Heavy-load train cruise control method based on fuzzy composite nonlinear feedback

Abstract

The invention discloses a heavy haul train cruise control method based on fuzzy composite nonlinear feedback. Comprising the following steps: establishing a T-S fuzzy error model based on basic resistance, ramp resistance, curve resistance and coupler force during train operation; 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 current relative displacement of the two adjacent trains, the control force of the train is adjusted through the speed tracking closed-loop control system, cruise speed tracking is achieved, the control force is traction force or electric braking force, the speed tracking dynamic response performance of the heavy-load train can be improved, and the speed tracking efficiency is improved. And the running efficiency and the running safety of the heavy-load train are improved.

Description

Technical Field

[0001] The invention relates to the technical field of heavy-load train cruise control, and in particular to a heavy-load train cruise control method based on fuzzy composite nonlinear feedback. Background Art

[0002] Due to its advantages in transportation capacity, energy consumption and cost, heavy-haul railway transportation has received more and more attention in the freight field. However, heavy-haul trains are long, heavy and have a complex operating environment, which poses great challenges to drivers. Therefore, it is necessary and urgent to develop automatic control strategies for heavy-haul trains to reduce drivers' workload and improve transportation efficiency.

[0003] At present, there are some research results on the operation control of heavy-load trains. To facilitate the design of controllers, some scholars use single-particle dynamics models to characterize the longitudinal motion of heavy-load trains, and propose a variety of control methods that can achieve speed tracking and energy consumption optimization. However, the single-particle model regards all the considered carriages as a particle, ignoring the coupler force between the carriages, and the coupler force is an important indicator to ensure driving safety, especially for heavy-load trains. To solve this problem, some scholars proposed a multi-particle dynamics model. In order to deal with the nonlinear characteristics of the multi-particle dynamics model, existing studies mainly use the Taylor expansion method to design linear controllers after linearizing the model at the equilibrium point, such as model predictive controller (MPC), periodic intermittent controller (PIC), etc. Such processing may bring about model mismatch problems and affect control accuracy. Therefore, some scholars also use nonlinear control methods such as sliding mode control or data-based heavy-load train operation control methods such as neural networks, genetic algorithms, and machine learning. These methods have large computational complexity, are difficult to use in actual on-board controllers, and are difficult to meet the needs of real-time computing. In addition, the existing heavy-haul train operation control methods focus on the steady-state performance of heavy-haul train speed tracking control, while ignoring the dynamic performance that can improve the operating efficiency of heavy-haul trains. Summary of the invention

[0004] In order to solve the above problems, the inventors have made the present invention, and provide a heavy-load train cruise control method based on fuzzy composite nonlinear feedback through a specific implementation method.

[0005] The embodiment of the present invention provides a heavy-load train cruise control method based on fuzzy composite nonlinear feedback, comprising: 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; Design a composite nonlinear feedback controller and 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 between 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, and the control force is traction force or electric braking force.

[0006] Furthermore, the establishment of 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.

[0007] Furthermore, the expression of the nonlinear multi-particle longitudinal dynamic model of the train is:

[0008] 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 the formula,

[0009]

[0010]

[0011]

[0012] 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.

[0013] Furthermore, 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.

[0014] Furthermore, the expression of the error dynamics model is:

[0015] in, , , ( ), is the number of locomotives, is the desired cruising speed, is the expected relative displacement, ( ) is the control force in equilibrium state, , , , , , is the matrix of corresponding dimension.

[0016] Furthermore, the expression of the TS fuzzy error model is: , In the formula, the antecedent variable , control input variables , , and is the maximum value of electric braking force and traction force and , is the additional term generated by the asymmetric constraint of the control quantity, , , , is the system matrix of the corresponding dimension.

[0017] Furthermore, 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.

[0018] Furthermore, the expression of the non-parallel distributed compensation fuzzy control law is: ; in, , , .

[0019] Furthermore, the expression of the additional control law is: ; Among them, the nonlinear function , , and All are adjustable parameters. is the Lyapunov matrix.

[0020] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least: establishing a TS fuzzy error model 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 the nonlinear multi-particle longitudinal dynamic model of the train; designing a composite nonlinear feedback controller that takes into account 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 current 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, which can improve the dynamic response performance of speed tracking of heavy-load trains and improve the operating efficiency and driving safety of heavy-load trains.

[0021] Other features and advantages of the present invention will be described in the following description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0022] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying 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 of the present invention. In the accompanying drawings: Figure 1 is a flow chart of a method in an embodiment of the present invention; Figure 2 Schematic diagram of a longitudinal dynamics model of a heavy-load train in an embodiment of the present invention; Figure 3 The curve of the running speed, traction force / electric braking force, coupler force and road slope (converted to height) of the heavy-load train under working condition 1 in the embodiment of the present invention; Figure 4 The curve of the running speed, traction force / electric braking force, coupler force and road slope (converted to height) of the heavy-load train under working condition 2 in the embodiment of the present invention; Figure 5 It is an average running speed comparison curve of a heavy-load train cruise control method based on fuzzy composite nonlinear feedback and a non-PDC fuzzy control method under working condition 1 in an embodiment of the present invention; Figure 6 It is a comparison curve of the running speed and traction force / electric braking force of the heavy-load train cruise control method based on fuzzy composite nonlinear feedback and the non-PDC fuzzy control method under working condition 1 in the embodiment of the present invention; Figure 7 It is an average running speed comparison curve of a heavy-load train cruise control method based on fuzzy composite nonlinear feedback and a non-PDC fuzzy control method under working condition 2 in an embodiment of the present invention; Figure 8 It is a comparison curve of the running speed and traction force / electric braking force of the heavy-load train cruise control method based on fuzzy composite nonlinear feedback and the non-PDC fuzzy control method under working condition 2 in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The 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 accompanying 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 to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0025] Due to its advantages in transportation capacity, energy consumption and cost, heavy-haul railway transportation has received more and more attention in the freight field. However, heavy-haul trains are long, heavy and have a complex operating environment, which poses great challenges to drivers. Therefore, it is necessary and urgent to develop automatic control strategies for heavy-haul trains to reduce drivers' workload and improve transportation efficiency.

[0026] At present, there are some research results on the operation control of heavy-load trains. To facilitate the design of controllers, some scholars use single-particle dynamics models to characterize the longitudinal motion of heavy-load trains, and propose a variety of control methods that can achieve speed tracking and energy consumption optimization. However, the single-particle model regards all the considered carriages as a particle, ignoring the coupler force between the carriages, and the coupler force is an important indicator to ensure driving safety, especially for heavy-load trains. To solve this problem, some scholars proposed a multi-particle dynamics model. In addition, in order to deal with the nonlinear characteristics of the multi-particle dynamics model, existing studies mainly use the Taylor expansion method to design linear controllers after linearizing the model at the equilibrium point, such as model predictive controller (MPC), periodic intermittent controller (PIC), etc. Such processing may bring about model mismatch problems and affect control accuracy. Therefore, some scholars also use nonlinear control methods such as sliding mode control or data-based heavy-load 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-board controllers, and are difficult to meet the needs of real-time computing.

[0027] In addition, existing research results focus on the steady-state performance of heavy-load train speed tracking control, and rarely study the dynamic performance. However, fast dynamic response can improve the operating efficiency of heavy-load trains, and zero overshoot can improve the operating safety of heavy-load trains.

[0028] In order to solve the problems existing in the prior art, an embodiment of the present invention provides a heavy-load train cruise control method based on fuzzy composite nonlinear feedback.

[0029] The embodiment of the present invention provides a heavy-load train cruise control method based on fuzzy composite nonlinear feedback, the process of which is as follows: Figure 1 As shown, the following steps are included: Step S1: Based on the basic resistance, slope resistance, curve resistance and coupler force of the train during operation, a TS fuzzy error model is established, wherein the TS fuzzy error model is established based on a nonlinear multi-particle longitudinal dynamic model of the train.

[0030] Among them, the nonlinear multi-particle longitudinal dynamic 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.

[0031] Step S2: designing a composite nonlinear feedback controller 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.

[0032] Step S3: According to the current speed of the train and the current relative displacement between 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.

[0033] In the above method of this embodiment, establishing a TS fuzzy error model includes the following steps: Establishing a nonlinear multi-particle longitudinal dynamics model of the train, and simplifying the nonlinear multi-particle longitudinal dynamics model based on the fencing concept; 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.

[0034] The above method of this embodiment establishes a TS fuzzy error model 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 the nonlinear multi-particle longitudinal dynamic model of the train; a composite nonlinear feedback controller that takes into account the asymmetric saturation constraint 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 current 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, which can improve the speed tracking dynamic response performance of heavy-load trains and achieve the improvement of the operating efficiency and driving safety of heavy-load trains.

[0035] In the above method of this embodiment, when the heavy-load train is Locomotive and trucks, and When Figure 2 As shown in the figure, the force analysis of the train is carried out, and a nonlinear multi-particle longitudinal dynamic model is established considering the basic resistance, slope resistance, curve resistance and coupler force of the train. The expression of the nonlinear multi-particle longitudinal dynamic model of the train is:

[0036] 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 The basic resistance, slope resistance and curve resistance of the carriage. , , and The expression is as follows:

[0037]

[0038]

[0039]

[0040] in, 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.

[0041] Assume that the air resistance of the basic resistance of each compartment is Only acts on the head train, mechanical resistance part Acts on all carriages. Based on the fencing concept, the locomotive and several freight cars behind the locomotive are regarded as a group, and the nonlinear multi-particle longitudinal dynamics model is simplified to obtain a simplified nonlinear multi-particle longitudinal dynamics model. The expression of the simplified nonlinear multi-particle longitudinal dynamics model is: , In the formula, , , , , and Respectively The mass, speed, relative displacement, road slope, curve radius and control force of the group vehicles.

[0042] Defining velocity tracking error , relative displacement error , ( ), then the state variable , control amount and output The matrix is ​​defined as follows:

[0043] in, is the desired cruising speed, is the expected relative displacement, ( ) is the control force in equilibrium state.

[0044] Then the expression of the error dynamics model is:

[0045] in, , , , , , .

[0046] After the simplified nonlinear multi-particle longitudinal dynamics model is transformed into an error dynamics model, the maximum values ​​of the electric braking force and the traction force are defined as: and ,and , the following constraints must be met: , Define new control quantity , then , The control volume symmetry constraint is defined as follows: , in, , then the error dynamics model can be transformed into: , in, , , , definition , then the error dynamics model can be further transformed into: , set up ,Will Expressed as , in, , , Then the expression of TS fuzzy error model is obtained: , in, , , , is the system matrix of the corresponding dimension.

[0047] In the method of this embodiment, a composite nonlinear feedback controller considering the asymmetric saturation constraint of the control amount is set based on the TS fuzzy error model. 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.

[0048] The expression of non-parallel distributed compensation fuzzy control law is: ; in, , , .

[0049] Then the expression of the speed tracking closed-loop control system is: , in, . Controller gain and It can be obtained by solving the following convex optimization problem: The closed-loop system is stable at Given a scalar , , and Performance Indicators , find the matrix , , So that the following linear matrix inequality holds:

[0050]

[0051] in, is a matrix No. OK, , , , , , , .

[0052] In the above inequality, the known parameters include and the defined system matrix , , , the matrix to be solved includes , and .

[0053] The expression of the additional control law is: ; Among them, the nonlinear function , , and All are adjustable parameters. is the Lyapunov matrix, .

[0054] The expression of the composite nonlinear feedback controller is: .

[0055] The method of this embodiment takes into account the nonlinear characteristics of the longitudinal dynamics of heavy-duty trains and the asymmetric saturation constraints of the control force, designs a non-parallel distributed compensation fuzzy control law to ensure system stability, selects nonlinear functions, designs additional control laws of a composite nonlinear feedback controller, improves response speed, and achieves fast speed tracking. The composite nonlinear feedback controller improves the dynamic performance of the system, has a simple controller form, a small amount of online calculation, and is convenient for practical application.

[0056] It is worth noting that in order to evaluate the performance of the proposed heavy-load train cruise control strategy, simulation experiments were carried out based on MATLAB / Simulink. Without loss of generality, the heavy-load train used in the simulation is assumed to contain four locomotives, and the locomotive model is SS4B locomotive. The sampling period is 0.1s. It should be noted that the following embodiments do not consider long downhill conditions, and the slope does not exceed ±4%. The train simulation parameters are as follows: , locomotive length 15.2m, freight car length 13.98m, , , , , , .

[0057] Figure 3 and Figure 4The curves of the running speed, traction force / electric braking force, coupler force and road slope (converted to height) of the heavy-load train under working conditions 1 and 2 respectively show that the heavy-load train cruise control method based on fuzzy composite nonlinear feedback of the present invention can track the expected speed well, the control amount is within the saturation constraint range, and the coupler force is small.

[0058] Figure 5 The average running speed comparison curve of the heavy-load train cruise control method based on fuzzy composite nonlinear feedback and the non-PDC fuzzy control method under working condition 1 is shown in FIG. Figure 6 The running speed and traction force / electric braking force comparison curves of the heavy-load train cruise control method based on fuzzy composite nonlinear feedback and the non-PDC fuzzy control method under working condition 1 are shown; wherein TS-CNF represents the heavy-load train cruise control method based on fuzzy composite nonlinear feedback, and Fuzzy Control represents the non-PDC fuzzy control method.

[0059] Figure 7 It is the average running speed comparison curve of the heavy-load train cruise control method based on fuzzy composite nonlinear feedback and the non-PDC fuzzy control method under working condition 2. Figure 8 The operating speed and traction / electric braking force comparison curves of the heavy-load 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 expected speed well, but the heavy-load train cruise control method based on fuzzy composite nonlinear feedback of the present invention has faster dynamic performance.

[0060] Those skilled in the art can change the above sequence without departing from the protection scope of the present disclosure.

[0061] Any modifications, supplements and equivalent substitutions made within the scope of the principles of the present invention shall still fall within the patent scope 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 between 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, and the control force is traction force or electric braking force.

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 the formula, 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 number of locomotives, is the desired cruising speed, is the expected relative displacement, ( ) is the control force in equilibrium state, , , , , , is the matrix of corresponding dimension.

6. The method according to claim 5, characterized in that The expression of the TS fuzzy error model is: ， Among them, the antecedent variable , control input variables , , and is the maximum value of electric braking force and traction force and , is the additional term generated by the asymmetric constraint of the control quantity, , , , is the system matrix of the corresponding dimension.

7. The method according to claim 6, 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.

8. The method according to claim 7, characterized in that The expression of the non-parallel distributed compensation fuzzy control law is: ； in, , , .

9. The method according to claim 8, characterized in that The expression of the additional control law is: ； Among them, the nonlinear function , , and All are adjustable parameters. is the Lyapunov matrix.

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