Hybrid braking control method, device and system, and storage medium for maglev trains

Abstract

This invention discloses a hybrid braking control method, device, system, and storage medium for maglev trains. The method includes the following steps: when the train enters the platform parking area to prepare for stopping, the traction control unit and the braking control unit receive a braking command and receive a first induction detection signal from the sleepers passing by the train during its journey, a second induction detection signal from a detection element pre-laid along the track in the platform parking area, and an acceleration signal in the direction of train travel; calculate the real-time speed of the train based on the received first induction detection signal, second induction detection signal, and acceleration signal; and control the traction control unit and the braking control unit to perform hybrid braking at a preset speed point until the train stops. This invention has the advantages of simple implementation, low cost, low wear of the brake shoes in the train's mechanical braking system, and high control accuracy and efficiency.

Description

Technical Field

[0001] This invention relates to the field of medium- and low-speed maglev train technology, and in particular to a hybrid braking control method, device and system for maglev trains. Background Technology

[0002] Medium- and low-speed maglev trains use F-rail attraction between levitation electromagnets and track beams for levitation, and are driven by short-stator linear motors. Because they lack the rotating components of conventional wheel-rail trains, train speed cannot be obtained through speed encoders mounted on rotating components; alternative speed measurement methods are required. Current technologies for medium- and low-speed maglev trains typically employ methods such as cross-inductive loop speed measurement, inductive sleeper counting speed measurement, and radar speed measurement, with inductive counting sleeper speed measurement being a relatively common method. However, due to the small spacing between sleepers, typically between 600mm and 1200mm, the relative position change of the sleepers relative to the speed sensor is slow at low speeds. Therefore, when using speed measurement methods such as inductive counting sleeper speed measurement, the train speed detection is inaccurate and the measurement precision is low at low speeds, resulting in lag and fluctuations between the detected and actual train speeds.

[0003] After the train enters the platform stopping area, it begins to brake. Since electric braking force is ineffective at low speeds, medium- and low-speed maglev trains typically employ a hybrid braking method combining electric and mechanical braking at low speeds. Specifically, the electric braking force gradually decreases from its maximum value to zero at a fixed rate, while the mechanical braking force gradually increases from zero to its maximum value at a fixed rate. However, because secondary speed measurement methods, such as inductive counting sleeper speed measurement, are used in medium- and low-speed maglev trains, the accuracy of train speed detection is not high at low speeds. This results in the starting speed point for the transition between electric and mechanical braking being typically set relatively high (usually between 10 km / h and 12 km / h). This leads to poor precision in electric braking force control near zero speed and greater wear on the mechanical brake shoes, thus increasing the daily maintenance workload and costs. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: in view of the technical problems existing in the prior art, the present invention provides a hybrid braking control method, device and system for maglev trains that is simple to implement, low in cost, has low wear of brake shoes for train mechanical braking, and has high control accuracy and efficiency, as well as a storage medium.

[0005] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0006] A hybrid braking control method for maglev trains, comprising the following steps:

[0007] When the train enters the platform parking area to stop, the traction control unit and the braking control unit receive the braking command and receive the first induction detection signal of the sleepers that the train passes by during its journey, the second induction detection signal of the detection object that is laid along the track in advance in the platform parking area, and the acceleration signal of the train's running direction during its journey.

[0008] The train speed value is calculated based on the received first sensor detection signal, second sensor detection signal, and acceleration signal;

[0009] Based on the calculated train speed, the traction control unit and braking control unit are controlled to perform mixed braking at a preset speed point until the train comes to a stop.

[0010] Furthermore, the step of calculating the train speed value based on the received first sensing detection signal, second sensing detection signal, and acceleration signal includes:

[0011] The optimal acceleration value is obtained using the particle swarm optimization algorithm based on the acceleration signal.

[0012] Based on the second inductive detection signal and the acceleration signal, a Kalman filter algorithm is used to obtain the filtered acceleration value;

[0013] The optimal acceleration value is corrected using the filtered acceleration value to obtain the corrected acceleration value;

[0014] The train speed obtained using the first induction detection signal is used as the base speed value, and the speed value obtained by integrating the corrected acceleration value is used to correct the base speed value to calculate the final train speed value.

[0015] Furthermore, the optimal acceleration value 'a' is obtained using a particle swarm optimization algorithm based on the first sensed detection signal and the acceleration signal. jm include:

[0016] The acceleration array a is constructed by acquiring the acceleration signals detected within the detection interval ΔT. jd That is, a jd =[a j1d ,a j2d ....a jnd ], where n is the number of acceleration signals obtained within the detection interval ΔT;

[0017] During each iteration, based on the acceleration array a jd The internal acceleration signal is used to calculate the current train's acceleration fitness value;

[0018] Based on the current train's acceleration fitness value, the global optimal acceleration during low-speed braking, and the historical optimal acceleration during low-speed braking, the optimal value is selected as the output optimal acceleration value.

[0019] Furthermore, the acceleration fitness value of the current train is constructed according to the following formula:

[0020]

[0021] Where 1≤i1≤n, 1≤i2≤n, and i1≤≠i2.

[0022] Furthermore, the optimal acceleration value can be obtained by taking the minimum value from the current train's acceleration fitness value and the historical optimal acceleration during the train's low-speed braking process, or by taking the minimum value from the global optimal acceleration during the train's low-speed braking process and the historical optimal acceleration during the train's low-speed braking process.

[0023] Furthermore, when selecting the optimal value as the output of the optimal acceleration value, each set of data is iteratively calculated according to the following formula:

[0024] v jid (t+1)=v jid (t)+a jid (t+1)

[0025] a jid (t+1)=ωa jid (t)+c1r1(p jid (t)-v jid (t))+c2r2(p gid (t)-v jid (t))

[0026] Where t represents time, v jid Indicates the optimal speed value, a jid Let represent the optimal acceleration value, 1≤i≤n, 1≤d≤2, ω be the inertia weight, c1 and c2 be preset learning factors, and r1 and r2 be random numbers in [0,1].

[0027] Furthermore, based on the second sensed detection signal and the acceleration signal, a Kalman filter algorithm is used to calculate the filtered acceleration value according to the following discrete state equation:

[0028]

[0029] Where x1 and x2 are the train position and velocity detected using acceleration signals, respectively; x3 and x4 are the train position and velocity detected using the second induction detection signal of the object to be detected, respectively; T is a discrete parameter; m is the mass of the maglev train; and acceleration signal... Acceleration obtained from the detection signal of the object to be sensed k is the number of discrete points.

[0030] Furthermore, the discrete state equations are constructed according to the following steps:

[0031] The state equations are constructed as follows:

[0032]

[0033] The continuous state transition equation matrix is ​​obtained as follows:

[0034]

[0035] And by discretization, we obtain:

[0036]

[0037] Where f(τ) is the discrete introduction function;

[0038] The discrete transition matrix is ​​obtained as follows: The discrete state equations are finally constructed.

[0039] Furthermore, controlling the traction control unit and the braking control unit to perform mixed braking at a preset speed point includes: at the preset speed point, the traction control unit controls the DC traction motor to apply electric braking and the electric braking force decreases at a fixed slope, and the braking control unit controls the application of mechanical braking and the mechanical braking force increases at a fixed slope.

[0040] A hybrid braking control device for maglev trains, comprising:

[0041] The first sensing and detection unit is used to sense and detect the sleepers that the train passes during its journey and outputs a first sensing and detection signal to the train speed calculation unit.

[0042] The second sensing and detection unit is used to sense the detection objects to be sensed and detected that are laid along the track in advance in the platform parking area, and outputs the second sensing and detection signal to the train speed calculation unit.

[0043] An acceleration detection unit is used to detect acceleration signals in the direction of travel during train operation and output them to the train speed calculation unit.

[0044] The train speed calculation unit is used to calculate the real-time speed of the train based on the received first sensor detection signal, second sensor detection signal and acceleration signal;

[0045] The control unit is used to control the train to perform electric braking to near zero speed at a preset speed point based on the real-time speed calculated by the train speed calculation unit.

[0046] Furthermore, the first sensing detection unit and / or the second sensing detection unit are sensing counting sensors.

[0047] Furthermore, the sensing element is a metal toothed plate.

[0048] Furthermore, at a preset speed point, the control unit controls the DC traction motor to apply electric braking at a preset speed point, and the electric braking force decreases according to a first preset slope. It also controls the application of mechanical braking at a preset speed point, and the mechanical braking force increases according to a second preset slope. The traction control unit controls the linear traction motor to apply electric braking force to zero speed, and the brake control unit controls the base brake to apply mechanical braking when the train approaches zero speed.

[0049] Furthermore, the train speed calculation unit includes:

[0050] The particle swarm optimization subunit is used to obtain the optimal acceleration value based on the acceleration signal using the particle swarm algorithm.

[0051] The filtering subunit is used to obtain the filtered acceleration value based on the second sensing detection signal and the acceleration signal using the Kalman filtering algorithm;

[0052] An acceleration calculation subunit is used to correct the optimal acceleration value using the filtered acceleration value to obtain the corrected acceleration value.

[0053] The speed calculation subunit is used to use the train speed obtained by using the first sensing detection signal as the base speed value, and to correct the base speed value by integrating the corrected acceleration value to calculate the final train speed value.

[0054] A maglev train control system includes a traction control unit, a braking control unit, and a control device as described above, wherein the control device is connected to the traction control unit and the braking control unit respectively.

[0055] A computer-readable storage medium storing a computer program that, when executed, implements the method described above.

[0056] Compared with the prior art, the advantages of the present invention are as follows:

[0057] 1. This invention calculates the train's speed by comprehensively detecting the first induction signal from the sleepers passing by the train during its journey, the second induction signal from the detection components laid along the track in the platform parking area, and the acceleration signal in the direction of train travel. Compared to a single induction counting sleeper speed measurement method, this invention can effectively improve the accuracy of train speed detection, especially in the low-speed range. This allows the transition speed point between electric braking and mechanical braking to be lowered or as close as possible to zero speed, thereby enabling the electric braking force to be utilized at zero speed. This effectively reduces the wear of the brake shoes of the train's mechanical brakes, thereby reducing the daily maintenance workload and cost of the train.

[0058] 2. This invention acquires train motion attitude parameters by introducing acceleration signals. For the induced sleeper output signal and the induced detection component output signal, the optimal acceleration value is obtained using the particle swarm optimization algorithm, and the train detection speed and acceleration value are corrected using the Kalman filter algorithm. Then, the optimal acceleration value is corrected by the filtered acceleration value, and the train speed value is corrected by the corrected acceleration value. This can combine the two types of induced detection signals with the particle swarm optimization algorithm and the Kalman filter algorithm to further improve the accuracy of train speed detection. Attached Figure Description

[0059] Figure 1 This is a schematic diagram illustrating the implementation process of the hybrid braking control method for maglev trains in this embodiment.

[0060] Figure 2 This is a schematic diagram of the structural principle of the hybrid braking control system for the maglev train in this embodiment.

[0061] Figure 3 This is a schematic diagram illustrating the arrangement principle of the high-precision inductive counting sensor and the high-precision metal toothed plate in this embodiment.

[0062] Figure 4 This is a schematic diagram of the coordination curve between the electric braking force and the mechanical braking force when the electric braking force is close to zero speed in this embodiment.

[0063] Figure 5 This is a detailed flowchart illustrating the implementation of hybrid braking control for maglev trains in a specific application embodiment of the present invention. Detailed Implementation

[0064] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0065] like Figure 1 As shown, the steps of the hybrid braking control method for maglev trains in this embodiment include:

[0066] S1. When the train enters the platform parking area to stop, the traction control unit and the braking control unit receive the braking command and receive the first induction detection signal of the sleepers that the train passes by during its journey, the second induction detection signal of the detection object that is laid along the track in the platform parking area in advance, and the acceleration signal of the train's running direction during its journey.

[0067] S2. The train speed value is calculated based on the received first sensor detection signal, second sensor detection signal, and acceleration signal;

[0068] S3. Based on the calculated train speed value, control the traction control unit and the braking control unit to perform mixed braking at the preset speed point until the train stops.

[0069] This embodiment calculates the train's speed by comprehensively detecting the first induction signal from the sleepers passing by the train during its journey, the second induction signal from the detection components laid along the track in the platform parking area, and the acceleration signal in the direction of train travel. Compared to a single induction counting sleeper speed measurement method, this method can effectively improve the accuracy of train speed detection, especially in low-speed areas. It allows the transition speed point between electric braking and mechanical braking to be reduced to or close to zero, thereby enabling the electric braking force to be utilized at zero speed. This effectively reduces the wear of the brake shoes of the train's mechanical brakes, thus reducing the daily maintenance workload and cost of the train.

[0070] like Figure 2 As shown, to implement the above control method, this embodiment specifically arranges inductive sleeper counting sensors in the medium-low speed maglev train. These sensors detect the metal sleepers on the track and output induced pulses to the train speed calculation unit for speed calculation. Simultaneously, to improve the accuracy of speed detection, accelerometers are arranged on the train to detect acceleration signals in the train's direction of travel, acquiring train motion attitude parameters to correct the detected speed. Figure 2 , 3As shown, the sensing element is a metal toothed plate, which is a metal grooved plate with a toothed structure. This metal toothed plate is laid on the track in the platform parking area. Corresponding induction counting sensors are installed on the train to sense the metal toothed plate, generating newer and faster sensing pulses. This further corrects the detection speed of the train at low speeds, ensuring the accuracy of the linear motor's electric braking force control at extremely low speeds or close to zero speed. The train speed calculation unit corrects the train speed based on the detection data from the induction sleeper counting sensor, accelerometer, and induction counting sensor to obtain an accurate train speed value. During the mixed braking process, the traction control unit controls the linear traction motor to apply electric braking force to zero speed, while the braking control unit controls the base brake to apply mechanical braking when the train approaches zero speed. This ensures that the transition speed point V1 between electric braking and mechanical braking is as low as possible or as close to zero speed as possible. Figure 4 As shown.

[0071] The aforementioned metal toothed plate can be a high-precision metal toothed plate, and the inductive counting sensor used to detect the metal toothed plate can also be a high-precision inductive counting sensor. The specific type can be selected according to actual needs.

[0072] In this embodiment, step S2, which calculates the train speed value based on the received first sensor detection signal, second sensor detection signal, and acceleration signal, includes:

[0073] S201. Use the particle swarm optimization algorithm based on the acceleration signal to obtain the optimal acceleration value;

[0074] S202. Based on the second induction detection signal and the acceleration signal, the Kalman filter algorithm is used to obtain the filtered acceleration value;

[0075] S203. Correct the optimal acceleration value using the filtered acceleration value to obtain the corrected acceleration value;

[0076] S204. Using the train speed obtained by using the first induction detection signal as the base speed value, and using the speed value obtained by integrating the corrected acceleration value to correct the base speed value, the final train speed value is calculated.

[0077] This embodiment acquires train motion attitude parameters by introducing acceleration signals. For the induced sleeper output signal (first induction detection signal) and the induced output signal of the detection device (second induction detection signal), the optimal acceleration value is first obtained using the particle swarm optimization algorithm, and the train detection speed and acceleration value are corrected using the Kalman filter algorithm. Then, the optimal acceleration value is corrected by the filtered acceleration value to obtain the final corrected acceleration value. Finally, the speed obtained by integrating the corrected acceleration value is used to correct the train speed value. This approach combines the two induction detection signals with the particle swarm optimization algorithm and the Kalman filter algorithm to further improve the accuracy of train speed detection.

[0078] In this embodiment, step S021 above uses a particle swarm optimization algorithm based on the first sensing detection signal and the acceleration signal to obtain the optimal acceleration value a. jm The specific steps include:

[0079] S211. Obtain the acceleration signals detected within the detection interval ΔT (which can be a very small interval less than a preset threshold) to form an acceleration array a. jd That is, a jd =[a j1d ,a j2d ....a jnd ], where n is the number of acceleration signals obtained within the detection interval ΔT, and the velocity array v within time ΔT is... jd For v jd =[v j1d ,v j2d ....v jnd ];

[0080] S212. In each iteration, based on the acceleration array a jd The internal acceleration signal is used to calculate the current train's acceleration fitness value f(a). jd );

[0081] S213. Based on the current train's acceleration fitness value f(a) jd The globally optimal acceleration p during low-speed braking of the train gd And the historical best acceleration p during low-speed braking of the train jd The optimal value is selected and output as the optimal acceleration value.

[0082] Accelerometers acquire measured acceleration data in the direction of train travel. j A series of accelerations a obtained from the train accelerometer j Particle swarm optimization (PSO) is used for data processing to improve the accuracy of acceleration detection data.

[0083] Let the train fitness function be... The root mean square function is used for accurate target value evaluation, resulting in a solution with a dimension of 2. Specifically, in step S212 above, the current train's acceleration fitness value is constructed according to the following formula:

[0084]

[0085] Where 1≤i1≤n, 1≤i2≤n, and i1≤≠i2.

[0086] The historical best acceleration p during low-speed braking of the train within time ΔT. jd =[p j1d ,p j2d ....p jnd The global optimal acceleration during low-speed braking of the train can be obtained by initializing using the particle swarm optimization algorithm described above. gd =[p g1d ,p g2d ,...p gnd ], which is the set of parameters for the best fitness of data collected by n accelerometers, can also be obtained by initialization using the particle swarm optimization algorithm described above.

[0087] In this embodiment, the current train's acceleration fitness value f(a) is used. jd The historical best acceleration p during low-speed braking of the train jd The minimum value is taken as the optimal acceleration value a. jm ,Right now:

[0088] a jm =min(f(a) jd ),p jd (2)

[0089] Or, from the globally optimal acceleration p during the train's low-speed braking process... gd The historical best acceleration p during low-speed braking of the train jd The minimum value is taken as the optimal acceleration value a. jm ,Right now:

[0090] a jm =min(p gd ,p jd (3)

[0091] In this embodiment, when selecting the optimal value as the output of the optimal acceleration value, the following formula is used iteratively to calculate the current set of detection data each time:

[0092] v jid (t+1)=v jid (t)+a jid (t+1)(4)

[0093] ajid (t+1)=ωa jid (t)+c1r1(p jid (t)-v jid (t))+c2r2(p gid (t)-v jid (t)) (5)

[0094] Where t represents the current time (specifically, the measurement data at any given time), t+1 represents the iterative update value for the next time step, and the number of iterations can be determined based on the initial design T. max Confirmed; v jid Indicates the optimal speed value, a jid The optimal acceleration value is represented by 1≤i≤n, 1≤d≤2, where d is the dimension in the evaluation direction, which can be the direction of operation or the vertical direction of operation, and ω is the inertia weight, which is obtained based on the algorithm's data set processing and debugging experience data; c1 and c2 are preset learning factors, and r1 and r2 are random numbers in [0,1], which are obtained based on the algorithm's data set processing and debugging experience data.

[0095] In this embodiment, the filtered acceleration value is calculated using a Kalman filter algorithm based on the second sensing detection signal and the acceleration signal, according to the following discrete state equation:

[0096]

[0097] Where x1 and x2 are the train position and velocity detected using the acceleration signals from the accelerometer, respectively; x3 and x4 are the train position and velocity detected using the second induction detection signal from the sensor to be sensed, respectively; T is a discrete parameter; m is the mass of the maglev train; and the acceleration signal... Acceleration obtained from the detection signal of the object to be sensed k is the number of discrete points, and k+1 corresponds to the filtered data value.

[0098] The detailed steps for constructing the above discrete state equations are as follows:

[0099] Assuming the vehicle mass accelerometer uses a continuous mathematical model, detecting position x1 and velocity x2, and the high-precision metal toothed plate test also uses a continuous mathematical model, detecting position x3 and velocity x4 respectively, then:

[0100]

[0101]

[0102] For the same maglev vehicle, the acceleration detected by the accelerometer is:

[0103]

[0104] The acceleration obtained from the second inductive detection signal detected by the high-precision metal toothed plate is:

[0105]

[0106] Where u1 and u2 are external input parameters, and m is the mass of the maglev vehicle.

[0107] The state equation obtained by detecting signals using an accelerometer and a high-precision metal toothed plate is:

[0108]

[0109] Therefore, the matrix of the continuous state transition equation can be obtained as follows:

[0110]

[0111] Discretizing the above-mentioned accelerometer and high-precision metal toothed plate detection system yields:

[0112]

[0113] Where T is a discrete parameter, which can be obtained from system design or debugging experience parameters, and f(τ) is a discrete introduction function.

[0114] Therefore, the discrete transition matrix can be obtained as follows:

[0115]

[0116] This leads to the discrete state equation shown in equation (6), which is:

[0117]

[0118] Based on the discrete state equation (6) above, the acceleration signal detected by the accelerometer and the second induction detection signal of the high-precision metal toothed plate can be predicted, filtered and corrected in real time to obtain the acceleration value a. g Through acceleration value a g Further refine the acceleration value a obtained using the particle swarm optimization algorithm. j Then, using the train speed detected by the inductive sleeper counter as the base speed value, the speed is calculated using the values ​​for a. j The speed obtained by integration is corrected against the base speed value to obtain the final corrected train speed v. i Correcting the speed v of the maglev train during low-speed braking operation. i This allows for achieving precise values.

[0119] In this embodiment, controlling the traction control unit and the braking control unit to perform mixed braking at a preset speed point V1 specifically includes: at the preset speed point V1, the traction control unit controls the DC traction motor to apply electric braking with the electric braking force decreasing at a fixed slope, while the braking control unit controls the application of mechanical braking with the mechanical braking force increasing at a fixed slope. This allows the electric braking to reach zero speed or near zero speed, improving the accuracy of electric braking near zero speed and reducing brake shoe wear of the train's mechanical braking. The fixed slopes corresponding to electric braking and mechanical braking can be configured according to actual needs.

[0120] like Figure 5 As shown, in specific application embodiments, the present invention follows... Figure 2 After setting up the control system, the detailed steps for implementing the hybrid braking control method for maglev trains are as follows:

[0121] Step 1: When the train is about to stop in the platform parking area, the traction control unit and the braking control unit receive the braking command;

[0122] Step 2: The sleeper counting sensor scans the sleepers and outputs pulses to the train speed calculation unit for real-time calculation of the train's current base speed v. i A series of speeds v are obtained by counting the train sleepers within a sampling time Δt. i Perform fitting interpolation data processing to smooth the velocity curve.

[0123] Step 3: Acquire the acceleration data in the direction of train travel using an accelerometer. j A series of accelerations a obtained from the train accelerometer j Data processing is performed using the particle swarm optimization algorithm.

[0124] Assuming the train is a point mass, the accelerometer acquires the measured acceleration data 'a' in the direction of the train's movement. j At that time, the corresponding train speed is v j The accelerometer detects the vehicle's output acceleration array a within each extremely short time interval ΔT. jd =[a j1d ,a j2d ....a jnd [ ] represents the vehicle's current acceleration, and the velocity array v within a very short time ΔT. jd =[v j1d ,v j2d ....v jnd ], and according to The train fitness function is calculated.

[0125] Let p be the historical best acceleration during the train's low-speed braking process. jd =[p j1d ,p j2d ....pjnd Let p be the globally optimal acceleration during the train's low-speed braking process. gd =[p g1d ,p g2d ,...p gnd ], which is the set of parameters representing the best fitness of data collected from n accelerometers. (According to a) jm =min(f(a) jd ),p jd ) for fitness value f(a jd The corresponding historical best acceleration during low-speed braking of the train is p. jd Filter the data; if the historical best acceleration p is among all test data... jd Due to the current global acceleration p gd Then according to formula a jm =min(p gd ,p jd The optimal value a for train acceleration testing was obtained. jm When filtering each set of data, each time it is done according to: v jid (t+1)=v jid (t)+a jid (t+1), a jid (t+1)=ωa jid (t)+c1r1(p jid (t)-v jid (t))+c2r2(p gid (t)-v jid (t) Iterative calculation can yield the corrected acceleration a. j =a jm .

[0126] Step 4: Use a high-precision inductive counting sensor to scan the high-precision metal toothed plates laid in the platform parking area. Calculate the vehicle speed using a Kalman filter algorithm based on the high-precision inductive counting speeds and the discrete state equations. Real-time prediction, filtering, and correction processing are performed on accelerometer test data and high-precision metal toothed plate test data, through a g Further refine and improve the acceleration value a obtained from the particle swarm optimization algorithm. j The final acceleration value a is obtained. j .

[0127] Step 5: Calculate the final acceleration value a obtained in Step 4. j Integral, use the speed value obtained by integration to correct the train speed v. i After correction, the speed v of the maglev train during low-speed braking operation... i The precise value.

[0128] Step 6: Based on the obtained precise velocity v i At the preset V1 speed point, the electric braking force decreases at a fixed slope, while the mechanical braking force increases at a fixed slope until the vehicle stops.

[0129] To achieve the above control method, the hybrid braking control device for the maglev train in this embodiment includes:

[0130] The first sensing and detection unit is used to sense and detect the sleepers that the train passes during its journey and outputs the first sensing and detection signal to the train speed calculation unit.

[0131] The second sensing and detection unit is used to sense the sensing and detection objects that are laid along the track in advance in the platform parking area and output the second sensing and detection signal to the train speed calculation unit.

[0132] The acceleration detection unit is used to detect the acceleration signal in the direction of travel during train operation and output it to the train speed calculation unit;

[0133] The train speed calculation unit is used to calculate the real-time speed of the train based on the received first sensor detection signal, second sensor detection signal and acceleration signal;

[0134] The control unit is used to control the train to perform electric braking to near zero speed at a preset speed point based on the real-time speed calculated by the train speed calculation unit.

[0135] In this embodiment, the first sensing detection unit and the second sensing detection unit are sensing counting sensors. For example... Figure 2 As shown, the first sensing detection unit specifically uses a sleeper counting sensor, the second sensing detection unit specifically uses a high-precision sensing counting sensor to detect the metal toothed plates laid on the track in the platform parking area to achieve speed detection in the low-speed area, and the acceleration detection unit uses an accelerometer.

[0136] In this embodiment, the aforementioned control unit specifically controls the DC traction motor to apply electric braking at a preset speed point through the traction control unit, and the electric braking force decreases according to a first preset slope. It also controls the application of mechanical braking through the brake control unit, and the mechanical braking force increases according to a second preset slope. The traction control unit controls the linear traction motor to apply electric braking force to zero speed, and the brake control unit controls the base brake to apply mechanical braking when the train approaches zero speed.

[0137] The aforementioned train speed calculation unit specifically includes:

[0138] The particle swarm optimization subunit is used to obtain the optimal acceleration value based on the acceleration signal using the particle swarm algorithm.

[0139] The filtering subunit is used to obtain the filtered acceleration value based on the second sensing detection signal and the acceleration signal using the Kalman filtering algorithm;

[0140] An acceleration calculation subunit is used to correct the optimal acceleration value using the filtered acceleration value to obtain the corrected acceleration value.

[0141] The speed calculation subunit is used to use the train speed obtained by using the first induction detection signal as the base speed value, and to correct the base speed value by integrating the corrected acceleration value to calculate the final train speed value.

[0142] The hybrid braking control device for maglev trains in this embodiment corresponds to the hybrid braking control method for maglev trains described above, and will not be repeated here.

[0143] This embodiment also provides a maglev train control system, including a traction control unit, a braking control unit, and a control device as described above, which is connected to the traction control unit and the braking control unit respectively.

[0144] This embodiment also provides a computer-readable storage medium storing a computer program, characterized in that the computer program implements the control method described above when executed.

[0145] As shown in this disclosure and the claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. The terms "first," "second," and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms "comprising" or "including" mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, without excluding other elements or objects. The terms "connected" or "linked" are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect.

[0146] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A hybrid braking control method for a maglev train, characterized in that the steps include... include: When the train enters the platform parking area to stop, the traction control unit and the braking control unit receive the braking command and receive the first induction detection signal of the sleepers that the train passes by during its journey, the second induction detection signal of the detection object that is laid along the track in advance in the platform parking area, and the acceleration signal of the train's running direction during its journey. The train speed value is calculated based on the received first sensor detection signal, second sensor detection signal, and acceleration signal; Based on the calculated train speed value, the traction control unit and the braking control unit are controlled to perform mixed braking at the preset speed point until the train comes to a stop. The calculation of the train speed value based on the received first sensor detection signal, second sensor detection signal, and acceleration signal includes: The optimal acceleration value is obtained using the particle swarm optimization algorithm based on the acceleration signal. Based on the second inductive detection signal and the acceleration signal, a Kalman filter algorithm is used to obtain the filtered acceleration value; The optimal acceleration value is corrected using the filtered acceleration value to obtain the corrected acceleration value; The train speed obtained using the first induction detection signal is used as the base speed value, and the speed value obtained by integrating the corrected acceleration value is used to correct the base speed value to calculate the final train speed value.

2. The hybrid braking control method for maglev trains according to claim 1, characterized in that, The optimal acceleration value is obtained by using the particle swarm optimization algorithm based on the acceleration signal. include: Obtain the detection intervals respectively The acceleration signals detected internally constitute an acceleration array. ,Right now , n For detection interval The number of acceleration signals obtained within; Each iteration is based on the acceleration array The internal acceleration signal is used to calculate the current train's acceleration fitness value; Based on the current train's acceleration fitness value, the global optimal acceleration during low-speed braking, and the historical optimal acceleration during low-speed braking, the optimal value is selected as the output optimal acceleration value.

3. The hybrid braking control method for maglev trains according to claim 2, characterized in that, The acceleration fitness value of the current train is constructed according to the following formula: in, , ,and .

4. The hybrid braking control method for maglev trains according to claim 2, characterized in that, The optimal acceleration value can be obtained by taking the minimum value from the current train's acceleration fitness value and the historical best acceleration during the train's low-speed braking process, or by taking the minimum value from the global best acceleration during the train's low-speed braking process and the historical best acceleration during the train's low-speed braking process.

5. The hybrid braking control method for maglev trains according to any one of claims 1 to 4, characterized in that, The filtered acceleration value is calculated using the Kalman filter algorithm based on the second sensing detection signal and the acceleration signal, according to the following discrete state equation: in, , These are the train position and speed detected using acceleration signals, respectively. , The train position and speed are obtained using the second sensor detection signal from the sensor to be detected, respectively. T For discrete parameters, For the mass of the maglev train, acceleration signal Acceleration obtained from the detection signal of the object to be sensed , k The number of discrete points.

6. The hybrid braking control method for maglev trains according to claim 5, characterized in that, The discrete state equations are constructed according to the following steps: The state equations are constructed as follows: The continuous state transition equation matrix is ​​obtained as follows: And by discretization, we obtain: in, Introducing functions for discrete processing; The discrete transition matrix is ​​obtained as follows: Finally, the discrete state equations are constructed.

7. The hybrid braking control method for maglev trains according to any one of claims 1 to 4, characterized in that, The mixed braking control of the traction control unit and the braking control unit at the preset speed point includes: at the preset speed point, the traction control unit controls the DC traction motor to apply electric braking and the electric braking force decreases at a fixed slope, and the braking control unit controls the application of mechanical braking and the mechanical braking force increases at a fixed slope.

8. A hybrid braking control device for a maglev train, characterized in that, include: The first sensing and detection unit is used to sense and detect the sleepers that the train passes during its journey and outputs a first sensing and detection signal to the train speed calculation unit. The second sensing and detection unit is used to sense the detection objects to be sensed and detected that are laid along the track in advance in the platform parking area, and outputs the second sensing and detection signal to the train speed calculation unit. An acceleration detection unit is used to detect acceleration signals in the direction of travel during train operation and output them to the train speed calculation unit. The train speed calculation unit is used to calculate the real-time speed of the train based on the received first sensor detection signal, second sensor detection signal and acceleration signal; The control unit is used to control the train to perform electric braking to near zero speed at a preset speed point based on the real-time speed calculated by the train speed calculation unit. The train speed calculation unit includes: The particle swarm optimization subunit is used to obtain the optimal acceleration value based on the acceleration signal using the particle swarm algorithm. The filtering subunit is used to obtain the filtered acceleration value based on the second sensing detection signal and the acceleration signal using the Kalman filtering algorithm; An acceleration calculation subunit is used to correct the optimal acceleration value using the filtered acceleration value to obtain the corrected acceleration value. The speed calculation subunit is used to use the train speed obtained by using the first sensing detection signal as the base speed value, and to correct the base speed value by integrating the corrected acceleration value to calculate the final train speed value.

9. The hybrid braking control device for maglev trains according to claim 8, characterized in that, The first sensing detection unit and / or the second sensing detection unit are sensing counting sensors.

10. The hybrid braking control device for maglev trains according to claim 8, characterized in that, The sensing element is a metal toothed plate.

11. The hybrid braking control device for maglev trains according to claim 8, characterized in that, At a preset speed point, the control unit controls the DC traction motor to apply electric braking at a preset speed point, and the electric braking force decreases according to a first preset slope. It also controls the application of mechanical braking at a preset speed point, and the mechanical braking force increases according to a second preset slope. The traction control unit controls the linear traction motor to apply electric braking force to zero speed, and the brake control unit controls the base brake to apply mechanical braking when the train approaches zero speed.

12. A maglev train control system, comprising a traction control unit and a braking control unit, characterized in that, It also includes a control device as described in any one of claims 8 to 11, wherein the control device is connected to the traction control unit and the braking control unit respectively.

13. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed, it implements the method as described in any one of claims 1 to 7.

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