A bridge crane PID control method, system, device and storage medium

Abstract

The present invention discloses a PID control method, system, equipment and storage medium for a bridge crane, comprising obtaining a parameter vector of a bridge crane PID controller, calculating a fitness function value according to the parameter vector, dividing sparrows in an optimization model into finders and joiners that snatch food from the finder according to the fitness function value, updating the finder position according to a finder position update formula, introducing normal variation into the joiner position update formula to update the finder position, updating the joiner position update result using an orthogonal opposition learning strategy, randomly selecting some sparrow individuals to calculate according to a detection formula, and obtaining optimal PID control parameters according to the optimal value sparrow position, thereby realizing intelligent operation of a large-scale operating machine bridge crane, overcoming the problems of large swing angle amplitude and slow positioning response of the bridge crane during operation, and improving work efficiency and safety performance.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge crane control, and in particular to a bridge crane PID control method, system, equipment and storage medium. Background Art

[0002] Bridge cranes are widely used in the lifting, transportation, loading and unloading of materials in various industries such as metallurgy, coal, and electricity due to their low cost, simple structure, and strong load-bearing capacity. The main research is on its control system, so as to improve the stability and reliability of the equipment and solve the problems of large swing angle and slow positioning during its operation, so that the operation can be completed with high quality under various working conditions. In the controllers that have been used, such as PID controllers, fuzzy controllers, and sliding membrane controllers, the operation is usually completed by the experience accumulated by workers in actual work, and the control effect is not very ideal. Therefore, the selection of various parameters of the control system is an important problem that needs to be overcome in the development process of bridge cranes.

[0003] Domestic and foreign research mainly solves the problem by improving mechanical structure and proposing control methods. Mechanical structure optimization is to conduct stress and strain static analysis on its most dangerous working conditions through modeling to obtain the limit effect force and deflection. Then, combined with the sensitivity parameter requirements, the algorithm is optimized and proofread and debugged through simulation of software such as Matlab and Ansys. This method is costly and there are limitations in physical debugging, so control strategies based on energy analysis, nonlinearity of sliding membranes, fuzzy control, etc. are proposed. With the introduction of various control strategies, active control methods are gradually divided into two categories by determining whether output feedback is required: open-loop control and closed-loop control. Among them, closed-loop control has the most ideal control effect. In closed-loop control, there are delayed feedback controllers, Lyapunov methods, linear quadratic regulators and other methods that rely on model establishment, and fuzzy control, neural networks and other control methods that do not rely on model establishment. Compared with control methods that do not rely on model establishment, control methods that rely on model establishment have the advantage of reliable control systems, and the disadvantages of slow response speed and many iterations, so that the self-adjustment ability is poor when the environment changes. PID controllers are widely used in industry and can also be used for bridge crane control. PID controllers have simple structures, easy parameter adjustment, and good stability. The commonly used PID controller control method is to use a PID controller to control the position of the crane or to use PID control for the position and angle separately, and finally weighted to obtain the controller output. Simulation results and specific applications show that single PID position control cannot obtain satisfactory control results.

[0004] At present, the common solutions to the problem are mainly divided into two types: mechanical structure optimization and adding active control systems. Bridge cranes are heavy machinery, and it is difficult to improve the mechanical structure and the precision is low. Among active control systems, closed-loop control systems are the most widely used because of their self-tuning ability and strong anti-interference ability. Closed-loop control is divided into three categories: linear control, nonlinear control and intelligent control. The bridge crane system is a typical nonlinear strong coupling system. It is obviously difficult to obtain ideal results with a linear control system; nonlinear control systems also have the disadvantage of being very sensitive to modeling errors; intelligent control has strong robustness to system modeling errors and external interference, but there is currently a problem of difficulty in selecting control system parameters. Summary of the invention

[0005] The present invention aims to at least solve the technical problems existing in the prior art. To this end, the present invention proposes a bridge crane PID control method, system, device and storage medium, which can quickly find the optimal PID control parameters, realize the intelligent operation of the large-scale operation equipment bridge crane, overcome the problems of large swing angle amplitude and slow positioning response of the bridge crane during operation, and improve work efficiency and safety performance.

[0006] A first aspect of the present invention provides a bridge crane PID control method, comprising the following steps:

[0007] Step S101: Obtaining a parameter vector of a bridge crane PID controller;

[0008] Step S102: Calculate the fitness function value according to the parameter vector;

[0009] Step S103: constructing an optimization model of the parameter vector of the PID controller according to the sparrow search algorithm, and dividing the sparrows in the optimization model into finders and joiners that snatch food from the finders according to the fitness function value;

[0010] Step S104: updating the finder position according to the finder position update formula to obtain a finder position update result;

[0011] Step S105: introducing normal variation into the participant position update formula to update the participant position, and obtaining the participant first position update result;

[0012] Step S106: using an orthogonal opposition learning strategy to update the first position update result of the joiner to obtain the second position update result of the joiner;

[0013] Step S107: randomly selecting some sparrow individuals and calculating according to the detection formula to obtain the detection position update result;

[0014] Step S108: If the number of iterations is less than the maximum number of iterations, jump to step S104, otherwise output the optimal value sparrow position and fitness function value among the discoverer position update result, the joiner second position update result and the reconnaissance position update result, and obtain the optimal PID control parameters according to the optimal value sparrow position.

[0015] According to the embodiments of the present invention, there are at least the following technical effects:

[0016] The method obtains the parameter vector of the PID controller of the bridge crane, calculates the fitness function value according to the parameter vector, constructs the optimization model of the parameter vector of the PID controller according to the sparrow search algorithm, divides the sparrows in the optimization model into discoverers and joiners who rob the discoverer's food according to the fitness function value, updates the discoverer's position according to the discoverer position update formula to obtain the discoverer position update result, introduces normal variation into the joiner position update formula to update the joiner position to obtain the joiner's first position update result, uses the orthogonal opposition learning strategy to update the joiner's first position update result to obtain the joiner's second position update result, randomly selects some sparrow individuals and calculates according to the detection formula to obtain the detection position update result, if the number of iterations is less than the maximum number of iterations, the cycle continues, otherwise the optimal value sparrow position and fitness function value among the discoverer position update result, the joiner's second position update result and the detection position update result are output, and the optimal PID control parameters are obtained according to the optimal value sparrow position, thereby realizing the intelligent operation of the large-scale operation equipment bridge crane, overcoming the problems of large swing angle amplitude and slow positioning response of the bridge crane during operation, and improving work efficiency and safety performance.

[0017] According to some embodiments of the present invention, before obtaining the parameter vector of the bridge crane PID controller, the bridge crane PID control method further includes:

[0018] Setting a population size according to the number of parameter vectors of the overhead crane PID controller;

[0019] The parameter vector of the overhead crane PID controller is randomly initialized.

[0020] According to some embodiments of the present invention, the calculation formula of the fitness function value is:

[0021] W=[w1,w2,w3,w4,w5l

[0022]

[0023] Where J is the fitness function value, t is the current iteration number, ζ(t) is the position deviation signal parameter of the t-th iteration, x3(t) is the swing angle signal parameter of the t-th iteration, u(t) is the driving force parameter of the t-th iteration, and u max is the maximum value of the crane driving force during the control process, x 3max is the maximum angle of load swing, W is the weight matrix, and W = [w1, w2, w3, w4, w5] = [0.5, 0.3, 0.2, 0.1, 0.1].

[0024] According to some embodiments of the present invention, the finder location update formula is:

[0025]

[0026] in, is the position of the j-th dimension of the i-th sparrow in the t-th iteration, iter max is the maximum number of iterations, α is a random number that obeys a normal distribution from 0 to 1, Q is a random number that obeys a normal distribution from 0 to 1, L is a 1×D-dimensional vector whose elements are all 1, R2 is the warning value, and ST is the safety value.

[0027] According to some embodiments of the present invention, the joiner location update formula is:

[0028] A + ==A'(A.A') -1

[0029]

[0030]

[0031]

[0032] Where A is a 1xD dimensional matrix composed of 1 and -1, A′ is the transpose of the A matrix, and r is a random number in the range [0,1]. is the optimal position of the jth dimension at the tth iteration, is the worst position of the j-th dimension at the t-th iteration.

[0033] According to some embodiments of the present invention, the method of updating the first position update result of the joiner by using the orthogonal opposition learning strategy to obtain the second position update result of the joiner includes:

[0034] Generate an opposing position of the update result of the first position of the joiner according to the opposing learning strategy;

[0035] Constructing an orthogonal candidate solution using an orthogonal table according to the update result of the first position of the joiner and the opposing position;

[0036] The orthogonal candidate solution is evaluated according to the fitness function value corresponding to the orthogonal candidate solution to obtain the second position update result of the joiner.

[0037] According to some embodiments of the present invention, the detection formula is:

[0038]

[0039] Among them, β is a random number that obeys a normal distribution from 0 to 1, lb is the lower bound of the preset search space, ub is the upper bound of the preset search space, and f i is the fitness function value of the i-th sparrow, f g is the fitness function value of the current optimal individual.

[0040] A second aspect of the present invention provides a bridge crane PID control system, which includes a bridge crane PID control method as described in the first aspect of the present invention, and the bridge crane PID control system includes:

[0041] The data acquisition module is used to obtain the parameter vector of the bridge crane PID controller;

[0042] The fitness function value calculation module is used to calculate the fitness function value according to the parameter vector;

[0043] The sparrow classification module is used to construct an optimization model of the parameter vector of the PID controller according to the sparrow search algorithm, and to divide the sparrows in the optimization model into finders and joiners who snatch food from the finders according to the fitness function value;

[0044] The finder location update module is used to update the finder location according to the finder location update formula to obtain a finder location update result;

[0045] The first position update module of the participant is used to introduce normal variation into the participant position update formula to update the participant position and obtain the participant first position update result;

[0046] The participant second position update module is used to update the participant first position update result by using an orthogonal opposition learning strategy to obtain the participant second position update result;

[0047] The detection position update module is used to randomly select some sparrow individuals and calculate according to the detection formula to obtain the detection position update result;

[0048] The iteration module is used to jump to the discoverer position update module if the number of iterations is less than the maximum number of iterations, otherwise output the optimal value sparrow position and fitness function value among the discoverer position update result, the joiner second position update result and the reconnaissance position update result, and obtain the optimal PID control parameters according to the optimal value sparrow position.

[0049] The system obtains the parameter vector of the PID controller of the bridge crane, calculates the fitness function value according to the parameter vector, constructs the optimization model of the parameter vector of the PID controller according to the sparrow search algorithm, divides the sparrows in the optimization model into discoverers and joiners who snatch the food of the discoverer according to the fitness function value, updates the discoverer position according to the discoverer position update formula to obtain the discoverer position update result, introduces normal variation into the joiner position update formula to update the joiner position to obtain the joiner first position update result, uses the orthogonal opposition learning strategy to update the joiner first position update result to obtain the joiner second position update result, randomly selects some sparrow individuals and calculates according to the detection formula to obtain the detection position update result, if the number of iterations is less than the maximum number of iterations, the cycle continues, otherwise the optimal value sparrow position and fitness function value among the discoverer position update result, the joiner second position update result and the detection position update result are output, and the optimal PID control parameters are obtained according to the optimal value sparrow position, which realizes the intelligent operation of the large-scale operation equipment bridge crane, overcomes the problems of large swing angle amplitude and slow positioning response of the bridge crane during operation, and improves work efficiency and safety performance.

[0050] The third aspect of the present invention provides a bridge crane PID control electronic device, comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to execute the above-mentioned bridge crane PID control method.

[0051] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the above-mentioned bridge crane PID control method.

[0052] It should be noted that the beneficial effects between the second to fourth aspects of the present invention and the prior art are the same as the beneficial effects between the above-mentioned bridge crane PID control system and the prior art, and will not be described in detail here.

[0053] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0055] Figure 1 This is a schematic diagram of the PID controller principle;

[0056] Figure 2 is a flow chart of a PID control method for a bridge crane according to an embodiment of the present invention;

[0057] Figure 3 yes Figure 2 Flow chart of step S106 in FIG.

[0058] Figure 4 It is a flow chart of an improved sparrow search algorithm of a bridge crane PID control method according to an embodiment of the present invention;

[0059] Figure 5 It is a flow chart of a PID control system for a bridge crane according to one embodiment of the present invention. DETAILED DESCRIPTION

[0060] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0061] In the description of the present invention, if there is a description of first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0062] In the description of the present invention, it should be understood that descriptions involving orientation, such as orientation or positional relationship indicated as up, down, etc., are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0063] In the description of the present invention, it should be noted that, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific content of the technical solution.

[0064] Before introducing the embodiments of the present invention, a brief description of the PID controller and the swarm intelligence optimization algorithm is given:

[0065] In order to make the bridge crane automatically controlled and achieve the effect of rapid positioning of the crane and small swing angle, the PID controller is the most commonly used automatic controller. It has three links, namely proportional control (P), integral control (I) and differential control (D). The control principle diagram is as follows Figure 1 : Figure 1 Where r(t) is the current position or swing angle of the bridge crane, e(t) is the error at time t, u(t) is the output of the controller, and c(t) is the current position of the crane or the load swing angle. Figure 1 The three control methods in the figure are all aimed at making the output reach the target value, or making the error equal to 0, but the effects of different control methods are also different.

[0066] Among them, proportional control is to multiply the error by an amplification factor KP, and then use the result as the input of the controlled object. Then the error e(t+1) at the next moment will be reduced, so that the current state is close to the target value (making the crane position or swing angle much closer to the target value). Proportional control is the basis of the other two control methods, and the other two control methods are based on proportional control and then optimized control.

[0067] Differential control is mainly used to improve the above-mentioned proportional control, so that the state quantity decays and tends to a constant value (making the crane stop, but not necessarily at the target value). Like proportional control, it has a key coefficient TD. This parameter affects the effect of differential control, such as premature convergence.

[0068] Integral control is used to further strengthen the above-mentioned PD control. Its function is to eliminate steady-state errors (i.e., to stop the crane at the target value). Its key parameter is TI. Over-adjustment of the parameter will lead to inaccurate control.

[0069] Parameter optimization of bridge crane PID controller: From the three control methods of the above PID controller, it can be seen that the most important thing about PID control is the determination of the coefficients of the three control methods: KP, TI, and TD. Moreover, because the three PID control methods affect each other, it is necessary to find suitable values ​​​​of the three parameters at the same time. Therefore, the parameter optimization of the bridge crane PID controller is to connect the PID controller with the bridge crane system model, and use the optimization algorithm to find a PID controller parameter that can enable the bridge crane to reach the specified position as quickly as possible and the swing angle is quickly suppressed.

[0070] The technical principle of the swarm intelligence optimization algorithm is: take the individual in the animal population as a point, and this point can change its position in the solution space according to its own update method, that is, the position of each individual is a solution. Substituting these solutions into the objective function, a value can be calculated, which is called the fitness function value. Each position of the individual in the population can correspond to a fitness function value of the objective function. It is usually set that the smaller the fitness function value, the better the solution represented by the position, that is, the optimal solution.

[0071] Therefore, the swarm intelligence optimization algorithm continuously iterates and updates (with a maximum number of times limit) the position of individuals in the population, calculates the fitness function value corresponding to the position, and then compares these fitness function values ​​to take the minimum value as the optimal value of the objective function, that is, the solution corresponding to the minimum fitness function value is taken as the optimal solution of the objective function.

[0072] The general algorithm regards the fitness function value as the abundance of food. The smaller the fitness value, the more abundant the food. In the swarm intelligence algorithm, the position update of individuals is generally designed based on the competition between individuals to find food. Therefore, in the later stage of the algorithm iteration update, the individuals in the population gather near the current optimal solution and converge. At this time, a situation will occur, that is, falling into the local optimal solution: it means that due to its own position update mechanism, the individual is difficult to find a better solution than the current position (difficult to jump out of the local optimum), but there are indeed better positions in the solution space. Therefore, the problems that swarm intelligence algorithms will encounter are derived.

[0073] Population diversity: It can be considered as the distribution of individuals in the solution space. If the population converges near the current optimal solution in the later stage, it means that the population diversity is low, which means that if it falls into the local optimal solution, it will be difficult to escape.

[0074] Convergence speed: As mentioned above, the algorithm must not only find the best solution, but also find the optimal solution quickly. At this time, the convergence speed of the algorithm must be considered. If the algorithm finds the optimal solution, the individuals in the population will slowly move towards the optimal solution due to the update mechanism, which will seriously slow down the operation speed of the algorithm, which is not worth the loss.

[0075] Stability: If the algorithm is too random, or individuals in the population change their positions with large steps at different iteration stages, the algorithm will not be robust enough and will not be widely applicable. It cannot be applied to other industrial engineering applications that require stability.

[0076] When the swarm intelligence algorithm is running, the population must be restricted, that is, the initial value must be set. Otherwise, it is not practical to search endlessly in a boundless space. Therefore, the swarm intelligence optimization algorithm must first initialize the population, set the upper and lower bounds of the solution space, the number of individuals in the population, the maximum number of iterations, the dimension, and the parameters required by different algorithms.

[0077] The search principle of the sparrow search algorithm is derived from the predation and anti-predation behavior of sparrows. In the sparrow search algorithm, sparrows are divided into discoverers and joiners. The discoverer is the individual with the highest fitness value, and the other individuals are joiners. When a sparrow finds danger, the current individual needs to give up food and escape to other places.

[0078] Adversarial learning is a commonly used strategy to escape from the local optimal solution. When the original sparrow search algorithm is used for detection, the sparrows in the optimal position move closer to the worst solution, and the sparrows in other positions move closer to the optimal value. Although searching for the worst solution position can avoid falling into the local optimum to a certain extent, it is not conducive to the convergence of the population. Adversarial learning can not only help individuals escape from the current position quickly, but also the fitness value of the opposing position is more likely to be better than the current worst position.

[0079] The orthogonal adversarial learning strategy is a method that uses the current solution and the opposing solution to find the best combination of the levels of different factors with a smaller number of experiments through orthogonal experimental design. The basic idea of ​​this strategy is to use the current position and its opposing position to construct orthogonal candidate solutions according to the orthogonal table, then evaluate each candidate solution, and finally find the best orthogonal combination. In this way, the information of each dimension in the individual and the opposing individual is fully utilized to find the best combination.

[0080] At present, the common solutions to the problem are mainly divided into two types: mechanical structure optimization and adding active control systems. Bridge cranes are heavy machinery, and it is difficult to improve the mechanical structure and the precision is low. Among active control systems, closed-loop control systems are the most widely used because of their self-tuning ability and strong anti-interference ability. Closed-loop control is divided into three categories: linear control, nonlinear control and intelligent control. The bridge crane system is a typical nonlinear strong coupling system. It is obviously difficult to obtain ideal results with a linear control system; nonlinear control systems also have the disadvantage of being very sensitive to modeling errors; intelligent control has strong robustness to system modeling errors and external interference, but there is currently a problem of difficulty in selecting control system parameters.

[0081] In order to solve the above technical defects, refer to Figure 2 One embodiment of the present invention provides a PID control method for a bridge crane, comprising:

[0082] Step S101: Obtain a parameter vector of the bridge crane PID controller, wherein the length of the parameter vector is the number of parameters of the bridge crane PID controller, and the value of the parameter vector is the parameter value of the PID controller.

[0083] Step S102: Calculate the fitness function value according to the parameter vector.

[0084] Step S103: construct an optimization model of the parameter vector of the PID controller according to the sparrow search algorithm, and divide the sparrows in the optimization model into discoverers and joiners who snatch the discoverer's food according to the fitness function value.

[0085] Step S104: updating the finder location according to the finder location update formula to obtain a finder location update result.

[0086] Step S105: Introduce normal variation into the joiner position update formula to update the joiner position and obtain the joiner's first position update result.

[0087] Step S106: using the orthogonal opposition learning strategy to update the first position update result of the joiner, and obtain the second position update result of the joiner.

[0088] Step S107: Randomly select some sparrow individuals and calculate according to the detection formula to obtain the detection position update result.

[0089] Step S108: If the number of iterations is less than the maximum number of iterations, jump to step S104, otherwise output the optimal value sparrow position and fitness function value among the discoverer position update result, the joiner second position update result and the reconnaissance position update result, and obtain the optimal PID control parameters according to the optimal value sparrow position.

[0090] The method obtains the parameter vector of the PID controller of the bridge crane, calculates the fitness function value according to the parameter vector, constructs the optimization model of the parameter vector of the PID controller according to the sparrow search algorithm, divides the sparrows in the optimization model into discoverers and joiners who rob the discoverer's food according to the fitness function value, updates the discoverer's position according to the discoverer position update formula to obtain the discoverer position update result, introduces normal variation into the joiner position update formula to update the joiner position to obtain the joiner's first position update result, uses the orthogonal opposition learning strategy to update the joiner's first position update result to obtain the joiner's second position update result, randomly selects some sparrow individuals and calculates according to the detection formula to obtain the detection position update result, if the number of iterations is less than the maximum number of iterations, the cycle continues, otherwise the optimal value sparrow position and fitness function value among the discoverer position update result, the joiner's second position update result and the detection position update result are output, and the optimal PID control parameters are obtained according to the optimal value sparrow position, thereby realizing the intelligent operation of the large-scale operation equipment bridge crane, overcoming the problems of large swing angle amplitude and slow positioning response of the bridge crane during operation, and improving work efficiency and safety performance.

[0091] In some embodiments, before obtaining the parameter vector of the bridge crane PID controller, the bridge crane PID control method further includes steps S1091 to S1092:

[0092] Step S1091: Set the population size according to the number of parameter vectors of the bridge crane PID controller.

[0093] Step S1092: Randomly initialize the parameter vector of the bridge crane PID controller.

[0094] Specifically: The bridge crane PID controller has two goals, namely displacement and swing angle, which are to move the load to the current position as quickly as possible and to minimize the swing angle. Therefore, two sets of PID controllers are needed to control the two target quantities of displacement and swing angle respectively. The two sets of PID controller parameters to be adjusted are named u(t)1, ζ(t)1, x3(t)1, u(t)2, ζ(t)2, x3(t)2, where u(t)1, ζ(t)1, x3(t)1 are the parameter values ​​of the first set of PID controllers, and u(t)2, ζ(t)2, x3(t)2 are the parameter values ​​of the second set of PID controllers.

[0095] In step S102 of some embodiments, the calculation formula of the fitness function value is:

[0096] W=[w1,w2,w3,w4,w5l

[0097]

[0098] Where J is the fitness function value, t is the current iteration number, ζ(t) is the position deviation signal parameter of the t-th iteration, x3(t) is the swing angle signal parameter of the t-th iteration, u(t) is the driving force parameter of the t-th iteration, and u max is the maximum value of the crane driving force during the control process, x 3max is the maximum angle of load swing, W is the weight matrix, and W = [w1, w2, w3, w4, w5] = [0.5, 0.3, 0.2, 0.1, 0.1].

[0099] In step S104 of some embodiments, the finder location update formula is:

[0100]

[0101] in, is the position of the j-th dimension of the i-th sparrow in the t-th iteration, iter max is the maximum number of iterations, α is a random number that obeys a normal distribution from 0 to 1, Q is a random number that obeys a normal distribution from 0 to 1, L is a 1×D-dimensional vector whose elements are all 1, R2 is the warning value, and ST is the safety value.

[0102] In step S105 of some embodiments, the joiner location update formula is:

[0103] A + =A'(A.A') -1

[0104]

[0105]

[0106]

[0107] Where A is a 1xD dimensional matrix composed of 1 and -1, A′ is the transpose of the A matrix, and r is a random number in the range [0,1]. is the optimal position of the jth dimension at the tth iteration, is the worst position of the j-th dimension at the t-th iteration.

[0108] The normal mutation operator is introduced to perturb the optimal position of the joiner, allowing the non-hungry joiner to learn from the mutation solution of the discoverer's optimal position, which can improve the diversity of the population and the ability to escape from the local optimum.

[0109] Reference Figure 3 In some embodiments, step S106 may include but is not limited to steps S1001 to S1003:

[0110] Step S1001: Generate an opposing position of the first position update result of the joiner according to the opposing learning strategy.

[0111] Step S1002: construct an orthogonal candidate solution using an orthogonal table according to the update result of the first position of the joiner and the opposing position.

[0112] Step S1003: Evaluate the orthogonal candidate solution according to the fitness function value corresponding to the orthogonal candidate solution to obtain the update result of the second position of the joiner.

[0113] Aiming at the problems of excessive randomness, insufficient stability and poor performance of the sparrow search algorithm in solving complex problems, the present invention proposes an orthogonal adversarial learning strategy and applies it to the position update of the joiner, thereby improving the robustness and practicality of the sparrow search algorithm.

[0114] In step S107 of some embodiments, the detection formula is:

[0115]

[0116] Among them, β is a random number that obeys a normal distribution from 0 to 1, lb is the lower bound of the preset search space, ub is the upper bound of the preset search space, and f i is the fitness function value of the i-th sparrow, f g is the fitness function value of the current optimal individual.

[0117] The invention improves the updating formula of the scout by introducing an adversarial learning strategy, thereby increasing the convergence speed of the algorithm.

[0118] In some embodiments, refer to Table 1, which is a pseudo code of the bridge crane PID control method:

[0119] The input is the parameter vector of the bridge crane PID controller, where the length of the parameter vector is the number of parameters of the bridge crane PID controller, and the value of the parameter vector is the parameter value of the PID controller.

[0120] The output is the optimal PID control parameters.

[0121] The steps are:

[0122] (1) According to the number of parameter vectors of the bridge crane PID controller, the population size is set and the parameter vectors of the bridge crane PID controller are randomly initialized.

[0123] (2) The fitness function value is calculated based on the parameter vector and sorted.

[0124] (3) If the number of iterations is less than the maximum number of iterations.

[0125] (4) Set the population size to N, construct an optimization model of the parameter vector of the PID controller based on the sparrow search algorithm, and divide the sparrows in the optimization model into discoverers and joiners who steal the discoverer's food according to the fitness function value.

[0126] (5) Update the finder position according to the finder position update formula to obtain the finder position update result.

[0127] (6) Introduce normal variation into the joiner position update formula to update the joiner position and obtain the joiner's first position update result.

[0128] (7) Use the orthogonal adversarial learning strategy to update the first position update result of the joiner to obtain the second position update result of the joiner.

[0129] (8) Randomly select some sparrow individuals and calculate according to the reconnaissance formula to obtain the reconnaissance position update result.

[0130] (9) Output the optimal sparrow position and fitness function value, and obtain the optimal PID control parameters according to the optimal sparrow position.

[0131]

[0132]

[0133] Table 1

[0134] Reference Figure 4 In order to facilitate the understanding of those skilled in the art, a set of optimal embodiments are provided below:

[0135] 1. Get parameter vector:

[0136] Set the population size according to the number of parameter vectors for the overhead crane PID controller.

[0137] The parameter vector of the bridge crane PID controller is randomly initialized. The bridge crane PID controller has two goals, namely displacement and swing angle, which are to move the load to the current position as quickly as possible and to minimize the swing angle. Therefore, two sets of PID controllers are needed to control the two target quantities of displacement and swing angle respectively. The two sets of PID controller parameters to be adjusted are named u(t)1, ζ(t)1, x3(t)1, u(t)2, ζ(t)2, x3(t)2, where u(t)1, ζ(t)1, x3(t)1 are the parameter values ​​of the first set of PID controllers, and u(t)2, ζ(t)2, x3(t)2 are the parameter values ​​of the second set of PID controllers.

[0138] Get the parameter vector of the bridge crane PID controller, where the length of the parameter vector is the number of parameters of the bridge crane PID controller, and the value of the parameter vector is the parameter value of the PID controller.

[0139] 2. Use the improved sparrow search algorithm to find the optimal parameters:

[0140] Step 1: Calculate the fitness function value based on the parameter vector and sort it in ascending order.

[0141] The fitness function calculation formula is:

[0142] W=[w1,w2,w3,w4,w5l

[0143]

[0144] Where J is the fitness function value, t is the current iteration number, ζ(t) is the position deviation signal parameter of the t-th iteration, x3(t) is the swing angle signal parameter of the t-th iteration, u(t) is the driving force parameter of the t-th iteration, and u max is the maximum value of the crane driving force during the control process, x 3max is the maximum angle of load swing, W is the weight matrix, and W = [w1, w2, w3, w4, w5] = [0.5, 0.3, 0.2, 0.1, 0.1].

[0145] Step 2: Construct an optimization model of the parameter vector of the PID controller based on the sparrow search algorithm, and divide the sparrows in the optimization model into discoverers and joiners who snatch the discoverer's food according to the fitness function value.

[0146] Step 3: Update the finder position according to the finder position update formula to obtain the finder position update result. The finder position update formula is:

[0147]

[0148] in, is the position of the j-th dimension of the i-th sparrow in the t-th iteration, iter max is the maximum number of iterations, α is a random number that obeys a normal distribution from 0 to 1, Q is a random number that obeys a normal distribution from 0 to 1, L is a 1×D-dimensional vector whose elements are all 1, R2 is the warning value, and ST is the safety value.

[0149] Step 4: Introduce normal variation into the participant position update formula to update the participant position and obtain the participant's first position update result. The participant update formula is:

[0150] A + =A'(A.A') -1

[0151]

[0152]

[0153]

[0154] Where A is a 1xD dimensional matrix composed of 1 and -1, A′ is the transpose of the A matrix, and r is a random number in the range [0,1]. is the optimal position of the jth dimension at the tth iteration, is the worst position of the j-th dimension at the t-th iteration.

[0155] Step 5: Use the orthogonal adversarial learning strategy to update the first position update result of the participant to obtain the second position update result of the participant:

[0156] Generate the opposing position of the joiner's first position update result according to the opposing learning strategy.

[0157] According to the update result of the first position of the joiner and the opposite position, an orthogonal table is used to construct an orthogonal candidate solution.

[0158] The orthogonal candidate solutions are evaluated according to the fitness function values ​​corresponding to the orthogonal candidate solutions to obtain the update result of the second position of the joiner.

[0159] Step 6: Randomly select some sparrow individuals and calculate according to the reconnaissance formula to obtain the updated reconnaissance position result.

[0160] The detection formula is:

[0161]

[0162] Among them, β is a random number that obeys a normal distribution from 0 to 1, lb is the lower bound of the preset search space, ub is the upper bound of the preset search space, and f i is the fitness function value of the i-th sparrow, f g is the fitness function value of the current optimal individual.

[0163] 3. Get the optimal parameter value:

[0164] If the number of iterations is less than the maximum number of iterations, the loop continues, otherwise the optimal sparrow position and fitness function value among the discoverer's position update result, the joiner's second position update result and the reconnaissance position update result are output, and the optimal PID control parameters are obtained based on the optimal sparrow position.

[0165] When the optimization is completed, the positions corresponding to the optimal set of results (u(t)1, ζ(t)1, x3(t)1, u(t)2, ζ(t)2, x3(t)2) will be transmitted to the controller as parameters to complete the control of the bridge crane.

[0166] In addition, refer to Figure 5 The present invention also provides a PID control system for a bridge crane, comprising a data acquisition module 1100, a fitness function value calculation module 1200, a sparrow classification module 1300, a discoverer position update module 1400, a joiner first position update module 1500, a joiner second position update module 1600, a detection position update module 1700 and an iteration module 1800, wherein:

[0167] The data acquisition module 1100 is used to obtain the parameter vector of the bridge crane PID controller, wherein the length of the parameter vector is the number of parameters of the bridge crane PID controller, and the value of the parameter vector is the parameter value of the PID controller.

[0168] The fitness function value calculation module 1200 is used to calculate the fitness function value according to the parameter vector.

[0169] The sparrow classification module 1300 is used to construct an optimization model of the parameter vector of the PID controller according to the sparrow search algorithm, and to classify the sparrows in the optimization model into discoverers and joiners who snatch the food of the discoverers according to the fitness function value.

[0170] The finder location update module 1400 is used to update the finder location according to the finder location update formula to obtain a finder location update result.

[0171] The participant first position update module 1500 is used to introduce normal variation into the participant position update formula to update the participant position and obtain the participant first position update result.

[0172] The participant second position update module 1600 is used to update the participant first position update result by using the orthogonal opposition learning strategy to obtain the participant second position update result.

[0173] The detection position update module 1700 is used to randomly select some sparrow individuals and calculate according to the detection formula to obtain the detection position update result.

[0174] The iteration module 1800 is used to jump to the discoverer position update module if the number of iterations is less than the maximum number of iterations, otherwise output the optimal value sparrow position and fitness function value among the discoverer position update result, the joiner second position update result and the reconnaissance position update result, and obtain the optimal PID control parameters according to the optimal value sparrow position.

[0175] The system obtains the parameter vector of the PID controller of the bridge crane, calculates the fitness function value according to the parameter vector, constructs the optimization model of the parameter vector of the PID controller according to the sparrow search algorithm, divides the sparrows in the optimization model into discoverers and joiners who snatch the food of the discoverer according to the fitness function value, updates the discoverer position according to the discoverer position update formula to obtain the discoverer position update result, introduces normal variation into the joiner position update formula to update the joiner position to obtain the joiner first position update result, uses the orthogonal opposition learning strategy to update the joiner first position update result to obtain the joiner second position update result, randomly selects some sparrow individuals and calculates according to the detection formula to obtain the detection position update result, if the number of iterations is less than the maximum number of iterations, the cycle continues, otherwise the optimal value sparrow position and fitness function value among the discoverer position update result, the joiner second position update result and the detection position update result are output, and the optimal PID control parameters are obtained according to the optimal value sparrow position, which realizes the intelligent operation of the large-scale operation equipment bridge crane, overcomes the problems of large swing angle amplitude and slow positioning response of the bridge crane during operation, and improves work efficiency and safety performance.

[0176] It should be noted that the present method embodiment and the above-mentioned system embodiment are based on the same inventive concept, so the relevant contents of the above-mentioned method embodiment are also applicable to the present system embodiment and will not be repeated here.

[0177] The present application also provides a bridge crane PID control electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements: the bridge crane PID control method as described above.

[0178] The processor and the memory may be connected via a bus or other means.

[0179] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0180] The non-transient software program and instructions required to implement the bridge crane PID control method of the above embodiment are stored in the memory. When executed by the processor, the bridge crane PID control method of the above embodiment is executed, for example, the above described Figure 1The method comprises steps S101 to S108.

[0181] The present application also provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute: the above-mentioned bridge crane PID control method.

[0182] The computer-readable storage medium stores computer-executable instructions, which are executed by a processor or a controller, for example, by a processor in the above electronic device embodiment, so that the above processor can execute the bridge crane PID control method in the above embodiment, for example, execute the above described Figure 1 The method comprises steps S101 to S108.

[0183] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program units or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media generally contain computer readable instructions, data structures, program elements, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0184] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. A PID control method for a bridge crane, characterized in that: The bridge crane PID control method comprises: Step S101: according to the number of parameter vectors of the bridge crane PID controller, set the population size; randomly initialize the parameter vector of the bridge crane PID controller; obtain the parameter vector of the bridge crane PID controller; Step S102: Calculate the fitness function value according to the parameter vector, wherein the calculation formula of the fitness function value is: W = [w1, w2, w3, w4, w5] Where J is the fitness function value, t is the current iteration number, ζ(t) is the position deviation signal parameter of the t-th iteration, x3(t) is the swing angle signal parameter of the t-th iteration, u(t) is the driving force parameter of the t-th iteration, and u max is the maximum value of the crane driving force during the control process, x 3max is the maximum angle of load swing, W is the weight matrix, and W = [w1, w2, w2, w4, w5] = [0.5, 0.3, 0.2, 0.1, 0.1]; Step S103: constructing an optimization model of a parameter vector of a PID controller according to a sparrow search algorithm, and dividing the sparrows in the optimization model into finders and joiners that snatch food from the finders according to the fitness function value; Step S104: updating the finder position according to the finder position update formula to obtain a finder position update result; Step S105: introducing normal variation into the participant position update formula to update the participant position, and obtaining the participant first position update result; Step S106: using an orthogonal opposition learning strategy to update the first position update result of the joiner to obtain the second position update result of the joiner; Step S107: randomly selecting some sparrow individuals and calculating according to the detection formula to obtain the detection position update result; Step S108: If the number of iterations is less than the maximum number of iterations, jump to step S104, otherwise output the optimal value sparrow position and fitness function value among the discoverer position update result, the joiner second position update result and the reconnaissance position update result, and obtain the optimal PID control parameters according to the optimal value sparrow position.

2. The PID control method for an overhead crane according to claim 1, characterized in that: The finder position update formula is: in, is the position of the j-th dimension of the i-th sparrow in the t-th iteration, iter max is the maximum number of iterations, α is a random number that obeys a normal distribution from 0 to 1, Q is a random number that obeys a normal distribution from 0 to 1, L is a 1×D-dimensional vector whose elements are all 1, R2 is the warning value, and ST is the safety value.

3. The PID control method for an overhead crane according to claim 2, characterized in that: The joiner position update formula is: A + =A′(A·A′) -1 Where A is a 1xD dimensional matrix composed of 1 and -1, A′ is the transpose of the A matrix, and r is a random number in the range [0,1]. is the optimal position of the jth dimension at the tth iteration, is the worst position of the j-th dimension at the t-th iteration.

4. The PID control method for an overhead crane according to claim 3, characterized in that: The method of updating the first position update result of the joiner by using the orthogonal opposition learning strategy to obtain the second position update result of the joiner includes: Generate an opposing position of the update result of the first position of the joiner according to the opposing learning strategy; Constructing an orthogonal candidate solution using an orthogonal table according to the update result of the first position of the joiner and the opposing position; The orthogonal candidate solution is evaluated according to the fitness function value corresponding to the orthogonal candidate solution to obtain the second position update result of the joiner.

5. The PID control method for an overhead crane according to claim 4, characterized in that: The detection formula is: Among them, β is a random number that obeys a normal distribution from 0 to 1, lb is the lower bound of the preset search space, ub is the upper bound of the preset search space, and f i is the fitness function value of the i-th sparrow, f g is the fitness function value of the current optimal individual.

6. A PID control system for a bridge crane, characterized in that: The bridge crane PID control system comprises: The data acquisition module is used to set the population size according to the number of parameter vectors of the bridge crane PID controller; randomly initialize the parameter vector of the bridge crane PID controller; and obtain the parameter vector of the bridge crane PID controller; The fitness function value calculation module is used to calculate the fitness function value according to the parameter vector, wherein the calculation formula of the fitness function value is: W = [w1, w2, w3, w4, w5] Where J is the fitness function value, t is the current iteration number, ζ(t) is the position deviation signal parameter of the t-th iteration, x3(t) is the swing angle signal parameter of the t-th iteration, u(t) is the driving force parameter of the t-th iteration, and u max is the maximum value of the crane driving force during the control process, x 3max is the maximum angle of load swing, W is the weight matrix, and W = [w1, w2, w3, w4, w5] = [0.5, 0.3, 0.2, 0.1, 0.1]; The sparrow classification module is used to construct an optimization model of the parameter vector of the PID controller according to the sparrow search algorithm, and to divide the sparrows in the optimization model into finders and joiners who snatch food from the finders according to the fitness function value; The finder location update module is used to update the finder location according to the finder location update formula to obtain a finder location update result; The first position update module of the participant is used to introduce normal variation into the participant position update formula to update the participant position and obtain the participant first position update result; The participant second position update module is used to update the participant first position update result by using an orthogonal opposition learning strategy to obtain the participant second position update result; The detection position update module is used to randomly select some sparrow individuals and calculate according to the detection formula to obtain the detection position update result; The iteration module is used to jump to the discoverer position update module if the number of iterations is less than the maximum number of iterations, otherwise output the optimal value sparrow position and fitness function value among the discoverer position update result, the joiner second position update result and the reconnaissance position update result, and obtain the optimal PID control parameters according to the optimal value sparrow position.

7. A PID control device for a bridge crane, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to execute a bridge crane PID control method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute a bridge crane PID control method as described in any one of claims 1 to 5.

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