Overloaded Train Cruise Control Method Based on Fuzzy Composite Nonlinear Feedback

Abstract

The present invention discloses a heavy-haul train cruise control method based on fuzzy composite nonlinear feedback. The method includes: establishing a T-S fuzzy error model based on the basic resistance, ramp resistance, curve resistance and coupler force during the operation of the train; designing a composite nonlinear feedback controller considering the asymmetric saturation constraint of the control quantity, and establishing a speed tracking closed-loop control system; according to the current speed of the train and the relative displacement between two adjacent cars at present, using the speed tracking closed-loop control system to adjust the control force of the train, so as to realize the cruise speed tracking. The control force is traction force or electric braking force, which can improve the dynamic response performance of heavy-haul train speed tracking and realize the improvement of the operation efficiency and traffic safety of heavy-haul trains.

Description

Technical Field

[0001] The present invention relates to the technical field of heavy-haul train cruise control, and particularly relates to a heavy-haul train cruise control method based on fuzzy composite non-linear feedback. Background Art

[0002] Due to the advantages in aspects such as transportation capacity, energy consumption, and cost, heavy-haul railway transportation has received increasing attention in the freight transportation field. However, heavy-haul trains are long in length, heavy in weight, and have a complex operating environment, which poses a huge challenge to the driver's operation. Therefore, it is very necessary and urgent to develop an automatic control strategy for heavy-haul trains to reduce the driver's workload and improve transportation efficiency.

[0003] Currently, there are already some research results on the operation control of heavy-haul trains. For the convenience of controller design, some scholars use a single-mass-point dynamics model to characterize the longitudinal motion of heavy-haul trains and propose various control methods that can achieve speed tracking and energy consumption optimization. However, the single-mass-point model regards all considered carriages as a single mass point, ignoring the coupler force between carriages, and the coupler force is an important indicator to ensure train operation safety, especially for heavy-haul trains. To solve this problem, some scholars have proposed a multi-mass-point dynamics model. To deal with the non-linear characteristics of the multi-mass-point dynamics model, existing research mainly uses the Taylor expansion method to linearize the model at the equilibrium point and then design a linear controller, such as a Model Predictive Control (MPC), a Periodic Intermittent Control (PIC), etc. Such a treatment may bring the problem of model mismatch and affect the control accuracy. Therefore, some scholars also use non-linear control methods such as sliding mode control or data-based heavy-haul train operation control methods such as neural networks, genetic algorithms, and machine learning. These methods have a large amount of calculation, are difficult to use in actual on-vehicle controllers, and are difficult to meet the requirements of real-time calculation. In addition, the existing heavy-haul train operation control methods focus on the steady-state performance of heavy-haul train speed tracking control and ignore the dynamic performance that can improve the operation efficiency of heavy-haul trains. Summary of the Invention

[0004] To solve the above problems, the inventor made the present invention and provided a heavy-haul train cruise control method based on fuzzy composite non-linear feedback through specific implementation manners.

[0005] An embodiment of the present invention provides a heavy-haul train cruise control method based on fuzzy composite non-linear feedback, including:

[0006] Based on the basic resistance, ramp resistance, curve resistance, and coupler force during the train operation, establish a T-S fuzzy error model, wherein the T-S fuzzy error model is established based on the non-linear multi-mass-point longitudinal dynamics model of the train;

[0007] Design a composite non - linear feedback controller and establish a speed - tracking closed - loop control system. Among them, the composite non - linear feedback controller includes a non - parallel distributed compensation fuzzy control law and an additional control law;

[0008] According to the current speed of the train and the relative displacement between the current adjacent two trains, use the speed - tracking closed - loop control system to adjust the control force of the train to achieve cruise speed tracking, and the control force is traction force or electric braking force.

[0009] Furthermore, the establishment of the T - S fuzzy error model includes the following steps:

[0010] Establish the non - linear multi - particle longitudinal dynamics model of the train and simplify the non - linear multi - particle longitudinal dynamics model;

[0011] Convert the simplified non - linear multi - particle longitudinal dynamics model into an error dynamics model;

[0012] Convert the asymmetric control quantity saturation constraint of the error dynamics model into a symmetric constraint, select the non - linear term in the error dynamics model as the antecedent variable, and obtain the T - S fuzzy error model.

[0013] Furthermore, the expression of the non - linear multi - particle longitudinal dynamics model of the train is:

[0014]

[0015] In the formula, is the mass of the th carriage, is the speed of the th carriage, is the control force of the th carriage, represents the coupler force between the th and the th carriages, , and respectively represent the basic resistance, gradient resistance and curve resistance received by the th carriage;

[0016] In the formula,

[0017]

[0018]

[0019]

[0020]

[0021] In the formula, is the acceleration due to gravity, , and are the basic resistance coefficients, and respectively represent the road surface gradient and the curve radius, is the relative displacement between the th and th carriages, is the spring stiffness coefficient.

[0022] Furthermore, the expression of the simplified non-linear multi-particle longitudinal dynamics model is:

[0023] ,

[0024] In the formula, , , , , and are respectively the mass, speed, relative displacement, road surface gradient, curve radius and control force of the th group of vehicles.

[0025] Furthermore, the expression of the error dynamics model is:

[0026]

[0027] where , , ( ), is the number of locomotives, is the desired cruise speed, is the desired relative displacement, ( ) is the control force under the equilibrium state, , , , , , are the corresponding dimensional matrices.

[0028] Furthermore, the expression of the T-S fuzzy error model is:

[0029] ,

[0030] In the formula, the antecedent variable , the control input variable , , and are the maximum values of electric braking force and traction force, and , is an additional term generated by the asymmetric constraint of the control quantity, , , , is the system matrix of the corresponding dimension.

[0031] Furthermore, the expression of the composite nonlinear feedback controller is:

[0032] ,

[0033] wherein, is the non-parallel distributed compensation fuzzy control law, is the additional control law.

[0034] Furthermore, the expression of the non-parallel distributed compensation fuzzy control law is:

[0035] ;

[0036] wherein, , , .

[0037] Furthermore, the expression of the additional control law is:

[0038] ;

[0039] wherein, the nonlinear functions , , and are all adjustable parameters, is the Lyapunov matrix.

[0040] The beneficial effects of the above technical solutions provided by the embodiments of the present invention at least include: by establishing a T-S fuzzy error model based on the basic resistance, ramp resistance, curve resistance and coupler force during the train operation, wherein the T-S fuzzy error model is established based on the nonlinear multi-particle longitudinal dynamics model of the train; designing a composite nonlinear feedback controller considering the asymmetric saturation constraint of the control quantity, and establishing a speed tracking closed-loop control system, wherein the composite nonlinear feedback controller includes a non-parallel distributed compensation fuzzy control law and an additional control law; according to the current speed of the train and the relative displacement between the current adjacent two trains, adjusting the control force of the train by using the speed tracking closed-loop control system to achieve cruise speed tracking, which can improve the dynamic response performance of the heavy-haul train speed tracking and realize the improvement of the operation efficiency and traffic safety of the heavy-haul train.

[0041] Other features and advantages of the present invention will be described in the following specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the written specification, claims, and drawings.

[0042] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0043] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0044] Figure 1 is a flowchart of the method in the embodiment of the present invention;

[0045] Figure 2 is a schematic diagram of the longitudinal dynamics model of the heavy-haul train in the embodiment of the present invention;

[0046] Figure 3 is a curve of the running speed, traction / electric braking force, coupler force, and road gradient (converted to height) of the heavy-haul train under Condition 1 in the embodiment of the present invention;

[0047] Figure 4 is a curve of the running speed, traction / electric braking force, coupler force, and road gradient (converted to height) of the heavy-haul train under Condition 2 in the embodiment of the present invention;

[0048] Figure 5 is a comparison curve of the average running speeds of the heavy-haul train cruise control method based on fuzzy composite nonlinear feedback and the non-PDC fuzzy control method under Condition 1 in the embodiment of the present invention;

[0049] Figure 6 is a comparison curve of the running speeds and traction / electric braking forces of the heavy-haul train cruise control method based on fuzzy composite nonlinear feedback and the non-PDC fuzzy control method under Condition 1 in the embodiment of the present invention;

[0050] Figure 7 is a comparison curve of the average running speeds of the heavy-haul train cruise control method based on fuzzy composite nonlinear feedback and the non-PDC fuzzy control method under Condition 2 in the embodiment of the present invention;

[0051] Figure 8 is a comparison curve of the running speeds and traction / electric braking forces of the heavy-haul train cruise control method based on fuzzy composite nonlinear feedback and the non-PDC fuzzy control method under Condition 2 in the embodiment of the present invention. Detailed Embodiments

[0052] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0053] Due to its advantages in terms of transportation capacity, energy consumption, and cost, heavy-haul railway transportation has received increasing attention in the freight transportation field. However, the long length, heavy weight, and complex operating environment of heavy-haul trains pose great challenges to driver operation. Therefore, it is very necessary and urgent to develop an automatic control strategy for heavy-haul trains to reduce the driver's workload and improve transportation efficiency.

[0054] At present, there are already some research results on the operation control of heavy-haul trains. For the convenience of controller design, some scholars use a single-mass-point dynamic model to characterize the longitudinal motion of heavy-haul trains and propose various control methods that can achieve speed tracking and energy consumption optimization. However, the single-mass-point model treats all considered carriages as a single mass point, ignoring the coupler forces between the carriages, and the coupler force is an important indicator to ensure train operation safety, especially for heavy-haul trains. To solve this problem, some scholars have proposed a multi-mass-point dynamic model. In addition, to deal with the non-linear characteristics of the multi-mass-point dynamic model, existing research mainly uses the Taylor expansion method to linearize the model at the equilibrium point and then design a linear controller, such as a Model Predictive Control (MPC), a Periodic Intermittent Control (PIC), etc. Such a treatment may bring problems of model mismatch and affect the control accuracy. Therefore, some scholars also use non-linear control methods such as sliding mode control or data-based heavy-haul train operation control methods such as neural networks, genetic algorithms, and machine learning. These methods have a large amount of calculation and are difficult to use in actual on-vehicle controllers, and it is difficult to meet the requirements of real-time calculation.

[0055] In addition, the existing research results focus on the steady-state performance of heavy-haul train speed tracking control and rarely study the dynamic performance. However, fast dynamic response can improve the operation efficiency of heavy-haul trains, and zero overshoot can improve the operation safety of heavy-haul trains.

[0056] To solve the problems existing in the prior art, an embodiment of the present invention provides a heavy-haul train cruise control method based on fuzzy composite non-linear feedback.

[0057] An embodiment of the present invention provides a heavy-haul train cruise control method based on fuzzy composite non-linear feedback, and its process is as Figure 1 shown, including the following steps:

[0058] Step S1: Based on the basic resistance, ramp resistance, curve resistance, and coupler force during the train operation, establish a T-S fuzzy error model, where the T-S fuzzy error model is established based on the nonlinear multi-particle longitudinal dynamics model of the train.

[0059] Among them, the nonlinear multi-particle longitudinal dynamics model of the train is used to describe the nonlinear motion characteristics of the train in the longitudinal direction, and the basic resistance includes air resistance and mechanical resistance.

[0060] Step S2: Design a composite nonlinear feedback controller and establish a speed tracking closed-loop control system, where the composite nonlinear feedback controller includes a non-parallel distributed compensation fuzzy control law and an additional control law.

[0061] Step S3: According to the current speed of the train and the relative displacement between the current adjacent two trains, use the speed tracking closed-loop control system to adjust the control force of the train to achieve cruise speed tracking, and the control force is traction force or electric braking force.

[0062] In the above method of this embodiment, establishing the T-S fuzzy error model includes the following steps:

[0063] Establish the nonlinear multi-particle longitudinal dynamics model of the train, and simplify the nonlinear multi-particle longitudinal dynamics model based on the fencing idea;

[0064] Convert the simplified nonlinear multi-particle longitudinal dynamics model into an error dynamics model;

[0065] Convert the asymmetric control quantity saturation constraint of the error dynamics model into a symmetric constraint, and select the nonlinear term in the error dynamics model as the antecedent variable to obtain the T-S fuzzy error model.

[0066] In the above method of this embodiment, by establishing a T-S fuzzy error model based on the basic resistance, ramp resistance, curve resistance, and coupler force during the train operation, where the T-S fuzzy error model is established based on the nonlinear multi-particle longitudinal dynamics model of the train; designing a composite nonlinear feedback controller considering the asymmetric saturation constraint of the control quantity and establishing a speed tracking closed-loop control system, where the composite nonlinear feedback controller includes a non-parallel distributed compensation fuzzy control law and an additional control law; according to the current speed of the train and the relative displacement between the current adjacent two trains, using the speed tracking closed-loop control system to adjust the control force of the train to achieve cruise speed tracking, it can improve the dynamic response performance of the heavy-haul train speed tracking and achieve the improvement of the heavy-haul train operation efficiency and traffic safety.

[0067] In the above method of this embodiment, when the heavy-haul train is from locomotives and freight cars, and when, as Figure 2 shown, the forces acting on the train are analyzed, and a non-linear multi-particle longitudinal dynamics model considering the basic resistance, ramp resistance, curve resistance, and coupler force during train operation is established. The expression of the non-linear multi-particle longitudinal dynamics model of the train is:

[0068]

[0069] In the formula, is the mass of the th carbody, is the speed of the th carbody, is the control force of the th carbody, represents the coupler force between the th and the th carbodies, , and respectively represent the basic resistance, ramp resistance, and curve resistance acting on the th carbody. , , and The expressions are as follows:

[0070]

[0071]

[0072]

[0073]

[0074] Among them, is the acceleration due to gravity, , and are the basic resistance coefficients, and respectively represent the road surface gradient and the curve radius, is the relative displacement between the th and the th carbodies, is the spring stiffness coefficient.

[0075] Assume that the air resistance part in the basic resistance acting on each carbody Act on all carriages. Based on the fencing idea, the locomotive and several freight carriages behind the locomotive are regarded as a group, and the non-linear multi-particle longitudinal dynamics model is simplified to obtain a simplified non-linear multi-particle longitudinal dynamics model. The expression of the simplified non-linear multi-particle longitudinal dynamics model is:

[0076] ,

[0077] In the formula, , , , , and are respectively the mass, speed, relative displacement, road surface gradient, curve radius and control force of the th group of vehicles.

[0078] Define the speed tracking error , the relative displacement error , ( ). Then the state variable , the control variable and the output variable matrices are defined as follows:

[0079]

[0080] Among them, is the expected cruise speed, is the expected relative displacement, ( ) is the control force under the equilibrium state.

[0081] Then the expression of the error dynamics model is:

[0082]

[0083] Among them, , ,

[0084] ,

[0085] , ,

[0086] .

[0087] After transforming the simplified non-linear multi-particle longitudinal dynamics model into an error dynamics model, define the maximum electric braking force and traction force as and , and , the following constraints need to be satisfied:

[0088] ,

[0089] Define a new control variable , then there is

[0090] ,

[0091] The control variable symmetry constraint is defined as follows:

[0092] ,

[0093] where, , then the error dynamics model can be transformed into:

[0094] ,

[0095] where, , , ,

[0096] Define , then the error dynamics model can be further transformed into:

[0097] ,

[0098] Suppose , substitute is expressed as ,

[0099] where, , ,

[0100] Then the expression of the T-S fuzzy error model is obtained:

[0101] ,

[0102] where, , , , is the system matrix of the corresponding dimension.

[0103] In the method of this embodiment, a composite nonlinear feedback controller considering the non-symmetric saturation constraint of the control variable is set based on the T-S fuzzy error model. The expression of the composite nonlinear feedback controller is:

[0104] ,

[0105] In the formula, is the non-parallel distributed compensation fuzzy control law, is the additional control law.

[0106] The expression of the non-parallel distributed compensation fuzzy control law is:

[0107] ;

[0108] Wherein, , , .

[0109] Then the expression of the speed tracking closed-loop control system is:

[0110] ,

[0111] Wherein, . The controller gains and can be obtained by solving the following convex optimization problem:

[0112] The closed-loop system is stable at a given scalar , , and a performance index , find the matrices , , such that the following linear matrix inequalities hold:

[0113]

[0114]

[0115] Wherein, is the -th row of the matrix , ,

[0116] , , , , , .

[0117] In the above inequalities, the known parameters include and the defined system matrices , , , and the matrices to be solved include , and .

[0118] The expression of the additional control law is:

[0119] ;

[0120] Among them, the non-linear function , , and are all adjustable parameters, is a Lyapunov matrix, .

[0121] The expression of the composite non-linear feedback controller is:

[0122] .

[0123] The method of this embodiment considers the non-linear characteristics of the longitudinal dynamics of heavy-haul trains and the asymmetric saturation constraints of the control force, designs a non-parallel distributed compensation fuzzy control law to ensure system stability, selects a non-linear function, and designs an additional control law for the composite non-linear feedback controller to improve the response speed and achieve fast speed tracking. The composite non-linear feedback controller improves the dynamic performance of the system. The controller has a simple form and small online calculation amount, which is convenient for practical applications.

[0124] It should be noted that in order to evaluate the performance of the proposed heavy-haul train cruise control strategy, simulation experiments are carried out based on MATLAB / Simulink. Without loss of generality, it is assumed that the heavy-haul train used in the simulation consists of four locomotives, and the locomotive model is SS4B. The sampling period is 0.1 s. It should be noted that the following embodiments do not consider the long and steep downhill conditions, and the slope does not exceed ±4%. The train simulation parameters are as follows: , the locomotive length is 15.2 m, the freight car length is 13.98 m, , , , , , .

[0125] Figure 3 and Figure 4 are the curves of the running speed, traction / electric braking force, coupler force and road slope (converted to height) of the heavy-haul train under working condition 1 and working condition 2 respectively. It can be seen that the heavy-haul train cruise control method based on fuzzy composite non-linear feedback of the present invention can well track the desired speed, the control quantity is within the saturation constraint range, and the coupler force is small.

[0126] Figure 5 is the comparison curve of the average running speed of the heavy-haul train cruise control method based on fuzzy composite non-linear feedback of the present invention and the non-PDC fuzzy control method under working condition 1, Figure 6The comparison curves of the running speed and traction / electric braking force of the heavy-haul train cruise control method based on fuzzy composite nonlinear feedback of the present invention and the non-PDC fuzzy control method under working condition 1; among them, TS-CNF represents the heavy-haul train cruise control method based on fuzzy composite nonlinear feedback, and Fuzzy Control is the non-PDC fuzzy control method.

[0127] Figure 7 The comparison curve of the average running speed of the heavy-haul train cruise control method based on fuzzy composite nonlinear feedback of the present invention and the non-PDC fuzzy control method under working condition 2, Figure 8 The comparison curves of the running speed and traction / electric braking force of the heavy-haul train cruise control method based on fuzzy composite nonlinear feedback of the present invention and the non-PDC fuzzy control method under working condition 2. It can be seen that both control methods can track the desired speed well, but the heavy-haul train cruise control method based on fuzzy composite nonlinear feedback of the present invention has faster dynamic performance.

[0128] Those skilled in the art can change the above order without departing from the protection scope of the present disclosure.

[0129] Any modifications, supplements, equivalent replacements, etc. made within the scope of the principles of the present invention shall still fall within the scope covered by the patent of the present invention.

Claims

1. A heavy-load train cruise control method based on fuzzy composite nonlinear feedback, characterized in that: The following steps are involved: A TS fuzzy error model is established based on the basic resistance, slope resistance, curve resistance and coupler force of the train during operation, wherein the TS fuzzy error model is established based on a nonlinear multi-particle longitudinal dynamics model of the train; A composite nonlinear feedback controller considering asymmetric saturation constraints of the control quantity is designed to establish a speed tracking closed-loop control system, wherein the composite nonlinear feedback controller includes a non-parallel distributed compensation fuzzy control law and an additional control law; According to the current speed of the train and the relative displacement of the two adjacent cars, the speed tracking closed-loop control system is used to adjust the control force of the train to achieve cruising speed tracking, wherein the control force is traction force or electric braking force; The expression of the TS fuzzy error model is: ， Among them, the antecedent variable , control input variables , is the additional term generated by the asymmetric constraint of the control quantity, , , , is the system matrix of the corresponding dimension, set up ,Will Expressed as ,in , ; The expression of the non-parallel distributed compensation fuzzy control law is: ， in, , , , The expression of the additional control law is: ； Among them, the nonlinear function , , and All are adjustable parameters. is the Lyapunov matrix.

2. The method according to claim 1, characterized in that The method of establishing the TS fuzzy error model comprises the following steps: Establishing a nonlinear multi-particle longitudinal dynamics model of the train, and simplifying the nonlinear multi-particle longitudinal dynamics model; Converting the simplified nonlinear multi-particle longitudinal dynamics model into an error dynamics model; The asymmetric control quantity saturation constraint of the error dynamics model is converted into a symmetric constraint, and the nonlinear term in the error dynamics model is selected as antecedent variable to obtain the TS fuzzy error model.

3. The method according to claim 2, characterized in that The expression of the nonlinear multi-particle longitudinal dynamic model of the train is: In the formula, For the Carriage quality, For the Carriage speed, For the The control force of the carriage, Indicates Section and The coupling force between the carriages, , and Respectively represent Basic resistance, slope resistance and curve resistance of the carriage; in, In the formula, is the acceleration due to gravity, , and is the basic drag coefficient, and They represent the road slope and curve radius respectively. It is Section and The relative displacement of the carriages, is the spring stiffness coefficient.

4. The method according to claim 3, characterized in that The simplified expression of the nonlinear multi-particle longitudinal dynamic model is: ， In the formula, , , , , and Respectively The mass, speed, relative displacement, road slope, curve radius and control force of the group vehicles.

5. The method according to claim 4, characterized in that The expression of the error dynamics model is: ， in: , , , is the desired cruising speed, is the expected relative displacement; , , , is the matrix of corresponding dimension.

6. The method according to claim 1, characterized in that The expression of the composite nonlinear feedback controller is: ， In the formula, is the non-parallel distributed compensation fuzzy control law, is an additional control law.

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