A high-speed train adaptive sliding mode control method, system and electronic device

Abstract

The present application relates to a kind of high-speed train adaptive sliding mode control method, system and electronic equipment, belong to train control technical field.The high-speed train adaptive sliding mode control method provided by the present application considers that high-speed train system model has the characteristics such as nonlinearity and parameter time-varying, establishes train nonlinear model, and establishes its error characteristic model according to nonlinear model derivation;Using recursive least squares method, the time-varying parameter of the characteristic model is described on-line identification;After this, on the basis of this characteristic model, fully use the ability that characteristic modeling can reduce model complexity and meet control performance requirements, set PID sliding mode surface, improve sliding mode approach law, design adaptive sliding mode controller based on characteristic model, while reducing the influence of chattering on system, complete its asymptotic tracking to given operation curve, to realize high-precision tracking control of high-speed train to given operation curve.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of train control technology, in particular to a high-speed train adaptive sliding mode control method, system and electronic equipment. BACKGROUND

[0002] High-speed railway plays a very important role in promoting China's economic construction and stimulating domestic demand, and has been widely valued and developed in recent years. With the continuous improvement of high-speed train running speed, the safe and reliable operation of trains and related technical research have become a hot spot. In order to ensure the sustainable development of high-speed railway and the safe operation of high-speed train, it is necessary to establish an effective high-speed train operation process model and optimization control method to realize high-precision tracking of the given running curve, which has important practical significance and use value for the development of train automatic driving related technology.

[0003] The core of train automatic driving is to establish an accurate high-speed train model and design an effective tracking control method. And establishing a model suitable for the dynamic characteristics of the train is the premise and key of the control design of the train. In the current research, the dynamic characteristics of the high-speed train operation process have problems such as nonlinearity, parameter uncertainty and time-varying, which makes it difficult to establish an accurate mathematical model of high-speed train. Many researchers have carried out in-depth research on high-speed train modeling methods without affecting the performance of high-speed trains, and have made a series of achievements: such as mechanism modeling, data-driven modeling, ANFIS model and different modeling methods. Designing an effective control algorithm based on a suitable train operation model can enable the train to achieve high-precision tracking of speed and displacement. The above conventional high-speed train dynamic modeling process is mostly based on theoretical analysis, and many assumed parameters are known a priori. However, for the complex and variable operating environment of high-speed train operation process, these parameters have the characteristics of time-varying and unmeasurable, which are difficult to obtain accurately.

[0004] As for the control of high-speed trains, the current conventional control method researches include: classic PID control, predictive control, neural network control, fuzzy control and other algorithms, but all have some defects. Among them, the PID control has single effect and the problem of difficult parameter tuning; the predictive control has low model requirement and high real-time performance, but the system stability is not strong and is not suitable for stable operation of high-speed trains; the neural network control also has poor stability and slow learning speed; and the fuzzy control can achieve accurate tracking of the train, but it depends on the establishment of a complex model and actual experience. SUMMARY

[0005] To solve the above problems existing in the prior art, the present application provides a high-speed train adaptive sliding mode control method, system and electronic equipment.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A high-speed train adaptive sliding mode control method includes:

[0008] A high-speed train characteristic model is described based on the nonlinear characteristics of the train system.

[0009] The recursive least squares method was used to identify the feature parameters of a high-speed train feature model online.

[0010] An error feature model is obtained based on the aforementioned feature parameters and the train output feature model;

[0011] A sliding surface in the form of a PID controller is set based on the aforementioned error characteristic model;

[0012] The sliding mode reaching law in the sliding surface of the PID form is improved to obtain the sliding mode reaching law;

[0013] An adaptive sliding mode controller is constructed based on the sliding mode reaching law to realize adaptive sliding mode control of high-speed trains.

[0014] Preferably, the high-speed train feature model is as follows:

[0015] v ( k +1)= f 1( k ) v ( k )+ f 2( k ) v ( k -1)+ g 0( k ) u ( k );

[0016] In the formula, v ( k For high-speed trains k The running speed at any given moment, v ( k +1) for high-speed trains k The running speed at time +1, v ( k -1) For high-speed trains k The running speed at time -1 u ( k For high-speed trains k The input of traction or braking force at any given moment, f 1( k ), f 2( k )and g 0(k ) are all k the to-be-identified characteristic parameters of the high-speed train characteristic model.

[0017] Preferably, the error characteristic model is:

[0018] e k +1)= f 1( k ) e k + f 2( k ) e k -1)- g 0( k ) u k + η k ;

[0019] In the formula, e k is the speed tracking error of the high-speed train at time k e k +1) is the speed tracking error at time k +1, e k -1) is the speed tracking error at time k -1, η k = v d k +1)- f 1( k ) v d k - f 2( k ) v d k -1), η k is an intermediate variable of the simplified error model, v d k is the given speed of the high-speed train at time k v d k -1) is the given speed of the high-speed train at time k -1, v d k +1) is the given speed of the high-speed train at time k ​​​​​​​​​​​​​​​​​​The given speed of the high-speed train at time k.

[0020] Preferably, the sliding mode surface in the form of PID is:

[0021] ;

[0022] In the formula, s ( k ) is the sliding mode surface at time k, k k p Kp is the proportional coefficient of the PID control, k i Ki is the integral coefficient of the PID control, k d Kd is the differential constant coefficient of the PID control, is the velocity error sum at time k-1. k

[0023] Preferably, the improved sliding mode reaching law in the sliding mode surface in the form of PID is a sliding mode reaching law, and specifically comprises:

[0024] An exponential reaching law is selected for the discrete sliding mode control, and the discrete form of the sliding mode surface in the form of PID is:

[0025] ;

[0026] A power function in active disturbance rejection control is used to replace the sign function of the sliding mode reaching law in the discrete form to obtain a sliding mode reaching law; the sliding mode reaching law is:

[0027] ;

[0028] In the formula, s ( k ) is the sliding mode surface at time k, q is an exponential reaching term parameter, T is a sampling time, is a constant of the velocity of the moving point of the system approaching the switching surface, sgn is a sign function, is a sliding mode reaching law, fal is a power function, is a nonlinear parameter for determining the tracking performance of the system, is a nonlinear parameter for determining the width of the nonlinear interval of the power function, s ( k -1) is the sliding mode surface at time k-1.

[0029] According to the specific embodiments provided by the present application, the following technical effects are disclosed:​​

[0030] The high-speed train adaptive sliding mode control method provided by the application considers that the high-speed train system model has the characteristics of nonlinearity and time-varying parameters, and a characteristic model is established by deducing a train nonlinear model; the recursive least square method is used to identify the time-varying parameters of the characteristic model online, and then on the basis of the characteristic model, the ability of the characteristic modeling to reduce the model complexity and meet the control performance requirements is fully utilized, an adaptive sliding mode controller based on the characteristic model is designed, the influence of chattering on the system is reduced, the given running curve is asymptotically tracked, and high-precision tracking control of the high-speed train on the given running curve is realized.

[0031] Corresponding to the high-speed train adaptive sliding mode control method provided above, the application also provides the following implementation structures:

[0032] One of them is a high-speed train adaptive sliding mode control system, which comprises:

[0033] A characteristic model construction module is configured to construct a high-speed train characteristic model based on the nonlinear characteristics of the train system.

[0034] A characteristic parameter identification module is configured to identify the characteristic parameters of the high-speed train characteristic model online by using the recursive least square method.

[0035] An error characteristic model construction module is configured to obtain an error characteristic model based on the characteristic parameters and the train output characteristic model.

[0036] A sliding surface setting module is configured to set a PID form sliding surface based on the error characteristic model.

[0037] A reaching law improvement module is configured to improve the sliding mode reaching law in the PID form sliding surface to obtain a sliding mode reaching law.

[0038] A controller construction module is configured to construct an adaptive sliding mode controller based on the sliding mode reaching law to realize high-speed train adaptive sliding mode control.

[0039] The other is an electronic device, which comprises:

[0040] A memory is configured to store a logic control instruction.

[0041] A processor is connected with the memory and is configured to call and implement the logic control instruction to execute the high-speed train adaptive sliding mode control method provided above.

[0042] Preferably, the memory is a computer readable storage medium.

[0043] The technical effects achieved by the system and the electronic device provided by the application are the same as the technical effects achieved by the high-speed train adaptive sliding mode control method provided by the application, and thus will not be described here again. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0045] Figure 1 A flowchart of the high-speed train adaptive sliding mode control method provided by the present application;

[0046] Figure 2 A force analysis diagram of a single-particle model of a CRH380A high-speed train provided by the embodiment of the present application;

[0047] Figure 3 A structure block diagram of the high-speed train adaptive sliding mode control based on a feature model provided by the embodiment of the present application;

[0048] Figure 4 A speed tracking curve diagram of the high-speed train provided by the embodiment of the present application; wherein, the solid curve is a target speed curve, and the dashed curve is a speed curve under the control of the present application;

[0049] Figure 5 A displacement tracking curve diagram of the high-speed train provided by the embodiment of the present application; wherein, the solid curve is a target speed curve, and the dashed curve is a speed curve under the control of the present application;

[0050] Figure 6 A speed tracking error curve diagram of the high-speed train in the running process provided by the embodiment of the present application;

[0051] Figure 7 An acceleration tracking curve diagram of the high-speed train provided by the embodiment of the present application; wherein, the solid curve is a target speed curve, and the dashed curve is a speed curve under the control of the present application;

[0052] Figure 8 A feature parameter identification curve diagram of the high-speed train in the running process provided by the embodiment of the present application; wherein, Figure 8 (a) part of the feature parameter f 1 identification curve diagram; Figure 8 (b) part of the feature parameter f 2 identification curve diagram; Figure 8 (c) part of the feature parameter g 0 identification curve diagram. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] To address the modeling and control problems of high-speed trains during operation, this invention provides an adaptive sliding mode control method, system, and electronic equipment for high-speed trains, which can achieve high-precision tracking control of high-speed trains on a given running curve.

[0055] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] like Figure 1 As shown, the high-speed train adaptive sliding mode control method provided by the present invention includes:

[0057] Step 100: Describe the high-speed train characteristic model based on the nonlinear characteristics of the train system. In this invention, a dynamic analysis of the high-speed train's operation is performed. Considering the nonlinear and time-varying parameter characteristics of the high-speed train system model, a high-speed train characteristic model is derived from the train's nonlinear model. Specifically, the train is considered as a rigid point mass, and the forces acting on the train during operation are analyzed. Based on Newton's second law, a single-mass point model of the train is established. For example, taking the CRH380A high-speed train as the research object, its force analysis diagram is as follows: Figure 2 As shown. Based on the overall dynamic process analysis of the train, considering the nonlinear characteristics of the system and the increase in train speed and changes in the environment, the longitudinal dynamic model of the train is constructed as follows:

[0058] (1)

[0059] (2)

[0060] (3)

[0061] In the formula, m For train quality, v For train speed, u For the traction or braking force of high-speed trains. F g As the basic resistance, F a To add resistance, a ,b , c All are drag coefficients, t is time, and g is gravitational acceleration.

[0062] High-speed trains operate in complex and variable environments with frequent changes in operating conditions. Furthermore, the train is a large, complex, and uncertain system. In practice, as the speed of high-speed trains continuously increases, the nonlinear characteristics of the train system become more pronounced. By discretizing the continuous mathematical model of the high-speed train operation process represented by formula (3), and selecting an appropriate sampling period, a second-order time-varying difference equation can be used to describe the characteristic model of the high-speed train. The characteristic model of the high-speed train is as follows:

[0063] v ( k +1)= f 1( k ) v ( k )+ f 2( k ) v ( k -1)+ g 0( k ) u ( k (4)

[0064] In the formula, v ( k For high-speed trains k The running speed at any given moment, v ( k +1) for high-speed trains k The running speed at time +1, v ( k -1) For high-speed trains k The running speed at time -1 u ( k For high-speed trains k The input of traction or braking force at any given moment. f 1( k ), f 2( k )and g 0( k All of them are k The feature parameters to be identified in the high-speed train feature model at any time.

[0065] Step 101: Identify the feature parameters to be identified in the high-speed train feature model online based on the recursive least squares method. In the process of establishing the high-speed train feature model in Step 100 above, its accuracy is mainly determined by the feature parameters. In order to achieve a good parameter identification convergence effect and the purpose of real-time tracking of parameter changes, the recursive least squares method is used to identify the feature parameters online in real time, so as to facilitate the design of the controller. Then the high-speed train operation process feature model represented by equation (4) is described as the parameter estimation equation:

[0066] (5)

[0067] In the formula, , For system input and output vectors, , and These respectively indicate that the train is at k -1、 k The operating speed and system control input at time -2 , Let be the time-varying coefficient vector of the high-speed train characteristic model, which contains complex high-order information related to train dynamics. The recursive least squares algorithm of equation (6) is applied to equation (5):

[0068] (6)

[0069] In the formula, K ( k Let be the gain matrix at time k. P ( k Let be the covariance matrix at time k. P ( k -1) is the covariance matrix at time k-1. , , These are the estimated values ​​for , respectively. This is the initial value, which is usually zero. I It is a unit vector. μ This is the forgetting factor, which is usually a positive number not less than 0.9 and close to 1.

[0070] Step 102: Obtain the error feature model based on the feature parameters and the train output feature model. Specifically, based on the feature parameters in the high-speed train feature model obtained by the online identification method in Step 101, substitute the estimated values ​​of each parameter into the train output feature model to design an error feature model in the following form:

[0071] e ( k +1)= f 1( k )e ( k )+ f 2( k ) e ( k -1)- g 0( k ) u ( k )+ η ( k (7)

[0072] Where: In the formula, e ( k ) for in k Speed ​​tracking error of high-speed trains at all times. e ( k +1) is k Velocity tracking error at time +1 e ( k -1) is k Velocity tracking error at time -1 η ( k )= v d ( k +1)- f 1( k ) v d ( k )- f 2( k ) v d ( k -1), η ( k To simplify the intermediate parameters of the error model, v d ( k )for k The given speed of the high-speed train at any given time. v d ( k -1) is k The given speed of the high-speed train at time -1 v d ( k +1) is k The given speed of the high-speed train at time +1.

[0073] The train output model in the present application refers to the form of the identified train characteristic model, which can be specifically referred to in the literature: Gao S, Dong H, Ning B, et al. Characteristic model-based all-coefficient adaptive control for automatic train control systems[J]. Science China (Information Sciences), 2014, 57(09): 218-229.

[0074] Step 103: setting the sliding mode surface in the form of PID based on the error characteristic model. The sliding mode surface in the form of PID is as follows:

[0075] (8)

[0076] In the formula, s ( k ) is the discrete sliding mode surface at the moment t, k k p is the proportional coefficient of the PID control, k i is the integral coefficient of the PID control, k d is the differential constant coefficient of the PID control, is the speed error sum at the moment t-1. k -1, then k the sliding mode surface at the moment t+1 is:

[0077] (9)

[0078] In the formula, is the speed error sum at the moment t-1. k

[0079] Step 104: improving the sliding mode reaching law in the sliding mode surface in the form of PID to obtain the sliding mode reaching law. For the discrete sliding mode control, the exponential reaching law in the traditional reaching law is selected, and the discrete form is as follows:

[0080] (10)

[0081] In the formula, q is the exponential reaching term parameter, T is the sampling time, ε is the constant of the system motion point approaching the switching surface rate, sgn is the sign function.

[0082] ​​The sliding mode control inevitably has a sliding mode chattering problem, in order to better realize weakening of the sliding mode chattering, the sign function affecting the chattering is replaced by a power function in active disturbance rejection control fal Alternatively, by improving the sliding mode reaching law, the sliding mode chattering is better inhibited, the purpose of reducing the influence of the chattering on the system is achieved, and then the control target is better realized, and high-precision tracking of the given target curve is completed.

[0083] The power function is used fal The sign function in the exponential reaching law is replaced, and the reaching law expression is described as:

[0084] (11)

[0085] In the formula, S 1( k +1) is a sliding mode reaching law, fal is a power function, α is a nonlinear parameter for determining system tracking performance, δ is a nonlinear parameter for determining the width of the nonlinear interval of the power function.

[0086] The power function is in the form of:

[0087]

[0088] s ( k ) is a discrete sliding surface, and s is the same as s ( k ), both are discrete sliding surfaces, α is a nonlinear parameter for determining system tracking performance, δ is a nonlinear parameter for determining the width of the nonlinear interval of the power function.

[0089] The sliding surface expression (9) at the moment of k +1 is substituted into the reaching law expression (11), and then the error characteristic model formula (7) is rewritten by combining the train running process output characteristic model formula, to obtain the following expression:

[0090] (12)

[0091] When the sliding surface satisfies S 1( k +1)=0, and then the equivalent control expression (13) of the discrete sliding mode is described as: kThe expression (9) of the sliding mode surface at the time of +1 is substituted into the expression (11) of the reaching law, and then the error characteristic model (7) rewritten from the output characteristic model of the train operation process is combined to obtain the expression (12) thereof described as:

[0092] (13)

[0093]

[0094] For the equivalent control law, C ( k )、 D ( k )、 F ( k ) are equivalent to the intermediate variables, and are used for simplifying each part in the above expression (12).

[0095] Meanwhile, the switching control law is redesigned as:

[0096] (14)

[0097] The expression of the total control amount of the sliding mode control u ( k ) is:

[0098] (15)

[0099] In the expression, is the estimated value of the characteristic parameter k at the time of 0. g Let

[0100] , since the sign of the characteristic parameter may be positive or negative, the control amount is greatly affected, and then the control effect is affected, the is changed to , the appropriate parameter is selected to ensure that it is positive, that is, the following condition is met: p

[0101] (16)

[0102] Step 105: based on the sliding mode reaching law, an adaptive sliding mode controller is constructed to realize adaptive sliding mode control of the high-speed train.

[0103] Based on the above description, the application proposes a high-speed train adaptive control method based on a characteristic model to realize high-precision tracking control of a given speed curve, and the control principle is as follows: Figure 3 ​The design process can be divided into three steps. Firstly, the characteristic parameters in the train characteristic model are obtained according to an online identification method. Secondly, the adaptive control method based on the characteristic model is applied to the train characteristic model. Finally, the adaptive sliding mode controller is constructed from the perspective of improving the sliding mode reaching law, so that the train can realize tracking performance and improve the control accuracy of the system during operation.

[0104] The above design process shows that the model of the high-speed train system has the characteristics of nonlinearity and parameter time-varying, and the intelligent adaptive control method based on the high-speed train characteristic model is used in combination with the design of the sliding mode control, so that the asymptotic tracking of the high-speed train to the given curve can be realized in theory. The intelligent adaptive control method based on the high-speed train characteristic model can be referred to in the literature: Wu Hongxin, Hu Jun, Jie Yongchun. Intelligent adaptive control based on characteristic model [M]. China Science and Technology Publishing House, 2009.

[0105] Based on the above description, in the modeling, the characteristic model of the train system is constructed from the nonlinear model of the high-speed train based on the characteristic modeling theory according to the dynamic analysis of the operation process of the high-speed train. The train characteristic model considers the surrounding environmental characteristics and the performance requirements of the controller, improves the model parameter uncertainty and the nonlinearity of the running resistance in the conventional modeling process of the train, reduces the complexity of the model, and is beneficial to the design of the controller.

[0106] In the control, the intelligent adaptive control method based on the characteristic model is applied to the train characteristic model in combination with the sliding mode control, a discrete adaptive sliding mode controller based on the PID sliding surface is designed, and the chattering is reduced by improving the sliding mode reaching law. Not only the high-precision tracking of the given target curve can be realized, but also the long-time sliding mode chattering can be avoided, and the system has good dynamic performance.

[0107] A specific embodiment is provided below to specifically illustrate the high-speed train adaptive sliding mode control method provided above.

[0108] Based on the establishment of the high-speed train characteristic model and the theoretical analysis of the intelligent adaptive control method based on the high-speed train characteristic model, the MATLAB software simulation is used to verify the accuracy of the model and the precision of the tracking control.

[0109] According to the train running section and the ATP speed limit characteristics, the actual operation data of the CRH380A type high-speed train on the Jinan-Xuzhou East section are selected as the original data for modeling (speed v The formula (6) recursive least squares method is used to estimate the characteristic parameters of the train characteristic model, and the train characteristic model is obtained as as the system input quantity, and the following is obtained The estimated value of the time-varying coefficient of the high-speed train characteristic model. According to the selection of the sampling time in the high-speed train characteristic model theory, the value range of the time-varying parameter of the high-speed train characteristic model can be determined in a bounded convex closed set D s .

[0110] (17)

[0111] According to the identified characteristic parameters, the estimated values of each characteristic parameter are substituted into the train output characteristic model to design an error characteristic model, and a PID sliding surface is selected, and the sign function in the exponential approach law is replaced by a power function fal to improve the sliding mode approach law. Through continuous debugging, appropriate system parameters are selected for simulation, and after debugging, the undetermined parameters based on the PID sliding surface can be selected as: k p = 0.039, k i = 0.15, k d = 0.01, the design parameters in the approach law are selected as: q = 100, T = 0.01, ε = 0.001, α = 0.2, δ = 0.1, p = 0.5. The intelligent adaptive control method based on the high-speed train characteristic parameters is applied to the high-speed train characteristic model, and the discrete adaptive sliding mode controller based on the PID sliding surface is used to track the actual running curve, and the simulation results are shown in Figures 4-6 , which are the train speed tracking curve, the train displacement tracking curve, and the speed tracking error curve.

[0112] As shown in Figure 4 and Figure 5 , the speed and displacement tracking curves can basically closely track the target speed curve, and in the local enlarged view, the speed and displacement curves obtained by the method of the embodiment maintain a certain degree of coincidence with the given speed and displacement curves, and high-precision tracking of the target speed and displacement is achieved. As shown in Figure 6As shown, the root mean square error of speed tracking obtained by the control method in this embodiment is 0.0814 km / h, and the maximum speed tracking error during high-speed train start-up and braking is 1.3191 km / h and 1.8253 km / h, respectively, which still meets the allowable error range for train operation. After short-term parameter adjustment control, the train speed error is reduced, and the error curve ranges from -0.3092 to 0.2831 km / h. The above simulation results show that the control method proposed in this embodiment has good tracking effect under traction, inertia, and braking conditions during train operation.

[0113] To verify that the control method in this embodiment can meet the requirements for comfortable train operation. For example... Figure 7 As shown, the acceleration tracking method in this embodiment achieves high-precision target acceleration tracking with an accuracy of less than 1 m / s². 2 The high-precision tracking of acceleration affects the safe operation of high-speed trains and passenger comfort, thus ensuring passenger comfort to a certain extent.

[0114] The recursive least squares method is used to identify the characteristic parameters. Three characteristic parameters are identified during the operation of a high-speed train. f 1. f 2 and g The recognition curve for 0 is as follows Figure 8 Part (a) to Figure 8 As shown in section (c), the characteristic parameter variation curve is relatively smooth due to less system interference. This parameter variation curve becomes smoother after 1000 seconds. f 1( k )and f 2( k The impact is relatively small, on g 0( k The impact is significant.

[0115] The simulation results above demonstrate that the speed and displacement tracking curves obtained by the high-speed train adaptive sliding mode control method provided by this invention can achieve high-precision tracking, meeting the requirements for safe, punctual, and comfortable train operation. The designed feature model-based adaptive sliding mode control method also achieves the expected performance, verifying the effectiveness of the high-speed train adaptive sliding mode control method provided by this invention, exhibiting good tracking performance and strong robustness.

[0116] In addition, corresponding to the high-speed train adaptive sliding mode control method provided above, the present invention also provides the following implementation structure:

[0117] One type is the high-speed train adaptive sliding mode control system, which includes:

[0118] The feature model construction module is configured to construct a feature model of the high-speed train based on the nonlinear characteristic description of the train system.

[0119] The feature parameter identification module is configured to identify feature parameters of the feature model of the high-speed train online by using a recursive least square method.

[0120] The error feature model construction module is configured to obtain an error feature model based on the feature parameters and the output feature model of the train.

[0121] The sliding mode surface setting module is configured to set a sliding mode surface in a PID form based on the error feature model.

[0122] The reaching law improvement module is configured to improve a sliding mode reaching law in the sliding mode surface in the PID form to obtain a sliding mode reaching law.

[0123] The controller construction module is configured to construct an adaptive sliding mode controller based on the sliding mode reaching law to implement adaptive sliding mode control of the high-speed train.

[0124] The other is an electronic device, comprising:

[0125] The memory is configured to store the logic control instructions. The memory used in the present application is a computer readable storage medium.

[0126] The processor is connected with the memory and is configured to call and implement the logic control instructions to execute the high-speed train adaptive sliding mode control method provided above.

[0127] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.

[0128] The principles and implementation manners of the present application are described by using specific examples in the present application. The above embodiment description is only used to help understand the method of the present application and its core idea; meanwhile, for the general technical personnel in the art, the specific implementation manner and application range can be changed according to the idea of the present application. In conclusion, the content of the specification should not be understood as the limitation of the present application.

Claims

1. A high-speed train adaptive sliding mode control method, characterized in that, The method comprises the following steps: a characteristic model of a high-speed train is described based on nonlinear characteristics of a train system; recursive least squares are used to identify characteristic parameters of the characteristic model of the high-speed train online; an error characteristic model is obtained based on the characteristic parameters and a train output characteristic model; a sliding mode surface in a PID form is set based on the error characteristic model; a sliding mode reaching law in the sliding mode surface in the PID form is improved to obtain a sliding mode reaching law; an adaptive sliding mode controller is constructed based on the sliding mode reaching law to realize adaptive sliding mode control of the high-speed train; wherein the sliding mode reaching law in the sliding mode surface in the PID form is improved to obtain the sliding mode reaching law, which comprises the following steps: an exponential reaching law is selected for discrete sliding mode control, and a discrete form of the sliding mode surface in the PID form is obtained as follows: ； a sign function of the sliding mode reaching law in the discrete form is replaced by a power function in active disturbance rejection control to obtain the sliding mode reaching law; the sliding mode reaching law is as follows: ； wherein s k is the discrete sliding surface at k time, q is the exponential approach term parameter, T is the sampling time, is the constant of the system motion point approaching the switching surface rate, sgn is the sign function, is the sliding mode approach law, fal is the power function, is the nonlinear parameter that determines the system tracking performance, is the nonlinear parameter that determines the nonlinear interval width of the power function, s k is the discrete sliding surface at k-1 time.​​ 2. The adaptive sliding mode control method for high-speed trains according to claim 1, characterized in that, the characteristic model of the high-speed train is as follows: v ( k +1)= f 1( k ) v ( k )+ f 2( k ) v ( k -1)+ g 0( k ) u ( k )； In the formula, v ( k For high-speed trains k The running speed at any given moment, v ( k +1) for high-speed trains k The running speed at time +1, v ( k -1) For high-speed trains k The running speed at time -1 u ( k For high-speed trains k The input of traction or braking force at any given moment, f 1( k ), f 2( k )and g 0( k All of them are k The feature parameters to be identified in the high-speed train feature model at any time.

3. The adaptive sliding mode control method for high-speed trains according to claim 2, characterized in that, the error characteristic model is as follows: e ( k +1)= f 1( k ) e ( k )+ f 2( k ) e ( k -1)- g 0( k ) u ( k )+ η ( k )； In the formula, e ( k ) for in k Speed ​​tracking error of high-speed trains at all times. e ( k +1) is k Velocity tracking error at time +1 e ( k -1) is k Velocity tracking error at time -1 η ( k )= v d ( k +1)- f 1( k ) v d ( k )- f 2( k ) v d ( k -1), η ( k To simplify the intermediate parameters of the error model, v d ( k )for k The given speed of the high-speed train at any given time. v d ( k -1) is k The given speed of the high-speed train at time -1 v d ( k +1) is k The given speed of the high-speed train at time +1.

4. The adaptive sliding mode control method for high-speed trains according to claim 3, characterized in that, the sliding mode surface in the PID form is as follows: ； wherein s k k a time-discrete sliding surface, k p a proportional coefficient of the PID control, k i an integral coefficient of the PID control, k d a differential constant of the PID control, a previous k -1 time velocity error sum.​​ 5. A high-speed train adaptive sliding mode control system, characterized in that, The method comprises the following steps: a characteristic model of a high-speed train is described based on nonlinear characteristics of a train system; recursive least squares are used to identify characteristic parameters of the characteristic model of the high-speed train online; an error characteristic model is obtained based on the characteristic parameters and a train output characteristic model; a sliding mode surface in a PID form is set based on the error characteristic model; a sliding mode reaching law in the sliding mode surface in the PID form is improved to obtain a sliding mode reaching law; ； wherein the sliding mode reaching law in the sliding mode surface in the PID form is improved to obtain the sliding mode reaching law, which comprises the following steps: ； wherein, s ( k ) is a discrete sliding surface at k time, q is an exponential approach term parameter, T is a sampling time, is a constant for approaching the switching surface rate of the system motion point, an exponential reaching law is selected for discrete sliding mode control, and a discrete form of the sliding mode surface in the PID form is obtained as follows: is a sign function, is a sliding mode approach law, a sign function of the sliding mode reaching law in the discrete form is replaced by a power function in active disturbance rejection control to obtain the sliding mode reaching law; the sliding mode reaching law is as follows: is a power function, is a nonlinear parameter that determines the tracking performance of the system, is a nonlinear parameter that determines the nonlinear interval width of the power function, s ( k -1) is a discrete sliding surface at k-1 time; sgn 6. An electronic device, comprising: fal a controller is constructed based on the sliding mode reaching law to realize adaptive sliding mode control of the high-speed train. The method comprises the following steps:

7. The electronic device of claim 6, wherein, a memory is used to store logical control instructions; a processor is connected to the memory and used to call and implement the logical control instructions to execute the adaptive sliding mode control method of the high-speed train according to any one of claims 1-4. The memory is a computer readable storage medium.

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