CN110647038A - Bridge crane sliding mode control parameter optimization method, device, equipment and storage medium - Google Patents
Bridge crane sliding mode control parameter optimization method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a bridge crane sliding mode control parameter optimization method, a bridge crane sliding mode control parameter optimization device, bridge crane sliding mode control parameter optimization equipment and a storage medium, wherein a traditional cuckoo search algorithm is improved, self-adaptive step length is introduced, a cross operation operator of a cross point is selected based on iteration times, the control parameters of a bridge crane sliding mode controller are optimized by adopting the improved cuckoo search algorithm, the optimal solution of the parameters of the sliding mode controller is obtained, the response speed and robustness of anti-swing control of a bridge crane system are improved, and the system has anti-interference capacity.
Description
Technical Field
The invention relates to the technical field of bridge cranes, in particular to a bridge crane sliding mode control parameter optimization method, device, equipment and storage medium.
Background
The bridge crane plays an important role as a large cargo handling mechanism in modern industrial production, and the realization of accurate bridge crane positioning and the elimination of load swing angle as much as possible are problems to be solved. The sliding mode controller has the control characteristics of strong robustness, quick response and the like, so that the sliding mode controller is applied to the field of bridge cranes.
The sliding mode controller of the bridge crane has close relation between the advantages and disadvantages of parameter adjustment and the effects of accurately positioning goods and eliminating load swing angles, most of the parameters of the sliding mode controller of the existing bridge crane are manually debugged, the parameter adjusting process is complicated and tedious due to certain coupling between the parameters of the controller, and finally obtained parameters are not good in control effect.
Disclosure of Invention
In order to solve the above problems, the present invention aims to provide a method, an apparatus, a device and a storage medium for optimizing bridge crane sliding mode control parameters, wherein an improved cuckoo search algorithm is adopted to optimize the control parameters of a bridge crane sliding mode controller, so that an optimal solution of the parameters of the bridge crane sliding mode controller can be obtained, and the response speed and robustness of the anti-swing control of the bridge crane system are improved, so that the system has the anti-interference capability.
The technical scheme adopted by the invention for solving the problems is as follows:
in a first aspect, the invention provides a method for optimizing control parameters of a sliding mode of a bridge crane, which comprises the following steps: setting the search space range of each dimension of the cuckoo search algorithm as follows: a, epsilon, k, e [0,100], b, e [ 100,100], wherein a, epsilon, b and k are respectively position error weight, switching gain, angle error weight and index coefficient of the trolley position sliding mode controller, the cuckoo search algorithm adopts self-adaptive step length and introduces an operation operator for self-adaptively selecting the intersection points based on iteration times;
setting the fitness function of the cuckoo search algorithm as
and solving the optimal solution of the a, the epsilon, the b and the k by adopting the cuckoo search algorithm according to the fitness function.
2. Further, the solving of the optimal solution of a, epsilon, b and k by adopting the cuckoo search algorithm according to the fitness function comprises the following steps:
s31, initializing the basic parameters and the bird nests of the cuckoo search algorithm, initializing the parameters of the trolley position sliding mode controller, setting the number of the bird nests as n and the found probability as paAnd the maximum number of iterations is gmaxRandomly generating an initial bird nest by a cuckoo algorithm, calculating and evaluating the fitness value of the initial bird nest, and storing the bird nest with the highest fitness value;
s32, updating the positions of the bird nests according to the self-adaptive step length formula and the Levy flight formula, calculating and evaluating the adaptability values of the bird nests, and storing the bird nest with the highest adaptability value according to a greedy algorithm;
s33, selecting a random probability p for each bird nesttDiscard pt＜paThe bird nest of (2);
s34, updating the positions of the discarded bird nests according to a preference random walk algorithm, calculating and evaluating the fitness values of the bird nests, and storing the bird nest with the highest fitness value according to a greedy algorithm;
s35, performing cross operation according to the operation operator formula based on the iteration times self-adaptive selection cross point, generating a new bird nest, calculating and evaluating the adaptability value of the new bird nest, and storing the bird nest with the highest adaptability value according to a greedy algorithm;
s36, if not, determining the maximum iteration number gmaxThen the generation is carried outAnd taking the bird nest position as the initial bird nest position of the next generation, returning to S32, continuing to perform the next iteration, and otherwise, outputting the optimal solution of a, epsilon, b and k.
Further, the performing of the intersection operation according to the operation operator formula for adaptively selecting the intersection point based on the iteration number comprises:
randomly selecting n/2 bird nests, and converting the decimal bird nest position into a binary sequence, wherein the length of the binary sequence is L;
pairing the converted bird nests pairwise;
generating a probability P according to the operation operator formula for adaptively selecting the intersection points based on the iteration times0；
Will P0With a uniform random probability PnMaking a comparison when Pn＜P0Then, the cross-over point is selected from the high-order region from L/2 to L of the sequence, when P isn>P0Then, the cross point is selected in the low region from 0 to L/2 of the sequence;
performing a crossover operation at the crossover point;
the binary sequence is converted to decimal bird nest positions.
Further, the operation operator formula for adaptively selecting the intersection points based on the iteration number is p0＝b2/{1+exp[c1(gn-g0)]In which b is2As a range of probability variation, gnFor the current evolutionary algebra, c1And g0Respectively, the corresponding rate and evolution algebra when the step length is changed.
Further, the trolley position sliding mode controller is u,
wherein sat(s) is a sign function, s is a sliding mode surface, f1Are state variables of the trolley position system,
the moving speed of the position of the trolley is,
angular velocity, g, of load swing angle1Is an input variable of the trolley position system.
Further, the adaptive step formula is α ═ α1+b1gn/gmaxWherein α is1Step size of initial time, b1For step size variation range, gmaxFor the corresponding maximum evolution algebra, gnIs the current evolution algebra.
In a second aspect, the invention provides a bridge crane sliding mode control parameter optimization device, which comprises: the search range setting unit is used for setting the search space ranges of each dimension of the cuckoo search algorithm as follows: a, epsilon, k, e [0,100], b, e [ 100,100], wherein a, epsilon, b and k are respectively position error weight, switching gain, angle error weight and index coefficient of the trolley position sliding mode controller, the cuckoo search algorithm adopts self-adaptive step length and introduces an operation operator for self-adaptively selecting the intersection points based on iteration times;
a fitness function setting unit for setting the fitness function of the cuckoo search algorithm as
WhereinThe integral of the absolute value of the error of the target value and the actual value of the position of the trolley is multiplied by the time, the integral of the absolute value of the error of the load swing angle and the actual value of the position of the trolley is multiplied by the time, and the integral of the absolute value of the output of the controller is multiplied by the time, wherein c is a constant term;
and the processing unit is used for solving the optimal solution of the a, the epsilon, the b and the k by adopting the cuckoo search algorithm according to the fitness function.
Further, the processing unit includes:
the initialization unit is used for initializing the basic parameters and the bird nests of the cuckoo search algorithm, initializing the parameters of the trolley position sliding mode controller, setting the number of the bird nests as n and the found probability as paAnd maximumThe number of iterations is gmaxRandomly generating an initial bird nest by a cuckoo algorithm, calculating and evaluating the fitness value of the initial bird nest, and storing the bird nest with the highest fitness value; the Levy flying unit updates the position of the bird nest according to the self-adaptive step length formula and the Levy flying formula, calculates and evaluates the adaptability value of the bird nest, and stores the bird nest with the highest adaptability value according to a greedy algorithm;
a discarding unit for selecting a random probability p for each bird nesttDiscard pt＜paThe bird nest of (2);
the preference walking unit is used for updating the positions of the abandoned bird nests according to a preference random walking algorithm, calculating and evaluating the fitness value of the bird nests, and storing the bird nest with the highest fitness value according to a greedy algorithm;
the crossing unit is used for carrying out crossing operation according to the operation operator formula based on the iteration times self-adaptive selection crossing point, generating a new bird nest, calculating and evaluating the adaptability value of the new bird nest, and storing the bird nest with the highest adaptability value according to a greedy algorithm;
an output unit for outputting the maximum iteration number g if the preset maximum iteration number g is not reachedmaxAnd taking the nest position of the generation as the initial nest position of the next generation, returning to the step S32, and continuing to perform the next iteration, otherwise, outputting the optimal solution of a, epsilon, b and k.
In a third aspect, the invention provides bridge crane sliding-mode control parameter optimization equipment,
comprises at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform a method of optimizing parameters of a bridge crane sliding mode control as described above.
In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the method for optimizing parameters of sliding mode control of a bridge crane as described above.
In a fifth aspect, the present invention also provides a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the method for optimizing parameters for a bridge crane sliding mode control as described above.
One or more technical schemes provided in the embodiment of the invention have at least the following beneficial effects: the method has the advantages that the traditional cuckoo search algorithm is improved, the self-adaptive step length is introduced, the cross operation operator of the cross point is selected based on the iteration times, the control parameters of the sliding mode controller of the bridge crane are optimized by adopting the improved cuckoo search algorithm, the optimal solution of the parameters of the sliding mode controller is obtained, the response speed and the robustness of the anti-swing control of the bridge crane system are improved, and the system has the anti-interference capability.
Drawings
The invention is further illustrated by the following figures and examples.
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
FIG. 2 is a flowchart of a method for solving an optimal solution of a, ε, b, and k by using the cuckoo search algorithm according to a fitness function in an embodiment of the present invention;
FIG. 3 is a flowchart of a method for performing crossover operations according to an operator formula for adaptively selecting crossover points based on iteration count, according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a method for performing a crossover operation according to an operator formula for adaptively selecting a crossover point based on iteration number according to an embodiment of the present invention;
FIG. 5 is a table comparing the search results of the improved cuckoo search algorithm with the standard cuckoo search algorithm and the particle swarm algorithm in the embodiment of the present invention;
FIG. 6 is a graph of the results of the optimization test of the improved cuckoo search algorithm, the standard cuckoo search algorithm, and the particle swarm algorithm in the embodiment of the present invention;
FIG. 7 is a graph of an improved cuckoo search algorithm, a standard cuckoo search algorithm, and a particle swarm algorithm applied to a control parameter optimization process of a trolley position sliding mode controller in an embodiment of the present invention;
FIG. 8 is a graph showing the control effect of the improved cuckoo search algorithm, the standard cuckoo search algorithm, the particle swarm algorithm and the manual adjustment parameter applied to the sliding mode controller for the trolley position in the embodiment of the present invention;
fig. 9 is a sliding mode controller control parameter table in which an improved cuckoo search algorithm, a standard cuckoo search algorithm, a particle swarm algorithm, and manual adjustment parameters are applied to a trolley position sliding mode controller in the embodiment of the present invention;
FIG. 10 is a table of position performance indicators of a trolley where an improved cuckoo search algorithm and a standard cuckoo search algorithm, a particle swarm algorithm, and manual adjustment parameters are applied in a trolley position sliding mode controller in an embodiment of the present invention;
fig. 11 is a table of load swing angle performance indexes of the improved cuckoo search algorithm, the standard cuckoo search algorithm, the particle swarm algorithm and the manual adjustment parameter applied in the trolley position sliding mode controller in the embodiment of the present invention;
FIG. 12 is a diagram of simulation experiment results in a first simulation scenario according to an embodiment of the present invention;
FIG. 13 is a diagram of simulation experiment results in a second simulation scenario according to an embodiment of the present invention;
FIG. 14 is a block diagram of a device according to an embodiment of the present invention;
fig. 15 is a schematic diagram of connections in a device according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It should be noted that, if not conflicted, the various features of the embodiments of the invention may be combined with each other within the scope of protection of the invention. Additionally, while functional block divisions are performed in apparatus schematics, with logical sequences shown in flowcharts, in some cases, steps shown or described may be performed in sequences other than block divisions in apparatus or flowcharts.
Referring to fig. 1, an embodiment of the present invention provides a method for optimizing control parameters of a sliding mode of a bridge crane, including:
step S10, setting the search space ranges of each dimension of the cuckoo search algorithm as follows: a, epsilon, k, e [0,100], b, e [ 100,100], wherein a, epsilon, b and k are respectively position error weight, switching gain, angle error weight and index coefficient of the trolley position sliding mode controller, the cuckoo search algorithm adopts self-adaptive step length and introduces an operation operator for self-adaptively selecting the intersection points based on iteration times;
step S20, setting the fitness function of the cuckoo search algorithm as
Wherein
The integral of the absolute value of the error of the target value and the actual value of the position of the trolley is multiplied by the time, the integral of the absolute value of the error of the load swing angle and the actual value of the position of the trolley is multiplied by the time, and the integral of the absolute value of the output of the controller is multiplied by the time, wherein c is a constant term;
and step S30, solving the optimal solution of a, epsilon, b and k by adopting the cuckoo search algorithm according to the fitness function.
According to the embodiment of the invention, an improved cuckoo search algorithm is adopted to optimize the control parameters of the trolley position sliding mode controller to obtain an optimal control parameter group, so that the trolley position sliding mode controller can be rapidly and accurately moved to a specified position, and the load swing angle is eliminated as much as possible.
The improved cuckoo search algorithm is applied to a trolley position sliding mode controller, the range of control parameters needs to be studied in advance, the stability of a sliding mode controller system is combined for judgment and research, the system can be ensured to have stability in the set parameter optimization range, if unstable points exist in the system, the cuckoo search algorithm can be wrong in the operation process and cannot be carried out, and the search space range of each dimension of the improved cuckoo search algorithm is set as follows in the embodiment of the invention: a, ε, k ∈ [0,100], b ∈ [ 100,100 ].
Referring to fig. 2, the solving of the optimal solution of a, epsilon, b and k by using the cuckoo search algorithm according to the fitness function includes the following steps:
s31, initializing the basic parameters and the bird nests of the cuckoo search algorithm, initializing the parameters of the trolley position sliding mode controller, setting the number of the bird nests as n and the found probability as paAnd the maximum number of iterations is gmaxRandomly generating an initial bird nest by a cuckoo algorithm, calculating and evaluating the fitness value of the initial bird nest, and storing the bird nest with the highest fitness value;
s32, updating the positions of the bird nests according to the self-adaptive step length formula and the Levy flight formula, calculating and evaluating the adaptability values of the bird nests, and storing the bird nest with the highest adaptability value according to a greedy algorithm;
s33, selecting a random probability p for each bird nesttDiscard pt＜paThe bird nest of (2);
s34, updating the positions of the discarded bird nests according to a preference random walk algorithm, calculating and evaluating the fitness values of the bird nests, and storing the bird nest with the highest fitness value according to a greedy algorithm;
s35, performing cross operation according to the operation operator formula based on the iteration times self-adaptive selection cross point, generating a new bird nest, calculating and evaluating the adaptability value of the new bird nest, and storing the bird nest with the highest adaptability value according to a greedy algorithm;
s36, if not, determining the maximum iteration number gmaxAnd taking the nest position of the generation as the initial nest position of the next generation, returning to the step S32, and continuing to perform the next iteration, otherwise, outputting the optimal solution of a, epsilon, b and k.
In the embodiment of the invention, an improved cuckoo search algorithm is adopted to solve the optimal solution of a control parameter group a, epsilon, b and k in a trolley position sliding mode controller, wherein the process of calculating and evaluating the adaptability value of a bird nest comprises the following steps: and returning the trolley position and the load swing angle parameters according to the trolley position sliding mode controller, calculating the adaptability value of each bird nest by adopting a fitness function, and taking the positioning error, the load swing angle and the output condition of the controller into consideration during algorithm optimization, so that the trolley position sliding mode controller can obtain a good control effect.
The optimal position of the bird nest is the optimal optimization scheme of the bridge crane sliding mode control parameters, and the bird egg in the corresponding optimal position of the bird nest is the optimal solution of the bridge crane sliding mode control parameters.
The cuckoo search algorithm is a group intelligent optimization algorithm provided by professor Yang and Deb of Cambridge university according to cuckoo brooding behaviors, and is also a novel meta-heuristic search algorithm, and the idea is mainly based on two strategies: the nest parasitism and Levy flight mechanism of cuckoos search for an optimal nest to hatch eggs in a random walk mode, an efficient optimization mode can be achieved, and a cuckoo search algorithm has four important parameters, namely the nest number n and the discovery probability paStep size α and a parameter λ of the levey flight. The cuckoo search algorithm has the main advantages of few parameters, simple operation, easy realization, excellent random search path, strong optimizing capability and the like, but the searching step length of the standard cuckoo searching algorithm in the initial stage and the later stage is not changed due to the fixed step length, so that the searching precision is not high, the Levy flying randomly distributed searching process of the algorithm, the diversity of the solution in the searching process has certain limitation, which causes the algorithm to be not high in the convergence speed of the solution, the embodiment of the invention adopts the self-adaptive step length to replace the fixed step length in the traditional cuckoo searching algorithm, an operation operator for adaptively selecting the intersection points based on the iteration times is introduced, so that the algorithm has a larger cross variation range in the initial search stage and a smaller cross variation range in the later search stage, the method can increase the diversity of the solution of the algorithm at the initial stage, and meanwhile, the convergence speed and the accuracy of the algorithm at the later stage cannot be influenced.
Referring to fig. 3 and 4, the performing of the intersection operation according to the operation operator formula for adaptively selecting the intersection point based on the iteration number includes:
step S351, randomly selecting n/2 bird nests, and converting the decimal bird nest positions into a binary sequence, wherein the length of the binary sequence is L;
step S352, pairing the converted bird nests pairwise;
step S353, generating a probability P according to the operation operator formula based on the iteration number self-adaptive selection intersection point0；
Step S354, adding P0With a uniform random probability PnMaking a comparison when Pn＜P0Then, the cross-over point is selected from the high-order region from L/2 to L of the sequence, when P isn>P0Then, the cross point is selected in the low region from 0 to L/2 of the sequence;
step S355 of performing a crossover operation at the crossover point;
step S356, convert the binary sequence into decimal bird nest positions.
In the embodiment of the invention, the thought of increasing individual diversity is achieved by cross operation with reference to a genetic algorithm, an operation operator for adaptively selecting cross points based on the number of iterations is introduced in the searching process of the cuckoo search algorithm, and the convergence speed of the algorithm is possibly slowed down due to the randomness of individual variation in the cross process.
Preferably, the operation operator formula for adaptively selecting the intersection point based on the iteration number is p0＝b2/{1+exp[c1(gn-g0)]In which b is2As a range of probability variation, gnFor the current evolutionary algebra, c1And g0Respectively, the corresponding rate and evolution algebra when the step length is changed.
Preferably, the trolley position sliding mode controller is u,
wherein sat(s) is a sign function, s is a sliding mode surface, f1Are state variables of the trolley position system,the moving speed of the position of the trolley is,
angular velocity, g, of load swing angle1Is an input variable of the trolley position system.
Preferably, the adaptive step size formula is α ═ α1+b1gn/gmaxWherein α is1Step size of initial time, b1For step size variation range, gmaxFor the corresponding maximum evolution algebra, gnIs the current evolution algebra.
In the embodiment of the invention, the fixed step length in the standard cuckoo search algorithm is replaced by the self-adaptive step length, so that the optimizing precision of the improved cuckoo search algorithm can be improved, and the control effect of the trolley position sliding mode controller is better.
In a preferred embodiment, the construction process of the trolley position sliding mode controller comprises the following steps: a bridge crane trolley position system is modeled by adopting a Lagrange equation, and various friction forces, air resistance of loads and elastic deformation of ropes are ignored. The lagrange equation is generally of the form:
wherein T is the total kinetic energy of the system, P is the total potential energy of the system, q isiIs a state variable (x, theta), where x is the position of the trolley, theta is the load swing angle, QiFor non-conservative generalized forces, the kinetic energy T of the trolley position system can be deduced as:
wherein M is the trolley mass, M is the load mass, l is the lifting rope length, and the potential energy P derivation result of the trolley position system is as follows: and P is-mgl (1-cos theta), wherein g is the gravity acceleration, and the dynamic model of the trolley position system is obtained by combining the Lagrange equation, the kinetic energy T of the trolley position system and the potential energy P formula of the trolley position system
Wherein F is a control force in the horizontal direction, and the system state variable of the bridge crane is defined as
Input is u ═ F, output is Y, where x1、x2、x3、x4The position of the trolley, the speed of the trolley, the pivot angle and the angular speed of the load, respectively, the state equation of the system dynamics model can be:
x2＝f1(X)+g1(X)u， 
Y＝[x1,x3]Twherein the non-linear function f1、g1、f2、g2Are respectively defined as:
g1＝1/(M+msin2x3)，
g2＝-cosx3/[l(M+msin2x3)]the input control force F of the trolley position system needs to be reachedThe accurate positioning of the control platform truck and the pendulum effect that disappears of load, so the design of slip form face needs to consider the situation of location and pendulum angle simultaneously, the slip form face that the embodiment of the invention adopted is: x ═ s2+a(x-xd) + b θ, where xdFor the target value of the trolley displacement, the position error weight a and the angle error weight b are constants, and an exponential approximation law function is selected as follows:
epsilon is more than 0, k is more than 0, epsilon is switching gain, k is exponential coefficient, and both constants are more than 0, in order to weaken buffeting problem in later period of the controller, sat(s) function is selected as switching control function, and for s, x2+a(x-xd) The derivative of + b theta can be obtained,
combining with the exponential approximation law function, obtaining
Further, the trolley position sliding mode controller u is:
it is to be noted that in the cuckoo search algorithm, each cuckoo randomly selects a host nest to produce an egg, i.e., a solution, calculates the fitness for comparison, and retains the nest with high fitness and enters the next generation, during which the host will have paAnd discarding the parasitic bird nest, and then selecting a new location to nest. The method has the characteristics of long-time short-distance migration and occasional long-distance jump, ensures the global search capability of the algorithm, and adopts the following formula:
wherein the content of the first and second substances,
and
the position of the ith bird nest at times t and t +1, respectively, alpha (alpha > 0) is a step factor,
the notation is dot product, L (β) is the obedience parameter β random search vector, L (β) is defined as:
where u and v obey a gaussian distribution, i.e. u:
v:N(0,1)，
indicating the optimal position found by the current generation,
Γ is the standard gamma function, β in the present example is 1.5, and p is the hostaAfter discarding partial solutions, a new solution is generated by preferring random walks as follows:
wherein the content of the first and second substances,
andfor two randomly selected solutions of the t-th generation, r is obedient [0-1]Uniformly distributed random numbers of intervals.
In order to test the optimization performance of the improved cuckoo search algorithm in the embodiment of the invention, the setting of the comparison test comprises the following steps: selecting a standard cuckoo search algorithm and a particle swarm algorithm to compare with the improved cuckoo search algorithm of the embodiment of the invention, selecting four test functions to perform an optimization test, and performing optimization on the optimization resultLine comparison, the global minimum values of the four test functions are all fmin(0,0, L,0) ═ 0, and the four test functions are defined as: sphere function:rastigin function:
D＝10，x∈[-10,10](ii) a Schafer function:D＝2，x1,x2∈[-100,100](ii) a Ackley function:
D＝200，x∈[-32,32]wherein the unimodal function f1For investigating convergence speed, multimodal function f2And f3For reviewing convergence speed and global search capability. When initializing the parameters of the search algorithm, in order to keep the conditions consistent, the improved cuckoo search algorithm and the standard cuckoo search algorithm of the embodiment of the invention take the bird nest number as n-15 and the maximum iteration number g asmax200, the length L log of the binary sequence2max(|xmin|,|xmax| step size parameter α)1＝0.9，b10.7, adaptive mutation probability b of intersection2＝1，c1＝20/gmax，g0＝g max2, probability of discovery paTo be 0.25, the parameters of the particle swarm algorithm are set as follows, wherein the population size n is 15, the inertia weight w is 0.7, the learning factor c1 is c2 is 0.7, the simulation environment is Windows 10, the memory is 16GB, the CPU is Inter i7 series, Matlab 2014b, the algorithms are independently operated for 100 times, and the operation condition of each algorithm is calculated, and the method comprises the following steps: the improved CS represents an improved cuckoo search algorithm in the embodiment of the invention, the CS represents a standard cuckoo search algorithm, and the PSO represents a particle swarm algorithm.
Referring to FIG. 5, wherein E-07 denotes scientific notation 10-7E-05 denotes scienceCounting method 10-5In the aspect of optimization accuracy, in the optimization accuracy comparison test of the improved cuckoo search algorithm, the standard cuckoo search algorithm and the particle swarm algorithm in the embodiment of the invention, f is subjected to1The optimal solutions of the three algorithms can be close to the optimal value of the function, wherein the improved cuckoo search algorithm in the embodiment of the invention has higher closeness and better stability; at f2In the function test, the improved cuckoo search algorithm in the embodiment of the invention can find the optimal solution, but the standard cuckoo search algorithm and the particle swarm algorithm can not be found; for f3The improved cuckoo search algorithm in the embodiment of the invention is superior to the particle swarm algorithm in stability; at f4In the test, compared with other two algorithms, the improved cuckoo search algorithm in the embodiment of the invention has the advantages that the optimization precision is optimal, the optimization precision effects of the four functions are compared, and the improved cuckoo search algorithm in the embodiment of the invention is superior to the standard cuckoo search algorithm and the particle swarm algorithm in the search precision.
Referring to fig. 6, wherein a solid line represents a test result situation of the improved cuckoo search algorithm in the embodiment of the present invention, a dotted line represents a test result situation of the standard cuckoo search algorithm, and a dotted line represents a test result situation of the particle swarm algorithm, in terms of global search capability, according to the test situations of four test function scenarios, the particle swarm algorithm is easy to get early although the convergence rate is fast, and the standard cuckoo search algorithm has a descending trend all the time but is significantly slower than the improved cuckoo search algorithm in the embodiment of the present invention, wherein, for a unimodal function f1In the test case of (f), the convergence rate of the improved cuckoo search algorithm in the embodiment of the invention is obviously higher than that of the standard cuckoo search algorithm, and f is higher than that of the standard cuckoo search algorithm2，f3And f4In the test situation, although the convergence rate of the particle swarm algorithm is high, the particle swarm algorithm is finally trapped in a local solution, the improved cuckoo search algorithm and the cuckoo search algorithm in the embodiment of the invention both have strong global search capability, but in the convergence rate, the improved cuckoo search algorithm in the embodiment of the invention is superior to the standard cuckoo search algorithm, and the global solution of four functions is synthesizedCompared with the search capability effect, the improved cuckoo search algorithm in the embodiment of the invention is superior to the standard cuckoo search algorithm and the particle swarm algorithm in the global search capability.
In order to further check the beneficial effect of the embodiment of the invention in the aspect of optimizing the control parameters of the sliding mode of the bridge crane, the setting comparison test comprises the following steps: the control parameters a, epsilon, b and k of the trolley sliding mode controller are optimized by respectively adopting the improved cuckoo search algorithm, the standard cuckoo search algorithm and the particle swarm algorithm in the embodiment of the invention, and the control effects of the sliding mode controller adopting the improved cuckoo search algorithm, the standard cuckoo search algorithm and the particle swarm algorithm in the embodiment of the invention are recorded. In the embodiment of the present invention, the fitness function J is:
the integral of the error absolute value of the target value and the actual value of the trolley position multiplied by the time, the integral of the load swing angle and the error absolute value of the actual value multiplied by the time and the integral of the absolute value multiplied by the time output by the controller are respectively shown, c is 1000, the mass M of the trolley is 5 kilograms, the mass M of the load is 1 kilogram, the length l of the rope is 1 meter, and the search space of each dimension is respectively as follows: a, ε, k ∈ [0,100]]，b∈[-100,100]The solid line represents a test result condition of the improved cuckoo search algorithm in the embodiment of the invention, the dot-dash line represents a test result condition of the standard cuckoo search algorithm, the dotted line represents a test result condition of the particle swarm algorithm, and the dotted line represents a manually adjusted test result.
Referring to fig. 7, by using the optimization of the improved cuckoo search algorithm, the standard cuckoo search algorithm and the particle swarm algorithm in the embodiment of the present invention, it can be known that the improved cuckoo search algorithm in the embodiment of the present invention starts to converge in 40 generations, and the standard cuckoo search algorithm and the particle swarm algorithm are in gradual convergence, so that, in terms of optimization of control parameters of the trolley position sliding mode controller, the performance of the improved cuckoo search algorithm in the embodiment of the present invention is better than that of the standard cuckoo search algorithm and the particle swarm algorithm in terms of convergence speed.
Referring to fig. 8, the control parameter control effect optimized by the improved cuckoo search algorithm in the embodiment of the present invention is compared with the standard cuckoo search algorithm, the particle swarm algorithm, and manual adjustment, and the control parameter optimized by the improved cuckoo search algorithm in the embodiment of the present invention enables the trolley to reach the target position faster, effectively suppresses the load swing angle, and has the optimal control effect.
Referring to fig. 9, 10 and 11, wherein the improved CS represents an improved cuckoo search algorithm in the embodiment of the present invention, CS represents a standard cuckoo search algorithm, PSO represents a particle swarm algorithm, SMC represents a sliding mode controller, and E-08 represents scientific notation 10-8E-06 denotes scientific notation 10-6In the aspect of parameters of the sliding mode controller, the improved cuckoo search algorithm in the embodiment of the invention has the highest fitness value of the optimized control parameters, namely the optimized control parameters are optimal in the aspect of optimization precision; in the aspect of trolley position control, the control parameters optimized by the improved cuckoo search algorithm in the embodiment of the invention enable the trolley to reach the target position most quickly; in the aspect of load swing angle suppression, the load swing angle is eliminated as fast as possible by the control parameters optimized by the improved cuckoo search algorithm in the embodiment of the invention.
In order to further test the robustness of the embodiment of the invention, simulation experiments of two scenes are set, wherein the first simulation scene is as follows: in order to simulate the influence of wind power or other external force, external force interference with the amplitude of 100 newtons is applied at the 5 th second; the second simulation scenario is: the parameters of the bridge crane system are changed, the load mass m and the rope length l, the first group of parameters is m-1 kg, l-1 m, the second group of parameters is m-1 kg, l-0.75 m, the third group of parameters is m-0.5 kg, l-0.75 m, the fourth group of parameters is m-0.5 kg, l-1 m.
Referring to fig. 12, in a first simulation scenario, after a system is interfered, a load swing angle swings to a certain extent at an interference moment, and is stabilized again after 2 seconds to return to a zero swing angle, and a simulation result shows that the embodiment of the present invention has a good anti-interference capability.
Referring to fig. 13, where a solid line represents a simulation result condition of the first set of parameters, a dashed-dotted line represents a simulation result condition of the first set of parameters, and a dotted-dotted line represents a simulation result condition of the first set of parameters, in a second simulation scenario, the embodiment of the present invention has little influence on positioning of the trolley position when the load mass and the rope length are changed; when the mass of the load is only changed, the swinging angle of the load is not obviously changed by comparing the curve change conditions of the first group and the fourth group; only when the rope length is changed, compared with the curve change conditions of the first group, the second group, the third group and the fourth group, the swing angle of the load can be influenced to a certain extent, and particularly, when the rope length is shortened, the swing angle can be reduced in a small range.
The embodiment of the invention also provides a device for optimizing the sliding mode control parameters of the bridge crane, wherein the device 1000 for optimizing the sliding mode control parameters of the bridge crane comprises but is not limited to: a search range setting unit 1100, a fitness function setting unit 1200, and a processing unit 1300.
The search range setting unit 1100 is configured to set a search space range of each dimension of the cuckoo search algorithm to be: a, ε, k ∈ [0,100]]，b∈[-100,100]Wherein a, epsilon, b and k are respectively a position error weight, a switching gain, an angle error weight and an index coefficient of the trolley position sliding mode controller, and the cuckoo search algorithm adopts a self-adaptive step length and introduces an operation operator for self-adaptively selecting an intersection point based on iteration times; a fitness function setting unit 1200, configured to set a fitness function of the cuckoo search algorithm to
Wherein
The integral of the absolute value of the error of the target value and the actual value of the position of the trolley is multiplied by the time, the integral of the absolute value of the error of the load swing angle and the actual value of the position of the trolley is multiplied by the time, and the integral of the absolute value of the output of the controller is multiplied by the time, wherein c is a constant term;
and the processing unit 1300 is configured to solve the optimal solution of a, epsilon, b, and k by using the cuckoo search algorithm according to the fitness function.
It should be noted that, since the apparatus for optimizing parameters for sliding mode control of a bridge crane in this embodiment is based on the same inventive concept as the above-mentioned method for optimizing parameters for sliding mode control of a bridge crane, the corresponding contents in the method embodiment are also applicable to this apparatus embodiment, and will not be described in detail here.
The embodiment of the invention also provides bridge crane sliding mode control parameter optimization equipment, and the bridge crane sliding mode control parameter optimization equipment 2000 can be any type of intelligent terminal, such as a mobile phone, a tablet personal computer, a personal computer and the like.
Specifically, the bridge crane sliding mode control parameter optimization apparatus 2000 includes: one or more control processors 2010 and memory 2020, one control processor 2010 being illustrated in fig. 15.
The control processor 2010 and the memory 2020 may be coupled by a bus or other means, such as by a bus as illustrated in FIG. 15.
The memory 2020, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the method for optimizing parameters of a bridge crane sliding mode control in the embodiment of the present invention, for example, the search range setting unit 1100, the fitness function setting unit 1200, and the processing unit 1300 shown in fig. 14. The control processor 2010 executes various functional applications and data processing of the bridge crane sliding mode control parameter optimization apparatus 1000 by running non-transitory software programs, instructions and modules stored in the memory 2020, that is, implements the bridge crane sliding mode control parameter optimization method of the above-described method embodiment.
The memory 2020 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the bridge crane sliding mode control parameter optimization apparatus 1000, and the like. Further, the memory 2020 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 2020 optionally includes a memory remotely located from the control processor 2010, which may be connected to the bridge crane sliding mode control parameter optimization device 2000 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 2020, and when executed by the one or more control processors 2010, perform the method for optimizing bridge crane sliding mode control parameters in the above method embodiment, for example, perform the above-described method steps S10 to S30 in fig. 1, and implement the functions of the unit 1100 and 1300 in fig. 14.
Embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which are executed by one or more control processors, for example, by one control processor 2010 in fig. 15, and may cause the one or more control processors 2010 to execute the method for optimizing bridge crane sliding mode control parameters in the method embodiment, for example, execute the above-described method steps S10 to S30 in fig. 1, and implement the functions of the unit 1100-1300 in fig. 14.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by software plus a general hardware platform. Those skilled in the art will appreciate that all or part of the processes of the methods of the above embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a computer readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.
While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the foregoing and various other changes, omissions and deviations in the form and detail thereof may be made without departing from the scope of this invention.
Claims (7)
1. A bridge crane sliding mode control parameter optimization method is characterized by comprising the following steps: setting the search space range of each dimension of the cuckoo search algorithm as follows: a, ε, k ∈ [0,100]]，b∈[-100,100]Wherein a, epsilon, b and k are respectively a position error weight, a switching gain, an angle error weight and an index coefficient of the trolley position sliding mode controller, the cuckoo search algorithm adopts a self-adaptive step length and introduces an operation operator for self-adaptively selecting an intersection point based on iteration times, the trolley position sliding mode controller is u,
wherein sat(s) is a sign function, s is a sliding mode surface, f1Are state variables of the trolley position system,
for speed of movement of trolley position，
Angular velocity, g, of load swing angle1The adaptive step size formula is alpha-alpha which is an input variable of a trolley position system1+b1gn/gmaxWherein α is1Step size of initial time, b1For step size variation range, gmaxFor the corresponding maximum evolution algebra, gnFor the current evolutionary algebra, the operation operator formula for adaptively selecting the intersection points based on the iteration number is p0＝b2/{1+exp[c1(gn-g0)]In which b is2As a range of probability variation, gnFor the current evolutionary algebra, c1And g0Respectively corresponding speed and evolution algebra when the step length is changed;
setting the fitness function of the cuckoo search algorithm as
Wherein
The integral of the absolute value of the error of the target value and the actual value of the position of the trolley is multiplied by the time, the integral of the absolute value of the error of the load swing angle and the actual value of the position of the trolley is multiplied by the time, and the integral of the absolute value of the output of the controller is multiplied by the time, wherein c is a constant term;
and solving the optimal solution of the a, the epsilon, the b and the k by adopting the cuckoo search algorithm according to the fitness function.
2. The method for optimizing the control parameters of the sliding mode of the bridge crane according to claim 1, wherein the step of solving the optimal solution of a, epsilon, b and k by adopting the cuckoo search algorithm according to the fitness function comprises the following steps:
s31, initializing the basic parameters and the bird nests of the cuckoo search algorithm, initializing the parameters of the trolley position sliding mode controller, setting the number of the bird nests as n, and finding out the bird nestsProbability of paAnd the maximum number of iterations is gmaxRandomly generating an initial bird nest by a cuckoo algorithm, calculating and evaluating the fitness value of the initial bird nest, and storing the bird nest with the highest fitness value;
s32, updating the positions of the bird nests according to the self-adaptive step length formula and the Levy flight formula, calculating and evaluating the adaptability values of the bird nests, and storing the bird nest with the highest adaptability value according to a greedy algorithm;
s33, selecting a random probability p for each bird nesttDiscard pt＜paThe bird nest of (2);
s34, updating the positions of the discarded bird nests according to a preference random walk algorithm, calculating and evaluating the fitness values of the bird nests, and storing the bird nest with the highest fitness value according to a greedy algorithm;
s35, performing cross operation according to the operation operator formula based on the iteration times self-adaptive selection cross point, generating a new bird nest, calculating and evaluating the adaptability value of the new bird nest, and storing the bird nest with the highest adaptability value according to a greedy algorithm;
s36, if not, determining the maximum iteration number gmaxAnd taking the nest position of the generation as the initial nest position of the next generation, returning to the step S32, and continuing to perform the next iteration, otherwise, outputting the optimal solution of a, epsilon, b and k.
3. The optimization method of the control parameters of the sliding mode of the bridge crane according to claim 2, wherein the cross operation according to the operation operator formula for adaptively selecting the cross point based on the iteration number comprises the following steps:
randomly selecting n/2 bird nests, and converting the decimal bird nest position into a binary sequence, wherein the length of the binary sequence is L;
pairing the converted bird nests pairwise;
generating a probability P according to the operation operator formula for adaptively selecting the intersection points based on the iteration times0；
Will P0With a uniform random probability PnMaking a comparison when Pn＜P0When in sequenceThe high order region from L/2 to L of the column selects the cross point when Pn>P0Then, the cross point is selected in the low region from 0 to L/2 of the sequence;
performing a crossover operation at the crossover point;
the binary sequence is converted to decimal bird nest positions.
4. The utility model provides a bridge crane slipform control parameter optimizing apparatus which characterized in that: the method comprises the following steps:
the search range setting unit is used for setting the search space ranges of each dimension of the cuckoo search algorithm as follows: a, ε, k ∈ [0,100]]，b∈[-100,100]Wherein a, epsilon, b and k are respectively a position error weight, a switching gain, an angle error weight and an index coefficient of the trolley position sliding mode controller, the cuckoo search algorithm adopts a self-adaptive step length and introduces an operation operator for self-adaptively selecting an intersection point based on iteration times, the trolley position sliding mode controller is u,
wherein sat(s) is a sign function, s is a sliding mode surface, f1Are state variables of the trolley position system,
the moving speed of the position of the trolley is,
angular velocity, g, of load swing angle1The adaptive step size formula is alpha-alpha which is an input variable of a trolley position system1+b1gn/gmaxWherein α is1Step size of initial time, b1For step size variation range, gmaxFor the corresponding maximum evolution algebra, gnFor the current evolutionary algebra, the operation operator formula for adaptively selecting the intersection points based on the iteration number is p0＝b2/{1+exp[c1(gn-g0)]In which b is2Is a range of probability variationGo around, gnFor the current evolutionary algebra, c1And g0Respectively corresponding speed and evolution algebra when the step length is changed;
a fitness function setting unit for setting the fitness function of the cuckoo search algorithm as
Wherein
The integral of the absolute value of the error of the target value and the actual value of the position of the trolley is multiplied by the time, the integral of the absolute value of the error of the load swing angle and the actual value of the position of the trolley is multiplied by the time, and the integral of the absolute value of the output of the controller is multiplied by the time, wherein c is a constant term;
and the processing unit is used for solving the optimal solution of the a, the epsilon, the b and the k by adopting the cuckoo search algorithm according to the fitness function.
5. The bridge crane sliding-mode control parameter optimization device according to claim 4, characterized in that: the processing unit includes:
the initialization unit is used for initializing the basic parameters and the bird nests of the cuckoo search algorithm, initializing the parameters of the trolley position sliding mode controller, setting the number of the bird nests as n and the found probability as paAnd the maximum number of iterations is gmaxRandomly generating an initial bird nest by a cuckoo algorithm, calculating and evaluating the fitness value of the initial bird nest, and storing the bird nest with the highest fitness value;
the Levy flying unit updates the position of the bird nest according to the self-adaptive step length formula and the Levy flying formula, calculates and evaluates the adaptability value of the bird nest, and stores the bird nest with the highest adaptability value according to a greedy algorithm;
a discarding unit for selecting a random probability p for each bird nesttDiscard pt＜paThe bird nest of (2);
the preference walking unit is used for updating the positions of the abandoned bird nests according to a preference random walking algorithm, calculating and evaluating the fitness value of the bird nests, and storing the bird nest with the highest fitness value according to a greedy algorithm;
the crossing unit is used for carrying out crossing operation according to the operation operator formula based on the iteration times self-adaptive selection crossing point, generating a new bird nest, calculating and evaluating the adaptability value of the new bird nest, and storing the bird nest with the highest adaptability value according to a greedy algorithm;
an output unit for outputting the maximum iteration number g if the preset maximum iteration number g is not reachedmaxAnd taking the nest position of the generation as the initial nest position of the next generation, returning to the step S32, and continuing to perform the next iteration, otherwise, outputting the optimal solution of a, epsilon, b and k.
6. The utility model provides a bridge crane slipform control parameter optimization equipment which characterized in that: comprises at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform a method of optimizing parameters of a bridge crane sliding mode control according to any one of claims 1 to 3.
7. A computer-readable storage medium characterized by: the computer-readable storage medium stores computer-executable instructions for causing a computer to perform the method for optimizing parameters for sliding mode control of a bridge crane according to any one of claims 1 to 3.
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