CN116027669A - Self-adaptive sliding mode control method and system for high-speed train and electronic equipment - Google Patents
Self-adaptive sliding mode control method and system for high-speed train and electronic equipment Download PDFInfo
- Publication number
- CN116027669A CN116027669A CN202310101189.2A CN202310101189A CN116027669A CN 116027669 A CN116027669 A CN 116027669A CN 202310101189 A CN202310101189 A CN 202310101189A CN 116027669 A CN116027669 A CN 116027669A
- Authority
- CN
- China
- Prior art keywords
- sliding mode
- train
- speed
- speed train
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Feedback Control In General (AREA)
Abstract
The invention relates to a self-adaptive sliding mode control method and system for a high-speed train and electronic equipment, and belongs to the technical field of train control. According to the self-adaptive sliding mode control method for the high-speed train, provided by the invention, the characteristics of nonlinearity, parameter time variation and the like of a high-speed train system model are considered, a train nonlinearity model is established, and an error characteristic model is deduced and established according to the nonlinearity model; adopting a recursive least square method to identify the time-varying parameter description of the characteristic model on line; and then, on the basis of the feature model, the capability of feature modeling to reduce the complexity of the model and meet the control performance requirement is fully utilized, a PID sliding mode surface is set, the sliding mode approach law is improved, an adaptive sliding mode controller based on the feature model is designed, meanwhile, the influence of buffeting on a system is reduced, the asymptotic tracking of the system on a given running curve is completed, and the high-precision tracking control of the high-speed train on the given running curve is realized.
Description
Technical Field
The invention relates to the technical field of train control, in particular to a self-adaptive sliding mode control method, a self-adaptive sliding mode control system and electronic equipment for a high-speed train.
Background
The high-speed railway plays a very important role in promoting the economic construction of China, pulling internal requirements and the like, and is widely valued and vigorously developed in recent years. With the continuous increase of the running speed of high-speed trains, safe and reliable running of the trains and related technical researches thereof also become hot spots. In order to ensure sustainable development of a high-speed railway and safe operation of a high-speed train, an effective high-speed train operation process model and an optimal control method are required to be established, high-precision tracking of a given operation curve is realized, and the method has important practical significance and use value for updating and developing a related technology of automatic driving of the train.
The core of the automatic driving of the train is to establish an accurate high-speed train model and design an effective tracking control method. The establishment of a model suitable for the dynamic characteristics of the train is also a precondition and key for the control design of the train. In the current research, the problems of nonlinearity, uncertain parameters, time variation and the like exist in the dynamics characteristics of the running process of the high-speed train, so that an accurate mathematical model of the high-speed train is difficult to establish. On the basis of not affecting the high-speed train performance research, numerous researchers develop in-depth research on a high-speed train modeling method and obtain a series of achievements: such as: different modeling methods such as mechanism modeling, data-driven modeling, ANFIS model and the like. The effective control algorithm is designed on the basis of building a proper train running model, so that the speed displacement of the train can be tracked with high precision. The conventional high-speed train dynamics modeling process is mostly based on theoretical analysis, and many assumed parameters are known a priori, however, for the high-speed train running process with complex and variable operating environments, the parameters have the characteristics of time varying, non-measurable and the like, and are difficult to accurately obtain.
In terms of control of a high-speed train, the conventional control method is researched at present: classical PID control, predictive control, neural network control, fuzzy control and other algorithms, but have partial defects, wherein the PID control effect is single, and the problem of difficult parameter setting exists; the predictive control has low requirements on the model and high instantaneity, but the system stability is not strong, and the predictive control is not suitable for the stable operation of a high-speed train; also, the neural network control has poor stability and also has the defect of low learning speed; while fuzzy control can realize accurate train tracking, the comparison depends on the establishment of a complex model and actual experience.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a self-adaptive sliding mode control method, a self-adaptive sliding mode control system and electronic equipment for a high-speed train.
In order to achieve the above object, the present invention provides the following solutions:
a self-adaptive sliding mode control method for a high-speed train comprises the following steps:
describing a high-speed train characteristic model based on nonlinear characteristics of a train system;
characteristic parameters of a high-speed train characteristic model are identified on line by adopting a recursive least square method;
obtaining an error characteristic model based on the characteristic parameters and the train output characteristic model;
setting a sliding mode surface in a PID form based on the error feature model;
improving the sliding mode approach law in the sliding mode surface of the PID form to obtain a novel sliding mode approach law;
and constructing an adaptive sliding mode controller based on the novel sliding mode approach law, and realizing the adaptive sliding mode control of the high-speed train.
Preferably, the high-speed train feature model is:
v(k+1)=f 1 (k)v(k)+f 2 (k)v(k-1)+g 0 (k)u(k)；
wherein v (k) is the running speed of the high-speed train at the moment k, v (k+1) is the running speed of the high-speed train at the moment k+1, v (k-1) is the running speed of the high-speed train at the moment k-1, u (k) is the traction force or braking force input of the high-speed train at the moment k, and f 1 (k)、f 2 (k) And g 0 (k) All are high-speed trains at k momentCharacteristic parameters to be identified of the characteristic model.
Preferably, the error feature model is:
e(k+1)=f 1 (k)e(k)+f 2 (k)e(k-1)-g 0 (k)u(k)+η(k)；
where 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 parameter of the simplified error model, v d (k) For a given speed of the high speed train at time k, v d (k-1) is a given speed, v, of the high speed train at time k-1 d (k+1) is a given speed of the high speed train at time k+1.
Preferably, the sliding mode surface of the PID form is:
wherein s (k) is a sliding mode surface with discrete k time, k p Is the proportional coefficient of PID control, k i Is the integral coefficient of PID control, k d Is the differential constant coefficient of the PID control,
is the sum of the velocity errors at the previous k-1 time.
Preferably, the improving the sliding mode approach law in the sliding mode surface of the PID form obtains a novel sliding mode approach law, which specifically comprises the following steps:
selecting an exponential approximation law for discrete sliding mode control to obtain a discrete form of the PID form sliding mode surface, wherein the discrete form is as follows:
replacing a symbol function of the sliding mode approach law in the discrete form by a power function in the active disturbance rejection control to obtain a novel sliding mode approach law; the novel sliding mode approach law is as follows:
in the formula, s (k) is a sliding mode surface with discrete k moment, q is an exponential approach parameter, T is sampling time,
constant approaching switching surface velocity for system motion point, sgn is a sign function, ++>
Is a novel sliding mode approach law, fal is a power function, and>
to determine the nonlinear parameters of the tracking performance of the system, < +.>
To determine the nonlinear parameter of the width of the nonlinear interval of the power function, s (k-1) is the sliding mode surface with discrete k-1 moments.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the self-adaptive sliding mode control method for the high-speed train, provided by the invention, the characteristics of nonlinearity, parameter time variation and the like of a system model of the high-speed train are considered, and a characteristic model of the system model is deduced and established by a nonlinear model of the train; and (3) adopting a recursive least square method to identify time-varying parameter description of the feature model on line, then fully utilizing feature modeling on the basis of the feature model to reduce the complexity of the model and meet the capability of control performance requirements, designing a self-adaptive sliding mode controller based on the feature model, reducing the influence of buffeting on a system, and completing asymptotic tracking of the buffeting on a given running curve so as to realize high-precision tracking control of a high-speed train on the given running curve.
Corresponding to the self-adaptive sliding mode control method of the high-speed train, the invention also provides the following implementation structure:
one of them is a high-speed train self-adaptive sliding mode control system, which comprises:
the feature model construction module is used for describing a high-speed train feature model based on nonlinear characteristics of the train system;
the characteristic parameter identification module is used for identifying characteristic parameters of the high-speed train characteristic model on line by adopting a recursive least square method;
the error feature model construction module is used for obtaining an error feature model based on the feature parameters and the train output feature model;
the sliding die surface setting module is used for setting a sliding die surface in a PID form based on the error characteristic model;
the approach law improving module is used for improving the slip form approach law in the slip form surface of the PID form to obtain a novel slip form approach law;
and the controller construction module is used for constructing an adaptive sliding mode controller based on the novel sliding mode approach law and realizing the adaptive sliding mode control of the high-speed train.
Another is an electronic device, comprising:
a memory for storing logic control instructions;
and the processor is connected with the memory and used for calling and implementing the logic control instruction so as to execute the self-adaptive sliding mode control method of the high-speed train.
Preferably, the memory is a computer readable storage medium.
The technical effects achieved by the system and the electronic equipment provided by the invention are the same as those achieved by the self-adaptive sliding mode control method of the high-speed train, so that the detailed description is omitted.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a high-speed train adaptive slip-form control method provided by the invention;
FIG. 2 is a stress analysis chart of a CRH380A high-speed train simple substance point model provided by the embodiment of the invention;
FIG. 3 is a block diagram of a high-speed train adaptive sliding mode control structure based on a feature model provided by an embodiment of the invention;
FIG. 4 is a graph of speed tracking of a high speed train provided by an embodiment of the present invention; wherein, the solid curve is the target speed curve, and the dotted curve is the speed curve under the control of the invention;
FIG. 5 is a graph of displacement tracking for a high speed train provided by an embodiment of the present invention; wherein, the solid curve is the target speed curve, and the dotted curve is the speed curve under the control of the invention;
FIG. 6 is a graph of velocity tracking error during operation of a high speed train provided by an embodiment of the present invention;
FIG. 7 is a graph of acceleration tracking for a high speed train provided by an embodiment of the present invention; wherein, the solid curve is the target speed curve, and the dotted curve is the speed curve under the control of the invention;
FIG. 8 is a graph of characteristic parameter identification during the operation of a high speed train according to an embodiment of the present invention; wherein part (a) of FIG. 8 is the characteristic parameter f 1 Is a graph of the identification of (a); part (b) of FIG. 8 is the characteristic parameter f 2 Is a graph of the identification of (a); part (c) of FIG. 8 is the characteristic parameter g 0 Is a graph of the identification of (a).
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Aiming at the modeling and control problems of the high-speed train in the running process, the invention provides a self-adaptive sliding mode control method, a self-adaptive sliding mode control system and electronic equipment for the high-speed train, which can realize high-precision tracking control of the high-speed train on a given running curve.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the self-adaptive sliding mode control method for the high-speed train provided by the invention comprises the following steps:
step 100: and describing a high-speed train characteristic model based on the nonlinear characteristics of the train system. In the invention, dynamics analysis is carried out on the running process of the high-speed train, the characteristics of nonlinearity, parameter time variation and the like of a system model of the high-speed train are considered, a characteristic model of the high-speed train is built by deducing the nonlinear model of the train, specifically, the train is regarded as a rigid particle, the stress condition of the train in the running process is analyzed, and a single particle model of the train is built based on Newton's second law. For example, a CRH380A type high-speed train is taken as a study object, and a stress analysis schematic diagram of the high-speed train is shown in fig. 2. According to the overall dynamics process analysis of the train, as the running speed of the train is improved and the environment is changed, the nonlinear characteristic of the system is considered, and a longitudinal dynamics model of the train is constructed as follows:
wherein m is the mass of the train, v is the running speed of the train, u is the traction or braking force of the high-speed train, F g As basic resistance, F a For additional resistance, a, b and c are all resistance coefficients, t is time, and g is gravitational acceleration.
The high-speed train running environment is complex and changeable, the working condition changes frequently, and the train is a large, complex and uncertain system. In practical situations, the more the nonlinear characteristics of the train system are apparent as the speed of operation of the high speed train is increased. Discretizing a continuous mathematical model of the running process of the high-speed train represented by the formula (3), and describing a characteristic model of the high-speed train by adopting a second-order time-varying differential equation by selecting a proper sampling period, wherein 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)；
wherein v (k) is the running speed of the high-speed train at the moment k, v (k+1) is the running speed of the high-speed train at the moment k+1, v (k-1) is the running speed of the high-speed train at the moment k-1, u (k) is the traction force or braking force input of the high-speed train at the moment k, and f 1 (k)、f 2 (k) And g 0 (k) And the characteristic parameters to be identified are the characteristic model of the high-speed train at the moment k.
Step 101: and identifying the characteristic parameters to be identified of the high-speed train characteristic model on line based on a recursive least square method. In the process of establishing the feature model of the high-speed train in the step 100, the accuracy is mainly determined by the feature parameters. In order to achieve the purposes of good parameter identification convergence effect and real-time parameter change tracking, a recursive least square method is adopted to identify characteristic parameters online in real time so as to facilitate the design of a controller. The high-speed train operation process feature model represented by the formula (4) is described as a parameter estimation equation:
in the method, in the process of the invention,
，/>
for system input/output vector, +.>
、/>
And->
Control inputs for the system and the speed of the train at the times k-1 and k-2, respectively>
，/>
The time-varying coefficient vector is a high-speed train characteristic model, and contains related information of complex high-order in train dynamics. The recursive least squares algorithm of the formula (6) is adopted for the formula (5):
where K (K) is the gain matrix at time K, P (K) is the covariance matrix at time K, P (K-1) is the covariance matrix at time K-1,
，/>
、/>
respectively, the estimated values. />
The initial value is usually zero, I is a unit vector, mu is a forgetting factor, and the initial value is usually a positive number which is not less than 0.9 and is close to 1.
Step 102: and obtaining an error characteristic model based on the characteristic parameters and the train output characteristic model. Specifically, according to the feature parameters in the high-speed train feature model obtained by the online identification method in step 101, substituting the obtained parameter estimation values into the train output feature model, and designing an error feature model in the following form:
e(k+1)=f 1 (k)e(k)+f 2 (k)e(k-1)-g 0 (k)u(k)+η(k) (7)
wherein: wherein e (k)) For the speed tracking error of the high-speed train at the k moment, e (k+1) is the speed tracking error at the k+1 moment, e (k-1) is the speed tracking error at the k-1 moment, η (k) =v d (k+1)-f 1 (k)v d (k)-f 2 (k)v d (k-1), η (k) is an intermediate parameter of the simplified error model, v d (k) For a given speed of the high speed train at time k, v d (k-1) is a given speed, v, of the high speed train at time k-1 d (k+1) is a given speed of the high speed train at time k+ 1.
In the invention, the train output model refers to a train characteristic model form after identification, and the specific reference can be made to 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.
Step 103: and setting a sliding mode surface in the form of PID based on the error characteristic model. The slip form surface for the PID form is as follows:
wherein s (k) is a sliding mode surface with discrete k time, k p Is the proportional coefficient of PID control, k i Is the integral coefficient of PID control, k d Is the differential constant coefficient of the PID control,
is the sum of the velocity errors at the previous k-1 time. The slip plane at time k+1 is:
in the method, in the process of the invention,
is the sum of the velocity errors at the first k times.
Step 104: and improving the sliding mode approach law in the sliding mode surface of the PID form to obtain a novel sliding mode approach law. For discrete sliding mode control, an exponential approach law in a traditional approach law is selected, wherein the discrete form is as follows:
where q is an exponential approach parameter, T is a sampling time, epsilon is a constant for the system motion point approach switching surface rate, sgn is a sign function.
In order to better realize weakening of sliding mode buffeting, the invention replaces a symbol function influencing buffeting with a power function fal in active disturbance rejection control, and well suppresses the sliding mode buffeting by improving a sliding mode approach law so as to achieve the aim of reducing the influence of buffeting on a system, further better realize a control target and finish high-precision tracking of a given target curve.
The new approach law expression is described as:
wherein S is 1 (k+1) is a novel sliding mode approach law, fal is a power function, alpha is a nonlinear parameter for determining the tracking performance of the system, and delta is a nonlinear parameter for determining the nonlinear interval width of the power function.
The power function is in the form of:
s (k) is a discrete sliding mode surface, where s and s (k) are both discrete sliding mode surfaces, α is a nonlinear parameter that determines the tracking performance of the system, and δ is a nonlinear parameter that determines the width of the nonlinear interval of the power function.
Substituting the sliding mode surface expression (9) at the moment k+1 into a novel approach law expression (11), and combining an error characteristic mode (7) rewritten by an output characteristic mode of a train operation process to obtain the following expression:
when the slip form surface meets S 1 (k+1) =0, and the equivalent control expression (13) for deriving the discrete sliding mode is described as: substituting the sliding mode surface expression (9) at the moment k+1 into a novel approach law expression (11), and combining an error characteristic mode pattern (7) rewritten by an output characteristic mode pattern of a train operation process to obtain an expression (12) which is described as follows:
At the same time redesign the switching control law
The method comprises the following steps: />
The expression of the total control amount u (k) of the sliding mode control is:
in the method, in the process of the invention,
for the characteristic parameter g of the k moment 0 Is used for the estimation of the estimated value of (a).
Order the
Due to characteristic parameters->
The sign of (2) can be positive or negative, the control quantity is greatly influenced, and the control effect is further influenced>
Become->
The appropriate parameter p is chosen to ensure that it is positive, i.e. the following conditions apply:
step 105: and constructing a self-adaptive sliding mode controller based on a novel sliding mode approach law, and realizing self-adaptive sliding mode control of the high-speed train.
Based on the above description, the invention provides a high-speed train self-adaptive control method based on a characteristic model to realize high-precision tracking control on a given speed curve, and the control principle is shown in fig. 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, an adaptive control method based on the characteristic model is acted on the train characteristic model, and finally, an adaptive sliding mode controller is constructed from an improved sliding mode approach law angle, so that the tracking performance of the train is realized in the running process, and the control precision of the system is improved.
The design process shows that the invention considers the characteristics of nonlinearity, parameter time-varying and the like of the high-speed train system model, and can theoretically realize the asymptotic tracking of the high-speed train to a given curve by combining the design of sliding mode control by utilizing an intelligent self-adaptive control method based on the high-speed train characteristic model. The intelligent self-adaptive control method based on the high-speed train feature model can be seen in the literature: wu Hongxin, hu Jun, solving Yongchun. Intelligent self-adaptive control based on feature model [ M ]. Chinese science and technology Press, 2009.
Based on the description, the invention builds the characteristic model of the train system from the nonlinear model of the high-speed train based on the characteristic modeling theory according to the dynamic analysis of the running process of the high-speed train. The train characteristic model considers the surrounding environment characteristics of the train and the performance requirements of the controller, improves the conditions of uncertain model parameters, nonlinear running resistance and the like in the conventional modeling process of the train, reduces the complexity of the model and is beneficial to the design of the controller.
According to the invention, in control, an intelligent self-adaptive control method based on a characteristic model is applied to a train characteristic model, and a discrete self-adaptive sliding mode controller based on a PID sliding mode surface is designed by combining sliding mode control, and buffeting is reduced by improving a sliding mode approach law, so that high-precision tracking of a given target curve can be realized, long-time sliding mode buffeting can be avoided, and good dynamic performance of the system is ensured.
The following provides a specific embodiment for specifically explaining the self-adaptive sliding mode control method for the high-speed train.
The embodiment is based on the establishment of the high-speed train feature model and the theoretical analysis of the intelligent self-adaptive control method based on the high-speed train feature model, and the accuracy of the model and the accuracy of tracking control are verified by utilizing MATLAB software simulation.
According to the train running section and ATP speed limiting characteristics, selecting actual running data of CRH380A high-speed train on the east section of Jinan-Xuzhou as modeling original data (speed v unit is km/h), wherein the modeling original data comprise working conditions such as traction, inertia, braking and the like in the running process of the train, and carrying out parameter estimation on characteristic parameters of a train characteristic model by adopting a formula (6) recursive least square method so as to obtain the model
As system input, get +.>
And the estimated value of the time-varying coefficient is the characteristic model of the high-speed train. According to the selection of sampling time in the theory of the high-speed train characteristic model, the value range of the time-varying parameters of the high-speed train characteristic model can be determined in a bounded convex closure set D s And (3) inner part.
Substituting the estimated value of each characteristic parameter into the train output characteristic model according to the identified characteristic parameter, designing an error characteristic model, selecting a PID sliding mode surface, replacing a symbol function in the exponential approach law by a power function fal, and improving to obtain the novel sliding mode approach law. Through continuous debugging, proper system parameters are selected for simulation, and after debugging, undetermined parameters based on the PID sliding mode surface can be selected as follows: k (k) p =0.039、k i =0.15、k d =0.01, the design parameters in the new approach law are selected as: q=100, t=0.01, epsilon=0.001, alpha=0.2, delta=0.1, p=0.5. The intelligent self-adaptive control method based on the characteristic parameters of the high-speed train is applied to the characteristic model of the high-speed train, a discrete self-adaptive sliding mode controller based on a PID sliding mode surface is used for tracking the actual running curve of the high-speed train, and simulation results are obtained, and are a train speed tracking curve, a train displacement tracking curve and a speed tracking error curve respectively as shown in fig. 4-6.
As shown in fig. 4 and 5, the speed and displacement tracking curves can track the target speed curve basically and tightly, and in the partial enlarged view, the speed and displacement curves obtained by the method provided by the embodiment keep a certain degree of coincidence with the given speed and displacement curves, so that the high-precision tracking of the target speed and displacement is realized. As shown in fig. 6, the root mean square error of the speed tracking obtained by the control method according to the embodiment is 0.0814km/h, and the maximum errors of the speed tracking errors during the starting and braking of the high-speed train are 1.3191km/h and 1.8253km/h respectively, and still meet the allowable error range of the train operation, and the speed error of the train is reduced and the error curve range is reduced through short-time parameter adjustment control: the simulation results prove that the control method provided by the embodiment has good tracking effect under traction, inertia and braking working conditions in the running process of the train.
In order to verify that the requirement of comfortable running of the train can be met under the control method of the embodiment. As shown in FIG. 7, the acceleration under the control method of the present embodiment tracks the target acceleration with high accuracy, and satisfies the accuracy of less than 1m +.s 2 Is a comfortable condition of the person. The high-precision tracking of the acceleration influences the safety operation of the high-speed train and the comfort of passengers, and the comfort of the passengers is guaranteed to a certain extent.
Identifying characteristic parameters by adopting recursive least square method, and three characteristic parameters f in the running process of high-speed train 1 、f 2 And g 0 As shown in part (a) of fig. 8 to part (c) of fig. 8, the characteristic parameter change curve is smoother due to less influence of system interference, the parameter change curve changes smoothly after 1000s, and the characteristic parameter f is calculated by the method 1 (k) And f 2 (k) Has less influence on g 0 (k) The influence is larger.
The simulation result shows that the speed and displacement tracking curve obtained by the self-adaptive sliding mode control method of the high-speed train can achieve tracking with higher precision, and meets the running requirements of safety, punctual and comfort of the train. The designed self-adaptive sliding mode control method based on the characteristic model can also achieve expected performance, verifies the effectiveness of the self-adaptive sliding mode control method of the high-speed train, and has good tracking effect and stronger robustness.
In addition, the invention also provides the following implementation structure corresponding to the self-adaptive sliding mode control method of the high-speed train:
one of them is a high-speed train self-adaptive sliding mode control system, which comprises:
and the characteristic model construction module is used for describing a high-speed train characteristic model based on the nonlinear characteristics of the train system.
And the characteristic parameter identification module is used for identifying the characteristic parameters of the high-speed train characteristic model on line by adopting a recursive least square method.
And the error feature model construction module is used for obtaining an error feature model based on the feature parameters and the train output feature model.
And the sliding mode surface setting module is used for setting the sliding mode surface in the PID form based on the error characteristic model.
And the approach law improving module is used for improving the slip form approach law in the slip form surface of the PID form to obtain a novel slip form approach law.
The controller construction module is used for constructing a self-adaptive sliding mode controller based on a novel sliding mode approach law and realizing self-adaptive sliding mode control of the high-speed train.
Another is an electronic device, comprising:
and the memory is used for storing logic control instructions. The memory employed in the present invention is a computer-readable storage medium.
And the processor is connected with the memory and used for retrieving and implementing logic control instructions so as to execute the self-adaptive sliding mode control method of the high-speed train.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. The self-adaptive sliding mode control method for the high-speed train is characterized by comprising the following steps of:
describing a high-speed train characteristic model based on nonlinear characteristics of a train system;
characteristic parameters of a high-speed train characteristic model are identified on line by adopting a recursive least square method;
obtaining an error characteristic model based on the characteristic parameters and the train output characteristic model;
setting a sliding mode surface in a PID form based on the error feature model;
improving the sliding mode approach law in the sliding mode surface of the PID form to obtain a novel sliding mode approach law;
and constructing an adaptive sliding mode controller based on the novel sliding mode approach law, and realizing the adaptive sliding mode control of the high-speed train.
2. The high-speed train adaptive slip-form control method according to claim 1, wherein the high-speed train feature model is:
v(k+1)=f 1 (k)v(k)+f 2 (k)v(k-1)+g 0 (k)u(k)；
wherein v (k) is the running speed of the high-speed train at the moment k, v (k+1) is the running speed of the high-speed train at the moment k+1, v (k-1) is the running speed of the high-speed train at the moment k-1, u (k) is the traction force or braking force input of the high-speed train at the moment k, and f 1 (k)、f 2 (k) And g 0 (k) And the characteristic parameters to be identified are the characteristic model of the high-speed train at the moment k.
3. The method for controlling the self-adaptive sliding mode of the high-speed train according to claim 2, wherein the error feature 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)；
where 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 parameter of the simplified error model, v d (k) For a given speed of the high speed train at time k, v d (k-1) is a given speed, v, of the high speed train at time k-1 d (k+1) is a given speed of the high speed train at time k+1.
4. The adaptive sliding mode control method for high-speed trains according to claim 3, wherein the sliding mode surface in the form of PID is:
wherein s (k) is a sliding mode surface with discrete k time, k p Is the proportional coefficient of PID control, k i Is the integral coefficient of PID control, k d Is the differential constant coefficient of the PID control,
is the sum of the velocity errors at the previous k-1 time.
5. The method for adaptively controlling a sliding mode of a high-speed train according to claim 4, wherein the step of improving the sliding mode approach law in the sliding mode surface of the PID form to obtain a new sliding mode approach law comprises the following steps:
selecting an exponential approximation law for discrete sliding mode control to obtain a discrete form of the PID form sliding mode surface, wherein the discrete form is as follows:
replacing a symbol function of the sliding mode approach law in the discrete form by a power function in the active disturbance rejection control to obtain a novel sliding mode approach law; the novel sliding mode approach law is as follows:
in the formula, s (k) is a sliding mode surface with discrete k moment, q is an exponential approach parameter, T is sampling time,
constant approaching switching surface velocity for system motion point, sgn is a sign function, ++>
Is a novel sliding mode approach law, fal is a power function, and>
to blockNonlinear parameters of the tracking performance of the system, +.>
To determine the nonlinear parameter of the width of the nonlinear interval of the power function, s (k-1) is the sliding mode surface with discrete k-1 moments. />
6. A high speed train adaptive slip form control system, comprising:
the feature model construction module is used for describing a high-speed train feature model based on nonlinear characteristics of the train system;
the characteristic parameter identification module is used for identifying characteristic parameters of the high-speed train characteristic model on line by adopting a recursive least square method;
the error feature model construction module is used for obtaining an error feature model based on the feature parameters and the train output feature model;
the sliding die surface setting module is used for setting a sliding die surface in a PID form based on the error characteristic model;
the approach law improving module is used for improving the slip form approach law in the slip form surface of the PID form to obtain a novel slip form approach law;
and the controller construction module is used for constructing an adaptive sliding mode controller based on the novel sliding mode approach law and realizing the adaptive sliding mode control of the high-speed train.
7. An electronic device, comprising:
a memory for storing logic control instructions;
the processor is connected with the memory and used for calling and implementing the logic control instruction so as to execute the high-speed train self-adaptive sliding mode control method according to any one of claims 1-5.
8. The electronic device of claim 7, wherein the memory is a computer-readable storage medium.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310101189.2A CN116027669A (en) | 2023-02-13 | 2023-02-13 | Self-adaptive sliding mode control method and system for high-speed train and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310101189.2A CN116027669A (en) | 2023-02-13 | 2023-02-13 | Self-adaptive sliding mode control method and system for high-speed train and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116027669A true CN116027669A (en) | 2023-04-28 |
Family
ID=86091067
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310101189.2A Pending CN116027669A (en) | 2023-02-13 | 2023-02-13 | Self-adaptive sliding mode control method and system for high-speed train and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116027669A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117850215A (en) * | 2024-03-08 | 2024-04-09 | 华东交通大学 | Model-free self-adaptive sliding mode control method and system for high-speed motor train unit and electronic equipment |
| CN117930666A (en) * | 2024-03-25 | 2024-04-26 | 华东交通大学 | Motor train unit control method, device and medium based on rapid power approach law |
-
2023
- 2023-02-13 CN CN202310101189.2A patent/CN116027669A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117850215A (en) * | 2024-03-08 | 2024-04-09 | 华东交通大学 | Model-free self-adaptive sliding mode control method and system for high-speed motor train unit and electronic equipment |
| CN117850215B (en) * | 2024-03-08 | 2024-05-17 | 华东交通大学 | Model-free self-adaptive sliding mode control method and system for high-speed motor train unit and electronic equipment |
| CN117930666A (en) * | 2024-03-25 | 2024-04-26 | 华东交通大学 | Motor train unit control method, device and medium based on rapid power approach law |
| CN117930666B (en) * | 2024-03-25 | 2024-05-24 | 华东交通大学 | Motor train unit control method, device and medium based on rapid power approach law |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116027669A (en) | Self-adaptive sliding mode control method and system for high-speed train and electronic equipment | |
| CN110806759B (en) | Aircraft route tracking method based on deep reinforcement learning | |
| CN103381826B (en) | Based on the self-adapting cruise control method of approximate Policy iteration | |
| Wang et al. | Self-learning cruise control using kernel-based least squares policy iteration | |
| CN111158349B (en) | Unmanned vehicle model prediction control method based on multi-step linearization strategy | |
| CN110615003B (en) | Cruise control system based on strategy gradient online learning algorithm and design method | |
| Yang et al. | Speed tracking control using an ANFIS model for high-speed electric multiple unit | |
| CN109204390B (en) | Train control method based on deep learning | |
| CN112389436A (en) | Safety automatic driving track-changing planning method based on improved LSTM neural network | |
| CN112486024A (en) | High-speed train self-adaptive control method and system based on multi-quality-point model | |
| CN112666833A (en) | Vehicle speed following self-adaptive robust control method for electric automatic driving vehicle | |
| CN113911172A (en) | High-speed train optimal operation control method based on self-adaptive dynamic planning | |
| CN114509949A (en) | Control method for presetting performance of robot | |
| CN107272419A (en) | A kind of driver's adaptive direction control method based on improvement PSO | |
| CN113253605A (en) | Active disturbance rejection unmanned transverse control method based on DDPG parameter optimization | |
| CN116560223A (en) | Intelligent vehicle dynamics model, ILQR control algorithm and track tracking controller based on physical information neural network | |
| Su et al. | Finite-time event-triggered consensus control for high-speed train with gradient resistance | |
| CN107450311A (en) | Inversion model modeling method and device and adaptive inverse control and device | |
| Guo et al. | Adaptive fuzzy sliding mode control for high‐speed train using multi‐body dynamics model | |
| CN114415507A (en) | Method for building and training dexterous hand grasping process dynamic model based on deep neural network | |
| CN112462608A (en) | Discrete sliding mode track and speed tracking control method for high-speed train | |
| CN117048667A (en) | Heavy-duty train control method and system based on vehicle dynamic response identification | |
| CN116859731A (en) | Method for enhancing punctuality of high-speed rail automatic driving control system based on reinforcement learning | |
| CN116373846A (en) | Back wheel steering vehicle stability control method based on BP neural network optimization | |
| CN113219840B (en) | Self-adaptive sliding mode cross-coupling contour control method for three-axis motion platform |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |