CN111152213A - Mechanical arm vibration compensation method and device based on hybrid control - Google Patents

Mechanical arm vibration compensation method and device based on hybrid control Download PDF

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CN111152213A
CN111152213A CN201911235359.6A CN201911235359A CN111152213A CN 111152213 A CN111152213 A CN 111152213A CN 201911235359 A CN201911235359 A CN 201911235359A CN 111152213 A CN111152213 A CN 111152213A
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vibration
mechanical arm
control quantity
vibration control
feedforward controller
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CN111152213B (en
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王兆国
徐丽媛
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Beijing Disi Technology Co ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1628—Programme controls characterised by the control loop

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  • Automation & Control Theory (AREA)
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Abstract

The embodiment of the invention discloses a mechanical arm vibration compensation method and a device based on hybrid control, which comprises the steps of obtaining a first vibration control quantity of a mechanical arm vibration feedforward controller which is established in advance; acquiring a current vibration signal of the mechanical arm, which is acquired by a sensor, and inputting the current vibration signal to a pre-established mechanical arm vibration feedback compensation controller by combining a preset vibration signal expected value to obtain a second vibration control quantity; and calculating the first vibration control quantity and the second vibration control quantity by adopting a weighting algorithm to obtain the vibration control quantity after the vibration compensation of the mechanical arm. The self-adaptive capacity and the response speed of the feedforward controller are improved by adopting the mechanical arm vibration feedforward controller and automatically adjusting the parameters of the controller; the method has the advantages that the vibration of the mechanical arm is subjected to feedback compensation, the robustness and the adaptability under the condition of system parameter change and external interference are guaranteed, in addition, the control method has certain learning capacity, and the complex problem can be solved under the condition that an object model is not constructed.

Description

Mechanical arm vibration compensation method and device based on hybrid control
Technical Field
The embodiment of the invention relates to the technical field of mechanical arm vibration, in particular to a mechanical arm vibration compensation method and device based on hybrid control.
Background
With the rapid development of industrial technology, industrial robots are used in various fields, which not only play an important role in heavy industry, but also play an indispensable role in some light industry and fine production industry, which requires that industrial robots have higher reliability and stability, i.e. when completing a specified task, the generated results can reach basic indexes. When an industrial robot works, tasks are mainly completed by the robot arm according to related instructions to execute specified operations, and therefore, the effect of the robot in completing the tasks mainly depends on the control of the working state of the robot arm.
The control of the vibration of the mechanical arm is an important factor influencing whether the robot can stably and accurately complete a task. At present, vibration suppression methods for mechanical arms mainly comprise two active control methods based on joint motors and intelligent materials, and the control method based on the joint motors is easier to realize and has better practical effect, so that more researchers select to study based on the method. The vibration control technology based on motor driving can be divided into open-loop control based on input shaping and closed-loop control based on system state feedback state, and the applied algorithm control technology comprises several algorithms such as classical control algorithm, self-adaptive control, sliding mode control, intelligent control and optimal control.
However, in the prior art, the mechanical arm is easily vibrated under the condition of self characteristics or external force interference, the stability and accuracy of the mechanical arm in task execution cannot be guaranteed, and the physical loss is large.
Disclosure of Invention
Therefore, embodiments of the present invention provide a method and an apparatus for compensating vibration of a mechanical arm based on hybrid control, so as to solve the problems in the prior art that stability and accuracy of the mechanical arm during task execution are not guaranteed and physical loss is large due to the fact that the mechanical arm is easily vibrated under the condition of self-characteristics or external force interference.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
according to a first aspect of the embodiments of the present invention, there is provided a method for compensating vibration of a mechanical arm based on hybrid control, including the steps of:
s1, obtaining a vibration control quantity of a current mechanical arm, inputting the vibration control quantity to a mechanical arm vibration feedforward controller which is established in advance, and obtaining a first vibration control quantity;
s2, acquiring the current vibration control quantity of the mechanical arm acquired by the sensor, and inputting the vibration control quantity to a pre-established mechanical arm vibration feedback compensation controller by combining a preset vibration control quantity expected value to acquire a second vibration control quantity;
and S3, calculating the first vibration control quantity and the second vibration control quantity by adopting a weighting algorithm to obtain the vibration control quantity after the vibration compensation of the mechanical arm.
Further, the establishment of the mechanical arm vibration feedforward controller specifically comprises the following steps:
s11, determining the number N of pulses contained in the feedforward controller;
s12, obtaining a time domain expression of the feedforward controller according to the number of pulses in the feedforward controller, namely:
Figure BDA0002304735910000021
wherein Ai is the amplitude of the ith pulse train and ti is the lag time;
s13, according to the optimal principle of the control performance, establishing a fitness function Q related to the system error e, the vibration control quantity u and the overshoot M, namely:
Figure BDA0002304735910000022
wherein, w1And w2As a weight value, the weight value,
Figure BDA0002304735910000023
is a penalty coefficient;
s14, under given constraint conditions: a1+ a2 ═ 1, t2And (4) performing algorithm calculation on the fitness function Q to obtain the optimal solution A of the fitness function Qi、ti1,2, N, i.e., a first vibration control amount;
the optimal solution A isi、tiAnd substituting the time domain expression of the feedforward controller to complete the establishment of the feedforward controller.
Further, a time domain expression of the vibration feedforward controller of the mechanical arm is constructed by adopting an input shaping algorithm; and calculating the optimal parameter solution of the fitness function Q by adopting an online signal acquisition and offline optimization mode and adopting a quantum particle swarm algorithm.
Further, the online signal acquisition and offline optimization method adopts a quantum particle swarm algorithm to calculate an optimal parameter solution of the fitness function Q, and specifically includes the steps of:
giving a preset input signal and inputting the preset input signal into the feedforward controller to obtain an output vibration signal;
and taking the vibration signal as the input quantity of the quantum particle swarm optimization algorithm to obtain the optimal parameter solution of the fitness function Q.
Further, the establishment of the mechanical arm vibration feedback compensation controller specifically comprises the following steps:
s21, constructing a fuzzy neural network model of a hierarchical structure comprising a front piece network and a back piece network, and respectively giving input and output of the front piece structure and the back piece structure;
and S22, determining the connection weight in the back part network and the width and the central value of the membership function in the front part network.
Further, adopting a Gaussian function as a membership function in the front-part network; and selecting a product fuzzy inference method, and calculating the fitness Z of each matched fuzzy rule.
Further, the calculation of the first vibration control quantity and the second vibration control quantity by using a weighting algorithm to obtain a specific calculation formula of the vibration control quantity after the vibration compensation of the mechanical arm is as follows:
u＝β1uf+β2ub
wherein u is the vibration control quantity after the vibration compensation of the mechanical arm; said u isfA first vibration control amount; u. ofbβ as a second vibration control amount1、β2Are each uf、ubThe weighting coefficient of (2).
According to a second aspect of the embodiments of the present invention, there is provided a hybrid control-based vibration compensation apparatus for a robot arm, including a first vibration control amount calculation module, a second vibration control amount calculation module, and a vibration control amount calculation module after robot arm vibration compensation; the first vibration control quantity calculation module is used for acquiring the current vibration control quantity of the mechanical arm, inputting the vibration control quantity to a pre-established mechanical arm vibration feedforward controller and acquiring a first vibration control quantity; the second vibration control quantity calculation module is used for acquiring the current vibration control quantity of the mechanical arm acquired by the sensor, and inputting the vibration control quantity to a pre-established mechanical arm vibration feedback compensation controller by combining a preset vibration control quantity expected value to acquire a second vibration control quantity; and the vibration control quantity calculation module after the mechanical arm vibration compensation is used for calculating the first vibration control quantity and the second vibration control quantity by adopting a weighting algorithm to obtain the vibration control quantity after the mechanical arm vibration compensation.
Further, the first vibration control amount calculation module includes a robot arm vibration feedforward controller construction module configured to:
determining the number N of pulses contained in the feedforward controller;
obtaining a time domain expression of the feedforward controller according to the number of pulses in the feedforward controller, namely:
Figure BDA0002304735910000041
wherein Ai is the amplitude of the ith pulse train and ti is the lag time;
according to the optimal principle of control performance, a fitness function Q related to the system error e, the vibration control quantity u and the overshoot M is established, namely:
Figure BDA0002304735910000042
wherein, w1And w2As a weight value, the weight value,
Figure BDA0002304735910000043
is a penalty coefficient;
under the given constraints: a1+ a2 ═ 1, t2And (4) performing algorithm calculation on the fitness function Q to obtainThe optimal solution A of the fitness function Qi、ti1,2, N, i.e., a first vibration control amount;
the optimal solution A isi、tiAnd substituting the time domain expression of the feedforward controller to complete the establishment of the feedforward controller.
Further, the second vibration control amount calculation module includes a mechanical arm vibration feedback compensation controller establishment module configured to:
constructing a fuzzy neural network model of a hierarchical structure comprising a front piece network and a back piece network, and respectively giving input and output of the front piece structure and the back piece structure;
and determining the connection weight in the back-part network and the width and the central value of the membership function in the front-part network.
Further, a time domain expression of the vibration feedforward controller of the mechanical arm is constructed by adopting an input shaping algorithm; and calculating the optimal parameter solution of the fitness function Q by adopting an online signal acquisition and offline optimization mode and adopting a quantum particle swarm algorithm.
Further, adopting a Gaussian function as a membership function in the front-part network; and selecting a product fuzzy inference method, and calculating the fitness Z of each matched fuzzy rule.
The embodiment of the invention has the following advantages:
according to the mechanical arm vibration compensation method based on hybrid control, which is provided by the embodiment 1 of the invention, a mechanical arm vibration feedforward controller is adopted, and the parameters of the controller are automatically adjusted, so that the self-adaptive capacity and the response speed of the feedforward controller can be improved; the vibration of the mechanical arm is subjected to feedback compensation, so that the robustness and the adaptability under the condition of system parameter change and external interference are ensured, and the control method has certain learning capacity and can solve the complex problem under the condition of not constructing an object model; and the vibration control quantities of the feedforward controller and the feedback controller are subjected to weighted calculation to obtain the final vibration control quantity of the mechanical arm, so that the vibration suppression effect is further improved, and the stability of the system is ensured.
Furthermore, the input shaping algorithm is adopted to construct the mechanical arm vibration feedforward controller, and the parameters of the controller are automatically adjusted through the quantum particle swarm algorithm, so that the self-adaptive capacity and the response speed of the feedforward controller can be improved; the method has the advantages that feedback compensation is carried out on mechanical arm vibration in a mode of combining fuzzy logic and a neural network, robustness and adaptability under system parameter change and external interference are guaranteed, the control method has certain learning capacity, and complex problems can be solved under the condition that an object model is not constructed.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
The structures, ratios, sizes, and the like shown in the present specification are only used for matching with the contents disclosed in the specification, so that those skilled in the art can understand and read the present invention, and do not limit the conditions for implementing the present invention, so that the present invention has no technical significance, and any structural modifications, changes in the ratio relationship, or adjustments of the sizes, without affecting the functions and purposes of the present invention, should still fall within the scope of the present invention.
Fig. 1 is a flow chart of a mechanical arm vibration compensation method based on hybrid control according to embodiment 1 of the present invention;
FIG. 2 is a flowchart of an implementation of a feedforward controller control method according to embodiment 2 of the present invention;
fig. 3 is a schematic view of an application scenario of the mechanical arm vibration compensation method based on hybrid control according to embodiment 3 of the present invention.
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a flow chart of a mechanical arm vibration compensation method based on hybrid control according to embodiment 1 of the present invention includes:
s1, acquiring a first vibration control quantity of a pre-established mechanical arm vibration feedforward controller;
s2, acquiring a vibration signal of the current mechanical arm acquired by a sensor, and inputting the vibration signal to a pre-established mechanical arm vibration feedback compensation controller by combining a preset vibration signal expected value to acquire a second vibration control quantity;
and S3, calculating the first vibration control quantity and the second vibration control quantity by adopting a weighting algorithm to obtain the vibration control quantity after the vibration compensation of the mechanical arm.
According to the embodiment of the invention, after the first vibration control quantity of the feedforward controller of the current mechanical vibration arm is obtained, the first vibration control quantity and the second vibration control quantity of the current mechanical arm vibration feedback compensation controller are subjected to weighted algorithm calculation to obtain the final vibration control quantity of the mechanical arm, so that the vibration suppression effect is improved, and the stability of the system is ensured.
The pre-established first vibration control quantity of the mechanical arm vibration feedforward controller is used for automatically adjusting the parameters of the feedforward controller before the first vibration control quantity of the current mechanical arm vibration feedforward controller is obtained, and the adjustment can improve the self-adaptive capacity and the response speed of the feedforward controller.
The current vibration signal of the mechanical arm acquired by the sensor is compared with the preset vibration signal expected value and is input to the mechanical arm vibration feedback compensation controller established in advance, the mechanical arm vibration is subjected to feedback compensation, and robustness and adaptability under the condition of system parameter change and external interference are guaranteed.
The establishment of the mechanical arm vibration feedforward controller specifically comprises the following steps:
s11, determining the number N of pulses contained in the feedforward controller;
s12, obtaining a time domain expression of the feedforward controller according to the number of pulses in the feedforward controller, namely:
Figure BDA0002304735910000071
wherein, A isiIs the amplitude of the ith pulse train, tiIs a time lag, uf(s) is a first vibration control amount;
s13, according to the optimal principle of the control performance, establishing a fitness function Q related to the system error e, the vibration control quantity u and the overshoot M, namely:
Figure BDA0002304735910000072
wherein, w1And w2As a weight value, the weight value,
Figure BDA0002304735910000073
is a penalty coefficient;
s14, under given constraint conditions: a. the1+A2＝1、t2And (4) performing algorithm calculation on the fitness function Q to obtain the optimal solution A of the fitness function Qi、ti1,2, N, i.e., a first vibration control amount;
the optimal solution A isi、tiAnd substituting the time domain expression of the feedforward controller to complete the establishment of the feedforward controller.
In an optional embodiment of the invention, a time domain expression of a vibration feedforward controller of the mechanical arm is constructed by adopting an input shaping algorithm; and calculating the optimal parameter solution of the fitness function Q by adopting an online signal acquisition and offline optimization mode and adopting a quantum particle swarm algorithm.
The online signal acquisition and offline optimization method adopts a quantum particle swarm algorithm to calculate the optimal parameter solution of the fitness function Q, and specifically comprises the following steps:
giving a preset input signal and inputting the preset input signal into the feedforward controller to obtain an output vibration signal;
and taking the vibration signal as the input quantity of the quantum particle swarm optimization algorithm to obtain the optimal parameter solution of the fitness function Q.
Referring to fig. 2, which is a flow chart for implementing the feedforward controller control method provided in embodiment 1 of the present invention, an input signal is first given to excite a mechanical arm system, then a vibration signal of the mechanical arm is obtained through a signal acquisition device, and the vibration signal is used as an input quantity of a quantum particle swarm algorithm to obtain an optimal parameter solution of a fitness function Q; and finally, transmitting the optimal parameter solution back to the feedforward controller as a first vibration control quantity.
The establishment of the mechanical arm vibration feedback compensation controller specifically comprises the following steps:
s21, constructing a fuzzy neural network model of a hierarchical structure comprising a front piece network and a back piece network, and respectively giving input and output of the front piece structure and the back piece structure;
and S22, determining the connection weight in the back part network and the width and the central value of the membership function in the front part network.
Specifically, the hierarchical structure of the front-end network is as follows: the first layer is an input layer with an input value of x1D (k) and
Figure BDA0002304735910000081
(d (k) is a positional error of the vibration of the robot arm,
Figure BDA0002304735910000082
the differential of the position error of the mechanical arm vibration to the time, and k is the feedback iteration number) and is transmitted to the next layer; the second layer, firstly determining the fuzzy segmentation number m of each input variable, and then selecting a Gaussian function as a membership function to solve the membership mu of each input component belonging to the fuzzy set of each linguistic variable value; the third layer is used for matching the antecedents of the fuzzy rules, and the fitness Z of each rule is calculated by selecting a product fuzzy inference method; the fourth layer carries out normalization processing on the fitness Z to obtain
Figure BDA0002304735910000085
The above-mentioned network hierarchy of the back-end is: the first layer is an input layer with an input value of x01 (to provide constant terms in fuzzy rule sequel), x1D (k) and
Figure BDA0002304735910000083
(d (k) is a positional error of the vibration of the robot arm,
Figure BDA0002304735910000084
the differential of the position error of the mechanical arm vibration to the time, and k is the feedback iteration number) and is transmitted to the next layer; the second layer obtains the back-piece output y of each fuzzy rule through network connection weight p weighting calculation; the third layer is a layer of
Figure BDA0002304735910000087
As an input to the layer, wherein
Figure BDA0002304735910000086
Weighting and summing the weighting coefficients as y, wherein the result is used as the final output of the network;
the learning algorithm for determining the parameters comprises a connection weight p in the back-part network and the width and the central value of the membership function mu in the front-part network;
wherein, the learning algorithm of p is as follows:
pij(k+1)＝pij(k)+Δpij
in the formula,. DELTA.pijComprises the following steps:
Figure BDA0002304735910000091
in the formula, i is 0,1,2, j is 1,2,3,.., m, m is a fuzzy division number, δ is a learning rate, u is an output of the fuzzy neural network controller, d (k) is a position error of the robot arm, i.e., d (k) r (k) -y (k), r (k) is a desired position of the robot arm, and y (k) is a robot arm vibration signal obtained by the signal collector.
The learning algorithms for the width δ and the center value c are respectively:
σij(k+1)＝σij(k)+Δσij
cij(k+1)＝cij(k)+Δcij
in the formula, Δ σijAnd Δ cijRespectively as follows:
Figure BDA0002304735910000092
Figure BDA0002304735910000093
where i is 0,1,2, j is 1,2,3, as, m, m is the number of fuzzy partitions, a and b are the learning rates of σ and c, respectively, and θ is the inverse error of each layer in the predecessor network.
Referring to fig. 3, which is an application scenario diagram of the hybrid control-based mechanical arm vibration compensation method according to embodiment 1 of the present invention, a mechanical arm vibration feedforward controller is constructed based on an input shaping algorithm, a parameter value of the controller is determined by using a quantum particle swarm algorithm and an online signal acquisition and offline optimization method, a mechanical arm vibration feedforward shaping controller is established, and a first vibration control quantity u is obtainedf；
Acquiring vibration information of the mechanical arm according to the sensing equipment, combining the vibration information of the mechanical arm with expected value vibration information, performing feedback compensation on mechanical arm vibration in a mode of combining fuzzy logic and neural network, establishing a mechanical arm vibration feedback compensation algorithm controller, and acquiring a second vibration control quantity ubWill ufAnd ubCalculating by adopting a weighting algorithm to obtain a final vibration control quantity u, u- β1uf+β2ub。
According to the mechanical arm vibration compensation method based on hybrid control, which is provided by the embodiment 1 of the invention, a mechanical arm vibration feedforward controller is adopted, and the parameters of the controller are automatically adjusted, so that the self-adaptive capacity and the response speed of the feedforward controller can be improved; the vibration of the mechanical arm is subjected to feedback compensation, so that the robustness and the adaptability under the condition of system parameter change and external interference are ensured, and the control method has certain learning capacity and can solve the complex problem under the condition of not constructing an object model; and the vibration control quantities of the feedforward controller and the feedback controller are subjected to weighted calculation to obtain the final vibration control quantity of the mechanical arm, so that the vibration suppression effect is further improved, and the stability of the system is ensured.
The second aspect of the present invention further provides a mechanical arm vibration compensation device based on hybrid control, including a first vibration control amount calculation module, a second vibration control amount calculation module, and a vibration control amount calculation module after mechanical arm vibration compensation; the first vibration control quantity calculation module is used for acquiring the current vibration control quantity of the mechanical arm, inputting the vibration control quantity to a pre-established mechanical arm vibration feedforward controller and acquiring a first vibration control quantity; the second vibration control quantity calculation module is used for acquiring the current vibration control quantity of the mechanical arm acquired by the sensor, and inputting the vibration control quantity to a pre-established mechanical arm vibration feedback compensation controller by combining a preset vibration control quantity expected value to acquire a second vibration control quantity; and the vibration control quantity calculation module after the mechanical arm vibration compensation is used for calculating the first vibration control quantity and the second vibration control quantity by adopting a weighting algorithm to obtain the vibration control quantity after the mechanical arm vibration compensation.
Further, the first vibration control amount calculation module includes a robot arm vibration feedforward controller construction module configured to:
determining the number N of pulses contained in the feedforward controller;
obtaining a time domain expression of the feedforward controller according to the number of pulses in the feedforward controller, namely:
Figure BDA0002304735910000101
wherein, A isiAnd tiThe amplitude and the time lag time of the ith pulse sequence are respectively;
according to the optimal principle of control performance, a fitness function Q related to the system error e, the vibration control quantity u and the overshoot M is established, namely:
Figure BDA0002304735910000102
wherein, w1And w2As a weight value, the weight value,
Figure BDA0002304735910000103
is a penalty coefficient;
under the given constraints: a. the1+A2＝1、t2And (4) performing algorithm calculation on the fitness function Q to obtain the optimal solution A of the fitness function Qi、ti1,2, N, i.e., a first vibration control amount;
the optimal solution A isi、tiAnd substituting the time domain expression of the feedforward controller to complete the establishment of the feedforward controller.
Further, the second vibration control amount calculation module includes a mechanical arm vibration feedback compensation controller establishment module configured to:
constructing a fuzzy neural network model of a hierarchical structure comprising a front piece network and a back piece network, and respectively giving input and output of the front piece structure and the back piece structure;
and determining the connection weight in the back-part network and the width and the central value of the membership function in the front-part network.
Further, a time domain expression of the vibration feedforward controller of the mechanical arm is constructed by adopting an input shaping algorithm; and calculating the optimal parameter solution of the fitness function Q by adopting an online signal acquisition and offline optimization mode and adopting a quantum particle swarm algorithm.
Further, adopting a Gaussian function as a membership function in the front-part network; and selecting a product fuzzy inference method, and calculating the fitness z of each matched fuzzy rule.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.

Claims (10)

1. A mechanical arm vibration compensation method based on hybrid control is characterized by comprising the following steps:
s1, acquiring a first vibration control quantity of a pre-established mechanical arm vibration feedforward controller;
s2, acquiring a vibration signal of the current mechanical arm acquired by a sensor, and inputting the vibration signal to a pre-established mechanical arm vibration feedback compensation controller by combining a preset vibration signal expected value to acquire a second vibration control quantity;
and S3, calculating the first vibration control quantity and the second vibration control quantity by adopting a weighting algorithm to obtain the vibration control quantity after the vibration compensation of the mechanical arm.
2. The method for compensating vibration of a mechanical arm based on hybrid control as claimed in claim 1, wherein the establishment of the mechanical arm vibration feedforward controller specifically comprises the steps of:
s11, determining the number N of pulses contained in the feedforward controller;
s12, obtaining a time domain expression of the feedforward controller according to the number of pulses in the feedforward controller, namely:
Figure FDA0002304735900000011
wherein A isiIs the amplitude of the ith pulse train, tiIs the time lag time;
s13, according to the optimal principle of the control performance, establishing a fitness function Q related to the system error e, the vibration control quantity u and the overshoot M, namely:
Figure FDA0002304735900000012
wherein, w1And w2As a weight value, the weight value,
Figure FDA0002304735900000013
is a penalty coefficient;
s14, under given constraint conditions: a. the1+A2＝1、t2And (4) performing algorithm calculation on the fitness function Q to obtain the optimal solution A of the fitness function Qi、ti1,2, N, i.e., a first vibration control amount;
the optimal solution A isi、tiAnd substituting the time domain expression of the feedforward controller to complete the establishment of the feedforward controller.
3. The vibration compensation method for the mechanical arm based on the hybrid control as claimed in claim 2, wherein an input shaping algorithm is adopted to construct a time domain expression of a vibration feedforward controller of the mechanical arm; and calculating the optimal parameter solution of the fitness function Q by adopting an online signal acquisition and offline optimization mode and adopting a quantum particle swarm algorithm.
4. The hybrid control-based mechanical arm vibration compensation method according to claim 3, wherein the online signal acquisition and offline optimization manner adopts a quantum-behaved particle swarm algorithm to calculate the optimal parameter solution of the fitness function Q, and specifically comprises the following steps:
giving a preset input signal and inputting the preset input signal into the feedforward controller to obtain an output vibration signal;
and taking the vibration signal as the input quantity of the quantum particle swarm optimization algorithm to obtain the optimal parameter solution of the fitness function Q.
5. The vibration compensation method for the mechanical arm based on the hybrid control as claimed in claim 3, wherein the establishment of the mechanical arm vibration feedback compensation controller specifically comprises the steps of:
s21, constructing a fuzzy neural network model of a hierarchical structure comprising a front piece network and a back piece network, and respectively giving input and output of the front piece structure and the back piece structure;
and S22, determining the connection weight in the back part network and the width and the central value of the membership function in the front part network.
6. The vibration compensation method for the mechanical arm based on the hybrid control as claimed in claim 5, wherein a Gaussian function is adopted as a membership function in the front-part network; and selecting a product fuzzy inference method, and calculating the fitness Z of each matched fuzzy rule.
7. The method for compensating vibration of a mechanical arm based on hybrid control according to claim 1, wherein the first vibration control amount and the second vibration control amount are calculated by a weighting algorithm, and a specific calculation formula of the vibration control amount after the vibration compensation of the mechanical arm is obtained is as follows:
u＝β1uf+β2ub
wherein u is the vibration control quantity after the vibration compensation of the mechanical arm; u. offA first vibration control amount; u. ofbβ as a second vibration control amount1、β2Are each uf、ubThe weighting coefficient of (2).
8. A mechanical arm vibration compensation device based on hybrid control is characterized by comprising a first vibration control quantity calculation module, a second vibration control quantity calculation module and a vibration control quantity calculation module after mechanical arm vibration compensation; the first vibration control quantity calculation module is used for acquiring a first vibration control quantity of a mechanical arm vibration feedforward controller which is established in advance; the second vibration control quantity calculation module is used for acquiring a current vibration signal of the mechanical arm acquired by the sensor, and inputting the current vibration signal to a pre-established mechanical arm vibration feedback compensation controller by combining a preset vibration control quantity expected value to acquire a second vibration control quantity; and the vibration control quantity calculation module after the mechanical arm vibration compensation is used for calculating the first vibration control quantity and the second vibration control quantity by adopting a weighting algorithm to obtain the vibration control quantity after the mechanical arm vibration compensation.
9. The hybrid control-based vibration compensation device for the mechanical arm according to claim 8, wherein the first vibration control amount calculation module comprises a mechanical arm vibration feedforward controller construction module configured to:
determining the number N of pulses contained in the feedforward controller;
obtaining a time domain expression of the feedforward controller according to the number of pulses in the feedforward controller, namely:
Figure FDA0002304735900000031
wherein A isiIs the amplitude of the ith pulse train, tiIs the time lag time;
according to the optimal principle of control performance, a fitness function Q related to the system error e, the vibration control quantity u and the overshoot M is established, namely:
Figure FDA0002304735900000032
wherein, w1And w2As a weight value, the weight value,
Figure FDA0002304735900000033
is a penalty coefficient;
under the given constraints: a. the1+A2＝1、t2And (4) performing algorithm calculation on the fitness function Q to obtain the optimal solution A of the fitness function Qi、ti，i＝1,2,...,N；
The optimal solution A isi、tiAnd substituting the time domain expression of the feedforward controller to complete the establishment of the feedforward controller.
10. The hybrid control-based vibration compensation device for mechanical arm according to claim 8, wherein the second vibration control amount calculation module comprises a mechanical arm vibration feedback compensation controller establishing module for:
constructing a fuzzy neural network model of a hierarchical structure comprising a front piece network and a back piece network, and respectively giving input and output of the front piece structure and the back piece structure;
and determining the connection weight in the back-part network and the width and the central value of the membership function in the front-part network.
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