CN102981408A - 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
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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 the on-line identification of motor train unit running status and optimize the manipulation technology field.
Background technology
The China New Generation bullet train, scooter was 350 kilometers when it continued operation, and the test speed per hour surpasses 400 kilometers, is the motor train unit that commercial operation is fastest in the world, scientific and technological content is the highest, system matches is optimum.The transportation demands such as relative other means of transportation (such as automobile, aircraft, middle low speed passenger and freight train), motor train unit can satisfy that long distance, large conveying quantity, high density, hourage are lacked.China has had whole world maximum-norm and the High-speed Railway Network of high overall trip speed at present, to the year two thousand twenty, with the high-speed railway that builds up 16000 kilometers.Yet the China Railway High-speed netting gear is had any different in some key characters of Europe and Japan High-speed Railway, and large such as road network scale, energy resource consumption obviously increases; Geography, geology, weather conditions are complicated and changeable; Friction speed grade High-Speed Passenger Railway condition difference is obvious etc.Research has the intelligent motor train unit control system from detection, self diagnosis, self-decision ability, realizes that safe and reliable operation has become technology trends with the optimization manipulation.
For motor train unit operational process modeling and control problem, relevant scholar has set up the linearization mechanism model for the motor train unit dynamic perfromance and has designed
Constant speed controller; Propose many particles unit telephone-moving reason model and described the dynamic process of motor train unit, and adopted fuzzy controller to come the tracking control of realization speed and displacement; Set up motor train unit traction and damped condition non-linear constrain model and designed the adaptive backstepping control device; Studied the energy consumption model under the motor train unit different target speed.But these modelling by mechanism methods are difficult to solve the nonlinear problem of motor train unit aerodynamics and the variation of handle level, thereby may cause the system comprehensive evaluation index to reduce.
Under the high-speed and high-density operation condition, the manual operation is difficult to satisfy the multiple-objection optimization requirement, and research smart steering optimized algorithm is for improving the motor train unit runnability, and is significant.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 such as relevant scholar; Proposition is optimized motor train unit high-speed and high-density runnability based on the intelligent train control system of fuzzy logic control; Adopt model switching and optimisation strategy to study electric motor car group least energy consumption and handle problem; Also there is the scholar to consider the co-design problem that the motor train unit economy under the uncertain factor impact is handled and moved on schedule, adopts genetic algorithm and fuzzy linear programming method to be optimized respectively.Yet these methods can not satisfy the requirement of motor train unit real-time optimization preferably mostly take offline optimization as main.As a kind of dynamic optimization method, the Nonlinear Model Predictive Control algorithm can solve preferably and become nonlinear optimal problem when slow.But because nonlinear optimization process on-line calculation is large, directly is applied in the motor train unit operation control and does not have obvious advantage.
Summary of the invention
The objective of the invention is, modeling exists modeling method to be difficult to solve the nonlinear problem of aerodynamics and the variation of handle level for the motor train unit operational process, thus the problem that may cause the system comprehensive evaluation index to reduce; In addition, optimize motor train unit high-speed and high-density runnability based on the intelligent train control system of fuzzy logic control, can't satisfy 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; Employing comes the dynamic calibration model parameter based on the local modeling strategy of instant learning, designs accordingly bilinearity adaptive model predictive controller and carries out rolling optimization and closed-loop control, realize the motor train unit safety and steady, energy-conservation comfortable, wait multiobjective optimal control on schedule.
Realize that technical scheme of the present invention is, the present invention sets up T-S bilinearity fuzzy model in conjunction with the characteristics of motor train unit kinetics equation and manipulation control, and adopts the self-adaptation adjustment of Lazy learning method implementation model parameter; T-S bilinear model discrimination method has been proposed.The present invention is according to motor train unit actual motion figure, the place ahead circuit situation, speed limit condition, tractive force/constraint conditions such as damping force saturation nonlinearity characteristic are set up motor train unit multi-objective restriction model, design accordingly the bilinear model predictive controller and realize motor train unit optimization operation.And improve motor train unit multi-objective restriction optimal control performance index according to above-mentioned modelling instant learning Model Predictive Control Algorithm.Namely when the model output error
In the time of in system's allowed band, T-S bilinear model parameter need not to optimize; When the model output error surpasses threshold value, adopt Lazy learning method that model parameter is carried out on-line correction.Dynamically adjust accordingly the parameter of bilinear model predictive controller, optimize in the time of implementation model parameter and controller parameter, reduced 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 the Lazy learning method calculated amount again, improved the multi-objective restriction optimization of system and handled level.
The modeling of motor train unit operational process and self-adaptation control method step are:
(1) from the ultimate principle of motor train unit operation control, take the characteristics of handling control and kinetics equation as the basis, sets up the bilinear model of describing motor train unit dynamic perfromance, energy consumption and motion time; Employing is carried out cluster analysis based on the FCM fuzzy clustering algorithm of genetic simulated annealing to the sample data that gathers, and obtains the economic control point of operational process, for the steward provides priori operational optimization information; Determine each fuzzy rule former piece parameter according to the model structure of determining, by each fuzzy rule consequent parameter of recursion weighted least-squares method identification, the dynamic perfromance of local each fuzzy rule is carried out accurate description; When the model output error surpasses predefined threshold value, adopt local regression method based on instant learning to the model parameter on-line correction.Can realize like this On-line Estimation of motor train unit operational process multi-mode model.
(2) by setting up motor train unit multi-objective restriction Optimized model, on the basis of the T-S of above-mentioned foundation bilinear model, in conjunction with Nonlinear Model Predictive Control, forecast model to be changed, design T-S bilinearity adaptive model predictive controller is studied motor train unit optimization and is handled problem.
Motor train unit is handled control and is mainly comprised traction, coasting and three kinds of operating conditions of braking, relates to startup, accelerates constant speed, the various control patterns such as coasting and braking.Wherein under traction and the damped condition a plurality of handle levels are arranged respectively, as shown in Figure 3 the handle handled of high ferro steward.All car controlling instructions all are to send from the handle level, and different handle control levels determine different control models.Curve of traction characteristics under the different handle levels of motor train unit, braking characteristic curve are respectively shown in Fig. 4 (a) and Fig. 4 (b).
The motor train unit mechanism model is determined by following principle and method among the present invention:
As can be seen from Figure 4, the required tractive force/damping force of motor train unit operational process
With travelling speed
With the handle level
Between be multivariable nonlinearity relation:
In addition, because different handle control level determines different control models, and
With
Closely related, its Nonlinear Dynamic can present control variable
With state variable
The phenomenon that multiplies each other can be described as a bilinear system with 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. is the function of distance, and is comparatively suitable take distance as independent variable, then
Motor train unit dynamic perfromance, energy consumption and the motion time of marshalling can be described with following bilinear model:
（3）
In the formula:
Be motor train unit gross mass of equal value;
Be the range ability of motor train unit, system's input is the control under the different handle levels that act on the motor train unit
(tractive force/damping force); System's output is speed
Symbol
The Kronecker operator, so that
With
Satisfy multiplication relationship; G is line parameter circuit value (electric phase separation point, the gradient and curvature);
With
The position that represents respectively starting point and terminal point;
Be motion time;
Be the energy consumption in the motor train unit operational process;
Be the mechanical resistance coefficient, size generally exists
About;
Be coefficient of air resistance, size generally exists
About, when
The time, Nonlinear Space atmidometer item
Proportion is less in formula (2).
In order to simplify the design of train travelling process modeling and control device, many engineering application and research persons ignore its impact.As in the situation of not considering the Nonlinear Space atmidometer, correlative study person has designed the fuzzy gain controller and has regulated the subway train travelling speed; Adopt the adaptive optimization control algolithm to solve subway train operational management and energy saving optimizing problem; Control has proposed respectively open loop heuristic optimization strategy and based on the closed loop LQR control algolithm of heuritic approach for the speed of middle low-speed heave-load goods train kinetic model.But work as
The time, Nonlinear Space atmidometer item
Proportion is increasing in formula (2), become the required main resistance that overcomes in the motor train unit operational process, its energy consumption is also increasing, is difficult to satisfy the control of motor train unit operational process high precision tracking and multiple-objection optimization requirement based on the linear modelling of common middle low speed train and control method.
Adopt T-S bilinear model self study predictive control algorithm that the motor train unit operational process is studied among 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 model is output as:
In the formula:
Expression is for system's input/output variable, and
The fuzzy membership of rule.
(1) T-S bilinear model Structure Identification
How T-S bilinear model structure is optimized, Model Distinguish speed and precision are had material impact.Structure Identification method commonly used has FCM clustering algorithm etc., but the Local Search of FCM, and to the susceptibility of cluster centre initial value, has limited its application.The present invention adopts and based on the FCM clustering method of Global Genetic Simulated Annealing Algorithm the motor train unit model structure is carried out identification.This algorithm has been inherited the stronger parallel and ability of searching optimum of genetic algorithm, and adopt the Metropolis acceptance criterion of simulated annealing to keep population diversity, improve local search ability, overcome precocious phenomenon and the low defective of simulated annealing speed of convergence of genetic algorithm.
Above-mentioned clustering algorithm can obtain specifying
Global optimum's cluster centre in the individual classification.But the motor train unit operating condition is complicated and changeable, is difficult to determine in advance the system works 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 the interior compactness of class and class.
(2) motor train unit T-S For Identification of Bilinear Model Parameters
Based on the identification of Model Parameters principle, formula (5) can be exchanged into following form:
In the formula:
Be observation vector, and
Satisfy:
But its objective function is global optimization, can not accurate description the dynamic perfromance of local each fuzzy rule.
The present invention adopts recursion weighted least-squares method method to come iteration identification model parameter and avoids matrix inversion.
(3) based on the model parameter on-line correction of instant learning
In order to improve the efficient of Lazy learning method, the present invention only enables Lazy learning method and proofreaies and correct also renewal learning collection when the model output error surpasses threshold value.How setting up the study collection is the principal element that affects model accuracy, and for improving online modeling accuracy, the present invention considers motor train unit bilinearity dynamic change trend in the criterion of selecting sample.
The present invention designs bilinearity adaptive model predictive controller and carries out rolling optimization and closed-loop control, realize the motor train unit safety and steady, energy-conservation comfortable, wait multiobjective optimal control on schedule, main calculation procedure is as follows:
By minimizing objective function, T-S bilinear model predictive control algorithm can be described its dynamic process exactly, provides optimal control sequence, but that the bilinear terms in the model has is 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:
In the motor train unit operational process, controller's design should make motor train unit operate steadily, comfortable, energy-conservation, punctual and guarantee accurately to stop.Then motor train unit operation multi-objective restriction Optimized model is:
（9）
In the formula:
With
Be respectively motion time weight and energy consumption weight,
Be target velocity,
Be the speed reference tracking error,
Be the motion time of service chart,
Be the energy consumption of section operation expectation,
Represent maximum braking force,
Be the controlled quentity controlled variable increment,
Represent maximum drawbar pull.
Because energy-saving index and steady comfort level can be described by the variation of controlled quentity controlled variable, percent of punctuality and accurate parking index can realize that by the accurate tracking to the optimal velocity curve then objective function can be expressed as:
In the formula:
Be following speed reference track,
Respectively minimum output length, prediction length and control length,
Be the weighting coefficient sequence, the constraint controlled quentity controlled variable;
Be following controlling increment sequence.
Based on rolling optimization mechanism, can get optimal control law:
Wherein
With what calculate
First value of individual controlling increment puts into practice, realizes rolling optimization.Current
Place, individual sampling location, controlled quentity controlled variable is expressed as:
In sum, for the modeling of motor train unit operational process and real-time optimization control problem, can according to its curve of traction characteristics, idle running resistance curve, braking mode curve and service data, set up the T-S bilinear model and effectively describe its multi-mode operation process; Design instant learning model predictive controller improves its multiobjective optimal control performance index.
The present invention's beneficial effect compared with the prior art is, the motor train unit operation relates 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 such as energy-conservation, comfortable handles.The present invention at first proposes motor train unit according to set service chart, regularly operation in set period and interval, dynamic relationship between control variable and the state variable is followed curve of traction characteristics, idle running resistance curve and braking mode curve and is changed, and the modeling and the optimal control method that drive for based on data provide possibility; Then set up T-S bilinearity fuzzy model in conjunction with its kinetics equation and manipulation control characteristics, and adopt the self-adaptation adjustment of Lazy learning method implementation model parameter; According to motor train unit actual motion figure, the place ahead circuit situation, speed limit condition, tractive force/constraint conditions such as damping force saturation nonlinearity characteristic are set up restricted model, design accordingly the bilinear model predictive controller and realize motor train unit optimization operation, for optimizing to handle, the steward provides prior imformation, thereby having changed the blindness of regulating by rule of thumb, is that supplementary means is handled in a kind of effective optimization.The present invention is more directly perceived, rapider, and is not subjected to the condition restriction such as place, environment, has simple and practically, improves the high ferro steward and optimizes the manipulation level, reduces human resources and drops into, and improves railway interests's efficiency of operation, reduces the advantage of cost.
The present invention is applicable to the on-line identification of motor train unit running status and multiobjective optimal control.
Description of drawings
Fig. 1 is motor train unit running-course control system construction drawing;
Fig. 2 is motor train unit operation control ultimate principle;
Fig. 3 is the motor train unit main control unit;
Fig. 4 (a) is the curve of traction characteristics under the different handle levels of motor train unit;
Fig. 4 (b) is the braking characteristic curve under the different handle levels of motor train unit;
Fig. 5 is circuit speed limit figure;
Fig. 6 is motor train unit actual moving process figure;
Fig. 7 is modeling method output of the present invention and graph of errors thereof;
Fig. 8 (a) follows the tracks of and graph of errors for the speed that control method of the present invention obtains;
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 certain Railway Bureau jurisdiction Beijing-Shanghai High-Speed Railway Jinan-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 stopped 2 minutes at station, Tai'an.Initial mileage is 393.74km, and station, Tai'an mileage is 465.77km, and the terminal point mileage is 693.74km.The EMU operating condition changes complicated, is subject to the constraints such as line slope (ruling grade has reached 20 ‰), working time, speed limit.Whole process has 9 tunnels, 11 places electricity phase separation point, and 17 place's value of slope surpass 12 ‰.Table 1 is motion time between each station of service chart regulation, and Fig. 5 is circuit speed limit figure.
Table 1 section operation timetable
| Interval title | The section operation time-division (time: minute: 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 has described on July 13rd, 2012 this model motor train unit actual motion time-division, overall trip speed.Wherein, actual run time was 1 hour 19 minutes 51 seconds, than 1 hour 18 minutes late 1 minute 51 seconds service chart stipulated time; Braking procedure adopts electric empty Associated brake mode, and the electric weight of wherein regenerating can feed back to electrical network, and total energy consumption deducts the regenerative braking electric weight for traction power consumption, and its value is 14230kwh.
The embodiment of the invention is moved 2000 groups of data based on the traction/brake curve under the different handle levels of CRH380AL type motor train unit to the scene and is carried out pre-service, obtains
1800 groups of valid data in the scope.Utilize these data of Cluster Validity Algorithm Analysis of the present invention, the DB value is minimum when the operating mode number is 6, 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;
High speed coasting operating mode;
Fig. 7 is based on the T-S bilinear model output of instant learning and the graph of errors of exporting with reality thereof.
As can be seen from Figure 7, in motor train unit multi-state operational process, the model of modeling method of the present invention is exported the situation of change that still can follow the tracks of preferably actual output, and (root-mean-square error is
).Particularly in different traction handle levels, different braking handle level transition period, maximum positive error and the minimal negative error of model output only are
With
, all in circuit speed limit scope, Model Distinguish precision and generalization ability are higher for its absolute value, can satisfy preferably CTCS-3 train control system error requirements, and namely 30
Below
2
, 30
More than be no more than 2% of velocity amplitude.
For the further validity of checking this paper modeling and control method and the variation of adaption object and disturbance characteristic, thereby make the system works zone be positioned at most economical operational zone, carry out the multiobjective optimal control emulation experiment according to the on-the-spot service data of motor train unit.
Employing is based on T-S bilinearity self learning model predictive control algorithm, Fig. 8 (a) shows that this paper method can further improve the complicated running environment medium velocity of motor train unit tracking performance index, Operational Safety indicators also is improved, and its maximum departure and minimal negative departure are respectively
With
, all satisfy the target velocity error requirements; The root-mean-square error of control system (
) obviously be better than modeling root-mean-square error (
).Can find out that from Fig. 8 (b) the control change curve meets the working conditions change situation, namely the traction working condition control is greater than zero, and coasting operating conditions power equals zero, and the damped condition control is less than zero; Start-up course control time-based principle of optimality changes, constant speed process control power carries out permanent power according to energy-conservation principle and the coasting operating mode changes in order, it is energy-conservation that braking procedure adopts regenerative braking that energy feedback is carried out to electrical network, reduced the frequency of utilization of maximum braking, improved running stability; In the whole section operation process, control keeps seamlessly transitting and switching, and has improved passenger comfort.Fig. 8 (c) described 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 increased, working time, rate of rise was slack-off; Energy consumption in the whole service process is 14095kwh, and be 1 hour 17 minutes 42 seconds working time, running on time, relative steward's experience method of operating, the energy-conservation 265kwh of this algorithm, relatively energy-conservation 1.88%, satisfy preferably safe, energy-conservation, on schedule, the steadily multiple-objection optimization requirement such as comfortable.
Claims (5)
1. motor train unit operational process modeling and self-adaptation control method is characterized in that, described method is set up T-S bilinearity fuzzy model, and adopt the self-adaptation adjustment of Lazy learning method implementation model parameter; T-S bilinear model discrimination method has been proposed; Described method is according to motor train unit actual motion figure, the place ahead circuit situation, speed limit condition, tractive force/constraint conditions such as damping force saturation nonlinearity characteristic are set up motor train unit operation multi-objective restriction Optimized model, design accordingly the bilinear model predictive controller and realize motor train unit optimization operation; And improve motor train unit multi-objective restriction optimal control performance index according to above-mentioned modelling instant learning Model Predictive Control Algorithm; When the model output error
In the time of in system's allowed band, T-S bilinear model parameter need not to optimize; When the model output error surpasses threshold value, adopt Lazy learning method that model parameter is carried out on-line correction; Dynamically adjust accordingly the parameter of bilinear model predictive controller, optimize in the time of implementation model parameter and controller parameter, reduced bilinear model predictive controller on-line calculation.
2. motor train unit T-S bilinearity adaptive model forecast Control Algorithm according to claim 1 is characterized in that described method step is:
(1) from the ultimate principle of motor train unit operation control, take the characteristics of handling control and kinetics equation as the basis, sets up the bilinear model of describing motor train unit dynamic perfromance, energy consumption and motion time; Employing is carried out cluster analysis based on the FCM fuzzy clustering algorithm of genetic simulated annealing to the sample data that gathers, and obtains the economic control point of operational process, for the steward provides priori operational optimization information; Determine each fuzzy rule former piece parameter according to the model structure of determining, by each fuzzy rule consequent parameter of recursion weighted least-squares method identification, the dynamic perfromance of local each fuzzy rule is carried out accurate description; When the model output error surpasses predefined threshold value, adopt local regression method based on instant learning to the model parameter on-line correction; Can realize like this On-line Estimation of motor train unit operational process multi-mode model;
(2) on the basis of the T-S of above-mentioned foundation bilinear model, in conjunction with Nonlinear Model Predictive Control, forecast model to be changed, design T-S bilinearity adaptive model predictive controller is studied motor train unit optimization and is handled problem.
3. motor train unit T-S bilinearity adaptive model forecast Control Algorithm according to claim 1 is characterized in that the bilinearity mechanism model of described motor train unit operational process is:
In the formula:
Be motor train unit gross mass of equal value;
It is the range ability of motor train unit; System's input is the control under the different handle levels that act on the motor train unit
(tractive force/damping force); System's output is speed
Symbol
The Kronecker operator so that
With
Satisfy multiplication relationship; G is line parameter circuit value (electric phase separation point, the gradient and curvature);
The position of expression starting point;
The position of expression terminal point;
Be motion time;
Be the energy consumption in the motor train unit operational process;
Be the mechanical resistance coefficient, size generally exists
About,
Be coefficient of air resistance, size generally exists
About.
4. motor train unit T-S bilinearity adaptive model forecast Control Algorithm according to claim 1 is characterized in that, described motor train unit operation multi-objective restriction Optimized model is:
In the formula:
Weight for motion time;
Be the energy consumption weight;
Be target velocity;
Be the speed reference tracking error;
Motion time for service chart;
Energy consumption for the section operation expectation;
Represent maximum braking force;
Be the controlled quentity controlled variable increment;
Represent maximum drawbar pull.
5. motor train unit T-S bilinearity adaptive model forecast Control Algorithm according to claim 1 is characterized in that the identification of described T-S bilinear model and self-adaptation control method comprise:
(1) T-S bilinear model Structure Identification adopts and based on the FCM clustering method of Global Genetic Simulated Annealing Algorithm the motor train unit model structure is carried out identification; This algorithm has been inherited the stronger parallel and ability of searching optimum of genetic algorithm, and adopt the Metropolis acceptance criterion of simulated annealing to keep population diversity, improve local search ability, overcome precocious phenomenon and the low defective of simulated annealing speed of convergence of genetic algorithm;
(2) motor train unit T-S For Identification of Bilinear Model Parameters adopts recursion weighted least-squares method method to come iteration identification model parameter and avoid matrix inversion;
(3) based on the model parameter on-line correction of instant learning, when the model output error surpasses threshold value, enable Lazy learning method and proofread and correct also renewal learning collection; Design bilinearity adaptive model predictive controller carries out rolling optimization and closed-loop control, realize the motor train unit safety and steady, energy-conservation comfortable, wait multiobjective optimal control on schedule.
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