CN103303299A - Emergency braking signal generation device for high-speed train based on orthogonal collocation optimization - Google Patents

Abstract

The invention discloses an emergency braking signal generation device for a high-speed train based on orthogonal collocation optimization. The emergency braking signal generation device comprises a train speed sensor, a dangerous case distance/processing time input unit, a high-speed train central control MCU (micro controller unit), a brake unit and emergency braking alarm and state display equipment. After the train speed sensor is started for measuring current train speed in real time, a train driver inputs a dangerous case distance and the dangerous case processing time into the dangerous case distance/processing time input unit; and the high-speed train central control MCU is used for performing an internal orthogonal collocation optimization method, calculating a braking strategy capable of enabling the train to safely pass through a dangerous case position and enabling the train delay time to be shortest at the same time, converting the braking strategy obtained by the calculation into a braking instruction, sending the braking instruction to the brake unit, and sending an emergency braking alarm signal at the same time. According to the emergency braking signal generation device, the high-speed train can be guaranteed to safely pass through the dangerous case position and the train delay time can be also enabled to be shortest at the same time.

Description

A kind of high speed train emergency brake signal generating means of optimizing based on orthogonal configuration

Technical field

The present invention relates to the track traffic security fields, mainly is a kind of high speed train emergency brake signal generating means of optimizing based on orthogonal configuration.When emergency appears in train the place ahead, can calculate the train of the sening as an envoy to the shortest braking strategy that wastes time, and it is implemented as speed-slackening signal.

Background technology

High speed train owing to various enchancement factors, may occur paroxysmal emergency in car the place ahead in the process of moving.If handle untimelyly, will lead to serious accident.

In Japan, the Germany and French of technology maturation, high speed train has an accident unrare.A typical case is: on April 25th, 2005, Japan's one row high speed train is through the rugged city of Bingku county Buddhist nun the time, because desiring to recover the overdue moment, the driver has little time to cause derailed in the bend deceleration, behind train and the train colliding, pour a housing block, cause first compartment and second compartment to ruin entirely, cause 107 people's death, 555 people are injured.This plays tragic incident and causes Japanese government and Congress to revise " railway cause method ", stipulates that each railroad must bear obligatioies such as installation " ATS Automatic Train Stopper (ATS) " along the railway.

China " 7.23 " Wenzhou motor-car rear-end collision has caused the great attention of people to train safe especially.The speed car of homemade independent research need be developed equally and promptly avoid braking technology and Related product.

Summary of the invention

Suppose outside one segment distance of high speed train the place ahead, dangerous situation to occur, and known removing the dangerous condition the needed time.For make train safe by the dangerous situation spot, simultaneously train is delayed time the shortest, the invention provides a kind of high speed train emergency brake signal generating means based on the orthogonal configuration optimization method, this device can calculate the braking strategy that satisfies above-mentioned requirements, and it is implemented as speed-slackening signal.

The math modeling of high-speed train braking process can be described as

${\stackrel{\·}{x}}_1\left(t\right)=x_2\left(t\right)$

${\stackrel{\·}{x}}_2\left(t\right)=F\left(t\right)$

x ₁(t ₀)=0

x ₂(t ₀)=x ₂₀

x ₁(t _f)≤s _b

Wherein t represents the time, x ₁(t) distance of expression train driving, Be x ₁(t) first derivative, x ₂(t) moving velocity of expression train,

Be x ₂(t) first derivative, t ₀The time point that the expression train begins to brake, x ₂(t ₀) be t ₀Speed constantly, s _bBe t ₀Train is apart from the distance of dangerous situation spot, t constantly _fThe expression train is by the time point of dangerous situation spot, at t _fConstantly require the distance of train driving to be no more than s _bDescribe as can be seen from this, the math modeling of the urgent train braking process of train is one group of differential algebraic equations.

The time that train is delayed is the shortest, and it is minimum to the braking force that train applies in fact to be equivalent to braking procedure.Represent time dependent braking force with F (t), then the final expression formula of this problem is:

$\min J\left[F\left(t\right)\right]={\∫}_{t_0}^{t_f}{F}^2\left(t\right)\mathrm{dt}$

$\begin{array}{cc}s.t.& {\stackrel{\·}{x}}_1\left(t\right)=x_2\left(t\right)\end{array}$

${\stackrel{\·}{x}}_2\left(t\right)=F\left(t\right)$

x ₁(t ₀)=0

x ₂(t ₀)=x ₂₀

x ₁(t _f)≤s _b

Be optimal control problem in this question essence.Wherein, J[F (t)] be the objective function of problem, determined by braking force F (t).

The technical solution adopted for the present invention to solve the technical problems is: the control integrated orthogonal configuration optimization method of current main-stream---control variable parametric method (Control variable parameterization among the MCU in the high speed high speed train, be called for short CVP), when the needs emergency braking, export braking instruction automatically by described MCU and give brake unit, realize urgent the deceleration or parking.Described MCU can be considered as the emergency brake signal generator, and its holonomic system comprises in car speed sensor, dangerous situation distance/processing time input block, the high speed train and controls MCU, brake unit, emergency braking alarm and status display unit as shown in Figure 2.Described intrasystem each component part connects by data bus in the car is unified.

The operational process of described system is as follows:

Steps A 1: high speed train is opened car speed sensor in the process of moving, is used for measuring in real time the moving velocity of current this train;

Steps A 2: carve t at a time ₀, the train operator is apprised of the place ahead apart from s _bHave dangerous situation to take place outward, the time that handling this dangerous situation needs is t _f-t ₀The train operator with dangerous situation apart from s _bAnd dangerous situation processing time t _f-t ₀Input dangerous situation distance/processing time input block;

Steps A 3: control MCU carries out inner orthogonal configuration optimization method in the high speed train, calculates the shortest braking strategy of time that can make train safe by dangerous situation spot, while train be delayed;

Steps A 4: control MCU will calculate the braking strategy that obtains and be converted to braking instruction in the high speed train, issue brake unit, send the emergency braking alerting signal simultaneously.

Integrated in the high speed train of orthogonal configuration optimization method control MCU be core of the present invention, as shown in Figure 3, its inside comprises information acquisition module, initialization module, ordinary differential equation group (Ordinary differential equations, abbreviation ODE) orthogonal configuration module, nonlinear programming problem (Non-linear Programming is called for short NLP) are found the solution module, control command output module.Wherein information acquisition module comprises dangerous situation apart from collection, the collection of dangerous situation processing time, three submodules of current vehicle speed collection, and NLP finds the solution module and comprises optimizing direction calculating, optimizing step size computation, three submodules of NLP convergence judgement.

The process of control MCU generation emergency brake signal is as follows in described:

Step B1: information acquisition module obtains the setting value of control MCU from dangerous situation distance/processing time input block is input to, and the current vehicle speed value of controlling MCU from car speed sensor is input to.The orthogonal configuration optimization method that execution begins from step B2;

Step B2: initialization module brings into operation, and the initial guess F of discrete hop count, braking trace and the state trajectory of braking procedure time is set ⁽⁰⁾(t) and x ⁽⁰⁾, set design accuracy tol;

Step B3: by ODE orthogonal configuration module with the ordinary differential equation group at time shaft [t ₀, t _f] go up all and disperse;

Step B4: obtain required braking trace and corresponding states track by the NLP problem solver module, this process comprises repeatedly inner iteration, and each iteration all will be calculated optimizing direction and optimizing step-length.The braking trace F that obtains for certain iteration ^(k)(t), if its corresponding target function value J[F ^(k)(t)] with the target function value J[F of a preceding iteration ^(k-1)(t)] difference judges then that less than accuracy requirement tol convergence satisfies, and with braking trace F ^(k)(t) output to brake unit as instruction.

Described ODE orthogonal configuration module, adopt following steps to realize:

Step C1: will control track u (t), state trajectory x (t) represents with the linear combination of M rank basic function, that is:

$u\left(t\right)\≈\underset{j=1}{\overset{M}{\mathrm{\Σ}}}{u}_{i,j}{\mathrm{\φ}}_{i,j}^{\left(M\right)}\left(t\right)i=\mathrm{1,2},...,N$

$x\left(t\right)\≈\underset{j=1}{\overset{M}{\mathrm{\Σ}}}{x}_{i,j}{\mathrm{\φ}}_{i,j}^{\left(M\right)}\left(t\right)i=\mathrm{1,2},...,N$

Wherein N is time shaft [t ₀, t _f] discrete hop count, φ (t) can select different types of basic functions such as Lagrange's interpolation basic function, spline base function, wavelet basis function, linear combination coefficient u _{I, j}And x _{I, j}Be respectively that u (t) and x (t) are at collocation point t _{I, j}On value.

Step C2: because the derived function expression formula of all basic functions is known, so the simultaneous differential equation of state trajectory is by the discretization quantic:

$\stackrel{\·}{x}\left(t\right)\≈\underset{j=1}{\overset{M}{\mathrm{\Σ}}}{x}_{i,j}{\stackrel{\·}{\mathrm{\φ}}}_{i,j}^{\left(M\right)}\left(t\right)i=\mathrm{1,2},...,N$

Step C3: replace original simultaneous differential equation with the simultaneous differential equation after the discretization, will obtain NLP problem to be asked.

Described NLP finds the solution module, adopts following steps to realize:

Step D1: with braking force F ^(k-1)(t) as certain point in the vector space, note is made P ₁, P ₁Corresponding target function value is exactly J[F ^(k-1)(t)];

Step D2: from a P ₁Set out, according to an optimizing direction d in the NLP algorithm construction vector space of selecting for use ^(k-1)With step-length α ^(k-1)

Step D3: through type F ^(k)(t)=F ^(k-1)(t)+α ^(k-1)d ^(k-1)Corresponding F in the structure vector space ^(k)(t) another one point P ₂, make P ₂Corresponding target function value J[F ^(k)(t)] compare J[F ^(k-1)(t)] more excellent.

Beneficial effect of the present invention mainly shows: can guarantee that 1, high speed train is safely by the dangerous situation spot; 2, the time that again train is delayed simultaneously is the shortest.

Description of drawings

Fig. 1 is functional schematic of the present invention;

Fig. 2 is structural representation of the present invention;

Fig. 3 is control MCU internal module constructional drawing among the present invention;

Fig. 4 is the emergency braking policy map of embodiment 1.

The specific embodiment

Embodiment 1

Suppose high speed train in the process of moving, the driver is apprised of forwardly and occurs obstacle suddenly on the 1km place track, and clearing of obstruction needs 30 seconds.The driver is with these two information input dangerous situation distance/processing time input blocks, and the current vehicle speed of control MCU was 300km/h during car speed sensor imported at this moment.In the control MCU inner orthogonal configuration optimization method that brings into operation immediately, its operational process as shown in Figure 3, for:

Step e 1: initialization module 32 brings into operation, and the segments that the braking procedure time is set is 20, the initial guess F of braking strategy is set ^(k)(t) be-0.5,

With

All be 1, setting numerical calculation precision tol is 0.01, with iterations k zero setting;

Step e 2: the initial value of establishing the ODE set of equations is x ₁(t ₀) and x ₂(t ₀), by ODE orthogonal configuration module with the ordinary differential equation group at time shaft [t ₀, t _f] go up all and disperse;

Step e 3: obtain required braking trace and corresponding states track by the NLP problem solver module, this process comprises repeatedly inner iteration, and each iteration all will be calculated optimizing direction and optimizing step-length.The braking trace F that obtains for certain iteration ^(k)(t), if its corresponding target function value J[F ^(k)(t)] with the target function value J[F of a preceding iteration ^(k-1)(t)] difference judges then that less than accuracy requirement 0.01 convergence satisfies, and with braking trace F ^(k)(t) output to brake unit as instruction.

Described ODE orthogonal configuration module, adopt following steps to realize:

Step F 1: will control track u (t), state trajectory x (t) represents with the linear combination of three rank Lagrange's interpolation basic functions, that is:

$u\left(t\right)\≈\underset{j=1}{\overset{3}{\mathrm{\Σ}}}{u}_{i,j}\underset{r=0,\≠j}{\overset{3}{\mathrm{\Π}}}\frac{t-t_{i,r}}{t_{i,j}-t_{i,r}}i=1,2,...,N$

$x\left(t\right)\≈\underset{j=1}{\overset{3}{\mathrm{\Σ}}}{x}_{i,j}\underset{r=0,\≠j}{\overset{3}{\mathrm{\Π}}}\frac{t-t_{i,r}}{t_{i,j}-t_{i,r}}i=\mathrm{1,2},...,N$

Wherein N is time shaft [t ₀, t _f] discrete hop count, linear combination coefficient u _{I, j}And x _{I, j}Be respectively that u (t) and x (t) are at collocation point t _{I, j}On value.

Step F 2: because the derived function expression formula of all basic functions is known, so the simultaneous differential equation of state trajectory is by the discretization quantic:

$\stackrel{\·}{x}\left(t\right)\≈\underset{j=1}{\overset{3}{\mathrm{\Σ}}}{x}_{i,j}{\stackrel{\·}{\mathrm{\φ}}}_{i,j}^{\left(3\right)}\left(t\right)i=\mathrm{1,2},...,N$

Step F 3: replace original simultaneous differential equation with the simultaneous differential equation after the discretization, will obtain NLP problem to be asked.

Described NLP finds the solution module, adopts following steps to realize:

Step G1: with braking force F ^(k-1)(t) as certain point in the vector space, note is made P ₁, P ₁Corresponding target function value is exactly J[F ^(k-1)(t)];

Step G2: from a P ₁Set out, select an optimizing direction d in the SQP algorithm construction vector space for use ^(k-1)With step-length α ^(k-1)

Step G3: through type F ^(k)(t)=F ^(k-1)(t)+α ^(k-1)d ^(k-1)Corresponding F in the structure vector space ^(k)(t) another one point P ₂, make P ₂Corresponding target function value J[F ^(k)(t)] compare J[F ^(k-1)(t)] more excellent.

The result of calculation of orthogonal configuration optimization method as shown in Figure 4.Coordinate is through normalized, and ordinate value is-1 expression maximum braking force, and value is 1 expression tractive force limit.The value of whole piece control curve F (t) all is no more than 0, shows that this is a braking control curve.It is 20 that asterisk number on the curve represents the time slice number.Value on the curve is 0 just when braking procedure finishes only, shows that train need not to brake during by the barrier in safety again.

At last, the braking control policy that middle control MCU will obtain outputs to brake unit as instruction, finishes brake operating mechanically, sends the emergency braking alerting signal simultaneously.

Above content be in conjunction with concrete preferred implementation to further describing that the present invention does, can not assert that concrete enforcement of the present invention is only limited to these explanations.For the general technical staff of the technical field of the invention, under the prerequisite that does not break away from inventive concept, can also make some simple deduction or replace, all should be considered as belonging to protection scope of the present invention.

Claims (1)

1. high speed train emergency brake signal generating means of optimizing based on orthogonal configuration can calculate the train of the sening as an envoy to the shortest braking strategy that wastes time when emergency occurring in train the place ahead, and it is implemented as speed-slackening signal.It is characterized in that: constitute by controlling MCU, brake unit, emergency braking alarm and status display apparatus in car speed sensor, dangerous situation distance/processing time input block, the high speed train, each component part connects by data bus in the car.The operational process of described device comprises:

Steps A 1: open car speed sensor and be used for measuring in real time current vehicle speed;

Steps A 2: the train operator is with dangerous situation distance and dangerous situation processing time input dangerous situation distance/processing time input block;

Steps A 3: control MCU carries out inner orthogonal configuration optimization method in the high speed train, calculates the shortest braking strategy of time that can make train safe by dangerous situation spot, while train be delayed;

Steps A 4: control MCU will calculate the braking strategy that obtains and be converted to braking instruction in the high speed train, issue brake unit, send the emergency braking alerting signal simultaneously.

Control MCU in the described high speed train, comprise information acquisition module, initialization module, ordinary differential equation group (Ordinary differential equations, abbreviation ODE) orthogonal configuration module, nonlinear programming problem (Non-linear Programming is called for short NLP) are found the solution module, control command output module.Wherein information acquisition module comprises dangerous situation apart from collection, the collection of dangerous situation processing time, three submodules of current vehicle speed collection, and NLP finds the solution module and comprises optimizing direction calculating, optimizing step size computation, three submodules of NLP convergence judgement.

Control MCU produces the orthogonal configuration optimization method of speed-slackening signal automatically in described, and operating procedure is as follows:

Step B1: information acquisition module (31) obtains the setting value of control MCU from dangerous situation distance/processing time input block is input to, and the current vehicle speed value of controlling MCU from car speed sensor is input to.The orthogonal configuration optimization method that execution begins from step B2;

Step B2: initialization module (32) brings into operation, and the initial guess F of discrete hop count, braking trace and the state trajectory of braking procedure time is set ⁽⁰⁾(t) and x ⁽⁰⁾, set design accuracy tol;

Step B3: by ODE orthogonal configuration module with the ordinary differential equation group at time shaft [t ₀, t _f] go up all and disperse;

Step B4: obtain required braking trace and corresponding states track by the NLP problem solver module, this process comprises repeatedly inner iteration, and each iteration all will be calculated optimizing direction and optimizing step-length.The braking trace F that obtains for certain iteration ^(k)(t), if its corresponding target function value J[F ^(k)(t)] with the target function value J[F of a preceding iteration ^(k-1)(t)] difference judges then that less than accuracy requirement tol convergence satisfies, and with braking trace F ^(k)(t) output to brake unit as instruction.

Described ODE orthogonal configuration module, adopt following steps to realize:

Step C1: will control track u (t), state trajectory x (t) represents with the linear combination of M rank basic function, that is:

$u\left(t\right)\≈\underset{j=1}{\overset{M}{\mathrm{\Σ}}}{u}_{i,j}{\mathrm{\φ}}_{i,j}^{\left(M\right)}\left(t\right)i=\mathrm{1,2},...,N$

$x\left(t\right)\≈\underset{j=1}{\overset{M}{\mathrm{\Σ}}}{x}_{i,j}{\mathrm{\φ}}_{i,j}^{\left(M\right)}\left(t\right)i=\mathrm{1,2},...,N$

Wherein N is time shaft [t ₀, t _f] discrete hop count, φ (t) can select different types of basic functions such as Lagrange's interpolation basic function, spline base function, wavelet basis function, linear combination coefficient u _{I, j}And x _{I, j}Be respectively that u (t) and x (t) are at collocation point t _{I, j}On value.

Step C2: because the derived function expression formula of all basic functions is known, so the simultaneous differential equation of state trajectory is by the discretization quantic:

$\stackrel{\·}{x}\left(t\right)\≈\underset{j=1}{\overset{M}{\mathrm{\Σ}}}{x}_{i,j}{\stackrel{\·}{\mathrm{\φ}}}_{i,j}^{\left(M\right)}\left(t\right)i=\mathrm{1,2},...,N$

Step C3: replace original simultaneous differential equation with the simultaneous differential equation after the discretization, will obtain NLP problem to be asked.

Described NLP finds the solution module, adopts following steps to realize:

Step D1: with braking force F ^(k-1)(t) as certain point in the vector space, note is made P ₁, P ₁Corresponding target function value is exactly J[F ^(k-1)(t)];

Step D2: from a P ₁Set out, according to an optimizing direction d in the NLP algorithm construction vector space of selecting for use ^(k-1)With step-length α ^(k-1)

Step D3: through type F ^(k)(t)=F ^(k-1)(t)+α ^(k-1)d ^(k-1)Corresponding F in the structure vector space ^(k)(t) another one point P ₂, make P ₂Corresponding target function value J[F ^(k)(t)] compare J[F ^(k-1)(t)] more excellent.

Images