CN113377007A - Fuzzy neural network-based concrete distributing robot control method - Google Patents
Fuzzy neural network-based concrete distributing robot control method Download PDFInfo
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Abstract
The invention discloses a fuzzy neural network-based concrete spreader robot control method, which comprises the following steps: collecting target position parameter signals to generate a target motion track; collecting angle signals of three rotary joints; calculating errors between the actual angles of the three rotary joints and the target position; calculating the errors through fuzzy controllers of the three rotary joints to obtain the torques of the three rotary joints; adding neural network training into the fuzzy controller to learn the fuzzy rules of the fuzzy controller; and adding input compensation control in the neural network training, connecting the fuzzy controller with the PD controller in parallel, and taking the output of the PD controller as the compensation error input of the fuzzy controller. The method is suitable for intelligent boom control of the concrete distribution robot, and compared with the traditional PID method, the method is low in calculation complexity and high in control precision, and control reliability is effectively guaranteed.
Description
Technical Field
The invention relates to the technical field of intelligent construction control, in particular to a concrete distributing robot control method based on a fuzzy neural network.
Background
With the rapid development of scientific technology and the improvement of national economy, China increasingly pays more attention to the construction of various infrastructures, a construction method of building engineering is a key of an engineering construction implementation stage, and concrete pouring is one of important components in the field of building construction and plays a crucial role in ensuring the quality of various building constructions.
Concrete pouring, namely material distribution, refers to the process of pouring concrete materials into a designed mold until plasticizing, and the material distribution operation is usually performed by adopting a concrete pump truck and a pumping pouring technology. However, in the actual concrete pump truck material distribution process, the cooperative operation and control of the boom of the pump truck often depends on the rich experience of operators on a construction site, and the end joint hose is manually operated to ensure that the pouring port at the tail end of the boom moves according to an expected track, so that the concrete pouring process is finished with high quality. However, under the operation mode, certain potential safety hazards exist, the pouring quality is directly influenced, and the personal safety is threatened. With the increasing scale of urban construction, the development of high-rise buildings is accelerated to relieve the situation of shortage of construction land, the complexity of building structures is increased continuously, and particularly in the concrete pouring aspect of ultra-large-volume concrete pouring surfaces, super high-rise buildings and underground steel open caisson, a certain test is carried out on the traditional cantilever concrete pump truck control method, so that the robotization of the concrete distribution operation process is particularly urgent and important for the development of building engineering and intelligent construction.
The concrete distributing robot is used as a part of an automatic building platform, bears the tasks of on-site distributing and pouring of concrete, is a novel technical derivative based on an arm-mounted concrete pump truck, can realize the control of the pose and the distributing speed of the tail end of a conveying pipeline and the like through the action control of each mechanical arm of the distributing robot according to the on-site working environment, and efficiently finishes the concrete pouring in a working space. At present, the control system of concrete distribution equipment in China mainly adopts a traditional PID control mode and combines expert experience to carry out controller parameter setting. However, the concrete distributing robot has a fluid-solid coupling characteristic, a dynamic model of the concrete distributing robot is a complex nonlinear equation, a theoretical model and an actual model often have large errors, and in an actual construction process, the concrete distributing robot often has uncertain external interference factors due to severe working conditions. Therefore, a more suitable nonlinear control method for the concrete distributing robot needs to be designed to meet the dynamic requirements of actual conditions and improve the control performance.
Disclosure of Invention
According to the embodiment of the invention, a fuzzy neural network-based concrete distributing robot control method is provided, which is used for controlling three rotary joints of a three-joint rotary concrete distributing robot and realizing the control of the position of a pouring opening at the tail end of a mechanical arm of the three-joint rotary concrete distributing robot, and comprises the following steps:
collecting target position parameter signals to generate a target motion track;
collecting angle signals of three rotary joints;
calculating errors between the actual angles of the three rotary joints and the target position;
calculating the errors through fuzzy controllers of the three rotary joints to obtain the torques of the three rotary joints;
adding neural network training into the fuzzy controller to learn the fuzzy rules of the fuzzy controller;
and adding input compensation control in the neural network training, connecting the fuzzy controller with the PD controller in parallel, and taking the output of the PD controller as the compensation error input of the fuzzy controller.
Further, the three-joint rotary type concrete distributing robot independently controls joint angles of the three rotary joints.
Further, the error calculation formula of the actual angles and the target positions of the three rotary joints is as follows:
Further, the fuzzy controller has two input signals and one output signal.
Further, the fuzzy controller adopts a non-single value fuzzifier, the non-single value fuzzifier adopts a triangular membership function, the input X of the fuzzy controller is converted into a Gaussian distribution function which is expected by taking the input X as mathematics, and the input X and the membership function are used for solving the ignition level in a simultaneous mode.
Further, the importance of the non-univocal fuzzy logic is positively correlated to its distance from the center, centered on the membership of each input X.
Further, the ignition intensity calculation formula of the controller is as follows:
Further, the ignition intensity is normalized to obtain a value of
And the output calculation formula of the fuzzy neural network controller is as follows:
According to the concrete spreader robot control method based on the fuzzy neural network, the calculation complexity is lower, the control precision is higher, the fuzzy neural network control mode designed based on heuristic knowledge and language decision rules is adopted, the simulation of the manual control process and method is facilitated, the adaptability of the control system is enhanced, and the control system has a certain intelligent level. The fuzzy control system has strong robustness, the influence of interference and parameter change on the control effect is greatly weakened, and the fuzzy control system is particularly suitable for the control of nonlinear, time-varying and pure hysteresis systems, so that the control performance of the concrete distributing robot with the fluid-solid coupling characteristic is ensured.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and are intended to provide further explanation of the claimed technology.
Drawings
FIG. 1 is an overall view of a concrete distribution robot (a three-joint rotary concrete distribution robot, previously filed patent 2020111625562);
FIG. 2 is a concrete pouring fuzzy neural network control framework designed by the present invention;
FIG. 3 is a block diagram of a parallel PD control module of the fuzzy neural network designed by the present invention;
FIG. 4 is a diagram of a non-univocal fuzzifier membership function.
Fig. 5 is a flow chart of an embodiment of the present invention.
In figure 1, 1 is an upright post assembly, 2 is a pipeline assembly, 3 is a pipe clamp, 4 is a cantilever support and 5 is a rotary table assembly.
Detailed Description
The present invention will be further explained by describing preferred embodiments of the present invention in detail with reference to the accompanying drawings.
First, a fuzzy neural network-based concrete distribution robot control method for concrete distribution control application according to an embodiment of the present invention will be described with reference to fig. 1 to 4.
As shown in fig. 1 to 4, the concrete distributing robot control method based on the fuzzy neural network according to the embodiment of the present invention is used for controlling three rotary joints of a three-joint rotary concrete distributing robot, so as to control a position of a pouring opening at a distal end of a mechanical arm of the three-joint rotary concrete distributing robot, and the three-joint rotary concrete distributing robot independently controls joint angles of the three rotary joints, all of which are in a motor driving form, and has two described motion spaces, namely a joint space and a cartesian space, wherein the joint space is used for describing a change of the joint angle in a distributing operation, and the cartesian space is used for describing a change of the position of the pouring opening at the distal end in the distributing operation. And establishing a three-joint concrete distribution robot kinematic model based on an industrial robot standard D-H modeling method and a track planning algorithm. Comprises the following steps:
as shown in figure 2 of the drawings, in which,
s1: and acquiring a target position parameter signal to generate a target motion track.
And establishing a concrete distribution robot dynamic model based on a Lagrange mechanical equation. Based on a dynamic model, the position of a pouring opening at the tail end of a mechanical arm is controlled through independent motor drive control of three rotary joints, so that the actual pouring track is generated according to target position parameters.
As shown in figure 2 of the drawings, in which,
s2: collecting angle signals of three rotary joints.
As shown in figure 2 of the drawings, in which,
s3: and calculating the errors of the actual angles of the three rotary joints and the target position.
The input target position parameter signals q1, q2 and q3 are subtracted from the output joint angle signals q1, q2 and q3, so that the error calculation formula of the actual angles and the target positions of the three rotary joints is obtained as follows:
As shown in figure 2 of the drawings, in which,
s4: and calculating the errors by using fuzzy controllers of the three rotary joints to obtain the torques of the three rotary joints.
And inputting the obtained torques of the three rotary joints into a concrete distribution robot dynamics model, and outputting joint signal motor driving torques to form closed-loop control.
As shown in FIGS. 3 to 4,
s5: and adding neural network training into the fuzzy controller to learn the fuzzy rules of the fuzzy controller.
The fuzzy controllers of the three independent rotary joints adopt FNN fuzzy neural network controllers which have two input signals respectively
And
the output signal is
. The fuzzy neural network controller adopts a TSK fuzzy model, wherein the most important part is an inference engine part, the inference engine consists of a series of fuzzy rules, the fuzzy rules adopt an if-then structure, an if front piece is a type of fuzzy set, a then back piece is a determined value, and a neural network algorithm is adopted to learn the fuzzy rules on line. The stability and the robustness of the system under the dynamic severe construction environment are improved, and the problem that the traditional fuzzy rule in the fuzzy controller is written and formulated excessively and depends on expert experience guidance is solvedThe fuzzy rule is difficult to be established in real time. The fuzzy controller adopts a non-single value fuzzifier, the non-single value fuzzifier adopts a triangular membership function, the input X of the fuzzy controller is converted into a Gaussian distribution function which is expected by taking the input X as mathematics, and the input X and the membership function solve the ignition level in a simultaneous mode.
The importance of the non-univocal fuzzy logic is positively correlated with the distance from the center with the membership of each input X as the center, and the importance of the fuzzy logic is weaker the farther away from the center. The ignition intensity calculation formula of the controller is as follows:
As shown in figure 3 of the drawings,
s6: and adding input compensation control in the neural network training, connecting the fuzzy controller with the PD controller in parallel, and taking the output of the PD controller as the compensation error input of the fuzzy controller.
Because the artificial neural network needs time to carry out on-line learning on the expert experience database, and input compensation control is added in the network training process, introducing a traditional PD controller into the fuzzy neural network controller to form the PD + FNN parallel controller, wherein the output of the PD controller is used as the compensation error input of the controller, so as to ensure the reliability of neural network training and the real-time performance and the control quality of a concrete pouring control system, the fuzzy neural network controller can provide real-time compensation control for the learning process of the early fuzzy neural network, effectively guarantees the overall stability and reliability of the overall pouring control process, and solves the problems that the fuzzy neural network controller is independently applied to a concrete distributing robot pouring control system, the expected high-reliability control effect is difficult to achieve in the early training process, and potential safety hazards exist in distributing operation.
The concrete spreader robot control method based on the fuzzy neural network according to the embodiment of the invention has lower computational complexity and higher control precision, and the fuzzy neural network control mode designed based on heuristic knowledge and language decision rules is adopted, so that the simulation of the process and method of manual control is facilitated, and the adaptive capacity of the control system is enhanced, so that the control system has a certain intelligent level. The fuzzy control system has strong robustness, the influence of interference and parameter change on the control effect is greatly weakened, and the fuzzy control system is particularly suitable for the control of nonlinear, time-varying and pure hysteresis systems, so that the control performance of the concrete distributing robot with the fluid-solid coupling characteristic is ensured.
It should be noted that, in the present specification, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (8)
1. A fuzzy neural network-based concrete distributing robot control method is used for controlling three rotary joints of a three-joint rotary concrete distributing robot and realizing the control of the position of a pouring opening at the tail end of a mechanical arm of the three-joint rotary concrete distributing robot, and is characterized by comprising the following steps of:
collecting target position parameter signals to generate a target motion track;
collecting angle signals of the three rotary joints;
calculating errors between the actual angles of the three revolute joints and the target position;
calculating the errors through fuzzy controllers of the three rotary joints to obtain torques of the three rotary joints;
adding neural network training in the fuzzy controller to learn fuzzy rules of the fuzzy controller;
and adding input compensation control in the neural network training, connecting the fuzzy controller with a PD controller in parallel, and taking the output of the PD controller as the compensation error input of the fuzzy controller.
2. The fuzzy neural network-based concrete distribution robot control method as claimed in claim 1, wherein the three-joint rotary concrete distribution robot independently controls joint angles of the three rotary joints.
3. The fuzzy neural network-based concrete distribution robot control method of claim 1, wherein the error calculation formula of the actual angles of the three revolute joints and the target position is:
4. The fuzzy neural network-based concrete spreading robot control method of claim 1, wherein the fuzzy controller has two input signals and one output signal.
5. The fuzzy neural network-based concrete distributor robot control method as claimed in claim 4, wherein the fuzzy controller uses a non-single-valued fuzzy device, the non-single-valued fuzzy device uses a triangular membership function, the input X of the fuzzy controller is converted into a Gaussian distribution function with which it is mathematically expected, and the ignition level is solved in parallel with the membership function.
6. The fuzzy neural network-based concrete distribution robot control method of claim 5, wherein the importance of the non-singular-value fuzzifier is positively correlated with the distance from the center thereof, centering on the membership of each input X.
7. The fuzzy neural network-based concrete distribution robot control method as claimed in claim 6, wherein the ignition intensity calculation formula of the controller is:
8. The fuzzy neural network-based concrete distribution robot control method of claim 7, wherein the firing intensity is normalized to obtain a value of
And the output calculation formula of the fuzzy neural network controller is as follows:
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