CN113071989B - Motor control method of crane - Google Patents

Motor control method of crane Download PDF

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CN113071989B
CN113071989B CN202110321218.7A CN202110321218A CN113071989B CN 113071989 B CN113071989 B CN 113071989B CN 202110321218 A CN202110321218 A CN 202110321218A CN 113071989 B CN113071989 B CN 113071989B
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signal
motor
comparator
speed
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CN113071989A (en
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王力超
李子阳
耿树巧
王义琼
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Anhui Polytechnic University
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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
    • B66C13/18—Control systems or devices
    • B66C13/22—Control systems or devices for electric drives
    • B66C13/30—Circuits for braking, traversing, or slewing motors
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P29/00—Arrangements for regulating or controlling electric motors, appropriate for both AC and DC motors
    • H02P29/10—Arrangements for regulating or controlling electric motors, appropriate for both AC and DC motors for preventing overspeed or under speed

Abstract

The invention discloses a motor control method of a crane, comprising the following steps of AThe heavy machine is provided with a data processing module, and at the moment of setting k, the data processing module reads the detection value u (k) of the gravity sensor and outputs the value y1(ii) a The motor is firstly started at a set initial starting speed V0Running, the running speed increasing exponentially every cycle until reaching a threshold speed VTThen operated at a set acceleration until a set operating speed V is reachedFWherein the operating speed V is setFThe determination steps are as follows: when a heavy object is hung on the crane, the output value calculated by the data processing module is recorded as g1(ii) a In the working process, the output value calculated by the data processing module is recorded as g2(ii) a G is prepared from2And g1For comparison, if g2Greater than g1Then the motor is controlled to rotate forward, VF＝β*(g2‑g1) (ii) a If g is2Less than g1Then the motor is controlled to rotate reversely, VF＝β*(g1‑g2) Wherein, beta is a set proportionality coefficient. The motor can run more stably and safely.

Description

Motor control method of crane
Technical Field
The invention relates to a motor control method of a crane.
Background
With the high-speed development of the logistics industry, the crane is used as an important labor tool to replace the traditional manual carrying, so that the labor cost and the time cost are greatly saved, and the working efficiency is greatly improved. The crane is widely applied to various occasions such as factory workshops, truck loading and unloading and the like, and is gradually developed towards the directions of intellectualization, digitization, automation and the like in order to meet the rapid demand of modern production. The traditional crane realizes the loading and unloading of cargoes up and down by manually controlling a machine handle, but in the actual operation, the lifted heavy object is generally large, the lifting height is high, the operation is difficult to be carried out through the handle, and the importance of a suspension mode is reflected at the moment.
In the suspension mode, a weight is applied with an upward or downward force by hands to realize the ascending or descending of the weight, namely, the suspension mode detects the force applied to a hook of the crane by using a gravity sensor, and when the force applied to the hook is greater than the gravity of the weight, the crane moves downwards; when the force borne by the hook is smaller than the gravity of the weight, the crane can move upwards.
In the suspension mode, the heavy object can shake in the air, so that the value measured by the gravity sensor is not the mass of the heavy object but fluctuates repeatedly, and the system is unstable. When the heavy object starts to move upwards (downwards) from a static state, the heavy object can generate overweight, the value read by the gravity sensor can suddenly become bigger (smaller), and then the heavy object moves in the opposite direction, and the safety accident can be caused and the loss of the machine can be accelerated.
Disclosure of Invention
Aiming at the problems, the invention provides a motor control method of a crane, so that the motor can run more stably and safely.
In order to achieve the technical purpose and achieve the technical effect, the invention is realized by the following technical scheme:
a motor control method of a crane comprises the following steps:
A. the crane is provided with a data processing module, and at the moment of setting k, the data processing module reads the detection value u (k) of the gravity sensor and outputs the value y1Average value of measurement noise is n and y1And the signal added or subtracted with the external noise is y (k), then:
the signal u (k) is amplified by B times by a first amplifier and then input to a first comparator, y1After a unit delay, the signal is amplified by A times by a second amplifier and then input into a first comparator, and after two signals of the first comparator are added, a state prediction quantity omega (k) output by a controlled object at the moment k is output;
respectively inputting the predicted state quantity omega (K) and n output by the controlled object at the moment K into a second comparator, adding the predicted state quantity y '(K) and outputting an error value after subtracting the predicted state quantity y' (K) and y (K) from a third comparator, and passing the error value through a Kalman filtering gain Kf(k) The state prediction quantity omega (k) output by the controlled object at the time k is respectively input into the fourth comparator to be added and output y1；
B. The motor control of the crane comprises:
the motor is firstly started at a set initial starting speed V0Running, the running speed increasing exponentially every cycle until reaching a threshold speed VTThen operated at a set acceleration until a set operating speed V is reachedFWherein the operating speed V is setFThe determination steps are as follows:
1) when a heavy object is hung on the crane, the output value calculated by the data processing module is recorded as g1；
2) In the working process, the output value calculated by the data processing module is recorded as g2；
3) G is prepared from2And g1For comparison, if g2Greater than g1Then the motor is controlled to rotate forward, VF＝β*(g2-g1) (ii) a If g is2Less than g1Then the motor is controlled to rotate reversely, VF＝β*(g1-g2) Wherein, beta is a set proportionality coefficient.
Preferably, the speed regulation algorithm of the motor is as follows:
Figure BDA0002993013720000031
Figure BDA0002993013720000032
V2＝V1+n2*VD
wherein V is the rotation speed of the motor, V0Is the initial starting speed, V, of the motorTIs a threshold speed, VDTo fix the growth rate, VFA is a proportionality coefficient and n is a set running speed which is finally reached by the motor1For the number of program execution cycles, n, before the motor speed reaches the threshold speed2The number of program execution cycles after the motor reaches the threshold speed.
Preferably, Kalman Filter gains
Figure BDA0002993013720000033
Where R is the variance of the measurement noise, P*(k) The variance is predicted for the state at time k,
Figure BDA0002993013720000034
wherein, the lambda is an fading factor,
Figure BDA0002993013720000035
variance estimates are predicted for the state at time k.
Preferably, the motor is controlled by an improved active disturbance rejection controller.
The method as claimed in claim 4, wherein the input signal of the improved active disturbance rejection controller is the control signal v sent by the processor, the output signal of the improved active disturbance rejection controller is u, and the rotation speed of the motor is y, then:
the control signal V is divided into two parts by the steepest tracking differentiator TD, one is the tracking signal V1And the other is a differential signal V of the control signal2Tracking signal V1The differential signal V is input into the nonlinear state error feedback control law NLSEF through a fifth comparator2The output signal U is input to the nonlinear state error feedback control law NLSEF through a sixth comparator, and the output signal U of the nonlinear state error feedback control law NLSEF0Input to a seventh comparator, the control signal V is differentiated to form a signal V3Then the signal is used as negative feedback to be input to a seventh comparator, the output signal of the seventh comparator is u, and the signal u passes through a disturbance compensation device b0The output signal and the output signal y are input into an Extended State Observer (ESO) for observation, and the observed signal is divided into a tracking signal Z1A differential signal Z2And Z3Wherein the tracking signal Z1As negative feedback input to the fifth comparator, differentiating the signal Z2As negative feedback input to a sixth comparator, Z3Pass through disturbance compensation device
Figure BDA0002993013720000041
And then the signal is input to a seventh comparator as negative feedback after being processed.
The invention has the beneficial effects that:
firstly, aiming at the problem that the mass of the weight read by the gravity sensor fluctuates back and forth due to the fact that the weight shakes in the air, and the control of the processor on the motor is seriously influenced, the control method carries out data processing before the value read by the gravity sensor is transmitted to the processor, and meanwhile, the control method adopts a Kalman filtering algorithm containing an evanescent factor, and compared with a traditional Kalman filter, the convergence speed of the filtering algorithm is accelerated under the condition of equivalent calculated amount.
Secondly, aiming at the problem that the normal operation of the system is influenced by the fact that the object starts to move in the suspension mode and the object generates excessive (losing) weight, the starting speed and the operation speed of the motor are improved. In the levitation mode, once the system is going to move up (down), the motor is first run at a very low initial speed, then it increases exponentially each cycle later, and after a threshold is reached, the speed increases at a fixed value until the set speed is reached. The influence on the system caused by the overload (loss) is solved, and the system can run stably.
Thirdly, before the processor controls the motor, the invention also adds an improved Active Disturbance Rejection Controller (ADRC) which is used for setting the motor so as to enable the operation of the motor to reach the expected state. The two combines to make the mode of suspension can even running under the state of expectation, and when the pivoted acceleration of motor was too big, the process burden feedforward device will descend the rotational acceleration of motor for the motor can slow stable increase rotational speed, prevents that the system from appearing the oscillation, and the system is more stable in the middle of the engineering uses, and is safer.
Drawings
FIG. 1 is a block diagram of a data processing module of the present invention;
FIG. 2 is a block diagram of the control architecture of the improved active disturbance rejection controller of the present invention;
FIG. 3 is a simulation of motor speed as controlled by a classical ADRC;
FIG. 4 is a graph of an acceleration simulation of the corresponding motor of FIG. 3;
FIG. 5 is a speed simulation of the motor of the present invention;
fig. 6 is a corresponding motor acceleration simulation diagram of fig. 5.
Detailed Description
The present invention will be better understood and implemented by those skilled in the art by the following detailed description of the technical solution of the present invention with reference to the accompanying drawings and specific examples, which are not intended to limit the present invention.
A motor control method of a crane, wherein:
A. the crane is provided with a data processing module, as shown in fig. 1, when the time k is set, the data processing module reads the detection value u (k) of the gravity sensor and the output value y1Average value of measurement noise is n and y1And the signal added or subtracted with the external noise is y (k), then:
the signal u (k) is amplified by B times by a first amplifier and then input to a first comparator, y1By a unit delay
Figure BDA0002993013720000051
And the signal is amplified by A times by a second amplifier and then input into a first comparator, and two signals of the first comparator are added to output a state prediction quantity omega (k) output by a controlled object at the moment k, wherein A, B is a proportionality coefficient.
Respectively inputting the predicted state quantity omega (K) and n output by the controlled object at the moment K into a second comparator, adding the predicted state quantity y '(K) and outputting an estimated state quantity y' (K), respectively inputting y (K) and the estimated state quantity y '(K) into a third comparator, subtracting the values to obtain an error value (y (K) -y' (K) which is an error between the controlled object output quantity and the predicted output quantity, and passing the error value through a Kalman filter gain Kf(k) The state prediction quantity omega (k) output by the controlled object at the time k is respectively input into the fourth comparator to be added and output y1。
Wherein, Kf(k) For Kalman filtering gain, the invention combines the measured noise variance R and the state prediction variance P*(k) Design Kalman filter gain as Kf(k)：
Figure BDA0002993013720000061
Where R is the variance of the measurement noise, P*(k) The variance is predicted for the state at time k,
Figure BDA0002993013720000062
wherein, the lambda is an fading factor,
Figure BDA0002993013720000063
variance estimates are predicted for the state at time k.
In order to improve the convergence rate of the filter algorithm without increasing the calculation amount of the filter algorithm, the method calculates the state prediction variance P*(k) An evanescent factor lambda is introduced to make the autocorrelation function of the output error take a minimum value as far as possible, thereby improving the convergence rate of the algorithm.
Aiming at the problem that the mass of the heavy object read by the gravity sensor fluctuates back and forth due to the fact that the heavy object shakes in the air, and the control of the processor on the motor is seriously influenced, therefore, data processing is carried out before the value read by the gravity sensor is transmitted to the processor, meanwhile, the Kalman filtering algorithm containing the fading factor is adopted, and compared with a traditional Kalman filter, the convergence speed of the filtering algorithm is accelerated under the condition of equivalent calculated amount.
B. The motor control of the crane comprises:
the motor is firstly started at a set initial starting speed V0Running, the running speed increasing exponentially every cycle until reaching a threshold speed VTThen operated at a set acceleration until a set operating speed V is reachedFWherein the operating speed V is setFThe determination steps are as follows:
1) when a heavy object is hung on the crane, the output value calculated by the data processing module is recorded as g1；
2) In the working process, the output value calculated by the data processing module is recorded as g2；
3) G is prepared from2And g1For comparison, if g2Greater than g1When a downward force is applied to the weight, the control is performedFor positive rotation of motor, VF＝β*(g2-g1) (ii) a If g is2Less than g1When the upward force is applied to the heavy object, the motor is controlled to rotate reversely, and V is controlledF＝β*(g1-g2) Wherein beta is a set proportionality coefficient, and the value range of beta is 1/35-1/7; g2And g1The greater the difference is, the set operating speed VFThe larger.
Preferably, the speed regulation algorithm of the motor is as follows:
Figure BDA0002993013720000071
Figure BDA0002993013720000072
V2＝V1+n2*VD
wherein V is the rotation speed of the motor, V0Is the initial starting speed, V, of the motorTIs a threshold speed, VDTo fix the growth rate, VFA is a proportionality coefficient and n is a set running speed which is finally reached by the motor1For the number of program execution cycles, n, before the motor speed reaches the threshold speed2The number of program execution cycles after the motor reaches the threshold speed.
Therefore, the speed of the heavy object in the early stage can be very low, and the heavy object can rapidly and stably run in the middle and later stages, so that the influence on the system caused by the heavy object exceeding (losing) is avoided, and the system can stably run. Before the processor controls the motor, an improved Active Disturbance Rejection Controller (ADRC) is added, and the ADRC is used for adjusting the motor so that the operation of the motor can reach an expected state. The combination of the two enables the levitation mode to operate in our envisioned state.
As shown in fig. 2, assuming that the input signal of the improved active disturbance rejection controller is the control signal v sent by the processor, the output signal of the improved active disturbance rejection controller is u, and the rotation speed of the motor is y, then:
the control signal V is divided into two parts by the steepest tracking differentiator TD, one is the tracking signal V1And the other is a differential signal V of the control signal2Tracking signal V1The differential signal V is input into the nonlinear state error feedback control law NLSEF through a fifth comparator2The output signal U is input to the nonlinear state error feedback control law NLSEF through a sixth comparator, and the output signal U of the nonlinear state error feedback control law NLSEF0Input to a seventh comparator, the control signal V is differentiated to form a signal V3Then the signal is used as negative feedback to be input to a seventh comparator, the output signal of the seventh comparator is u, and the signal u passes through a disturbance compensation device b0The output signal and the output signal y are input into an Extended State Observer (ESO) for observation, and the observed signal is divided into a tracking signal Z1A differential signal Z2And Z3Wherein the tracking signal Z1As negative feedback input to the fifth comparator, differentiating the signal Z2As negative feedback input to a sixth comparator, Z3Pass through disturbance compensation device
Figure BDA0002993013720000081
And the processed signal is input to a seventh comparator as negative feedback and finally fed back to the motor. The motor can run stably in an expected state. When the rotating acceleration of the motor is overlarge, the rotating acceleration of the motor can be reduced through the negative feedforward device, so that the rotating speed of the motor can be slowly and stably increased, and the system is prevented from oscillating.
The simulation verification of the invention is shown in FIGS. 3-6: as shown in fig. 3, the motor speed controlled by the classic ADRC is shown, and fig. 4 shows the acceleration of the motor, the motor speed changes too fast, which easily causes the oscillation of the system; fig. 5 shows the motor speed of the present invention, and fig. 6 shows the motor acceleration of the present invention, and it can be seen from fig. 5 and 6 that the speed change of the motor is much more stable, and the system is more stable and safer in engineering use.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (6)

1. A motor control method of a crane is characterized in that:
A. the crane is provided with a data processing module, and at the moment of setting k, the data processing module reads the detection value u (k) of the gravity sensor and outputs the value y1Average value of measurement noise is n and y1And the signal added or subtracted with the external noise is y (k), then:
the signal u (k) is amplified by B times by a first amplifier and then input to a first comparator, y1After a unit delay, the signal is amplified by A times by a second amplifier and then input into a first comparator, and after two signals of the first comparator are added, a state prediction quantity omega (k) output by a controlled object at the moment k is output;
respectively inputting the predicted state quantity omega (K) and n output by the controlled object at the moment K into a second comparator, adding the predicted state quantity y '(K) and outputting an error value after subtracting the predicted state quantity y' (K) and y (K) from a third comparator, and passing the error value through a Kalman filtering gain Kf(k) The state prediction quantity omega (k) output by the controlled object at the time k is respectively input into the fourth comparator to be added and output y1；
B. The motor control of the crane comprises:
the motor is firstly started at a set initial starting speed V0Running, the running speed increasing exponentially every cycle until reaching a threshold speed VTThen operated at a set acceleration until a set operating speed V is reachedFWherein the operating speed V is setFThe determination steps are as follows:
1) when a heavy object is hung on the crane, the output value calculated by the data processing module is recorded as g1；
2) In the working process, the output value calculated by the data processing module is recorded as g2；
3) G is prepared from2And g1For comparison, if g2Greater than g1Then the motor is controlled to rotate forward, VF＝β*(g2-g1) (ii) a If g is2Less than g1Then the motor is controlled to rotate reversely, VF＝β*(g1-g2) Wherein, beta is a set proportionality coefficient.
2. The motor control method of the crane according to claim 1, wherein the speed control algorithm of the motor is as follows:
Figure FDA0002993013710000021
Figure FDA0002993013710000022
V2＝V1+n2*VD
wherein V is the rotation speed of the motor, V0Is the initial starting speed, V, of the motorTIs a threshold speed, VDTo fix the growth rate, VFA is a proportionality coefficient and n is a set running speed which is finally reached by the motor1For the number of program execution cycles, n, before the motor speed reaches the threshold speed2The number of program execution cycles after the motor reaches the threshold speed.
3. The motor control method of a crane according to claim 2, wherein kalman filter gain
Figure FDA0002993013710000023
Where R is the variance of the measurement noise, P*(k) The variance is predicted for the state at time k,
Figure FDA0002993013710000024
wherein, the lambda is an fading factor,
Figure FDA0002993013710000025
variance estimates are predicted for the state at time k.
4. The motor control method of the crane according to claim 2, wherein the motor is controlled by an improved active disturbance rejection controller.
5. The method as claimed in claim 4, wherein the input signal of the improved active disturbance rejection controller is the control signal v sent by the processor, the output signal of the improved active disturbance rejection controller is u, and the rotation speed of the motor is y, then:
the control signal V is divided into two parts by the steepest tracking differentiator TD, one is the tracking signal V1And the other is a differential signal V of the control signal2Tracking signal V1The differential signal V is input into the nonlinear state error feedback control law NLSEF through a fifth comparator2The output signal U is input to the nonlinear state error feedback control law NLSEF through a sixth comparator, and the output signal U of the nonlinear state error feedback control law NLSEF0Input to a seventh comparator, the control signal V is differentiated to form a signal V3Then the signal is used as negative feedback to be input to a seventh comparator, the output signal of the seventh comparator is u, and the signal u passes through a disturbance compensation device b0The output signal and the output signal y are input into an Extended State Observer (ESO) for observation, and the observed signal is divided into a tracking signal Z1A differential signal Z2And Z3Wherein the tracking signal Z1As negative feedback input to the fifth comparator, differentiating the signal Z2As negative feedback input to a sixth comparator, Z3Pass through disturbance compensation device
Figure FDA0002993013710000031
And then the signal is input to a seventh comparator as negative feedback after being processed.
6. The motor control method of a crane according to claim 1, wherein β is in a range of 1/35-1/7.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2248225A1 (en) * 1973-10-17 1975-05-16 Drilling Systems Internal Inc
CN102040161A (en) * 2010-04-01 2011-05-04 长沙中联重工科技发展股份有限公司 System and method for preventing gliding during secondary lifting of crane
CN110316657A (en) * 2019-08-07 2019-10-11 上海昂丰装备科技有限公司 A kind of anti-swing control system and its control method of heavy object of crane
CN211895823U (en) * 2019-12-25 2020-11-10 广东电网有限责任公司 Novel electric hoist
CN112173967A (en) * 2020-10-28 2021-01-05 武汉港迪电气传动技术有限公司 Method and device for inhibiting initial swinging of weight

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2248225A1 (en) * 1973-10-17 1975-05-16 Drilling Systems Internal Inc
CN102040161A (en) * 2010-04-01 2011-05-04 长沙中联重工科技发展股份有限公司 System and method for preventing gliding during secondary lifting of crane
CN110316657A (en) * 2019-08-07 2019-10-11 上海昂丰装备科技有限公司 A kind of anti-swing control system and its control method of heavy object of crane
CN211895823U (en) * 2019-12-25 2020-11-10 广东电网有限责任公司 Novel electric hoist
CN112173967A (en) * 2020-10-28 2021-01-05 武汉港迪电气传动技术有限公司 Method and device for inhibiting initial swinging of weight

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