Train robust adhesion tracking control method and system and storage medium

Abstract

The invention relates to the technical field of high-speed train control, and discloses a train robust adhesion tracking control method and system and a storage medium, and the method comprises the steps: firstly obtaining the input information of a target train control unit; state information of a target train is obtained in real time according to the input information, and state feedback information of the train is obtained based on the state information; designing a robust adhesion tracking controller, taking the train state feedback information as input of the adhesion tracking controller, and solving a robust control law of the target train; and based on the control signal output by the robust controller, carrying out adhesion tracking control on the target train by using the robust controller. Thus, the adhesion tracking controller is used for achieving adhesion tracking control over the train, unknown bounded disturbance is estimated and restrained, various actual factors such as input saturation limitation and partial state constraint existing in actual problems can be processed, and practical application is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of high-speed train control, and in particular to a train robust adhesion tracking control method, system and storage medium. Background Art

[0002] Nowadays, high-speed trains work in extreme weather environments such as extreme cold and extreme heat for a long time, and the information generated by the external environmental changes of the traction / braking system cannot be directly obtained through sensors, resulting in incomplete key information, making it impossible for the on-board central control unit to accurately track the train in real time based on key information. Therefore, how to deal with these unknown environmental changes while the train is running and maintain stability when both the train input and output are limited is crucial for the adhesion tracking control of high-speed trains. The commonly used robust methods in traditional adhesion control technology do not consider the impact of input saturation, so it is impossible to give a suitable control signal.

[0003] In order to deal with the above-mentioned train adhesion tracking control problem, high-gain robust technology and auxiliary dynamic systems are introduced to deal with input saturation. Most of the existing train adhesion control technologies are aimed at keeping the train in the state of the optimal operating point corresponding to the adhesion coefficient, and the estimation of this optimal operating point can be completed through the extreme value search method. However, in the actual application of trains, the main control task of the train during operation is to track the preset "speed-displacement" trajectory, which means that the train does not need to track the optimal value of the slip ratio in real time, but only limits it to a preset set. Based on this idea, some works have designed a new adhesion control strategy to track the trajectory of the required state, but this reference trajectory is unknown in engineering applications. In addition, some works have proposed an iterative learning tracking controller for adhesion problems, but some linearization methods are used in the design of this controller, and it can only be used in the train cruise mode, such as train constant speed control. It can be seen that the adhesion tracking control method for trains in the prior art has the problem of difficulty in estimating and suppressing unknown bounded disturbances. Summary of the invention

[0004] The present invention provides a train robust adhesion tracking control method, system and storage medium to estimate and suppress unknown bounded disturbances and to deal with the problem of adaptive adhesion tracking control of high-speed trains under many practical factors.

[0005] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0006] In a first aspect, a train robust adhesion tracking control method comprises:

[0007] S1: Establish a single wheel adhesion dynamics model of the train;

[0008] S2: Obtain input information of the target train control unit;

[0009] S3: determining the state information of the target train in real time according to the input information, and obtaining state feedback information of the target train based on the state information;

[0010] S4: dividing the train adhesion dynamics model into a traction process and a braking process according to the running state of the train, designing a robust adhesion tracking controller based on the traction process and the braking process, taking the train state feedback information as an input of the robust adhesion tracking controller, and solving a robust control law of the target train;

[0011] S5: Perform adhesion tracking control on the target train based on the control signal output by the robust controller.

[0012] In a second aspect, the present application provides a robust control system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the computer program.

[0013] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method steps described in the first aspect.

[0014] Beneficial effects:

[0015] The train robust adhesion tracking control method provided by the present invention first obtains the input information of the target train control unit; obtains the state information of the target train in real time according to the input information, and obtains the state feedback information of the train based on the state information; designs a robust adhesion tracking controller, uses the train state feedback information as the input of the adhesion tracking controller, and solves the robust control law of the target train; and uses the control signal output by the robust controller to perform adhesion tracking control on the target train. In this way, the adhesion tracking control of the train is realized by using the adhesion tracking controller, which not only estimates and suppresses unknown bounded disturbances, but also can handle various practical factors existing in practical problems, such as input saturation restrictions and partial state constraints, which is convenient for practical application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a train robust adhesion tracking control method, system and storage medium according to a preferred embodiment of the present invention;

[0017] Figure 2 A comparison diagram of the displacement of the train under control (system output) and the reference trajectory according to the preferred embodiment of the present invention;

[0018] Figure 3It is a comparison diagram of the limited slip velocity (state 2) and the boundary results under the proposed control of the preferred embodiment of the present invention;

[0019] Figure 4 This is a result diagram of the control input signal under the proposed control of the preferred embodiment of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention is described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the usual meanings understood by persons with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one" or "one" do not indicate quantity restrictions, but indicate the existence of at least one. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship also changes accordingly.

[0022] It should be understood that the train robust adhesion tracking control method provided in the present application can be applied to the traction / braking control system of the train, which can be a third-order uncertain pure feedback nonlinear system. This is only an example and not a limitation.

[0023] See also Figure 1 , a train robust adhesion tracking control method, comprising:

[0024] S1: Establish a single wheel adhesion dynamics model of the train;

[0025] S2: Obtain input information of the target train control unit;

[0026] S3: determining the state information of the target train in real time according to the input information, and obtaining state feedback information of the target train based on the state information;

[0027] S4: dividing the train adhesion dynamics model into a traction process and a braking process according to the running state of the train, designing a robust adhesion tracking controller based on the traction process and the braking process, taking the train state feedback information as an input of the robust adhesion tracking controller, and solving a robust control law of the target train;

[0028] S5: Perform adhesion tracking control on the target train based on the control signal output by the robust controller.

[0029] In this embodiment, based on the train dynamics law and wheel-rail adhesion mechanism, a train adhesion dynamics model with unknown uncertainty and input saturation influence can be established, and subdivided into traction and braking.

[0030] The input information includes: the desired trajectory of train displacement, train speed signal, wheel angular velocity signal, and actuator saturation boundary;

[0031] The above-mentioned train robust adhesion tracking control method, based on the train dynamics law and wheel-rail adhesion mechanism, establishes a train adhesion dynamics model under the influence of unknown uncertainty and input saturation, and subdivides it into traction process and braking process; obtains the input information of the target train control unit, the input information includes: the expected trajectory of train displacement, train speed signal, wheel angular velocity signal, actuator saturation boundary; obtains the state information of the target train in real time according to the input information, and obtains the state feedback information of the train based on the state information; designs an adaptive adhesion tracking controller, takes the train state feedback information as the input of the adaptive adhesion tracking controller, and solves the adaptive control law of the target train; based on the control signal output by the adaptive controller, uses it to perform adhesion tracking control on the target train. In this way, the adaptive estimator and the adhesion tracking controller are used to realize adhesion tracking control of trains with three uncertainties, which not only estimates and suppresses unknown bounded disturbances, but also can deal with various practical factors (input saturation limit, partial state constraint) in practical problems, which is convenient for practical application.

[0032] Optionally, the S1 includes:

[0033] S11: Based on the train dynamics and wheel-rail adhesion mechanism, a single wheel adhesion dynamics model of the train is constructed:

[0034]

[0035] Where s represents the position of the train, It means that the real number domain is used as the standard. represents the speed, b and J represent the viscous friction coefficient and the moment of inertia of the wheel, respectively. It is piecewise continuous at time t ≥ 0; represents the acceleration of the train, F r represents the resistance of the train when it is running, v represents the speed of the train, Ψ2 represents the unknown environmental parameter vector, T a represents the adhesion torque, ω represents the angular velocity, u(ν) represents the traction / braking torque limited by the physical saturation constraint, represents angular acceleration, λ represents slip ratio, F a (λ,Ψ1) represents the adhesion that can affect the movement of the train and satisfies the following relationship:

[0036] F a (λ,Ψ1)=μ(λ,Ψ1)mg, (2)

[0037] Where, m is the total mass of the train, g is the natural gravity coefficient, μ represents the adhesion coefficient, and is composed of:

[0038]

[0039] in, is an unknown nonlinear parameter vector related to the track surface environment. The slip ratio λ ≥ 0 describes the traction process of the train; otherwise, it describes the braking process of the train. In particular, the slip ratio is related to the train speed and wheel angular velocity, and is composed as follows:

[0040]

[0041] in, represent the slip velocity and creep velocity respectively; r is the wheel radius; in addition, F r (v,Ψ2) represents the running resistance, which is composed of aerodynamic force and track resistance, that is:

[0042]

[0043] Among them, the unknown parameter vector The adhesion moment can be expressed as:

[0044] T a (λ,Ψ1)=F a (λ,Ψ1)r=μ(λ,Ψ1)mgr. (6)

[0045] In addition to the mentioned adhesion, the external torque u(v) generated by the train's power unit (motor or brake cylinder) can be applied to the wheels to further control the train speed. Because the traction process and the braking process do not occur at the same time, both the traction torque and the braking torque can be used In other words, v is used to represent the torque; but there is a known upper limit μ on the torque max and the lower limit of torque μ min , and the saturation function can be expressed as follows:

[0046]

[0047] S12: Construct the constraint space required for train anti-skid: The slip ratio of the train during operation needs to be limited to a certain creep area To ensure that the wheels do not slip; use To represent the slip zone,

[0048]

[0049] Where λ = λ opt Corresponds to the maximum adhesion coefficient, denoted as λ opt = arg max{μ(λ,Ψ1)}. Since the slip ratio λ may have a control singularity when both the train speed and the wheel angular velocity are relatively small, the described constraint set is processed as follows: The creep region of λ can be rewritten as Although opt is usually unknown, so the slip velocity provides a more conservative anti-slip region:

[0050]

[0051] Among them, the positive parameter λ a and v0 represent the upper limit of the slip ratio λ and a small constant selected by the designer respectively; v smax Represents v s Corresponding anti-slip cap.

[0052] Optionally, before step S2, the method further includes:

[0053] Convert the adhesion dynamics model of the train into a nonlinear state equation. Select the states x1 = s, x2 = v, and x3 = v s , then v ω becomes v ω (x2,x3)=max{x2+x3,x2} and ω=(x2+x3) / r, so the dynamic model becomes

[0054]

[0055] Among them, x1 represents the train displacement, x2 represents the train speed, x3 represents the train angular velocity, g o Represents environmental parameters, Indicates the train speed, represents the train acceleration, represents the angular acceleration of the wheel, d1(t), d2(t), d3(t) represent interference or noise; according to Design, design is the upper bound of the constraint state x2;

[0056] Optionally, the S2 includes:

[0057] Obtain input information of the target train control unit, which includes: the expected trajectory of train displacement, train speed signal x2(t), wheel angular velocity signal x3(t), actuator saturation boundary, i.e., torque upper limit μ max and the lower limit of torque μ min , where the expected trajectory of train displacement is described as follows:

[0058] s r (t) = f d (t)(11)

[0059] in, is the reference signal of the system displacement, f d (·) is a nonlinear smooth function.

[0060] Optionally, step S3 includes:

[0061] S31: Construct an auxiliary dynamic system that can handle the system input saturation constraint as follows:

[0062]

[0063] in, i=1,2,3 represents the state of the auxiliary dynamic system, φ i ,i=1,2,3 represents artificially set positive parameters.

[0064] S32: build system state error z i ,Right now

[0065] z1=x1-x r (t)-λ1,z2=x2-α1-λ2,z2=x3-α2-λ3, (13)

[0066] Among them, α1 and α2 represent intermediate control signals designed for the controller. According to the input information, the state information of the target train, that is, the displacement error signal z1 of the train, is acquired in real time, and the state feedback information of the train is obtained based on the state information.

[0067] Optionally, step S4 includes:

[0068] S41: According to the running state of the train, it is divided into two processes: traction and braking. According to the running state of the train, it is divided into two processes: traction and braking. According to the given adhesion dynamics model, it can be rewritten as:

[0069]

[0070] in, x 2 represents the state vector, x 2=[x1,x2]T ;

[0071]

[0072] Using the middle value theorem to deal with the adhesion dynamics model of the train, we can get:

[0073]

[0074] in, Represents a random number from 0 to x2, Represents a random number from 0 to x3, u 0 (v) represents a random number from 0 to u(v); the partial derivative is defined as follows:

[0075]

[0076] S42: Design a robust adhesion tracking controller according to the control parameters corresponding to the traction-braking process, that is,

[0077]

[0078] Among them, ρ3 represents the parameter to be designed, k z3 represents the newly constructed state error, represents the parameters with design, represents the newly constructed state error, λ3 2 represents the parameters to be designed, represents the parameter to be designed, Q3 2 represents the parameters to be designed, represents the parameters to be designed, represents the parameters to be designed, represents the parameter to be designed, Λ3 represents the derivative variable; and Then the intermediate control signal α2 is designed as:

[0079]

[0080] Among them, z2 = x2-α1-λ2, and then the intermediate control signal α1 is

[0081]

[0082] Where z1 = x1-s r (t)-λ1, and

[0083]

[0084] In this way, the control signal calculated by this patent can be updated in real time during the operation of the train. Considering the problem of robust adhesion tracking control of a train under the conditions of unknown parameters, input saturation, and partial state constraints, an adhesion tracking controller with a closed-loop structure is designed. Through such a design, the train can adjust the size of the control signal by itself, and has a certain robustness to unknown environmental conditions, thereby improving the safety and reliability of the system operation.

[0085] In a complete example, for the adhesion dynamics model of the train (10), the corresponding real control signal (18) and intermediate control signal (19)(20) are designed, and an auxiliary dynamic system (12) is constructed. Given the input saturation constraint boundary μ max =20(kN),u min =-20(kN), the unknown bounded disturbance d s (t) = 0.1sin2t, d v (t) = 0.2cos1.5t, d ω =0.5sintcost. The initial state of the train is set as follows: x1(0)=0(m), x2(0)=0(m / s), ω(0)=0.01(rad / s), x3(0)=ω(0)r-x2(0), and the reference signals to be tracked are as follows:

[0086]

[0087] The parameters of the train are set as follows:

[0088] (1) Unknown parameters corresponding to running resistance: c0 = 8.6299 N / ton, c1 = 0.0730 N / (h / km·ton), c2 = 0.0011 N / (h 2 / km 2 ·ton).

[0089] (2) Unknown parameters corresponding to the wheel-rail environment:

[0090]

[0091] (3) Other system parameters: J = 80 kg·m 2 , m=12ton, r=0.445m.

[0092] Parameter settings of the adaptive adhesion tracking controller:

[0093]

[0094] For such a complex situation, the train robust adhesion tracking control method proposed in the present invention can still achieve asymptotic tracking of the system displacement and velocity (state 1 and state 2) to the preset reference trajectory, such as Figure 2 It is worth noting that the limited slip speed or slip ratio (state 3) can also meet the constraints, such as Figure 3 As shown in , it will not exceed the preset upper and lower limits. Among them, the result of the control input signal under the proposed control is shown in Figure 4 As shown, finally, although the output of the actual controller (system input) is limited by the saturation constraint, the system is still stable due to the design of the proposed algorithm.

[0095] The present application provides a train robust adhesion tracking control system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program. The train robust adhesion tracking control system can implement various embodiments of the above method and achieve the same beneficial effects, which will not be described in detail here.

[0096] The embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the method steps described above are implemented. The computer-readable storage medium can implement various embodiments of the above method and achieve the same beneficial effects, which will not be described in detail here.

[0097] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A train robust adhesion tracking control method, characterized in that: include: S1: Establish a single wheel adhesion dynamics model of the train; S2: Obtain input information of the target train control unit; S3: determining the state information of the target train in real time according to the input information, and obtaining state feedback information of the target train based on the state information; S4: dividing the train adhesion dynamics model into a traction process and a braking process according to the running state of the train, designing a robust adhesion tracking controller based on the traction process and the braking process, taking the train state feedback information as an input of the robust adhesion tracking controller, and solving a robust control law of the target train; S5: Perform adhesion tracking control on the target train based on the control signal output by the robust controller.

2. The train robust adhesion tracking control method according to claim 1, characterized in that: The S1 includes: S11: Based on the train dynamics and wheel-rail adhesion mechanism, a single wheel adhesion dynamics model of the train is constructed: Where s represents the position of the train, It means that the real number domain is used as the standard. represents the speed, b and J represent the viscous friction coefficient and the moment of inertia of the wheel, respectively. It is piecewise continuous at time t ≥ 0; represents the acceleration of the train, F r represents the resistance of the train when it is running, v represents the speed of the train, Ψ2 represents the unknown environmental parameter vector, T a represents the adhesion torque, ω represents the angular velocity, u(v) represents the traction / braking torque limited by the physical saturation constraint, represents angular acceleration, λ represents slip ratio, F a (λ,Ψ1) represents the adhesion that can affect the movement of the train and satisfies the following relationship: F a (λ,Ψ1)=μ(λ,Ψ1)mg, Where m is the total mass of the train, g is the natural gravity coefficient, and μ represents the adhesion coefficient, and is composed of: in, It is an unknown nonlinear parameter vector related to the track surface environment. The slip ratio λ ≥ 0 describes the traction process of the train; λ < 0 describes the braking process of the train. The slip ratio is related to the train speed and wheel angular velocity, and is composed as follows: in, represent the slip velocity and creep velocity respectively; r is the wheel radius; F r (v,Ψ2) represents the running resistance, which is composed of aerodynamic force and track resistance, that is: F r (v,Ψ2)=mf r (v,Ψ2) f r (v,Ψ2)=c0+c1v+c2v 2 ; Among them, the unknown parameter vector The adhesion moment satisfies the following relationship: T a (λ,Ψ1)=F a (λ,Ψ1)r=μ(λ,Ψ1)mgr; Use both traction torque and braking torque Indicates that there is a known upper limit of the torque μ max and the lower limit of torque μ min , and the saturation function satisfies the following relationship: S12: Construct the constraint space required for train anti-skid: set the creep area by limiting the slip ratio of the train during operation inside, use To represent the slip zone, that is: Where λ = λ opt Corresponds to the maximum adhesion coefficient, denoted as λ opt =argmax{μ(λ,Ψ1)}; The described constraint set is processed as follows: The creep region about λ Rewrite as The more conservative anti-slip region provided by calculating the slip velocity satisfies the following relationship: Among them, the positive parameter λ a and v0 represent the upper limit of the slip ratio λ and a small constant selected by the designer respectively; v smax Represents v s Corresponding anti-slip cap.

3. The train robust adhesion tracking control method according to claim 1, characterized in that: Before S2, the method further includes: Convert the adhesion dynamics model of the train into a nonlinear state equation and select the states x1 = s, x2 = v, and x3 = v s , then v ω becomes v ω (x2,x3)=max{x2+x3,x2} and ω=(x2+x3) / r, so the dynamic model is transformed to satisfy the following relationship: g o =-g(1+r 2 mJ -1 ) d1(t)=σ(t) Among them, x1 represents the train displacement, x2 represents the train speed, x3 represents the train angular velocity, g o Represents environmental parameters, Indicates the train speed, represents the train acceleration, represents the angular acceleration of the wheel, d1(t), d2(t), d3(t) all represent interference or noise; according to Design, design is the upper bound of the constraint state x2.

4. The train robust adhesion tracking control method according to claim 1, characterized in that: The S2 includes: Obtain input information of the target train control unit, which includes: the expected trajectory of train displacement, train speed signal x2(t), wheel angular velocity signal x3(t), actuator saturation boundary, i.e., torque upper limit μ max and the lower limit of torque μ min , where the expected trajectory of train displacement is described as follows: s r (t)=f d (t); in, is the reference signal of the system displacement, f d (·) is a nonlinear smooth function.

5. The adaptive control method according to claim 1, characterized in that: The S3 includes: S31: Construct an auxiliary dynamic system that can handle the system input saturation constraint and satisfy the following relationship: in, Represents the state of the auxiliary dynamic system, i=1,2,3,φ i represents an artificially set positive parameter, i=1,2,3; S32: build system state error z i , satisfying the following relationship: z1=x1-x r (t)-λ1,z2=x2-α1-λ2,z2=x3-α2-λ3 Among them, α1 and α2 represent intermediate control signals designed for the controller. According to the input information, the displacement error signal z1 of the target train is obtained in real time as the state information of the target train, and the state feedback information of the train is obtained based on the state information.

6. The train robust adhesion tracking control method according to claim 1, characterized in that: The S4 includes: S41: According to the running state of the train, it is divided into two processes: traction and braking. According to the given adhesion dynamics model, it is rewritten as: in, x 2 represents the state vector, x 2=[x1,x2] T ; f1(x1,x2)=x2, f2( x 2,x3)=μ(x3 / (x2+x3),Ψ1)g-f r (x2,Ψ2), Using the middle value theorem to deal with the adhesion dynamics model of the train, we get: In the formula, Represents a random number from 0 to x2, represents a random number from 0 to x3, cc represents a random number from 0 to u(ν); Among them, the partial derivative is defined as follows: S42: Design a robust adhesion tracking controller according to the control parameters corresponding to the traction-braking process, namely: In the formula, ρ3 represents the parameter to be designed, represents the newly constructed state error, represents the parameters with design, represents the newly constructed state error, represents the parameters to be designed, represents the parameters to be designed, represents the parameters to be designed, represents the parameters to be designed, represents the parameters to be designed, represents the parameter to be designed, Λ3 represents the derivative variable; Among them, z3=x3-α2-λ3, and The intermediate control signal α2 is designed as: Among them, z2=x2-λ1-λ2, the intermediate control signal λ1 is: Where z1 = x1-s r (t)-λ1, and:

7. A robust control system, characterized in that: Including processor and memory; Memory, used to store computer programs; A processor, for implementing the method steps described in any one of claims 1 to 6 when executing a program stored in a memory.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method steps described in any one of claims 1 to 6 are implemented.

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