CN113184705A - Bridge crane control method and system with uncertain load - Google Patents
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C13/00—Other constructional features or details
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
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Abstract
The invention relates to a bridge crane control method with uncertain load and a system thereof, comprising the following steps: receiving collected operation parameters of a crane system in real time; bringing the collected operation parameters into estimators corresponding to different loads to obtain estimators of the operation parameters, and obtaining monitoring signals according to the difference between the operation parameters and the estimators; extracting a controller corresponding to the minimum monitoring signal in the plurality of monitoring signals; the crane system is controlled according to the extracted controller, and the method can play a good positioning anti-swing effect and can adapt to large-range uncertainty of the load.
Description
Technical Field
The invention relates to the technical field of automatic control of mechanical systems, in particular to a bridge crane control method and system with uncertain loads.
Background
The statements herein merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The bridge crane is a common carrying machine and plays an irreplaceable role in the manufacturing industry, the building industry, the steel industry, the logistics industry and the like. Traditional bridge crane mainly relies on manual operation, relies on workman's experience seriously, not only causes the location inaccurate easily, the amplitude of oscillation is too big to there is the potential safety hazard and influences production efficiency, still probably causes the accident that leads to because of the maloperation even. With the rapid development of modern control theory and technology, how to utilize a plurality of advanced control methods to realize the accurate positioning and rapid oscillation elimination of a bridge crane becomes one of research hotspots. The bridge crane system has the characteristics of underactuation, strong coupling, intrinsic nonlinearity and the like, is easy to be interfered by swinging caused by inertia and the like and different loads in the running process, and how to realize the positioning and swing elimination of the bridge crane with high performance is still very challenging work. Although many research results are obtained for positioning and swing-eliminating control of a bridge crane at present, and many advanced control methods, such as a PID control strategy, a swing-eliminating control strategy based on trajectory planning, a time optimal control strategy, a sliding mode control strategy, a control strategy based on passivity, an adaptive control strategy, etc., are developed, the inventor finds that many limitations still exist: firstly, most of the existing control strategies are difficult to deal with large-range uncertainty, for example, when the weight difference of loads lifted by a crane is large, the control strategies are difficult to apply; although partial strategies such as an adaptive control strategy, an passivity-based control strategy and the like can be suitable for large-range uncertainty, the control performance is seriously degraded, and particularly an extremely long adaptive adjustment process is needed; and thirdly, estimation of uncertain load quality is difficult to realize while positioning and pendulum elimination are realized.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a bridge crane control method with an uncertain load, which can effectively deal with the influence of the uncertain load on crane positioning and pendulum elimination and can quickly and accurately estimate the quality of the uncertain load.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, an embodiment of the present invention provides a bridge crane control method with indeterminate load, including the following steps:
receiving collected operation parameters of a crane system in real time;
bringing the collected operation parameters into estimators corresponding to a plurality of different loads to obtain estimators of the operation parameters, and obtaining monitoring signals according to the difference between the operation parameters and the estimators;
extracting a controller corresponding to the minimum monitoring signal in the plurality of monitoring signals;
and controlling the crane system according to the extracted controller.
Optionally, if the sum of the minimum monitoring signal at the current moment and the set value is not greater than the minimum monitoring signal at the last moment, switching to the controller corresponding to the minimum monitoring signal at the current moment to control the crane system to operate, otherwise, continuing to control the crane system to operate by using the controller corresponding to the minimum monitoring signal at the last moment.
Optionally, a dynamic model of the crane system is established, a system model of the crane is obtained according to the dynamic model of the crane system, and an estimator and a controller are constructed according to the system model.
Optionally, modeling is performed on the crane system by using an euler-lagrange method, and a dynamic model of the crane system is obtained according to the established model.
Optionally, an estimation error is obtained according to a difference between the operating parameter and the estimator, and an integral operation is performed according to the estimation error norm to obtain the monitoring signal.
Optionally, the operation parameters include trolley displacement, trolley speed, load swing angle and swing angle speed of the crane system collected in real time.
Optionally, the candidate load mass in the estimator corresponding to the minimum monitoring signal is used as the estimated load mass of the crane system.
In a second aspect, embodiments of the present invention provide a bridge crane control system with indeterminate load comprising:
a data acquisition module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring the operating parameters of the crane system in real time;
a monitor signal acquisition module: the load estimator is used for bringing the collected operation parameters into estimator models corresponding to different loads to obtain estimators of the operation parameters and obtaining monitoring signals according to the difference between the operation parameters and the estimators;
a controller acquisition module: the controller model is used for extracting the minimum monitoring signal in the plurality of monitoring signals;
a control module: and the controller is used for controlling the crane system according to the extracted controller.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for controlling a bridge crane with an indeterminate load according to the first aspect.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the bridge crane control method with indeterminate load as set forth in the first aspect.
The beneficial effects of the above-mentioned embodiment of the present invention are as follows:
1. according to the method, the controller corresponding to the minimum monitoring signal is adopted to control the operation of the crane system, under the minimum monitoring signal, the estimation effect of the estimator is best, and the corresponding load is closest to the actual load, so that the controller corresponding to the estimator is the best controller, and the best positioning anti-swing effect can be achieved.
2. According to the method, the sum of the minimum monitoring signal and the set value at the current moment is not greater than the minimum monitoring signal at the last moment, the controller corresponding to the minimum monitoring signal at the current moment is switched to control the crane system to work, otherwise, the controller corresponding to the minimum monitoring signal at the last moment is continuously adopted to control the crane system to work, the control method belongs to discontinuous adaptive control, and compared with a traditional continuous adaptive control strategy and a control strategy based on passivity, the control method is higher in adjusting speed and does not need an extremely long adaptive adjusting process.
3. According to the method, the load size corresponding to the estimator corresponding to the minimum monitoring signal is close to the actual load size, so that the estimation of uncertain load quality can be realized while the positioning oscillation elimination is realized.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flowchart of a method of example 1 of the present invention;
FIG. 2 is a system model diagram of a crane system according to embodiment 1 of the present invention;
FIG. 3 is a flow chart of a handover logic according to embodiment 1 of the present invention;
FIG. 4 is a simulation diagram of the trolley displacement, load swing angle, switching signal and control input obtained by the method of example 1 when the load mass is 5 kg;
FIG. 5 is a simulation diagram of the trolley displacement, load swing angle, switching signal and control input obtained by the method of example 1 when the load mass is 50 kg;
FIG. 6 is a simulation diagram of the trolley displacement, load swing angle, switching signal and control input obtained by the method of example 1 when the load mass is 90 kg;
FIG. 7 is a simulation diagram of the trolley displacement, load swing angle, switching signal and control input obtained by the method of example 1 when the load mass is 70.5 kg;
fig. 8 is a simulation diagram of the carriage displacement, load swing angle, switching signal and control input obtained by the method of example 1 when the load mass (30+0.02t) kg.
Detailed Description
Example 1
The embodiment discloses a bridge crane control method with uncertain load, as shown in fig. 1, comprising the following steps:
step 1: and receiving the operation parameters of the crane system in real time, wherein the operation parameters comprise the position of the trolley, the swing angle of the load, the moving speed of the trolley and the swing angular speed of the load. The above-mentioned operating parameters can be obtained by means of sensors arranged in the crane system.
Step 2: and bringing the collected operating parameters into pre-constructed estimators corresponding to different loads to obtain estimators, and obtaining monitoring signals according to the difference between the operating parameters and the estimators, wherein the number of the loads can be set according to the actual required precision.
The construction method of the estimator and the controller corresponding to different loads comprises the following steps:
as shown in fig. 2, firstly, a bridge crane system is modeled by using an euler-lagrange method according to the operation parameters, and a dynamic model of the bridge crane system is obtained according to the established model.
Wherein x represents the position of the trolley, theta represents the load swing angle, M, M and l respectively represent the mass of the trolley, the mass of the load and the length of the lifting rope, g represents the gravity acceleration, and F represents the input signal of the controller. The control objective being to drive the trolley to the desired position xd(xdA fixed position given in advance) while eliminating load swings.
In practice, the load swing angle is typically small, so by simplifying the cos θ ≈ 1, sin θ ≈ θ with a reasonable model, the motive dynamics model becomes:
further, a coordinate transformation xi is introduced1＝x-xd，
ξ3＝θ，
Obtaining a system model of the crane:
the compact expression form of the system model is as follows:
where xi is ═ xi1，ξ2，ξ3，ξ4]T
Through conversion, the positioning anti-swing control problem of the bridge crane is converted into the feedback stabilization problem of the system (1). Furthermore, assuming that the trolley mass M and the hoisting rope length l are both known constants, the load mass M is an unknown constant and belongs to a certain tight set:
it is particularly noted that the assumption of unknown load quality does not affect the applicability of the method of this embodiment. On one hand, the bridge crane has the maximum load constraint, so the load mass necessarily belongs to a certain compact set; on the other hand, although tight set is assumed
The number of elements in (a) is limited, but as the number of closely concentrated elements increases, an approximation can be found that is very close to the load mass.
And constructing estimators under different loads according to the established system model.
For i 1.. and n, the following multiple estimators are constructed:
n estimators are constructed in total, and each estimator corresponds to different load mass miEstimator state
The estimation method can be regarded as the estimation of the system state xi, namely, the collected operation parameters of the crane system are brought into the estimator, and the estimation quantity of the operation parameters can be obtained.
And constructing controllers under different loads according to the established system model.
According to the expression of the matrixes A and B in the system model, any normal number m can be easily verified, and both the matrixes A and B are controllable pairs. Thus, for i 1.. times, n, the following controllers are constructed:
wherein, by selecting KiSo that A isi+BKiIs a Helveltz matrix, and
the multiple controllers comprise n controllers, each controller corresponds to different load mass mi. It is noted that when the uncertain load mass m degrades to a known constant miWhen m is equal to miAnd the designed controller can directly ensure the global stability of the crane system.
And after obtaining the estimation quantity of the operation parameter, obtaining an estimation error according to the operation parameter and the estimation quantity of the operation parameter, performing integral operation on the norm of the estimation error to form a monitoring signal generator, and obtaining a monitoring signal through the monitoring signal generator.
Order to
1, n, wherein eiTo estimate the error, and thus for i 1
Ji(t)＝∫0(||ei(τ)||2+||ei(τ)||6)dτ
In particular, the signal may be dynamically generated by:
through the step 2, corresponding estimators of the crane system under a plurality of different loads at the current moment can be obtained, and then a plurality of monitoring signals are obtained, wherein each monitoring signal corresponds to one controller.
And step 3: and extracting the controller corresponding to the minimum monitoring signal in the plurality of monitoring signals.
In this embodiment, in order to realize discontinuous adaptive adjustment and shorten the adaptive adjustment time, the extracted controller switches according to the set switching logic.
As shown in fig. 3, the set switching logic is: if the sum of the minimum monitoring signal at the current moment and the set value h is not more than the minimum monitoring signal at the last moment, switching to the controller corresponding to the minimum monitoring signal at the current moment to be used as the controller for the operation of the crane system, and otherwise, continuously adopting the controller corresponding to the minimum monitoring signal at the last moment to be used as the controller for the crane system.
In particular, the control input signal may be described as
F＝Fσ(t)，
Wherein, σ: [0, + ∞) → { 1.,. n } is a piecewise constant function, determined by the following switching logic.
Initialization of switching logic: determining the value of the normal number h; setting σ (0) to σ0Where σ is0Any positive integer of 1, n can be taken; setting ts＝0。
Switching logic implementation: for each time t > tsIf, if
Then t is updatedsT and based on the updated tsLet us order
To this end, a high performance supervisory controller is constructed consisting of multiple estimators, multiple controllers and corresponding switching logic:
wherein σ (·) depends on the monitoring signal Ji(-) and executing according to the designed switching logic.
And 4, step 4: and 3, sending control signals to driving components such as related motors of the crane system according to the controller extracted in the step 3, and controlling the crane system.
In this embodiment, the candidate load mass corresponding to the estimator corresponding to the minimum monitoring signal is closest to the actual load mass, and can be used as the estimated load mass of the crane system, so that the actual load mass can be accurately estimated.
The method of the embodiment has the following characteristics:
there are only a limited number of handovers that occur. With the counter-syndrome method, it is assumed that the handover occurs an infinite number of times. Then, the formula (6) is determined by switching, and the monitoring signal is noticed
Is a monotonically increasing function, resulting in mini＝1，2，...，n{Ji(t) } tends to infinity with time t and tends to infinity. On the other hand, in the case of a liquid,
then there must be some mkM is satisfiedk. Thus, according to the system model and the multiple estimators, the method obtains
This means that
Jk(t)＝∫0(||ek(τ)||2+||ek(τ)||6)dτ＜+∞.
This is obviousAnd mini＝1，2，...，n{Ji(t) } tends to be infinite over time t and tends to be infinite contradiction. Thus, switching occurs only a limited number of times under the designed switching logic.
② the closed loop system state can be adjusted to the origin. In (r), it has been demonstrated that handover occurs only a limited number of times. Thus, for i ═ 1., n, there are
∫0 t(||ei(τ)||2+||ei(τ)||6)dτ＜+∞. (1)
Order to
Is the moment when the switching occurs for the last time, and the value of sigma (-) is i after the switching is stopped*I.e. σ (t) ═ i* 
Whereby, based on multiple estimators, multiple controllers, and control inputs, to
To obtain
Wherein,
and A in a multiple controller configurationiAre defined identically, an
Thus, the nature of the foregoing multiple controller configuration, according to equation (1), can be derived
Is a Helwitz matrix, to obtain
Finally we demonstrate the convergence of the system state ξ. From system model, multiple controllers, control inputs
Wherein K is a matrix dependent on the load mass m, such that A + BK is a Helvelz matrix, and
from (1) and (2), it can be verified
Since A + BK is a Helvelz matrix, lim is obtainedt→+∞And | ξ (t) | | ═ 0. Note that: xi1＝x-xd，
The resulting trolley is driven to the desired position xdWhile the load swing is eliminated.
The method of the embodiment is subjected to simulation analysis, the matlab is used for verifying the effectiveness of the method, and the superiority of the method is verified by comparing the method with the existing scheme.
The values of relevant parameters of the bridge crane system are as follows: 20kg of M, 2M of L and 9.8M/s of g2. In addition, let the maximum load of the crane be 100kg, so choose
Wherein m isi＝i，i＝1，...，100。
And verifying the effectiveness of the control strategy.
The following monitoring controller was constructed according to the method of the present embodiment:
wherein ξ1＝x-xd，
miI, i 1., 100, σ () is generated by the switching logic designed above, and σ (0) ═ 1, h ═ 0.01 in the switching logic. In addition, the initial value of the original system model state is
The states of the multiple estimators are:
the effectiveness of the method of the present embodiment is shown by 5 sets of simulation results. When the unknown load belongs to the set M and takes values of 5kg, 50kg, 90kg, respectively, we get fig. 4, fig. 5, fig. 6. When the unknown load was 70.5kg and (30+0.02t) kg, we obtained fig. 7 and fig. 8, respectively. As can be seen from fig. 4 to 8, the monitoring controller is constructed to ensure that the trolley reaches the desired position in around 11 seconds, while the load swing is eliminated in around 12 seconds. These 5 simulations demonstrate that the developed monitoring control strategy is extremely robust to uncertain loads, even if the loads do not belong to a set and
even slowly, the method of the present embodiment has excellent control effect as long as the maximum load is not exceeded. It should be noted that the method of this embodiment is also fast and accurateAs can be seen from the switching signals σ (-) in fig. 4 to 8, σ (-) is switched to 5, 50, 90, 70, 30 in a short time, respectively corresponding to the load values set in the simulation of 5kg, 50kg, 90kg, 70.5kg, and (30+0.02t) kg. It should be noted that the method of the present embodiment has an online estimation function for uncertain load quality, which is not available in other existing methods.
And (2) comparison with the existing control strategy.
Compared with the passive control method with better control effect in the existing related documents, the effectiveness of the control algorithm provided by the invention is further verified, and the good control performance is shown. The following expressions are mentioned in the document IEEE Transactions on Industrial Electronics,59(12): 4723-:
wherein k isp，kd，kaIs a positive design parameter. When the values of the system parameters are consistent with those of the embodiment, the literature gives values of the following design parameters:
kp＝8，kd＝40，ka＝2.
the experimental results obtained based on the passive control strategy and the method proposed in this example are shown in table 1, where the adjustment time indicates that the trolley displacement satisfies | x-x |dThe time used when the absolute value is less than 0.02 m; the maximum input represents the maximum value of the control input F; the maximum slip angle represents the maximum slip angle of the load during operation of the trolley.
TABLE 1 comparative experimental results
As can be seen from table 1, although the passive control strategy shows a good control effect when m is 50kg, when the load mass changes in a large range, the performance degradation is severe, and especially the adjustment time is greatly increased. The method of the embodiment shows good control effect under different conditions. It should be noted that, in the passive control strategy, when m is 90kg, the maximum distance reaches 5.3m, and after exceeding the desired position, the position returns to the desired position, which is not adopted in engineering practice. More importantly, the method of the present embodiment can quickly and accurately estimate the size of the unknown load, which is not available based on the passive control strategy.
Example 2:
the embodiment discloses a bridge crane control system with uncertain load, includes:
a data acquisition module: the state variable acquisition module is used for receiving the acquired state variable in real time;
a monitor signal acquisition module: the load estimator is used for bringing the collected operation parameters into estimators corresponding to different loads which are constructed in advance to obtain estimators, and obtaining monitoring signals according to the difference value of the operation parameters and the estimators;
a controller acquisition module: a controller for extracting a minimum monitoring signal from the plurality of monitoring signals;
a control module: and the controller is used for controlling the crane system according to the extracted controller.
Example 3:
the embodiment discloses an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, and is characterized in that the processor executes the program to realize the bridge crane control method with uncertain load in embodiment 1.
Example 4:
the present embodiment discloses a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the bridge crane control method with indeterminate load as described in embodiment 1.
"computer-readable storage medium" shall be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
It will be appreciated by those skilled in the art that the steps of the invention described above may be implemented using general purpose computer means, or alternatively they may be implemented using program code executable by computing means, whereby the steps may be stored in memory means for execution by the computing means, or may be implemented as separate integrated circuit modules, or may have a plurality of modules or steps implemented as a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A bridge crane control method with uncertain load is characterized by comprising the following steps:
receiving collected operation parameters of a crane system in real time;
bringing the collected operation parameters into estimators corresponding to a plurality of different loads to obtain estimators of the operation parameters, and obtaining monitoring signals according to the difference between the operation parameters and the estimators;
extracting a controller corresponding to the minimum monitoring signal in the plurality of monitoring signals;
and controlling the crane system according to the extracted controller.
2. The bridge crane control method according to claim 1, wherein if the sum of the minimum monitoring signal at the current time and the set value is not greater than the minimum monitoring signal at the previous time, switching to the controller corresponding to the minimum monitoring signal at the current time to control the crane system to operate, otherwise, continuing to control the crane system to operate by using the controller corresponding to the minimum monitoring signal at the previous time.
3. The bridge crane control method with uncertain load as in claim 1, wherein the dynamic model of the crane system is established, the system model of the crane is obtained based on the dynamic model of the crane system, and the estimator and controller are constructed based on the system model.
4. The bridge crane control method with uncertain load as in claim 3, characterized in that the crane system is modeled using the Euler-Lagrangian method, and the dynamics model of the crane system is obtained from the established model.
5. The bridge crane control method as claimed in claim 1, wherein the estimation error is obtained from a difference between the operation parameter and the estimation amount, and the monitoring signal is obtained by performing an integration operation based on the norm of the estimation error.
6. The bridge crane control method with indeterminate load as set forth in claim 1, wherein the operating parameters include trolley displacement, trolley speed, load swing angle and swing angle speed of the crane system collected in real time.
7. A bridge crane control method with indeterminate load as claimed in claim 1 wherein the load mass candidate of the estimator for the minimum monitor signal is the estimated load mass of the crane system.
8. A bridge crane control system with indeterminate load comprising:
a data acquisition module: the state variable acquisition module is used for receiving the acquired state variable in real time;
a monitor signal acquisition module: the load estimator is used for bringing the collected operation parameters into estimators corresponding to different loads which are constructed in advance to obtain estimators, and obtaining monitoring signals according to the difference value of the operation parameters and the estimators;
a controller acquisition module: a controller for extracting a minimum monitoring signal from the plurality of monitoring signals;
a control module: and the controller is used for controlling the crane system according to the extracted controller.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of bridge crane control with indeterminate load as claimed in any one of claims 1 to 7 when the program is executed.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out a bridge crane control method with uncertain load according to any of claims 1-7.
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