A speed control method for bridge crane based on 0-1 programming

Abstract

The present invention relates to the technical field of motion control of industrial equipment, and discloses a speed control method for a bridge crane based on 0-1 planning, comprising the steps of: S3 judging whether the deviation between the actual displacement and the set displacement has fallen into an allowable range, if so, stopping the motor; otherwise, calculating the motor start-stop action sequence as 2 N Step S4 defines the motor start / stop sequence identification variable and the objective function minimization variable. Step S6 calculates the speed value time series for the next N sampling periods, and calculates the sum of the squared errors between the speed value time series and γ as θ1. Step S7 counts the number of state changes in the motor's future start / stop state time series, calculating this as θ2. Step S10 increments the motor start / stop sequence identification variable by 1, and the process returns to step S5. This invention achieves speed regulation of a bridge crane without a frequency converter, balancing the positioning accuracy requirements and equipment investment costs of the bridge crane.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial equipment motion control, and in particular to a bridge crane speed regulation control method based on 0-1 planning. Background Art

[0002] Industrial enterprises often use bridge cranes for material transfer. A bridge crane consists of three main components: a trolley, a small carriage, and an electrical control system. The trolley is the bridge of the bridge crane. A motor drives the active wheels on either side of the bridge along the longitudinal track of the workshop. To improve safety, ensure smoother and faster starting, braking, acceleration, and deceleration, and ensure more accurate positioning, a frequency converter (VFD) is often used to adjust the drive motor speed. This speed regulation is achieved by varying the motor power supply frequency. However, in material handling scenarios in raw material warehouses, bridge cranes typically perform repetitive, single loading operations with simple tasks and low positioning accuracy. Relays and contactors are typically used to control the motor start and stop. Bridge crane operators control the crane's motion through only two control actions: start and stop. Retrofitting the bridge crane's drive motor with VFD speed control solely to improve control accuracy is costly. Furthermore, bridge cranes in raw material warehouses typically operate continuously with minimal idle time, and complex hardware modifications can severely impact normal production. Furthermore, at the motion control algorithm level, traditional control optimization methods are complex, requiring multiple complex arithmetic operations and requiring high computational effort, making them difficult to implement in PLC controllers. Therefore, there is an urgent need for a bridge crane speed control method that is suitable for material transportation scenarios in factory raw material warehouses, has low requirements for bridge crane positioning accuracy, and can be implemented under limited parking and renovation time and renovation budget conditions. Summary of the Invention

[0003] In view of the technical problems existing in the prior art, the present invention provides a bridge crane speed control method based on 0-1 planning.

[0004] To achieve the above object, the present invention provides the following technical solution: a bridge crane speed control method based on 0-1 planning, comprising the following steps:

[0005] S1, calculate the time series γ of the reference trajectory of the bridge crane under the sampling period T;

[0006] S2. Wait for the new control cycle to arrive and execute the following steps:

[0007] S3, judge whether the deviation between the actual displacement of the bridge crane and the set displacement has been reduced to the allowable range, if so, stop the motor and exit the loop, otherwise, calculate all possible motor start and stop action sequences as 2 according to the pre-set prediction step number N N indivual;

[0008] S4. Define a motor start-stop action sequence identification variable, which is a non-negative integer and has an initial value of 0. Define an objective function minimization variable, which has a storage type of a single-precision floating-point number specified in IEEE754 in the computer and has an initial value of the maximum possible value of a single-precision floating-point number specified in IEEE754.

[0009] S5, when the motor start-stop action sequence identifier variable is less than 2 N , the motor start-stop action sequence identification variable is converted into an N-bit binary number B, each bit of the N-bit binary number B from low to high represents the future start-stop state time sequence of the bridge crane motor from near to far, where the digital value 1 corresponds to the motor start state, and the digital value 0 corresponds to the motor stop state, and then execute step S6. When the motor start-stop action sequence identification variable is greater than or equal to 2 N When , execute step S11;

[0010] S6. Calculate the speed value time series of the bridge crane in the next N sampling periods based on the future start and stop state time series of the motor, and calculate the sum of squares of errors between the speed value time series and the corresponding motion reference trajectory time series γ of the bridge crane as θ1;

[0011] S7, counting the number of state changes in the motor's future start and stop state time series, and calculating it as θ2;

[0012] S8. Set the optimization target to the sum of θ1 and θ2, denoted as θ total ;

[0013] S9, θ total Compared with the objective function minimization variable defined in S4, if θ total If it is less than the objective function minimization variable, then set the objective function minimization variable equal to θ total , and record θ at this time total The corresponding motor start and stop state time series μ;

[0014] S10, the motor start and stop action sequence identification variable is incremented by 1, and the process returns to step S5;

[0015] S11. Get the first state data from the motor start / stop state time series μ, send it to the motor electronic control system for execution, and return to execute S3.

[0016] Preferably, in step S1, the target displacement of the bridge crane is set to S, and the maximum allowable speed V of the bridge crane is set to max , maximum permissible acceleration a max , maximum allowable deceleration a neg and sampling period T, the following method is used to calculate the time series γ of the reference trajectory of the bridge crane:

[0017] (1) If S≤ , then the motion speed reference trajectory time series γ consists of two parts: acceleration trajectory and deceleration trajectory.

[0018] (1-1) Let the time interval be [0, ], in this time interval, the bridge crane starts to accelerate from speed 0, and the acceleration is a max , after sampling the velocity using the sampling period T, the first part of the time series γ of the motion reference trajectory of the bridge crane is obtained;

[0019] (1-2) Assume the time interval is

[0020] ( ], in this time interval, the bridge crane performs deceleration motion, and the deceleration is a neg , after sampling the velocity using the sampling period T, the second part of the time series γ of the motion reference trajectory of the bridge crane is obtained;

[0021] (2) If S> , then the motion reference trajectory time series γ consists of three parts: acceleration trajectory, uniform velocity trajectory and deceleration trajectory.

[0022] (2-1) Assume that the time interval is [0, ], in this time interval, the bridge crane starts to accelerate from speed 0, and the acceleration is a max , after sampling the velocity using the sampling period T, the first part of the time series γ of the motion reference trajectory of the bridge crane is obtained;

[0023] (2-2) Assume the time interval is ( ], during this time interval, the bridge crane moves at a constant speed V max Traveling, after sampling the velocity using the sampling period T, the second part of the time series γ of the motion reference trajectory of the bridge crane is obtained;

[0024] (2-3) Assume the time interval is

[0025] ( ], in this time interval, the bridge crane performs deceleration motion, and the deceleration is a neg , after sampling the velocity using the sampling period T, the third part of the reference trajectory time series γ of the bridge crane is obtained.

[0026] In the present invention, the target displacement of the bridge crane is S, the maximum permissible speed of the bridge crane is V ma , maximum allowable acceleration a maxand sampling period T, and the step of S1 is used to calculate the time series γ of the reference trajectory of the bridge crane motion including the acceleration, uniform speed, and deceleration processes according to the given formula. This can simplify the operation and greatly reduce the calculation time of the time series γ of the reference trajectory of the bridge crane motion.

[0027] The optimization objective in step S8 consists of the sum of two parts. The first part calculates the speed value time series of the bridge crane in the future N sampling periods based on the future start and stop state time series of the motor, and then squares and sums the errors between the speed value time series and the corresponding reference trajectory time series γ. The second part is the number of state changes in the future start and stop state time series.

[0028] In each sampling cycle, the optimal motor start-stop state time series μ is calculated through a series of steps from S4 to S10, and only the first state in μ is selected for the motor control quantity of the next beat, thereby reducing the measurement noise interference in the motion control of the bridge crane and ensuring the control accuracy.

[0029] The present invention also includes other components that enable the normal use of a bridge crane speed control method based on 0-1 planning, which are all conventional technical means in the field. In addition, devices or components not limited in the present invention all adopt conventional technical means in the field.

[0030] Compared with the prior art, the present invention is beneficial in that:

[0031] 1. The present invention has clear principles, small calculation amount, and is easy to implement through PLC programming.

[0032] 2. The present invention adopts a method of tracking the reference speed trajectory, which only requires a small amount of electrical modification while meeting the control accuracy requirements, resulting in a small workload, reduced hardware investment costs, and reduced hardware installation time.

[0033] 3. The present invention adopts a rolling optimization calculation method, which has strong anti-interference ability and good robustness.

[0034] The present invention can realize the speed regulation of the bridge crane without a frequency converter, takes into account the positioning accuracy requirements for the bridge crane and the equipment investment cost, and meets the use requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Schematic diagram of the time series of the reference trajectory of the bridge crane in Example 1 Figure 1 .

[0036] Figure 2 Schematic diagram of the time series of the reference trajectory of the bridge crane in Example 1 Figure 2 .

[0037] Figure 3This is a flow chart of the bridge crane speed control method in Example 1.

[0038] Figure 4 Schematic diagram of the time series of the reference trajectory of the bridge crane in Example 2 Figure 1 .

[0039] Figure 5 Schematic diagram of the time series of the reference trajectory of the bridge crane in Example 2 Figure 2 . DETAILED DESCRIPTION

[0040] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0041] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0042] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0043] Example 1

[0044] A speed control method for a bridge crane based on 0-1 programming includes the following steps:

[0045] S1, calculate the time series γ of the reference trajectory of the bridge crane under the sampling period T;

[0046] S2. Wait for the new control cycle to arrive and execute the following steps:

[0047] S3, judge whether the deviation between the actual displacement of the bridge crane and the set displacement has been reduced to the allowable range, if so, stop the motor and exit the loop, otherwise, calculate all possible motor start and stop action sequences as 2 according to the pre-set prediction step number N N indivual;

[0048] S4. Define a motor start-stop action sequence identification variable, which is a non-negative integer and has an initial value of 0. Define an objective function minimization variable, which has a storage type of a single-precision floating-point number specified in IEEE754 in the computer and has an initial value of the maximum possible value of a single-precision floating-point number specified in IEEE754.

[0049] S5, when the motor start-stop action sequence identifier variable is less than 2 N , the motor start-stop action sequence identification variable is converted into an N-bit binary number B, each bit of the N-bit binary number B from low to high represents the future start-stop state time sequence of the bridge crane motor from near to far, where the digital value 1 corresponds to the motor start state, and the digital value 0 corresponds to the motor stop state, and then execute step S6. When the motor start-stop action sequence identification variable is greater than or equal to 2 N When , execute step S11;

[0050] S6. Calculate the speed value time series of the bridge crane in the next N sampling periods based on the future start and stop state time series of the motor, and calculate the sum of squares of errors between the speed value time series and the corresponding motion reference trajectory time series γ of the bridge crane as θ1;

[0051] S7, counting the number of state changes in the motor's future start and stop state time series, and calculating it as θ2;

[0052] S8. Set the optimization target to the sum of θ1 and θ2, denoted as θ total ;

[0053] S9, θ total Compared with the objective function minimization variable defined in S4, if θ total If it is less than the objective function minimization variable, then set the objective function minimization variable equal to θ total , and record θ at this time total The corresponding motor start and stop state time series μ;

[0054] S10, the motor start and stop action sequence identification variable is incremented by 1, and the process returns to step S5;

[0055] S11. Get the first state data from the motor start / stop state time series μ, send it to the motor electronic control system for execution, and return to execute S3.

[0056] In step S1, the target displacement of the bridge crane is set to S, and the maximum allowable speed of the bridge crane is V. max , maximum permissible acceleration a max , maximum allowable deceleration a neg and sampling period T, the following method is used to calculate the time series γ of the reference trajectory of the bridge crane:

[0057] (1) If S≤ , then the motion speed reference trajectory time series γ consists of two parts: acceleration trajectory and deceleration trajectory.

[0058] (1-1) Let the time interval be [0, ], in this time interval, the bridge crane starts to accelerate from speed 0, and the acceleration is a max , after sampling the velocity using the sampling period T, the first part of the time series γ of the motion reference trajectory of the bridge crane is obtained;

[0059] (1-2) Assume the time interval is

[0060] ( ], in this time interval, the bridge crane performs deceleration motion, and the deceleration is a neg , after sampling the velocity using the sampling period T, the second part of the time series γ of the motion reference trajectory of the bridge crane is obtained;

[0061] (2) If S> , then the motion reference trajectory time series γ consists of three parts: acceleration trajectory, uniform velocity trajectory and deceleration trajectory.

[0062] (2-1) Assume that the time interval is [0, ], in this time interval, the bridge crane starts to accelerate from speed 0, and the acceleration is a max , after sampling the velocity using the sampling period T, the first part of the time series γ of the motion reference trajectory of the bridge crane is obtained;

[0063] (2-2) Assume the time interval is ( ], during this time interval, the bridge crane moves at a constant speed V max Traveling, after sampling the velocity using the sampling period T, the second part of the time series γ of the motion reference trajectory of the bridge crane is obtained;

[0064] (2-3) Assume the time interval is

[0065] ( ], in this time interval, the bridge crane performs deceleration motion, and the deceleration is a neg , after sampling the velocity using the sampling period T, the third part of the reference trajectory time series γ of the bridge crane is obtained.

[0066] This example takes the aluminum oxide and aluminum hydroxide warehouse in the nonferrous industry as an example. Assume that the sampling period is 1s and the maximum allowable speed of the bridge crane is V max =1.5m / s, maximum permissible acceleration a max =0.25m / s, the deceleration of the bridge crane measured in the experiment when the motor is stopped is a neg =0.2m / s. After completing the setting of the motion parameters of the bridge crane system, perform speed control according to the above method.

[0067] The target displacement of the bridge crane is set to 80m. The bridge crane is controlled using the above method. At this time, the total displacement S=80m. Since S> , then the time series γ of the reference trajectory of the bridge crane is composed of three segments, such as Figure 1 and Figure 2 As shown, Figure 1 In the figure, the coordinates of point P0 are ( ,0), the coordinates of point P1 are ( ,0), the coordinates of point P2 are ( ,0), the sum of the three parts of area S1+S2+S3 equals S; Figure 2 In the example, Step 1 is the acceleration time interval, Step 2 is the uniform speed time interval, and Step 3 is the deceleration time interval. max is the acceleration curve of accelerated motion, a neg It is the deceleration curve of deceleration motion.

[0068] The control process of the bridge crane using the method of the present invention is as follows: Figure 3 As shown in the figure, the horizontal axis represents sampling time. The solid black line represents the accumulated displacement of the bridge crane, the dashed black line represents the speed change of the bridge crane, and the black bar graph represents the motor start-stop control. The control algorithm successfully controlled the bridge crane to a displacement of 80.12 m 76 seconds after it began operating. During the initial phase of the bridge crane's movement, the motor did not frequently start and stop. However, during the deceleration process, the motor started and stopped several times to achieve accurate displacement control.

[0069] Example 2

[0070] The difference between this embodiment and embodiment 1 is that the target displacement of the crane is set to 10m, and the bridge crane is controlled using the above method. At this time, S=10m. Since S≤ , at this time, the reference trajectory time series γ of the bridge crane motion consists of two parts: the acceleration trajectory and the deceleration trajectory, as shown in Figure 4 and Figure 5 As shown, Figure 4 In the figure, the coordinates of point P0 are ( ,0), the coordinates of point P1 are ( , 0), the sum of the two areas S1+S2 equals S; Figure 5 In the example, Step 1 is the acceleration time interval, Step 2 is the deceleration time interval, and a max is the acceleration curve of accelerated motion, a neg It is the deceleration curve of deceleration motion.

[0071] While the embodiments of the present invention have been described above, the above description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A bridge crane speed control method based on 0-1 planning, characterized in that: The steps include: S1, calculate the time series γ of the reference trajectory of the bridge crane under the sampling period T; S2. Wait for the new control cycle to arrive and execute the following steps: S3, judge whether the deviation between the actual displacement of the bridge crane and the set displacement has been reduced to the allowable range, if so, stop the motor and exit the loop, otherwise, calculate all possible motor start and stop action sequences as 2 according to the pre-set prediction step number N N indivual; S4. Define a motor start-stop action sequence identification variable, which is a non-negative integer and has an initial value of 0. Define an objective function minimization variable, which has a storage type of a single-precision floating-point number specified in IEEE754 in the computer and has an initial value of the maximum possible value of a single-precision floating-point number specified in IEEE754. S5, when the motor start-stop action sequence identifier variable is less than 2 N , the motor start-stop action sequence identification variable is converted into an N-bit binary number B, each bit of the N-bit binary number B from low to high represents the future start-stop state time sequence of the bridge crane motor from near to far, where the digital value 1 corresponds to the motor start state, and the digital value 0 corresponds to the motor stop state, and then execute step S6. When the motor start-stop action sequence identification variable is greater than or equal to 2 N When , execute step S11; S6. Calculate the speed value time series of the bridge crane in the next N sampling periods based on the future start and stop state time series of the motor, and calculate the sum of squares of errors between the speed value time series and the corresponding motion reference trajectory time series γ of the bridge crane as θ1; S7, counting the number of state changes in the motor's future start and stop state time series, and calculating it as θ2; S8. Set the optimization target to the sum of θ1 and θ2, denoted as θ total ; S9, θ total Compared with the objective function minimization variable defined in S4, if θ total If it is less than the objective function minimization variable, then set the objective function minimization variable equal to θ total , and record θ at this time total The corresponding motor start and stop state time series μ; S10, the motor start and stop action sequence identification variable is incremented by 1, and the process returns to step S5; S11. Get the first state data from the motor start / stop state time series μ, send it to the motor electronic control system for execution, and return to execute S3.

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