A motion platform fuzzy control method based on genetic algorithm optimization, a computer device and a storage medium

Abstract

The application discloses a motion platform fuzzy control method based on a genetic algorithm optimization, a computer device and a storage medium, relates to the field of fuzzy control, and solves the problems that the determination of initial values of PID parameters, membership functions and fuzzy rules is complicated, a particle swarm optimization algorithm is only suitable for continuous problems, and is prone to local convergence and unable to achieve a global optimal solution. The application provides the following technical scheme: a motion platform model is established, the lengths of multiple push rods are calculated through inverse solution of a target attitude on the platform; a cascade PID control system is designed, the cascade PID control system is formed by connection of an attitude angle fuzzy controller and a push rod cascade controller; the attitude angle fuzzy controller is designed; genetic algorithm optimization is carried out; a genetic algorithm optimization fuzzy PID control system is constructed; the artificial fuzzy rule fuzzy PID control system and the genetic algorithm optimization fuzzy PID control system are compared; and simulation control results are obtained. The application is suitable for setting of PID parameters of a motion platform control system and determination of fuzzy rules.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of fuzzy control. BACKGROUND

[0002] The water-borne carrier will be disturbed by water flow when performing tasks such as laser scanning three-dimensional reconstruction, the attitude will be transformed and errors will be generated. In order to study the influence of the attitude on the measurement, a motion platform is usually used to simulate the attitude transformation of the carrier under the influence of the environment. The motion platform usually adopts a PID method for control. The setting method of the PID parameters is complicated, depends on manual experience, and the control effect is difficult to achieve the optimal, and once the structure parameters of the motion platform and the mass of the carried device change, the PID parameters need to be set again. The motion platform control system is multi-degree-of-freedom and nonlinear, and the attitude simulation effect will directly affect the task quality. In order to improve the effect of the motion platform attitude simulation, the control method of the motion platform needs to be optimized.

[0003] In recent years, fuzzy PID control method is adopted, the real-time adjustment of the PID control parameters is realized through fuzzy logic reasoning, the control parameters can adapt to the changes of the system in real time, the control precision and stability of the deep motion system are improved, and the adaptive performance of the motion platform to changes can also be improved. The patent with the document number CN105955026A discloses a fuzzy PID control method, device and system, but the fuzzy PID membership function and fuzzy rule in the patent are also obtained through experience. The determination of the PID initial value, fuzzy membership function and fuzzy rule is very complicated. Therefore, many methods for optimizing the fuzzy PID control are derived. The patent with the document number CN109190675A discloses a fuzzy classification method and device based on a particle swarm optimization algorithm. The patent introduces a fuzzy classification method based on a particle swarm optimization algorithm, but the particle swarm optimization algorithm is only suitable for continuous problems and is easy to fall into local convergence, and cannot reach the global optimal solution. SUMMARY

[0004] The present application aims to solve the problems that the determination of the PID parameter initial value, membership function and fuzzy rule is complicated, the particle swarm optimization algorithm is only suitable for continuous problems and is easy to fall into local convergence, and cannot reach the global optimal solution.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a motion platform fuzzy control method based on genetic algorithm optimization, comprising the following steps:

[0006] S1: establishing a motion platform model, calculating the lengths of a plurality of push rods through inverse solution of the target attitude of the platform;

[0007] S2: designing a cascade PID control system, the cascade PID control system 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: genetic algorithm optimization for the attitude angle fuzzy controller constructed in S3 above;

[0010] S5: construct a genetic algorithm optimized fuzzy PID control system, compare the fuzzy PID control system with artificial fuzzy rules with the genetic algorithm optimized fuzzy PID control system, and obtain the control results through simulation.

[0011] Further, a preferred embodiment is provided, in which the push rods in S1 are three.

[0012] Further, a preferred embodiment is provided, in which the process of calculating the lengths of the plurality of push rods through inverse solution of the target attitude of the upper platform is:

[0013] The moving platform model forms a moving 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] In the formula: D 1i is the coordinate matrix of the three points D 11 ~D 13 on the moving platform in the initial state, D 1i * is the coordinate matrix of the three points D 11 ~D 13 on the moving platform in the updated state, R is the direction cosine matrix of the attitude of the upper platform, and T is the translation matrix of the attitude of the upper platform.

[0016]

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

[0018] T = [Δx Δy Δz - h] T

[0019] Wherein, h is the height difference between the upper and lower platforms in the initial state.

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

[0021]

[0022] D 1ix , D 1iy , D 1iz is the D1i x, y, z spatial coordinates of D 0ix x, y, z spatial coordinates of D 0iy x, y, z spatial coordinates of D 0iz x, y, z spatial coordinates of D 0i x, y, z spatial coordinates of D

[0023] Further, a preferred embodiment is provided, wherein S3 is specifically that a fuzzy controller in a PID control system is placed in a motion platform attitude angle position loop for tracking of a real-time attitude of the platform to a target attitude.

[0024] Further, a preferred embodiment is provided, wherein S3 is that a PID controller is placed in a motion platform attitude angle position loop for tracking of a real-time attitude of the platform to a target attitude.

[0025] Further, a preferred embodiment is provided, wherein S3 is specifically that:

[0026] S3.1: setting a range of system output, and setting a sampling period for real-time calculation of a control algorithm;

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

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

[0029] S3.4: performing fuzzy processing on input variables and output variables of the system, determining fuzzy sets and membership functions, and setting a fuzzy rule base, wherein the fuzzy rule base includes fuzzy rules between input variables and output variables in a fuzzy controller in a PID control system; and formulating a fuzzy rule table according to the fuzzy rules;

[0030] S3.5: calculating a gain value of proportional gain of a fuzzy controller in a PID control system by using the fuzzy rule table, and combining a controller in a traditional PID control system to obtain a final control signal.

[0031] Further, a preferred embodiment is provided, wherein a formula for obtaining a final control signal by the PID controller and the fuzzy control on PID parameter setting in S3.5 is:

[0032]

[0033]

[0034]

[0035]

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

[0037] Further, a preferred embodiment is provided, in which the genetic algorithm optimizes the fuzzy control process in S4 as follows:

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

[0039]

[0040] wherein Q(x) is the target function, and e(t) is the system error;

[0041] S4.2: encoding is performed to generate an initial population;

[0042] S4.3: the fitness is selected to reflect the degree of closeness of the individual to the optimal value of the population in the genetic algorithm optimization process;

[0043] S4.4: genetic algorithms are used to perform mutation, crossover, replication and other operations, thereby generating a new generation of individuals and realizing the evolution process of survival of the fittest;

[0044] S4.5: when the fitness of the optimal individual and the population fitness no longer increases, or the fitness of the optimal individual reaches a set threshold, or the number of iterations reaches a preset algorithm termination.

[0045] Further, a preferred embodiment is provided, in which S5 is specifically as follows:

[0046] S5.1: the genetic algorithm is executed to optimize the fuzzy rule and the PID controller parameter;

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

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

[0049] S5.4: the system sampling period and the control time are set;

[0050] S5.5: the step / following attitude angle setting is input;

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

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

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

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

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

[0056] Scheme three, a computer device, comprising a memory and a processor, the memory stores a computer program, when the processor runs the computer program stored in the memory, the processor executes the method of any one of the above implementations.

[0057] Scheme four, a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program is executed by the processor to realize the steps of the method of any one of the above implementations.

[0058] The present application has the advantages of:

[0059] The present application provides a motion platform fuzzy control method based on genetic algorithm optimization, which needs a fuzzy control method that can determine the appropriate PID parameter initial value, membership function and fuzzy rule of the control system, and also has global convergence algorithm optimization. The present application uses the global convergence ability and parallel optimization ability of genetic algorithm to perform multi-objective optimization on the quantization factor of fuzzy PID controller and the correction coefficient of the controller. Simulation proves the effectiveness of the method, which can be applied to the setting of PID parameters of motion platform control system and the determination of fuzzy rules.

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

[0061] This invention is also applicable to the tuning of PID parameters and the determination of fuzzy rules in motion platform control systems. Attached Figure Description

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

[0063] Figure 2 This is a block diagram of a motion platform control system based on a fuzzy control method for a motion platform optimized by a genetic algorithm, as described in Implementation Method 1.

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

[0065] Figure 4 The graph shows the fitness transformation curve after 100 generations of optimization using the genetic algorithm of this invention.

[0066] Figure 5 ΔK is the result of the genetic algorithm optimization in this invention before and after optimization. p Membership function comparison chart. Where (a) represents ΔK before optimization. p Membership function, (b) is the optimized ΔK p Membership function.

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

[0068] In the diagram, 1 is the attitude sensor, 2 is the upper platform, 3D reconstruction instrument, 3 is the camera, 4 is the universal joint, 5 is the base, 6 is the push rod, 7 is the motor and encoder, and 8 is the line laser. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0070] Implementation Method 1, see [link] Figures 1 to 6 This embodiment describes a fuzzy control method for a motion platform based on genetic algorithm optimization. The method includes the following steps:

[0071] S1: Establish a motion platform model and calculate the lengths of multiple push rods by inverse kinematics of the target posture on 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 above by using a genetic algorithm;

[0075] S5: construct a genetic algorithm optimized fuzzy PID control system, compare the fuzzy PID control system with artificial fuzzy rules with the genetic algorithm optimized fuzzy PID control system, and obtain the control results by simulation.

[0076] Reference Figure 1 In this embodiment, the attitude simulation motion platform structure is used, which is composed of a base 6, three push rods 7, a universal joint 5, an upper platform 2 and an attitude sensor 1. The base 6 is used to fix the push rods 7; the push rods 7 are driven to extend and retract by a direct current motor 8, so as to drive the attitude transformation of the upper platform 2; the universal joint 5 connects the push rods 7 and the upper platform 2; the attitude sensor 1 is connected to the upper platform 2 and is used to feed back the attitude signal; the upper platform 2 is connected to the laser three-dimensional reconstruction instrument 3 used in the experiment, such as the translation scanning system used in the laser three-dimensional reconstruction instrument 3; the camera 4 is used to record the extension and retraction of the push rods 7.

[0077] The three supporting points of the upper platform 2 and the lower platform are distributed in the positions of the isosceles right triangle with the hypotenuse D 11 D 12 , D 01 D 02 The upper platform 2 can make the translation movement along the z axis and the spatial rotation movement around the x axis and the y axis in the movement process of the three telescopic supporting rods, and when the lengths of the three push rods 7 are fixed, the position and posture of the upper motion platform can be uniquely determined.

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

[0079] T=[ΔxΔyΔz]

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

[0081] R=[α β γ]

[0082] Wherein, alpha, beta, gamma are rotation angles of the upper platform 2 around the X axis, Y axis and Z axis of the coordinate system. The space posture relationship of the motion platform refers to the relationship between the upper platform 2 posture and the lengths of the three push rods 7, and the motion platform control uses the inverse solution of the posture to solve the rod length and control the push rod 7 extension.

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

[0084] Embodiment two, this embodiment is a further limitation of the motion platform fuzzy control method based on genetic algorithm optimization described in embodiment one, the push rod in S1 is three.

[0085] Embodiment three, this embodiment is a further limitation of the motion platform fuzzy control method based on genetic algorithm optimization described in embodiment one, in S1, the process of calculating the lengths of the plurality of push rods through the inverse solution of the upper platform target posture is:

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

[0087]

[0088] In the formula: D 1i is the coordinate matrix of the three points D 11 ~D 13 on the dynamic platform in the initial state, and D 1i * is the coordinate matrix of the three points D 11 ~D 13 on the dynamic platform after updating the state, R is the direction cosine matrix of the upper platform posture, and T is the translation matrix of the upper platform posture.

[0089]

[0090] Alpha, beta, and gamma are respectively the rotation angles of the upper platform around the X axis, Y axis and Z axis of the coordinate system.

[0091] T = [Delta x Delta y Delta z - h] T

[0092] Wherein, h is the height difference between the initial state of the upper and lower platforms.

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

[0094]

[0095] D 1ix 、D 1iy 、D 1iz x, y, z space coordinates of D 1i 0ix 、D 0iy 、D 0iz x, y, z space coordinates of D 0i

[0096] Embodiment four, this embodiment is a further limitation of the motion platform fuzzy control method based on genetic algorithm optimization described in embodiment one, S3 is specifically: placing the fuzzy controller in the PID control system in the motion platform attitude angle position loop, for the real-time attitude of the upper platform to track the target attitude.

[0097] Reference 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 input of the attitude angle fuzzy controller is the expected target attitude angle, the feedback is the attitude angle measured by the fusion of the gyroscope, accelerometer and magnetometer in the IMU, and the output is the expected control attitude parameter; the expected control attitude parameter is calculated by the inverse solution of the attitude to obtain 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 a motor position loop, the input is the target position of the push rod 7, the feedback is the push rod 7 position measured by the encoder, and the output is the expected speed of the motor, the inner loop is a motor speed loop, the input is the target speed of the push rod 7, the feedback is the motor speed measured 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 the attitude to the expected attitude.

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

[0099] Embodiment five, this embodiment is a further limitation of the motion platform fuzzy control method based on genetic algorithm optimization described in embodiment one, S3 is specifically:

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

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

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

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

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

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

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

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

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

[0109] Embodiment six, the embodiment is a further limitation of the motion platform fuzzy control method based on genetic algorithm optimization described in embodiment five, the formula for obtaining the final control signal of the PID control algorithm and the fuzzy control of the PID parameter setting in S3.5 is:

[0110]

[0111]

[0112]

[0113]

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

[0115] Embodiment seven, this embodiment is a further limitation of the motion platform fuzzy control method based on genetic algorithm optimization described in Embodiment five, 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 target function of parameter selection, and its formula is:

[0117]

[0118] wherein, Q(x) is the target function, and e(t) is the system error;

[0119] S4.2: encoding is carried out to generate an initial population;

[0120] S4.3: the fitness is selected to reflect the degree of closeness of individuals to the optimal value of the population in the genetic algorithm optimization process;

[0121] S4.4: genetic algorithm is used to carry out mutation, crossover, replication and other operations, so as to generate a new generation of individuals, and realize the evolution process of survival of the fittest;

[0122] S4.5: when the fitness of the optimal individual and the population fitness no longer rises, or the fitness of the optimal individual reaches the set threshold value, or the iteration number reaches the preset algorithm termination.

[0123] In S4.2 of the embodiment, preferably, a binary coding mode is used, and the quantization factor and the proportional factor in the fuzzy controller are used as genes 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, an initial population is randomly generated in the initial calculation stage, and the value is unchanged in the iteration process.

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

[0125]

[0126] In this embodiment S4.4, the individual with higher fitness is selected to be inherited to the next generation population; the crossover means that the two parent individuals exchange their partial structures to generate new individuals; the mutation means that some gene values in the individual code string are replaced by other gene values to form a new individual. In this way, 100 iterations are performed, so that the optimization range is gradually reduced, and the individual approaches 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 this embodiment S4.5, specifically, the genetic algorithm program is set to automatically stop when the number of iterations reaches 100, and the optimal solution is output.

[0128] Embodiment eight, this embodiment is a further limitation of the motion platform fuzzy control method based on genetic algorithm optimization described in embodiment five, S5 specifically:

[0129] S5.1: execute genetic algorithm, optimize fuzzy rule and PID controller parameter;

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

[0131] S5.3: construct motion platform control system transfer function;

[0132] S5.4: set system sampling period and control time;

[0133] S5.5: input step / following attitude angle setting;

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

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

[0136] S5.8: execute PID control to get attitude angle control result;

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

[0138] Referring toFigures 4-6 The present embodiment is described, Figure 4 The fitness transformation curve of the genetic algorithm optimization for 100 generations after the simulation ends, and the fuzzy rules before and after optimization are shown in Fig. 4. p The membership function of the fuzzy PID is taken as an example, and the details are shown in Fig. 3. Figure 5

[0139] The simulation output control results are compared between the fuzzy PID according to the artificial fuzzy rules and the fuzzy PID optimized by the genetic algorithm, and the comparison results are shown in Fig. 5, in which (a) is the control effect of the pitch angle step response, and (b) is the control effect of the pitch angle following. Figure 6

[0140] In the pitch angle step response, the rise time of the fuzzy PID is 5.66 s, the rise time after the genetic algorithm optimization is 4.97 s, which is reduced by 1.24% compared with the same period; the adjustment time is 11.01 s, the adjustment time after the genetic algorithm optimization is 7.42 s, which is reduced by 32.67% compared with the same period; the steady-state error is 0.14°, the steady-state error after the genetic algorithm optimization is 0.03°, which is reduced by 78.57% compared with the same period; the overshoot is 2.49°, the overshoot after the genetic algorithm optimization is 0.61°, which is reduced by 75.50% compared with the same period. In the pitch angle following, the cumulative error Q(x) of the fuzzy PID is 21.170, the cumulative error Q(x) after the genetic algorithm optimization is 9.816, which is reduced by 53.6% compared with the same period; the overshoot is 1.24°, and the overshoot after the genetic algorithm optimization is almost 0.

[0141] In the ninth embodiment, a computer device is provided, which comprises a memory and a processor, and the memory stores a computer program. When the processor executes the computer program stored in the memory, the processor executes the method in any one of the above methods.

[0142] In the tenth embodiment, a computer readable storage medium is provided, which stores a computer program. When the processor executes the computer program, the steps of the method in any one of the first to eighth embodiments are implemented.

[0143] ​​The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be used to perform a specified function.

[0144] The above description is merely 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 foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0145] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A fuzzy control method for a motion platform based on genetic algorithm optimization, characterized in that, The motion platform includes an upper platform and a lower platform, and the method includes the following steps: S1: Establish a motion platform model and calculate the lengths of multiple push rods by inverse kinematics of the target posture on the upper platform; S2: Design a cascaded PID control system, which is composed of an attitude angle fuzzy controller and a push rod cascaded controller connected in series. S3: Design the attitude angle fuzzy controller constructed in S2 above; S4: Optimize the attitude angle fuzzy controller constructed in S3 above using a genetic algorithm; S5: Constructing a genetic algorithm to optimize a fuzzy PID control system; In S1, the process of calculating the lengths of multiple push rods using the inverse kinematics of the target attitude on the upper platform is as follows: After the motion platform model moves, it forms a moving platform D. 11 ~D 13 D 11 ~D 13 The coordinates of the three points in the static coordinate system are: In the formula: D on the moving platform in the initial state 11 ~D 13 Three-point coordinate matrix To update the status of the D platform 11 ~D 13 The three-point coordinate matrix, R is the direction cosine matrix of the upper platform attitude, and T is the translation matrix of the upper platform attitude; , , These represent the rotation angles of the upper platform around the X, Y, and Z axes of the coordinate system, respectively. Where h is the initial height difference between the upper and lower platforms; The lengths of each push rod are then obtained as follows: for x, y, z spatial coordinates for The x, y, z spatial coordinates; S5 specifically refers to: S5.1: Execute the genetic algorithm to optimize the parameters of the fuzzy rules and the fuzzy controller in the 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 period and control time; S5.5: Input step or follow attitude angle setting; S5.6: Calculate the error e and the rate of change of error e c ; S5.7: Perform fuzzy logic deduction to obtain... , , And calculate , , ; S5.8: Execute PID control to obtain attitude angle control results; S5.9: Jump to S5.6 to perform cyclic control; S5.10: The fuzzy control system pauses after the set control time is reached; The fuzzy PID control system with manually defined fuzzy rules was compared with the fuzzy PID control system optimized by the genetic algorithm. The simulation control results showed that the performance of the fuzzy PID control system optimized by the genetic algorithm was better than that of the fuzzy PID control system with manually defined fuzzy rules.

2. The fuzzy control method for a motion platform based on genetic algorithm optimization according to claim 1, characterized in that, There are three pushers in S1.

3. The fuzzy control method for a motion platform based on genetic algorithm optimization according to claim 1, characterized in that, Specifically, S3 involves placing the fuzzy controller in the PID control system within the motion platform's attitude angle position loop for real-time attitude tracking of the target attitude on the upper platform.

4. The fuzzy control method for a motion platform based on genetic algorithm optimization according to claim 1, characterized in that, S3 specifically refers to: S 3.1: Set the range of system output and set the sampling period to control the real-time calculation of the algorithm; S3.2: The input variables are the systematic error e and the rate of change e of the systematic error between the target attitude angle and the actual attitude angle. c The output variables are the three correction values ​​of the control parameters in the fuzzy controller of the PID control system; S3.3: Adjust the proportional gain, integral gain, and derivative gain parameters of the fuzzy controller in the PID control system; S3.4: Fuzzyenize the system's input and output variables, determine the fuzzy set and membership function, and set up a fuzzy rule base, including fuzzy rules between the input and output variables in the fuzzy controller of the PID control system; formulate a fuzzy rule table based on 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.

5. The fuzzy control method for a motion platform based on genetic algorithm optimization according to claim 4, characterized in that, The formula for deriving the final control signal by combining the controller in a traditional PID control system, as described in S3.5, is as follows: In the formula: For the output of the control system; The cumulative error is de(k) / dk; the rate of change of error is de(k) / dk. , , The controller output value; , , These are the basic values ​​for the PID parameters; , , These are the online tuning values ​​for the PID parameters.

6. The fuzzy control method for a motion platform based on genetic algorithm optimization according to claim 1, characterized in that, The attitude angle fuzzy controller built in S4 is optimized using a genetic algorithm as follows: S4.1: The time integral performance index of absolute error is used as the objective function for parameter selection; S4.2: Encode to generate the initial population; S4.3: Select fitness to reflect how well an individual approaches the population optimum during the genetic algorithm optimization process; S4.4: Use genetic algorithms to perform mutation, crossover, and replication operations to generate a new generation of individuals, thus achieving the evolutionary process of survival of the fittest; S4.5: Optimization is complete 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 limit.

7. A computer device, the device comprising a memory and a processor, characterized in that, The memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor performs the method according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

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