CN102981408B - Running process modeling and adaptive control method for motor train unit - Google Patents
Running process modeling and adaptive control method for motor train unit Download PDFInfo
- Publication number
- CN102981408B CN102981408B CN201210524440.8A CN201210524440A CN102981408B CN 102981408 B CN102981408 B CN 102981408B CN 201210524440 A CN201210524440 A CN 201210524440A CN 102981408 B CN102981408 B CN 102981408B
- Authority
- CN
- China
- Prior art keywords
- train unit
- motor train
- model
- parameter
- bilinear
- 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.)
- Active
Links
Landscapes
- Feedback Control In General (AREA)
Abstract
The invention discloses a running process modeling and adaptive control method for a motor train unit. The running process modeling and adaptive control method comprises the following steps: according to characteristics that the running process of the motor train unit is complex, information is incomplete and nonlinearity is obvious, putting forward a T-S bilinear model identification method by a data-driven modeling method; according to constraints such as an actual running chart, a front route condition, a limited speed condition, and traction/ braking force saturation nonlinearity of the motor train unit, establishing a constraint model of the motor train unit; and according to the model design, studying a model prediction control algorithm immediately to improve performance indexes for multi-objective optimization control. By the running process modeling and adaptive control method, a set of reliable basis is provided for optimizing operation of trainmen of the motor train unit, ensuring safe and punctual running of the motor train unit, improving the running comfort and lowering energy consumption; and the running process modeling and adaptive control method is applicable to on-line identification and the multi-objective optimization control on the running state of the motor train unit.
Description
Technical field
The present invention relates to the modeling of a kind of motor train unit operational process and self-adaptation control method, belong to motor train unit running status on-line identification and optimized handling technical field.
Background technology
China New Generation bullet train, scooter 350 kilometers when it continues to run, test speed per hour, more than 400 kilometers, is the motor train unit that commercial operation is fastest, scientific and technological content is the highest, system matches is optimum in the world.Relatively other means of transportation (as automobile, aircraft, middle low speed passenger and freight train), motor train unit can meet the transportation demands such as long distance, large conveying quantity, high density, hourage be short.China has had the High-speed Railway Network of whole world maximum-norm and the highest overall trip speed at present, to the year two thousand twenty, will build up the high-speed railway of 16000 kilometers.But China Railway High-speed netting gear is had any different in Europe and some key characters of Japan High-speed Railway, as road network scale greatly, energy resource consumption obviously increases; Geography, geology, weather conditions are complicated and changeable; Friction speed grade High-Speed Passenger Railway condition difference is obvious.Research has the intelligent motor train unit control system of Autonomous test, self diagnosis, self-decision ability, realizes safe and reliable operation and becomes technology trends with optimized handling.
For motor train unit operational process modeling and control problem, relevant scholar establishes linearization mechanism model for motor train unit dynamic perfromance and devises
constant speed controller; Propose many particles unit and move mechanism model to describe the dynamic process of motor train unit, and adopt fuzzy controller to realize the tracing control of speed and displacement; Establish motor train unit traction and damped condition non-linear constrain model devise adaptive backstepping control device; Have studied the energy consumption model under motor train unit different target speed.But these modelling by mechanism methods are difficult to the nonlinear problem solving motor train unit aerodynamics and the change of handle level, thus system comprehensive evaluation index may be caused to reduce.
Under high-speed and high-density operation condition, manual operation is difficult to meet multiple-objection optimization requirement, and research smart steering optimized algorithm is for raising motor train unit runnability, significant.If relevant scholar is for motor train unit working time and energy resource consumption optimization problem, the fuzzy control model based on Fuzzy C-Means Clustering analysis and genetic algorithm optimization is proposed; Propose to optimize motor train unit high-speed and high-density runnability based on the intelligent train control system of fuzzy logic control; Adopt models switching and optimisation strategy to study electric EMU least energy consumption and handle problem; Also the co-design problem having scholar to consider the lower motor train unit economy of uncertain factor impact to handle and run on schedule, employing genetic algorithm and fuzzy linear programming method are optimized respectively.But these methods are mostly based on offline optimization, the requirement of motor train unit real-time optimization can not be met preferably.As a kind of dynamic optimization method, Nonlinear Model Predictive Control algorithm can solve slow time-varying nonlinear optimal problem preferably.But because nonlinear optimization procedure on-line calculation is large, be directly applied in during motor train unit operation controls and not there is obvious advantage.
Summary of the invention
The object of the invention is, there is for the modeling of motor train unit operational process the nonlinear problem that modeling method is difficult to solve aerodynamics and the change of handle level, thus the problem that system comprehensive evaluation index reduces may be caused; In addition, the intelligent train control system based on fuzzy logic control optimizes motor train unit high-speed and high-density runnability, can't meet the requirement of motor train unit real-time optimization.For these problems, the present invention discloses the modeling of a kind of motor train unit operational process and self-adaptation control method, sets up T-S bilinear model and multi-objective restriction model; Adopt and carry out dynamic calibration model parameter based on the locally fine point strategy of instant learning, design bilinearity adaptive model predictive controller accordingly and carry out rolling optimization and closed-loop control, realize motor train unit safety and steady, energy-conservation comfortable, the multiobjective optimal control such as on schedule.
Realizing technical scheme of the present invention is, the present invention sets up T-S bilinearity fuzzy model in conjunction with the feature of motor train unit kinetics equation and maneuvering and control, and adopts the self-adaptative adjustment of Lazy learning method implementation model parameter; Propose T-S bilinear model discrimination method.The present invention is according to motor train unit actual motion figure, forward box situation, speed limit condition, the constraint conditions such as tractive force/damping force saturation nonlinearity characteristic set up motor train unit multi-objective restriction model, design bilinear model predictive controller accordingly and realize motor train unit optimizing operation.And improve motor train unit multi-objective constrained optimization Control performance standard according to above-mentioned modelling instant learning Model Predictive Control Algorithm.Namely when model output error
time in system allowed band, T-S bilinear model parameter is without the need to optimizing; When model output error exceedes threshold value, Lazy learning method is adopted to carry out on-line correction to model parameter.The parameter of dynamic conditioning bilinear model predictive controller accordingly, optimizes while implementation model parameter and controller parameter, decreases bilinear model predictive controller on-line calculation.Whole identification process both can reduce the impact of T-S bilinear model unmodel parts and unknown failure or interference, can reduce Lazy learning method calculated amount again, and the multi-objective constrained optimization improving system handles level.
The modeling of motor train unit operational process and self-adaptation control method step are:
(1) run from motor train unit the ultimate principle controlled, based on the feature of maneuvering and control and kinetics equation, set up the bilinear model describing motor train unit dynamic perfromance, energy ezpenditure and motion time; Adopt the FCM fuzzy clustering algorithm based on genetic simulated annealing to carry out cluster analysis to the sample data gathered, obtain the economic control point of operational process, for steward provides priori operational optimization information; Determine each fuzzy rule former piece parameter according to the model structure determined, by each fuzzy rule consequent parameter of recursive weighted least squares estimation identification, accurate description is carried out to the dynamic perfromance of each fuzzy rule in local; When model output error exceedes the threshold value preset, adopt local regression method based on instant learning to model parameter on-line correction.The On-line Estimation of motor train unit operational process multi-mode model can be realized like this.
(2) by setting up motor train unit multi-objective constrained optimization model, on the basis of the T-S bilinear model of above-mentioned foundation, in conjunction with Nonlinear Model Predictive Control, changed by forecast model, design T-S bilinearity adaptive model predictive controller studies motor train unit optimized handling problem.
Motor train unit maneuvering and control mainly comprises traction, coasting and braking three kinds of operating conditions, relates to startup, accelerates, constant speed, the various control such as coasting and braking pattern.Wherein there is multiple handle level respectively under traction and damped condition, as shown in Figure 3 the handle handled of high ferro steward.All car controlling instructions are all send from handle level, and different handle controls level and determines different control models.Curve of traction characteristics under the different handle level of motor train unit, braking characteristic curve are respectively as shown in Fig. 4 (a) He Fig. 4 (b).
In the present invention, motor train unit mechanism model is determined by following principle and method:
As can be seen from Figure 4, the tractive force/damping force needed for motor train unit operational process
with travelling speed
with handle level
between be multivariable nonlinearity relation:
（1）
In addition, because different handle controls level determine different control models, and
with
closely related, its Nonlinear Dynamic can present control variable
with state variable
the phenomenon be multiplied, can be described as a bilinear system by the equivalence of motor train unit operational process.
For the bilinear system of motor train unit operational process, consider that the relative displacement between each power unit is approximately zero usually, each vehicle speed approximately equal, electric phase separation point, ramp and curvature etc. are the functions of distance, are that independent variable is comparatively suitable, then with distance
motor train unit dynamic perfromance, energy ezpenditure and the motion time of marshalling can describe with following bilinear model:
（2）
（3）
（4）
In formula:
for motor train unit gross mass of equal value;
be the range ability of motor train unit, system input is the control acted under the different handle levels in motor train unit
(tractive force/damping force); It is speed that system exports
; Symbol
kronecker operator, makes
with
meet multiplication relationship; G is line parameter circuit value (electric phase separation point, the gradient and curvature);
with
represent the position of starting point and terminal respectively;
for motion time;
for the energy ezpenditure in motor train unit operational process;
for mechanical resistance coefficient, size generally exists
left and right;
for coefficient of air resistance, size generally exists
left and right, when
time, Nonlinear Space atmidometer item
in formula (2), proportion is less.
In order to simplify the design of train travelling process modeling and control device, many engineer applied and researcher ignore its impact.As when not considering Nonlinear Space atmidometer, correlative study person devises fuzzy gain controller to regulate subway train running speed; Adaptive congestion control algorithm algorithm is adopted to solve subway train operational management and energy saving optimizing problem; Speeds control for middle low-speed heave-load goods train kinetic model proposes open loop heuristic optimization strategy and the closed loop LQR control algolithm based on heuritic approach respectively.But work as
time, Nonlinear Space atmidometer item
in formula (2), proportion is increasing, become the main resistance overcome needed in motor train unit operational process, its energy ezpenditure is also increasing, is difficult to meet motor train unit operational process high precision tracking controls and multiple-objection optimization requirement based on the linear modelling of common middle low speed train and control method.
T-S bilinear model self study predictive control algorithm is adopted to study motor train unit operational process in the present invention:
For each fuzzy rule, adopt product inference machine, the average ambiguity solution of monodrome fuzzy device and center, T-S fuzzy bilinear models exports and is:
（5）
In formula:
represent for system input/output variable, the
the fuzzy membership of rule.
(1) T-S bilinear model Structure Identification
How T-S bilinear model structure is optimized, has material impact to Model Distinguish speed and precision.Conventional Structure Identification method has FCM clustering algorithm etc., but the Local Search of FCM, and the susceptibility to cluster centre initial value, limit its application.The present invention adopts and carries out identification based on the FCM clustering method of Global Genetic Simulated Annealing Algorithm to motor train unit model structure.This algorithm inherits the stronger parallel and ability of searching optimum of genetic algorithm, and adopt the Metropolis acceptance criterion of simulated annealing to keep the diversity of population, improve local search ability, overcome the precocious phenomenon of genetic algorithm and the low defect of simulated annealing speed of convergence.
Above-mentioned clustering algorithm can be specified
global optimum's cluster centre in individual classification.But motor train unit operating condition is complicated and changeable, be difficult to prior certainty annuity working point.The quality of different classes of lower clustering algorithm performance can be weighed with Validity Index.Davies-Bouldin (DB) index is the Cluster Validity evaluation index of class classics, adopts the quality of separation property evaluation cluster result between compactness and class in class.
(2) motor train unit T-S For Identification of Bilinear Model Parameters
Based on identification of Model Parameters principle, formula (5) can be exchanged into following form:
（6）
In formula:
for observation vector, and
meet:
；
for parameter to be identified.This is a typical least-squares estimation problem, and available following formula tries to achieve parameter
:
（7）
But its objective function is global optimization, can not accurate description local each fuzzy rule dynamic perfromance.
The present invention adopts recursive weighted least squares estimation method carry out iteration identification model parameter and avoid matrix inversion.
(3) based on the model parameter on-line correction of instant learning
In order to improve the efficiency of Lazy learning method, the present invention only enables Lazy learning method and carries out correction and renewal learning collection when model output error exceedes threshold value.How setting up study collection is the principal element affecting model accuracy, and for improving online modeling accuracy, motor train unit bilinearity dynamic change trend is considered in the criterion selecting sample by the present invention.
The present invention designs bilinearity adaptive model predictive controller and carries out rolling optimization and closed-loop control, and realize motor train unit safety and steady, energy-conservation comfortable, the multiobjective optimal control such as on schedule, main calculation procedure is as follows:
By minimizing objective function, T-S bilinear model predictive control algorithm can describe its dynamic process exactly, provides optimal control sequence, but the bilinear terms in model has non-linear, and the on-line calculation of multi-step prediction is larger.In order to reduce the calculated amount of on-line optimization, formula (5) can be exchanged into:
（8）
In motor train unit operational process, the design of controller should make motor train unit operate steadily, comfortable, energy-conservation, punctual also guarantee is accurately stopped.Then motor train unit operation multi-objective constrained optimization model is:
（9）
In formula:
with
be respectively motion time weight and energy consumption weight,
for target velocity,
for speed tracing error range,
for the motion time of service chart,
for the energy consumption that section operation is expected,
represent maximum braking force,
for controlled quentity controlled variable increment,
represent maximum drawbar pull.
Because the change by controlled quentity controlled variable of energy-saving index and steady comfort level describes, percent of punctuality and accurate parking selection are by realizing the accurate tracking of optimal velocity curve, then objective function can be expressed as:
（10）
In formula:
for following speed reference track,
minimum output length, prediction length and control length respectively,
for weighting coefficient sequence, constraint controlled quentity controlled variable;
for following controlling increment sequence.
Based on rolling optimization mechanism, can optimal control law be obtained:
（11）
Wherein
By what calculate
first value of individual controlling increment puts into practice, realizes rolling optimization.Current
individual sampling location place, controlled quentity controlled variable is expressed as:
（12）
In sum, for the modeling of motor train unit operational process and real-time optimal control problem, according to its curve of traction characteristics, idle running resistance curve, braking mode curve and service data, T-S bilinear model can be set up and effectively describes its multi-mode operation process; Design instant learning model predictive controller improves its multiobjective optimal control performance index.
The present invention's beneficial effect is compared with the prior art, motor train unit is run and is related to the several scenes such as ramp, tunnel, bridge, electric phase-splitting and Changes in weather, operational process is complicated, information is imperfect, nonlinear characteristic is obvious, traditional control method be difficult to set up effective descriptive model and implement safety, on schedule, the multiple-objection optimization manipulation such as energy-conservation, comfortable.First the present invention proposes motor train unit according to set service chart, run regularly in set period and interval, dynamic relationship between control variable and state variable follows the change of curve of traction characteristics, idle running resistance curve and braking mode curve, for the Modeling and optimization control method based on data-driven provides possibility; Then set up T-S bilinearity fuzzy model in conjunction with its kinetics equation and maneuvering and control feature, and adopt the self-adaptative adjustment of Lazy learning method implementation model parameter; According to motor train unit actual motion figure, forward box situation, speed limit condition, the constraint conditions such as tractive force/damping force saturation nonlinearity characteristic set up restricted model, design bilinear model predictive controller accordingly and realize motor train unit optimizing operation, for steward's optimized handling provides prior imformation, thus change the blindness regulated by rule of thumb, be a kind of effective optimized handling supplementary means.The present invention more intuitively, more rapidly, and does not limit by the condition such as place, environment, has simple and practical, improves high ferro steward optimized handling level, reduces human resources and drops into, improve railway interests's efficiency of operation, the advantage reduced costs.
The present invention is applicable to the on-line identification of motor train unit running status and multiobjective optimal control.
Accompanying drawing explanation
Fig. 1 is motor train unit running-course control system construction drawing;
Fig. 2 is that motor train unit runs control ultimate principle;
Fig. 3 is motor train unit main control unit;
Fig. 4 (a) is the curve of traction characteristics under the different handle level of motor train unit;
Fig. 4 (b) is the braking characteristic curve under the different handle level of motor train unit;
Fig. 5 is circuit speed limit figure;
Fig. 6 is motor train unit actual moving process figure;
Fig. 7 is that modeling method of the present invention exports and graph of errors;
The speed tracing that Fig. 8 (a) obtains for control method of the present invention and graph of errors;
The control change curve that Fig. 8 (b) obtains for the inventive method;
The optimization energy consumption that Fig. 8 (c) obtains for the inventive method and working time curve.
Embodiment
The present invention is embodied in the administrative Beijing-Shanghai High-Speed Railway Jinan of certain Railway Bureau-east, Xuzhou downlink interval and carries out, and service data is collection in worksite on motor train unit CRH380AL.Middle through station, Tai'an, eastern station, Qufu, eastern station, Tengzhou and station, Zaozhuang, but only to stop 2 minutes at station, Tai'an.Initial mileage is 393.74km, and station, Tai'an mileage is 465.77km, and terminal mileage is 693.74km.The change of EMU operating condition is complicated, is subject to the constraints such as line slope (ruling grade reaches 20 ‰), working time, speed limit.Whole process has 9 tunnels, and 11 place's electricity phase separation points, 17 place's value of slope are more than 12 ‰.Motion time between each station that table 1 specifies for service chart, Fig. 5 is circuit speed limit figure.
Table 1 section operation timetable
| Interval title | The section operation time-division (time: point: second) |
| Jinan → Tai'an | 09:38:30→10:03:19 |
| Tai'an → Qu Fudong | 10:05:19→10:21:39 |
| Qu Fudong → east, Tengzhou | 10:21:39→10:33:02 |
| East → Zaozhuang, Tengzhou | 10:33:02→10:40:22 |
| Zaozhuang → Xu Zhoudong | 10:40:22→10:56:30 |
Fig. 6 describes on July 13rd, 2012 this model motor train unit actual motion time-division, overall trip speed.Wherein, actual run time be 1 hour 19 points 51 seconds, time less than the service chart stipulated time 1 18 minutes late 1 point 51 seconds; Braking procedure adopts electric empty Associated brake mode, and wherein regenerating electricity can feed back to electrical network, and total energy consumption deducts regenerative braking electricity for drawing power consumption, and its value is 14230kwh.
The embodiment of the present invention is run 2000 groups of data based on the traction/brake curve under the different handle level of CRH380AL type motor train unit to scene and is carried out pre-service, obtains
1800 groups of valid data in scope.Utilize these data of Cluster Validity Algorithm Analysis of the present invention, when operating mode number is 6, DB value is minimum, and namely optimum fuzzy rule number is 6, and corresponding model best operating point is respectively:
start operating performance;
middle low speed coasting operating mode;
invariable power traction working condition;
high speed coasting operating mode;
high speed Speed Braking operating mode;
low speed Brake stop operating mode;
Fig. 7 is the graph of errors exporting based on the T-S bilinear model of instant learning and export with reality.
As can be seen from Figure 7, in motor train unit multi-state operational process, the model of modeling method of the present invention exports the situation of change still following the tracks of actual output preferably, and (root-mean-square error is
).Particularly in different traction handle level, different braking handle level transition period, the maximum positive error that model exports and minimal negative error are only
with
, its absolute value all within the scope of circuit speed limit, Model Distinguish precision and generalization ability higher, CTCS-3 train control system error requirements can be met preferably, namely 30
below
2
, 30
more than be no more than 2% of velocity amplitude.
In order to verify the validity of modeling and control method herein further and the change of adaption object and disturbance characteristic, thus making system works region be positioned at most economical operational zone, carrying out multiobjective optimal control emulation experiment according to the on-the-spot service data of motor train unit.
Adopt based on T-S bilinearity self learning model predictive control algorithm, Fig. 8 (a) shows that context of methods can improve motor train unit complicated running environment medium velocity tracking performance index further, Operational Safety indicators is also improved, and its maximum departure and minimal negative departure are respectively
with
, all meet target velocity error requirements; The root-mean-square error of control system (
) be obviously better than modeling root-mean-square error (
).As can be seen from Fig. 8 (b), control change curve meets working conditions change situation, and namely traction working condition control is greater than zero, and coasting operating conditions power equals zero, and damped condition control is less than zero; Start-up course control changes based on time optimal principle, constant speed process control power carries out invariable power according to energy-conservation principle and coasting operating mode changes in order, braking procedure adopts regenerative braking to carry out energy-conservation by energy feedback to electrical network, decreases the frequency of utilization of maximum braking, improves running stability; In whole section operation process, control keeps seamlessly transitting and switching, and improves passenger comfort.Fig. 8 (c) describe optimization energy consumption that the inventive method obtains and working time curve, energy consumption curve is on a declining curve at braking procedure; When energy consumption obviously increases, working time, rate of rise was slack-off; Be 14095kwh in the energy consumption of whole service process, working time be 1 hour 17 points 42 seconds, running on time, relative steward's experience method of operating, the energy-conservation 265kwh of this algorithm, relatively energy-conservation 1.88%, meet preferably safe, energy-conservation, on schedule, the steadily multiple-objection optimization requirement such as comfortable.
Claims (3)
1. motor train unit operational process modeling and self-adaptation control method, it is characterized in that, described method is according to motor train unit actual motion figure, forward box situation, speed limit condition, tractive force/damping force saturation nonlinearity restrain condition condition is set up motor train unit and is run multi-objective constrained optimization model, designs bilinear model predictive controller accordingly and realizes motor train unit optimizing operation; And improve motor train unit multi-objective constrained optimization Control performance standard according to above-mentioned modelling instant learning Model Predictive Control Algorithm; When model output error e is in system allowed band, T-S bilinear model parameter is without the need to optimizing; When model output error exceedes threshold value, Lazy learning method is adopted to carry out on-line correction to model parameter; The parameter of dynamic conditioning bilinear model predictive controller accordingly, optimizes while implementation model parameter and controller parameter, decreases bilinear model predictive controller on-line calculation;
Described motor train unit runs multi-objective constrained optimization model:
(ν
r-δ)≤ν≤(ν
r+δ)
t≤t
*
U
min≤U≤U
max
Δu
min≤Δu≤Δu
max
In formula: F represents objective function; W
tfor the weight of motion time; W
efor energy consumption weight; v
rfor target velocity; δ is speed tracing error range; t
*for the motion time of service chart; E
*for the energy consumption that section operation is expected; U
minrepresent maximum braking force; Δ u is controlled quentity controlled variable increment; U
maxrepresent maximum drawbar pull; Δ u
minfor the lower limit of controlled quentity controlled variable increment; Δ u
maxfor the higher limit of controlled quentity controlled variable increment;
Described bilinear model is:
The bilinear model of N the motor train unit dynamic perfromance of organizing into groups, energy ezpenditure and motion time is:
In formula: M is motor train unit gross mass of equal value; X is the range ability of motor train unit; System input is the control U (x) acted under the different handle levels in motor train unit; It is speed v (x) that system exports; Symbol
operator makes U (x) and v (x) meet multiplication relationship; G is line parameter circuit value; x
1represent the position of starting point; x
2represent the position of terminal; T is motion time; E (x) is the energy ezpenditure in motor train unit operational process; C
1for mechanical resistance coefficient, size is 7.4 × 10
-3, C
2for coefficient of air resistance, size is 1.24 × 10
-4.
2. the modeling of a kind of motor train unit operational process and self-adaptation control method according to claim 1, it is characterized in that, described method step is:
(1) run from motor train unit the ultimate principle controlled, based on the feature of maneuvering and control and kinetics equation, set up the bilinear model describing motor train unit dynamic perfromance, energy ezpenditure and motion time; Determine each fuzzy rule former piece parameter according to the model structure determined, by each fuzzy rule consequent parameter of recursive weighted least squares estimation identification, accurate description is carried out to the dynamic perfromance of each fuzzy rule in local; When model output error exceedes the threshold value preset, adopt local regression method based on instant learning to model parameter on-line correction; The On-line Estimation of motor train unit operational process multi-mode model can be realized like this;
(2) on the basis of the T-S bilinear model of above-mentioned foundation, in conjunction with Nonlinear Model Predictive Control, changed by forecast model, design T-S bilinearity adaptive model predictive controller studies motor train unit optimized handling problem.
3. the modeling of a kind of motor train unit operational process and self-adaptation control method according to claim 1, it is characterized in that, the identification of described T-S bilinear model and self-adaptation control method comprise:
(1) motor train unit T-S For Identification of Bilinear Model Parameters, adopts recursive weighted least squares estimation method carry out iteration identification model parameter and avoid matrix inversion;
(2) based on the model parameter on-line correction of instant learning, enable Lazy learning method when model output error exceedes threshold value and carry out correction and renewal learning collection; Design bilinearity adaptive model predictive controller carries out rolling optimization and closed-loop control, realizes motor train unit safety and steady, energy-conservation comfortable, multiobjective optimal control on schedule.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210524440.8A CN102981408B (en) | 2012-12-10 | 2012-12-10 | Running process modeling and adaptive control method for motor train unit |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210524440.8A CN102981408B (en) | 2012-12-10 | 2012-12-10 | Running process modeling and adaptive control method for motor train unit |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102981408A CN102981408A (en) | 2013-03-20 |
| CN102981408B true CN102981408B (en) | 2015-05-27 |
Family
ID=47855566
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210524440.8A Active CN102981408B (en) | 2012-12-10 | 2012-12-10 | Running process modeling and adaptive control method for motor train unit |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102981408B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110758413A (en) * | 2019-10-24 | 2020-02-07 | 北京航盛新能科技有限公司 | Train speed self-adaptive control method based on system parameter identification |
Families Citing this family (38)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103246200B (en) * | 2013-04-17 | 2016-01-13 | 华东交通大学 | A kind of motor train unit synchronization and tracking control method based on distributed model |
| DE102013220432A1 (en) * | 2013-10-10 | 2015-04-16 | Robert Bosch Gmbh | Model calculation unit for an integrated control module for the calculation of LOLIMOT |
| CN103869809B (en) * | 2014-03-26 | 2016-07-06 | 中车信息技术有限公司 | A kind of railway locomotive operation state simulation testing device and emulation mode thereof |
| CN103870892B (en) * | 2014-03-26 | 2017-05-10 | 中车信息技术有限公司 | Method and system for achieving railway locomotive operation control from off-line mode to on-line mode |
| CN104134378A (en) * | 2014-06-23 | 2014-11-05 | 北京交通大学 | Urban rail train intelligent control method based on driving experience and online study |
| CN104102776B (en) * | 2014-07-14 | 2018-06-19 | 浙江众合科技股份有限公司 | A kind of model automatic identification method of urban railway transit train |
| CN104816738A (en) * | 2014-10-23 | 2015-08-05 | 华东交通大学 | Method and apparatus for multi-velocity control of high-speed train electromagnetic active levitation system |
| CN104656452B (en) * | 2015-02-04 | 2017-12-15 | 广西大学 | A kind of subway train optimal control method discrete based on matrix and device |
| CN104866925B (en) * | 2015-05-27 | 2018-04-20 | 上海工程技术大学 | A kind of time-table optimization method based on ATS adjustment functions |
| CN105573123B (en) * | 2016-01-19 | 2018-08-21 | 东南大学 | A kind of boiler coordination control method for thermal power unit based on the modeling of improved T-S fuzzy predictions |
| CN106019935B (en) * | 2016-04-28 | 2019-04-19 | 天津市职业大学 | Multiple target boiler combustion optimization based on constraint fuzzy association rules |
| CN106647279B (en) * | 2017-01-13 | 2019-11-12 | 清华大学 | A kind of locomotive smart steering optimized calculation method based on fuzzy rule |
| CN106842924B (en) * | 2017-01-18 | 2019-08-13 | 华东交通大学 | EMU optimal control method based on multi-state ANFIS model |
| CN106842925B (en) * | 2017-01-20 | 2019-10-11 | 清华大学 | A kind of locomotive smart steering method and system based on deeply study |
| CN106707765A (en) * | 2017-02-27 | 2017-05-24 | 华东交通大学 | Running-tracking, real-time optimization control method for high speed train |
| CN106707764B (en) * | 2017-02-27 | 2019-10-22 | 华东交通大学 | EMU braking process RBF Model Reference Adaptive Control Method based on multistage switching |
| US10832581B2 (en) * | 2017-03-31 | 2020-11-10 | General Electric Company | Flight management via model-based iterative optimization |
| CN107403196B (en) * | 2017-07-28 | 2020-05-12 | 江南大学 | Method for predicting concentration of butane at bottom of debutanizer by instant learning modeling based on spectral clustering analysis |
| CN108919782A (en) * | 2018-07-12 | 2018-11-30 | 中铁磁浮科技（成都）有限公司 | A kind of method that suspension control parameter adapts to line adjustment |
| CN109204391A (en) * | 2018-09-29 | 2019-01-15 | 交控科技股份有限公司 | A kind of target velocity curve based on multiobjective decision-making determines method |
| CN109783890B (en) * | 2018-12-26 | 2022-06-14 | 华东交通大学 | Heavy-load train operation curve multi-objective optimization method based on coupler and draft gear model |
| CN110155089A (en) * | 2019-05-28 | 2019-08-23 | 北京交通大学 | A kind of municipal rail train kinetic parameters automatic adjusting method |
| CN110320806B (en) * | 2019-07-24 | 2021-06-01 | 东北大学 | Sewage treatment process self-adaptive prediction control method based on integrated instant learning |
| CN111142374B (en) * | 2020-01-03 | 2023-04-28 | 江西理工大学 | Speed control method of automatic driving system of suspension type permanent magnet maglev train |
| CN111427263A (en) * | 2020-01-07 | 2020-07-17 | 湘潭大学 | Freight train operation adaptive speed fuzzy control and efficient energy-saving operation method based on speed regulation experience base |
| CN111580391B (en) * | 2020-05-29 | 2022-04-15 | 中车青岛四方车辆研究所有限公司 | Motor train unit traction torque control method based on model prediction |
| CN111898628B (en) * | 2020-06-01 | 2023-10-03 | 淮阴工学院 | Novel T-S fuzzy model identification method |
| CN112406822B (en) * | 2020-10-20 | 2022-04-22 | 湖南工业大学 | High-speed train braking force optimal distribution method considering adhesion and comfort |
| CN112488474B (en) * | 2020-11-19 | 2022-05-20 | 贵州电网有限责任公司 | Energy Internet comprehensive energy consumption model and parameter identification method |
| CN112346346A (en) * | 2020-12-04 | 2021-02-09 | 华东交通大学 | Heavy-load train speed tracking control method and system |
| CN113110045B (en) * | 2021-03-31 | 2022-10-25 | 同济大学 | Model prediction control real-time optimization parallel computing method based on computation graph |
| CN113411774B (en) * | 2021-08-17 | 2021-11-19 | 深圳电通信息技术有限公司 | Ad-hoc network-based train group control method and device |
| CN113715877B (en) | 2021-09-16 | 2022-09-02 | 交控科技股份有限公司 | Train control method, system, computer device and storage medium |
| CN114323706B (en) * | 2021-11-22 | 2024-04-12 | 卡斯柯信号有限公司 | Train ATO control fault detection method, device, equipment and medium |
| CN114815621B (en) * | 2022-05-09 | 2023-06-30 | 电子科技大学 | T-S fuzzy-based finite time adaptive control method for solidification process with time lag |
| CN114919632A (en) * | 2022-06-14 | 2022-08-19 | 通号城市轨道交通技术有限公司 | Traction calculation simulation method and device |
| CN115793472B (en) * | 2023-02-13 | 2023-06-20 | 华东交通大学 | Modeling method, modeling system, control method and control system of heavy-duty train |
| CN116577997B (en) * | 2023-07-06 | 2023-10-03 | 西北工业大学 | Omnidirectional trolley parameter identification method based on concurrent learning |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101941450A (en) * | 2010-08-26 | 2011-01-12 | 北京交通大学 | Method and system for controlling working condition switching of train |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3940649B2 (en) * | 2002-08-09 | 2007-07-04 | 株式会社東芝 | Automatic train driving device |
-
2012
- 2012-12-10 CN CN201210524440.8A patent/CN102981408B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101941450A (en) * | 2010-08-26 | 2011-01-12 | 北京交通大学 | Method and system for controlling working condition switching of train |
Non-Patent Citations (4)
| Title |
|---|
| 基于T_S模型的模糊预测控制研究;邢宗义等;《控 制 与 决 策》;20050530;第20卷(第5期);全文 * |
| 高速列车多模型广义预测控制方法;杨辉等;《铁道学报》;20110831;第33卷(第8期);全文 * |
| 高速动车组多模型切换主动容错预测控制;杨辉等;《控制理论与应用》;20120930;第29卷(第9期);全文 * |
| 高速动车组多模型建模与预测控制方法;张坤鹏;《中国知网》;20111023;正文第三-五章，图3-1至图5-1 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110758413A (en) * | 2019-10-24 | 2020-02-07 | 北京航盛新能科技有限公司 | Train speed self-adaptive control method based on system parameter identification |
| CN110758413B (en) * | 2019-10-24 | 2021-04-27 | 北京航盛新能科技有限公司 | Train speed self-adaptive control method based on system parameter identification |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102981408A (en) | 2013-03-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102981408B (en) | Running process modeling and adaptive control method for motor train unit | |
| Cao et al. | Application of fuzzy predictive control technology in automatic train operation | |
| Zhu et al. | An eco-driving algorithm for trains through distributing energy: A Q-Learning approach | |
| CN104881527B (en) | Urban railway transit train ATO speed command optimization methods | |
| CN108764571B (en) | Multi-objective optimization method for heavy-duty train operation | |
| CN103019267B (en) | Bullet train ANFIS modeling and speed of service forecast Control Algorithm | |
| CN105243430B (en) | The optimization method of the target velocity curve of energy-saving train operation | |
| CN103914985B (en) | A kind of hybrid power passenger car following speed of a motor vehicle trajectory predictions method | |
| Fernández-Rodríguez et al. | Balancing energy consumption and risk of delay in high speed trains: A three-objective real-time eco-driving algorithm with fuzzy parameters | |
| CN106056238B (en) | Planning method for train interval running track | |
| CN104134378A (en) | Urban rail train intelligent control method based on driving experience and online study | |
| CN103847749A (en) | Method for generating double-deck multi-objective locomotive optimized manipulating sequence | |
| CN107368920A (en) | A kind of off-peak period multi-train movement energy conservation optimizing method | |
| CN103879414A (en) | Locomotive optimal manipulation method based on self-adaption A-Star algorithm | |
| Park et al. | Power management controller for a hybrid electric vehicle with predicted future acceleration | |
| CN106777717A (en) | A kind of ATO speed command energy conservation optimizing methods for considering handling capacity of passengers change | |
| Guo et al. | Hierarchical velocity control considering traffic signal timings for connected vehicles | |
| Wang et al. | Ecological cruising control of connected electric vehicle: a deep reinforcement learning approach | |
| Zhang et al. | Integrated velocity optimization and energy management strategy for hybrid electric vehicle platoon: A multi-agent reinforcement learning approach | |
| CN108873691A (en) | Bullet train Generalized Prediction tuning control method | |
| CN113276829B (en) | Vehicle running energy-saving optimization weight-changing method based on working condition prediction | |
| CN108749816B (en) | Method for regulating and controlling speed of intelligent vehicle by using energy dissipation theory | |
| CN111598311B (en) | Novel intelligent optimization method for train running speed curve | |
| CN117227502A (en) | Layered vehicle queue economical driving cooperative control method | |
| Yin et al. | Balise arrangement optimization for train station parking via expert knowledge and genetic algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |