is 1.24°, and it is almost 0 after algorithm optimization.

[0141] Embodiment 9: This embodiment proposes a computer device including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes any one of the methods described above.

[0142] Embodiment 10: This embodiment proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method described in any one of embodiments 1 to 8 are implemented.

[0143] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a portion of a code, and the above-mentioned module, program segment, or a portion of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be used to perform the specified functions.

[0144] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0145] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A fuzzy control method for a motion platform based on genetic algorithm optimization, characterized in that: The following steps are involved: S1: Establish a motion platform model and calculate the lengths of multiple push rods by inversely solving the target posture of the upper platform; S2: Design a cascade PID control system, which is composed of an attitude angle fuzzy controller and a push rod cascade controller in series; S3: Design the attitude angle fuzzy controller constructed in S2 above; S4: Optimize the attitude angle fuzzy controller constructed in S3 using a genetic algorithm; S5: construct a genetic algorithm optimized fuzzy PID control system, compare the fuzzy PID control system with manually formulated fuzzy rules with the fuzzy PID control system optimized by the genetic algorithm, and simulate the control results.

2. The motion platform fuzzy control method based on genetic algorithm optimization according to claim 1 is characterized in that: There are three push rods in the S1.

3. The motion platform fuzzy control method based on genetic algorithm optimization according to claim 1 is characterized in that: In S1, the process of calculating the lengths of multiple push rods by inversely solving the upper platform target posture is as follows: The motion platform model forms a dynamic platform D after movement 11 ~D 13 , D 11 ~D 13 The coordinates of the three points in the static coordinate system are: Where: D 1i D on the moving platform when it is in the initial state 11 ~D 13 The three-point coordinate matrix, D 1i * To update the status, move the platform D 11 ~D 13 Three-point coordinate matrix, R is the direction cosine matrix of the upper platform attitude, T is the translation matrix of the upper platform attitude; α, β, and γ are the rotation angles of the upper platform around the X-axis, Y-axis, and Z-axis of the coordinate system, respectively; T=[Δx Δy Δz -h] T Among them, h is the height difference between the upper and lower platforms in the initial state; Then the length of each push rod is: D 1ix , D 1iy , D 1iz D 1i The x, y, and z spatial coordinates of 0ix , D 0iy , D 0iz D 0i The x, y, and z spatial coordinates of .

4. The motion platform fuzzy control method based on genetic algorithm optimization according to claim 1 is characterized in that: The S3 is specifically: placing the fuzzy controller in the PID control system in the motion platform attitude angle position loop, so as to track the target attitude of the upper platform in real time.

5. The motion platform fuzzy control method based on genetic algorithm optimization according to claim 1 is characterized in that: S3 is specifically: S3.1: Set the range of system output and set the sampling period for real-time calculation of the control algorithm; S3.2: Input variables are the system error e between the target attitude angle and the actual attitude angle and the system error change rate e c , the output variables are the three correction values ​​of the control parameters in the fuzzy controller in the PID control system; S3.3: Adjust the parameters such as proportional gain, integral gain and differential gain of the fuzzy controller in the PID control system; S3.4: fuzzify the input variables and output variables of the system, determine the fuzzy set and membership function, and set a fuzzy rule base, which includes fuzzy rules between input variables and output variables in the fuzzy controller in the PID control system; formulate a fuzzy rule table according to the fuzzy rules; S3.5: Calculate the gain value of the proportional gain of the fuzzy controller in the PID control system using the fuzzy rule table, and combine it with the controller in the traditional PID control system to obtain the final control signal.

6. The motion platform fuzzy control method based on genetic algorithm optimization according to claim 5 is characterized in that: The formula for the final control signal obtained by combining the controller in the traditional PID control system described in S3.5 is: Where: u(k) is the output of the control system; is the cumulative error; de(k) / dk is the error change rate; K p , K i , K d Output value for the controller; is the basic value of PID parameters; ΔK p , ΔK i , ΔK d It is the online setting value of PID parameters.

7. The motion platform fuzzy control method based on genetic algorithm optimization according to claim 1 is characterized in that: The attitude angle fuzzy controller constructed in S4 is optimized by genetic algorithm as follows: S4.1: Use the error absolute value time integral performance index as the objective function for parameter selection; S4.2: Encoding to generate the initial population; S4.3: The fitness is selected to reflect the degree to which an individual approaches the optimal value of the group during the genetic algorithm optimization process; S4.4: Use genetic algorithms to perform mutation, crossover, replication and other operations to generate a new generation of individuals and achieve the evolutionary process of survival of the fittest; S4.5: When the fitness of the optimal individual and the fitness of the population no longer increase, or the fitness of the optimal individual reaches the set threshold, or the number of iterations reaches the preset value, the optimization is completed.

8. The motion platform fuzzy control method based on genetic algorithm optimization according to claim 1 is characterized in that: The S5 is specifically: S5.1: Execute genetic algorithm to optimize fuzzy rules and parameters of fuzzy controller in PID control system; S5.2: Load the fuzzy control system file; S5.3: Construct the transfer function of the motion platform control system; S5.4: Set the system sampling cycle and control time; S5.5: Input step or follow attitude angle setting; S5.6: Calculate the error e and the error change rate e c ; S5.7: Perform fuzzy logic calculation to obtain ΔK p , ΔK i , ΔK d , and calculate K p , K i , K d ; S5.8: Execute PID control to obtain attitude angle control result; S5.9: Jump to S5.6 for loop control; S5.10: After reaching the set control time, the fuzzy control system pauses.

9. A computer device, comprising a memory and a processor, characterized in that: A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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