CN107857196A - A kind of bridge-type container crane swings optimal control system - Google Patents
A kind of bridge-type container crane swings optimal control system Download PDFInfo
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- CN107857196A CN107857196A CN201711115228.5A CN201711115228A CN107857196A CN 107857196 A CN107857196 A CN 107857196A CN 201711115228 A CN201711115228 A CN 201711115228A CN 107857196 A CN107857196 A CN 107857196A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C13/00—Other constructional features or details
- B66C13/04—Auxiliary devices for controlling movements of suspended loads, or preventing cable slack
- B66C13/06—Auxiliary devices for controlling movements of suspended loads, or preventing cable slack for minimising or preventing longitudinal or transverse swinging of loads
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C13/00—Other constructional features or details
- B66C13/18—Control systems or devices
- B66C13/22—Control systems or devices for electric drives
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C17/00—Overhead travelling cranes comprising one or more substantially horizontal girders the ends of which are directly supported by wheels or rollers running on tracks carried by spaced supports
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Abstract
The invention discloses a kind of bridge-type container crane to swing optimal control system, and by traction electric machine, container position sensor, analog-digital converter, digital analog converter, fieldbus networks, scattered control system (DCS), control room is shown, traction motor controller is formed.Control room engineer specifies container to need position and the lay day reached, DCS is by performing internal optimal control algorithm, output makes this container handling swing the minimum control strategy of energy, and be converted to the control instruction of traction electric machine, traction motor controller is sent to by fieldbus networks, electric machine controller makes traction electric machine perform corresponding actions by analog-digital converter output control amount, simultaneously, position sensor gathers the positional information of container and is passed back to control room in real time, engineer is grasped cargo handling process at any time.The present invention can make it swing security and efficiency minimum, and then that improve harbour container handling during container handling.
Description
Technical field
The present invention relates to bridge crane control field, mainly a kind of bridge-type container crane swings optimum control system
System.The handling of bridge-type container crane can be automatically controlled, container is swung energy minimum in moving process, to improve port
The security and efficiency of mouth container handling.
Background technology
The handling of container run most important for harbour.However, with the high speed of handling, container, which reaches, specifies
During position due to crane acceleration and deceleration and load enhancing action and wind, friction caused by disturb etc. caused by load it is residual
Stay swing also to increase therewith, the speed that not only reduce and carry precision, slow down carrying, also increase the possibility that accident occurs
Property.The capital equipment that bridge-type side container crane loads and unloads as harbour container ship, peace of its control strategy for container
Overall height effect, which is loaded and unloaded, has material impact, so requiring to carry out automatically most bridge-type container crane according to design parameter and operation
Excellent weave control is significant.
Currently, the theory of optimal control and corresponding method, controller seldom are used in the control method of domestic bridge crane
In parameter often with having there is experience setting, the efficiency of loading and unloading and security need further to be improved.After method for optimally controlling
Bridge crane Control platform and security can be protected, the efficiency of loading and unloading can be improved further.
The content of the invention
In order to improve the efficiency of harbour container handling, swung the invention provides a kind of bridge-type container crane optimal
Control system.
The purpose of the present invention is achieved through the following technical solutions：A kind of bridge-type container crane swings optimal control
System processed, the handling of bridge-type container crane can be automatically controlled, container is swung energy minimum in moving process, to carry
The security and efficiency of harbour high cube container handling.Turned by traction electric machine, container position sensor, analog-digital converter, digital-to-analogue
Parallel operation, fieldbus networks, scattered control system (DCS), control room is shown, traction motor controller is formed.The control system
The running of system includes：
Step 1)：Control room engineer specify container need reach position coordinates, cargo handling process time restriction and
The performance parameter constraint of traction electric machine；
Step 2)：DCS performs internal optimal control algorithm, obtains the traction electricity for making container handling process angle of oscillation minimum
Machine strategy of speed control；
Step 3)：DCS is converted to obtained motor speed control strategy the control instruction of traction electric machine, by live total
Gauze network is sent to the digital analog converter of traction motor controller leading portion, makes traction motor controller according to the control instruction received
Traction electric machine is controlled to perform corresponding actions；
Step 4)：Container position sensor gathers the positional information of container in real time, by being used after analog-to-digital conversion
Fieldbus networks are passed back to DCS, and are shown in master control room, engineer is monitored cargo handling process at any time.
Described DCS, including information acquisition module, initialization module, subordination principle (Ordinary
Differential equations, abbreviation ODE) rapid solving module, gradient solve module, nonlinear programming problem solve mould
Block, control instruction output module.Wherein information acquisition module includes container position collection, traction electric machine performance constraints gather,
Lay day sets three submodules of collection, Non-Linear Programming (Non-linear Programming, abbreviation NLP) problem solving
Module includes search direction solution, optimizing step-length solves, optimizing corrects, NLP convergences judge four submodules.
The model of the cargo handling process of harbour container collection crane can be described as：
Wherein, t represents the time, and u (t) represents the velocity vector being made up of all directions velocity component；X (t) represents to load and unload
The status information of journey；F (x (t), u (t), t) is the differential equation group established according to the physics principle of crane.From the description
As can be seen that the cargo handling process of container can be represented with one group of differential equation group mathematically.
The control targe of the system is container is swung energy minimum in cargo handling process, therefore object function represents
For：
Wherein, t0Represent the initial time of cargo handling process, tfWhen representing the cargo handling process final value that control room engineer specifies
Between, J represents object function, L0Swing angle function during (x (t), u (t), t) expression container handling in object function.
Meanwhile bridge-type container crane is towed motor performance parameter and influences to be moved to status requirement with container,
Existence restraint condition, its function representation are：
Wherein, E (x (t), u (t), t) represents that container reaches position constraint function, and G (x (t), u (t), t) represents traction
Motor performance parameter constraint function.Therefore, bridge-type container crane swings energy minimum control problem and can be ultimately expressed as：
The technical solution adopted for the present invention to solve the technical problems is：It is integrated with most in scattered control system (DCS)
Excellent control algolithm, and a kind of optimal control system is constructed based on this.
The complete structure of described control system includes container position sensor 21, analog-digital converter 22, fieldbus
Network 23, DCS24, control room show 25, digital analog converter 26, traction motor controller 27, traction electric machine 28.
The running of described system includes：
Step 1)：Control room engineer specify container need reach position coordinates, cargo handling process time restriction and
The performance parameter constraint of traction electric machine；
Step 2)：DCS performs internal optimal control algorithm, and obtaining makes container handling process swing the minimum traction of energy
Motor speed control strategy；
Step 3)：DCS is converted to obtained motor speed control strategy the control instruction of traction electric machine, by live total
Gauze network is sent to the digital analog converter of traction motor controller leading portion, makes traction motor controller according to the control instruction received
Traction electric machine is controlled to perform corresponding actions；
Step 4)：Container position sensor gathers the positional information of container in real time, by being used after analog-to-digital conversion
Fieldbus networks are passed back to DCS, and are shown in master control room, engineer is monitored cargo handling process at any time.
Described DCS, including information acquisition module, initialization module, ODE solve module, gradient calculation module, NLP and asked
Topic solves module, control instruction output module.Wherein information acquisition module includes container start-stop station acquisition, performance indications are adopted
Collection, speed control constraint collection three submodules, NLP problem solver modules include search direction calculating, optimizing step size computation,
NLP convergences judge three submodules.
Described DCS, including information acquisition module, initialization module, ODE rapid solvings module, gradient solve module, non-
Linear programming problem solves module, control instruction output module.Wherein information acquisition module includes container position collection, traction
Motor performance constraint collection, the lay day set collection three submodules, NLP problem solver modules include search direction solve,
Optimizing step-length solves, optimizing corrects, NLP convergences judge four submodules.
Described DCS performs internal optimal control algorithm and obtains the traction electric machine for making container handling process angle of oscillation minimum
Strategy of speed control, operating procedure are as follows：
Step 1)：Information acquisition module 31 obtain container initial position and engineer specify in-position, traction
Motor performance constrains and the lay day is set；
Step 2)：Initialization module 32 brings into operation, and is parameterized using piece-wise constant, sets the segments of cargo handling process
The vectorial initial guess u of NE, the parametrization of traction electric machine controlled quentity controlled variable(k), setup algorithm precision tol, by iterations k zero setting；
Step 3)：The status information x of current iteration is obtained by ODE rapid solvings module 33(k)And target function value J (t)(k)。
Step 4)：Module 34 is solved by gradient and obtains current iteration target function gradient information dJ(k)With constraints ladder
Spend information g(k)；Step 5) is skipped as k=0 and directly performs step 7)；
Step 5)：NLP problem solver modules 35 are run, and convergence judgement is carried out by NLP convergences judge module, if
The target function value J of current iteration(k)With the target function value J of last iteration(k-1)The difference of absolute value be less than precision tol, then
Judge convergence sexual satisfaction, and the traction electric machine strategy of speed control of current iteration is converted to the control instruction output of motor；Such as
Fruit convergence is unsatisfactory for, then continues executing with step 6)；
Step 6)：Use u(k),J(k),dJ(k),g(k)Value cover last iteration u(k-1),J(k-1),dJ(k-1),g(k-1)Value,
And iterations k is added 1；
Step 7)：NLP problem solver modules 35 are utilized in step 3) and 4) the middle target function value and gradient information obtained,
Search direction and optimizing step-length are solved, and carries out optimizing amendment, u is compared in acquisition(k-1)More excellent new strategy of speed control u(k).Should
Step jumps to step 3) again after the completion of performing, untill NLP convergences judge module meets.
Described ODE rapid solving modules, using the rank Runge Kutta method of level Four five, solution formula is：
Wherein, t represents time, tiThe integration moment of Runge Kutta method choice is represented, h is integration step, and F () is to retouch
State the function of state differential equation, K1、K2、K3、K4The functional value of 4 nodes in runge kutta method integral process is represented respectively,
x(k)(ti) represent container t in kth time iterationiThe status information at moment,
Described gradient solves module, using sensitivity equation of locus method：
Step 1)：Define the sensitivity equation of locus Γ of kth time iteration(k)(t) it is：
Γ(k)(t) solution formula is：
Wherein, t represents the time,Represent kth time iteration medium sensitivity equation of locus for time t derivative, F (u(k),x(k)(t), t) be describe state differential equation function, Γ(k)(t0) represent sensitivity equation of locus in kth time iteration
Initial time state value, x0Represent the initial time state value of state differential equation function.
Step 2)：Sensitivity equation of locus Γ is solved using the rank Runge Kutta method of level Four five(k)(t) at each integration moment
Value, solution formula is：
Wherein, t represents time, tiRepresent that sometime point, h are integration step in the control process of Runge Kutta method choice
It is long, x(k)(ti) represent container t in kth time iterationiThe status information at moment, S () are the letters for describing sensitivity equation
Number, Q1、Q2、Q3、Q4The functional value of 4 nodes in runge kutta method integral process is represented respectively.
Step 3)：According to obtained container state information x(k)And sensitivity equation of locus Γ (t)(k)(t) target, is solved
The gradient information dJ of function(k)：
Wherein, Φ0(u(k),x(k)(t),tf) represent object function end conswtraint item, L0(u(k),x(k)(t), t) represent mesh
The integral term of scalar functions.
Step 4)：According to obtained container state information x(k)And sensitivity equation of locus Γ (t)(k)(t) constraint, is solved
The gradient information g of condition(k)：
Wherein, Φj(u(k),x(k)(t),tf) represent j-th of constraints function end conswtraint item, Lj(u(k),x(k)
(t), t) represent j-th of constraints function integral term, m represent constraints number.
Described NLP problem solver modules, are realized using following steps：
Step 1)：If the target function value J of current iteration(k)With the target function value J of last iteration(k-1)It is absolute
The difference of value is less than precision tol, then judges convergence sexual satisfaction, and the strategy of speed control of current iteration is converted into traction electric machine
Control instruction exports；If convergence is unsatisfactory for, step 2) is continued executing with；
Step 2)：Use u(k),J(k),dJ(k),g(k)Value cover last iteration u(k-1),J(k-1),dJ(k-1),g(k-1)Value,
And iterations k is increased by 1；
Step 3)：By motor speed control strategy u(k-1)As some point in vector space, P is denoted as1, P1Corresponding mesh
Offer of tender numerical value is exactly J(k-1)；
Step 4)：From point P1Set out, according to the NLP algorithms of selection and point P1The target function gradient information dJ at place(k-1)With
Constraints gradient information g(k-1), construct a search direction d in vector space(k-1)With step-length α(k-1)；
Step 5)：Pass through formula u(k)=u(k-1)+α(k-1)d(k-1)U is corresponded in construction vector space(k)Another point P2；
Step 6)：Correct to obtain using optimizing and u is corresponded in vector space(k)Another point P3So that P3Corresponding mesh
Offer of tender numerical value J(k)Compare J(k-1)It is more excellent.
Beneficial effects of the present invention are mainly manifested in：Bridge-type container crane pendulum based on dominant vector parametric method
Dynamic optimal control system, can calculate the optimal control strategy of bridge-type container crane.Using the rank Runge Kutta of level Four five
Method solves sensitivity equation of locus group, can obtain more accurate result.Give the solution of the gradient information of the problem
Method, the convergence rate of problem can be accelerated, reduce the calculating time for the optimal policy that bridge-type container crane is swung.This hair
It is bright to realize that bridge-type container crane container handling process swings energy minimum, put forward the safety of harbour high cube container handling
Property and efficiency.
Brief description of the drawings
Fig. 1 is the functional schematic of the present invention；
Fig. 2 is the structural representation of the present invention；
Fig. 3 is DCS internal modules structure chart of the present invention；
Fig. 4 is the traction electric machine strategy of speed control figure obtained to embodiment 1；
Fig. 5 is container swing angle variation diagram corresponding to traction electric machine strategy of speed control in Fig. 4.
Embodiment
As shown in figure 1, the model of the cargo handling process of harbour container collection crane can be described as：
Wherein, t represents the time, and u (t) represents the velocity vector being made up of all directions velocity component；X (t) represents to load and unload
The status information of journey；F (x (t), u (t), t) is the differential equation group established according to the physics principle of crane.From the description
As can be seen that the cargo handling process of container can be represented with one group of differential equation group mathematically.
The control targe of the system is container is swung energy minimum in cargo handling process, therefore object function represents
For：
Wherein, t0Represent the initial time of cargo handling process, tfWhen representing the cargo handling process final value that control room engineer specifies
Between, J represents object function, L0Swing angle function during (x (t), u (t), t) expression container handling in object function.
Meanwhile bridge-type container crane is towed motor performance parameter and influences to be moved to status requirement with container,
Existence restraint condition, its function representation are：
Wherein, E (x (t), u (t), t) represents that container reaches position constraint function, and G (x (t), u (t), t) represents traction
Motor performance parameter constraint function.Therefore, bridge-type container crane swings energy minimum control problem and can be ultimately expressed as：
The technical solution adopted for the present invention to solve the technical problems is：It is integrated with most in scattered control system (DCS)
Excellent control algolithm, and a kind of optimal control system is constructed based on this.
The complete structure of described control system is as shown in Fig. 2 including container position sensor 21, analog-digital converter
22nd, fieldbus networks 23, DCS24, control room show 25, digital analog converter 26, traction motor controller 27, traction electric machine
28。
The running of described system includes：
Step 1)：Control room engineer specify container need reach position coordinates, cargo handling process time restriction and
The performance parameter constraint of traction electric machine；
Step 2)：DCS performs internal optimal control algorithm, and obtaining makes container handling process swing the minimum traction of energy
Motor speed control strategy；
Step 3)：DCS is converted to obtained motor speed control strategy the control instruction of traction electric machine, by live total
Gauze network is sent to the digital analog converter of traction motor controller leading portion, makes traction motor controller according to the control instruction received
Traction electric machine is controlled to perform corresponding actions；
Step 4)：Container position sensor gathers the positional information of container in real time, by being used after analog-to-digital conversion
Fieldbus networks are passed back to DCS, and are shown in master control room, engineer is monitored cargo handling process at any time.
Described DCS, including information acquisition module, initialization module, ODE solve module, gradient calculation module, NLP and asked
Topic solves module, control instruction output module.Wherein information acquisition module includes container start-stop station acquisition, performance indications are adopted
Collection, speed control constraint collection three submodules, NLP problem solver modules include search direction calculating, optimizing step size computation,
NLP convergences judge three submodules.
Described DCS, including information acquisition module, initialization module, ODE rapid solvings module, gradient solve module, non-
Linear programming problem solves module, control instruction output module.Wherein information acquisition module includes container position collection, traction
Motor performance constraint collection, the lay day set collection three submodules, NLP problem solver modules include search direction solve,
Optimizing step-length solves, optimizing corrects, NLP convergences judge four submodules.
Described DCS performs internal optimal control algorithm and obtains the traction electric machine for making container handling process angle of oscillation minimum
Strategy of speed control, operating procedure are as follows：
Step 1)：Information acquisition module 31 obtain container initial position and engineer specify in-position, traction
Motor performance constrains and the lay day is set；
Step 2)：Initialization module 32 brings into operation, and is parameterized using piece-wise constant, sets the segments of cargo handling process
The vectorial initial guess u of NE, the parametrization of traction electric machine controlled quentity controlled variable(k), setup algorithm precision tol, by iterations k zero setting；
Step 3)：The status information x of current iteration is obtained by ODE rapid solvings module 33(k)And target function value J (t)(k)。
Step 4)：Module 34 is solved by gradient and obtains current iteration target function gradient information dJ(k)With constraints ladder
Spend information g(k)；Step 5) is skipped as k=0 and directly performs step 7)；
Step 5)：NLP problem solver modules 35 are run, and convergence judgement is carried out by NLP convergences judge module, if
The target function value J of current iteration(k)With the target function value J of last iteration(k-1)The difference of absolute value be less than precision tol, then
Judge convergence sexual satisfaction, and the traction electric machine strategy of speed control of current iteration is converted to the control instruction output of motor；Such as
Fruit convergence is unsatisfactory for, then continues executing with step 6)；
Step 6)：Use u(k),J(k),dJ(k),g(k)Value cover last iteration u(k-1),J(k-1),dJ(k-1),g(k-1)Value,
And iterations k is added 1；
Step 7)：NLP problem solver modules 35 are utilized in step 3) and 4) the middle target function value and gradient information obtained,
Search direction and optimizing step-length are solved, and carries out optimizing amendment, u is compared in acquisition(k-1)More excellent new strategy of speed control u(k).Should
Step jumps to step 3) again after the completion of performing, untill NLP convergences judge module meets.
Described ODE rapid solving modules, using the rank Runge Kutta method of level Four five, solution formula is：
Wherein, t represents time, tiThe integration moment of Runge Kutta method choice is represented, h is integration step, and F () is to retouch
State the function of state differential equation, K1、K2、K3、K4The functional value of 4 nodes in runge kutta method integral process is represented respectively,
x(k)(ti) represent container t in kth time iterationiThe status information at moment,
Described gradient solves module, using sensitivity equation of locus method：
Step 1)：Define the sensitivity equation of locus Γ of kth time iteration(k)(t) it is：
Γ(k)(t) solution formula is：
Wherein, t represents the time,Represent kth time iteration medium sensitivity equation of locus for time t derivative, F (u(k),x(k)(t), t) be describe state differential equation function, Γ(k)(t0) represent sensitivity equation of locus in kth time iteration
Initial time state value, x0Represent the initial time state value of state differential equation function.
Step 2)：Sensitivity equation of locus Γ is solved using the rank Runge Kutta method of level Four five(k)(t) at each integration moment
Value, solution formula is：
Wherein, t represents time, tiRepresent that sometime point, h are integration step in the control process of Runge Kutta method choice
It is long, x(k)(ti) represent container t in kth time iterationiThe status information at moment, S () are the letters for describing sensitivity equation
Number, Q1、Q2、Q3、Q4The functional value of 4 nodes in runge kutta method integral process is represented respectively.
Step 3)：According to obtained container state information x(k)And sensitivity equation of locus Γ (t)(k)(t) target, is solved
The gradient information dJ of function(k)：
Wherein, Φ0(u(k),x(k)(t),tf) represent object function end conswtraint item, L0(u(k),x(k)(t), t) represent mesh
The integral term of scalar functions.
Step 4)：According to obtained container state information x(k)And sensitivity equation of locus Γ (t)(k)(t) constraint, is solved
The gradient information g of condition(k)：
Wherein, Φj(u(k),x(k)(t),tf) represent j-th of constraints function end conswtraint item, Lj(u(k),x(k)
(t), t) represent j-th of constraints function integral term, m represent constraints number.
Described NLP problem solver modules, are realized using following steps：
Step 1)：If the target function value J of current iteration(k)With the target function value J of last iteration(k-1)It is absolute
The difference of value is less than precision tol, then judges convergence sexual satisfaction, and the strategy of speed control of current iteration is converted into traction electric machine
Control instruction exports；If convergence is unsatisfactory for, step 2) is continued executing with；
Step 2)：Use u(k),J(k),dJ(k),g(k)Value cover last iteration u(k-1),J(k-1),dJ(k-1),g(k-1)Value,
And iterations k is increased by 1；
Step 3)：By motor speed control strategy u(k-1)As some point in vector space, P is denoted as1, P1Corresponding mesh
Offer of tender numerical value is exactly J(k-1)；
Step 4)：From point P1Set out, according to the NLP algorithms of selection and point P1The target function gradient information dJ at place(k-1)With
Constraints gradient information g(k-1), construct a search direction d in vector space(k-1)With step-length α(k-1)；
Step 5)：Pass through formula u(k)=u(k-1)+α(k-1)d(k-1)U is corresponded in construction vector space(k)Another point P2；
Step 6)：Correct to obtain using optimizing and u is corresponded in vector space(k)Another point P3So that P3Corresponding mesh
Offer of tender numerical value J(k)Compare J(k-1)It is more excellent.
Embodiment 1
Container is loaded and unloaded on the transhipment lorry of harbour in specified time range from cargo ship with bridge crane,
Ask the swing energy of the container in cargo handling process minimum, the mathematical modulo of the problem is obtained with reference to the performance constraints of crane
Type is：
Wherein, J represents the swing energy object function for the container to be minimized, x1(t) horizontal position of container is represented
Put, x2(t) upright position of container, x are represented3(t) swing angle of container, x are represented4(t) the level speed of crane is represented
Degree, x5(t) be crane vertical speed, x6(t) be container swing angular velocity, u1And u (t)2(t) crane water is represented
The rate controlling amount of gentle vertical direction.In embodiment 1, control room engineer requires that container is from position (0,22) in 10 seconds
(10,14) are moved to, in order to obtain the traction electric machine strategy of speed control for making the minimization of object function, DCS operation optimum controls
Algorithm, its running is as shown in figure 3, execution step is：
Step 1)：Control room engineer is by the initial position x (t of container0)=[0,22,0,0,0,0], be assigned to up to position
Put x (tf)=[10,14,0,0,0,0], the lay day set tf=10 and performance constraints -2.5≤x of traction electric machine4(t)≤
2.5, -1≤x5(t)≤1, -2.83374≤u1(t)≤2.83374, -0.80865≤u2(t) information gathering≤0.71265 is inputted
Module 31；
Step 2)：Initialization module 32 brings into operation, and is parameterized using piece-wise constant, and the segments for setting cargo handling process is
The vectorial initial guess u of NE=50, the parametrization of traction electric machine controlled quentity controlled variable(k)For 0.5, setup algorithm precision tol is 10-4, will
Iterations k zero setting；
Step 3)：The status information x of current iteration is obtained by ODE rapid solvings module 33(k)And target function value J (t)(k)。
Step 4)：Module 34 is solved by gradient and obtains current iteration target function gradient information dJ(k)With constraints ladder
Spend information g(k)；Step 5) is skipped as k=0 and directly performs step 7)；
Step 5)：NLP problem solver modules 35 are run, and convergence judgement is carried out by NLP convergences judge module, if
The target function value J of current iteration(k)With the target function value J of last iteration(k-1)The difference of absolute value be less than precision 10-4,
Then judge convergence sexual satisfaction, and the traction electric machine strategy of speed control of current iteration is converted to the control instruction output of motor；
If convergence is unsatisfactory for, step 6) is continued executing with；
Step 6)：Use u(k),J(k),dJ(k),g(k)Value cover last iteration u(k-1),J(k-1),dJ(k-1),g(k-1)Value,
And iterations k is added 1；
Step 7)：NLP problem solver modules 35 are utilized in step 3) and 4) the middle target function value and gradient information obtained,
Search direction and optimizing step-length are solved, and carries out optimizing amendment, u is compared in acquisition(k-1)More excellent new strategy of speed control u(k).Should
Step jumps to step 3) again after the completion of performing, untill NLP convergences judge module meets.
Described ODE rapid solving modules, using the rank Runge Kutta method of level Four five, solution formula is：
Wherein, t represents time, tiRepresent sometime point in the control process of Runge Kutta method choice, x(k)(ti) table
Show container t in kth time iterationiThe status information at moment, F () be describe state differential equation function, K1、K2、
K3、K4The functional value of 4 nodes in runge kutta method integral process is represented respectively, and integration step h determination formula is：
ti+1Represent t in Runge Kutta method choice control processiLatter time node.
Described gradient solves module, using sensitivity equation of locus method：
Step 1)：Define the sensitivity equation of locus Γ of kth time iteration(k)(t) it is：
Γ(k)(t) solution formula is：
Wherein, t represents the time,Represent kth time iteration medium sensitivity equation of locus for time t derivative, F (u(k),x(k)(t), t) be describe state differential equation function, Γ(k)(t0) represent sensitivity equation of locus in kth time iteration
Initial time state value, x0Represent the initial time state value of state differential equation function.
Step 2)：Sensitivity equation of locus Γ is solved using the rank Runge Kutta method of level Four five(k)(t) at each integration moment
Value, solution formula is：
Wherein, t represents time, tiRepresent that sometime point, h are integration step in the control process of Runge Kutta method choice
It is long, x(k)(ti) represent container t in kth time iterationiThe status information at moment, S () are the letters for describing sensitivity equation
Number, Q1、Q2、Q3、Q4The functional value of 4 nodes in runge kutta method integral process is represented respectively.
Step 3)：According to obtained container state information x(k)And sensitivity equation of locus Γ (t)(k)(t) target, is solved
The gradient information dJ of function(k)：
Wherein, Φ0(u(k),x(k)(t),tf) represent object function end conswtraint item, L0(u(k),x(k)(t), t) represent mesh
The integral term of scalar functions.
Step 4)：According to obtained container state information x(k)And sensitivity equation of locus Γ (t)(k)(t) constraint, is solved
The gradient information g of condition(k)：
Wherein, Φj(u(k),x(k)(t),tf) represent j-th of constraints function end conswtraint item, Lj(u(k),x(k)
(t), t) represent j-th of constraints function integral term, m represent constraints number.
Described NLP problem solver modules, are realized using following steps：
Step 1)：If the target function value J of current iteration(k)With the target function value J of last iteration(k-1)It is absolute
The difference of value is less than precision tol, then judges convergence sexual satisfaction, and the strategy of speed control of current iteration is converted into traction electric machine
Control instruction exports；If convergence is unsatisfactory for, step 2) is continued executing with；
Step 2)：Use u(k),J(k),dJ(k),g(k)Value cover last iteration u(k-1),J(k-1),dJ(k-1),g(k-1)Value,
And iterations k is increased by 1；
Step 3)：By motor speed control strategy u(k-1)As some point in vector space, P is denoted as1, P1Corresponding mesh
Offer of tender numerical value is exactly J(k-1)；
Step 4)：From point P1Set out, according to the NLP algorithms of selection and point P1The target function gradient information dJ at place(k-1)With
Constraints gradient information g(k-1), construct a search direction d in vector space(k-1)With step-length α(k-1)；
Step 5)：Pass through formula u(k)=u(k-1)+α(k-1)d(k-1)U is corresponded in construction vector space(k)Another point P2；
Step 6)：Correct to obtain using optimizing and u is corresponded in vector space(k)Another point P3So that P3Corresponding mesh
Offer of tender numerical value J(k)Compare J(k-1)It is more excellent.
Finally, DCS is converted to the strategy of speed control obtained by continuous fast response method the control instruction of motor,
Electric machine controller is sent to by fieldbus networks, actuating motor is performed corresponding actions, at the same it is real-time with position sensor
Gather the positional information of container and be passed back to DCS, control room engineer is grasped cargo handling process at any time.
Above content is to combine specific preferred embodiment further description made for the present invention, it is impossible to is assert
The specific implementation of the present invention is only limited to these explanations.For general technical staff of the technical field of the invention, not
On the premise of departing from inventive concept, some simple deduction or replace can also be made, should all be considered as belonging to the protection of the present invention
Scope.
Claims (1)
1. a kind of bridge-type container crane swings optimal control system, the handling of bridge-type container crane can be automatically controlled,
Container is set to swing energy minimum in moving process, to put forward the security and efficiency of harbour high cube container handling.Its feature exists
In：By traction electric machine, container position sensor, analog-digital converter, digital analog converter, fieldbus networks, decentralised control system
Unite (DCS), control room is shown, traction motor controller is formed.The running of the control system includes：
Step 1)：Control room engineer specifies container to need position coordinates, cargo handling process time restriction and the traction reached
The performance parameter constraint of motor；
Step 2)：DCS performs internal optimal control algorithm, obtains the traction electric machine speed for making container handling process angle of oscillation minimum
Spend control strategy；
Step 3)：DCS is converted to obtained motor speed control strategy the control instruction of traction electric machine, passes through fieldbus network
Network is sent to the digital analog converter of traction motor controller leading portion, traction motor controller is controlled according to the control instruction received
Traction electric machine performs corresponding actions；
Step 4)：Container position sensor gathers the positional information of container in real time, by using scene after analog-to-digital conversion
Bus network is passed back to DCS, and is shown in master control room, engineer is monitored cargo handling process at any time.
Described DCS, including information acquisition module, initialization module, subordination principle (Ordinary
Differential equations, abbreviation ODE) rapid solving module, gradient solve module, nonlinear programming problem solve mould
Block, control instruction output module.Wherein information acquisition module includes container position collection, traction electric machine performance constraints gather,
Lay day sets three submodules of collection, Non-Linear Programming (Non-linear Programming, abbreviation NLP) problem solving
Module includes search direction solution, optimizing step-length solves, optimizing corrects, NLP convergences judge four submodules.
Described DCS performs internal optimal control algorithm and obtains making container handling process swing the minimum traction electric machine speed of energy
Control strategy is spent, operating procedure is as follows：
Step 1)：Information acquisition module 31 obtain container initial position and engineer specify in-position, traction electric machine
Performance constraints and lay day are set；
Step 2)：Initialization module 32 brings into operation, and is parameterized using piece-wise constant, sets the segments NE of cargo handling process, leads
Draw the initial guess u of the parametrization vector of motor control amount(k), setup algorithm precision tol, by iterations k zero setting；
Step 3)：The status information x of current iteration is obtained by ODE rapid solvings module 33(k)And target function value J (t)(k)。
Step 4)：Module 34 is solved by gradient and obtains current iteration target function gradient information dJ(k)Believe with constraints gradient
Cease g(k)；Step 5) is skipped as k=0 and directly performs step 7)；
Step 5)：NLP problem solver modules 35 are run, and convergence judgement is carried out by NLP convergences judge module, if this
The target function value J of iteration(k)With the target function value J of last iteration(k-1)The difference of absolute value be less than precision tol, then judge
Sexual satisfaction is restrained, and the traction electric machine strategy of speed control of current iteration is converted to the control instruction output of motor；If receive
Holding back property is unsatisfactory for, then continues executing with step 6)；
Step 6)：Use u(k),J(k),dJ(k),g(k)Value covering before an iteration u(k-1),J(k-1),dJ(k-1),g(k-1)Value, and will
Iterations k adds 1；
Step 7)：NLP problem solver modules 35 are using in step 3) and 4) the middle target function value and gradient information obtained, solution
Search direction and optimizing step-length, and optimizing amendment is carried out, u is compared in acquisition(k-1)More excellent new strategy of speed control u(k).The step
Step 3) is jumped to after the completion of execution again, untill NLP convergences judge module meets.
Described ODE rapid solving modules, using the rank Runge Kutta method of level Four five, solution formula is：
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Step 1)：Define the sensitivity equation of locus Γ of kth time iteration(k)(t) it is：
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<msub>
<mi>Q</mi>
<mn>4</mn>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, t represents time, tiRepresent sometime point in the control process of Runge Kutta method choice, h is integration step, x(k)(ti) represent container t in kth time iterationiThe status information at moment, S () are the functions for describing sensitivity equation,
Q1、Q2、Q3、Q4The functional value of 4 nodes in runge kutta method integral process is represented respectively.
Step 3)：According to obtained container state information x(k)And sensitivity equation of locus Γ (t)(k)(t) object function, is solved
Gradient information dJ(k)：
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<mn>0</mn>
</msub>
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<mn>0</mn>
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</mrow>
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<msup>
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<mi>k</mi>
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</mrow>
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</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>N</mi>
<mi>E</mi>
</mrow>
</munderover>
<msubsup>
<mo>&Integral;</mo>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
</msubsup>
<mo>{</mo>
<mfrac>
<mrow>
<mo>&part;</mo>
<msub>
<mi>L</mi>
<mn>0</mn>
</msub>
<mrow>
<mo>(</mo>
<msup>
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</mrow>
</msup>
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<msup>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
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</mrow>
</msup>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
<mo>,</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mo>&part;</mo>
<msup>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
<mrow>
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<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<msup>
<mi>&Gamma;</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mfrac>
<mrow>
<mo>&part;</mo>
<msub>
<mi>L</mi>
<mn>0</mn>
</msub>
<mrow>
<mo>(</mo>
<msup>
<mi>u</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>,</mo>
<msup>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
<mo>,</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mo>&part;</mo>
<msup>
<mi>u</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
</mrow>
</mfrac>
<mo>}</mo>
<mi>d</mi>
<mi>t</mi>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, Φ0(u(k),x(k)(t),tf) represent object function end conswtraint item, L0(u(k),x(k)(t), t) represent target letter
Several integral terms.
Step 4)：According to obtained container state information x(k)And sensitivity equation of locus Γ (t)(k)(t) constraints, is solved
Gradient information g(k)：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<msup>
<msub>
<mi>g</mi>
<mi>j</mi>
</msub>
<mrow>
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<mi>k</mi>
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<mo>=</mo>
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<msub>
<mi>&Phi;</mi>
<mi>j</mi>
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<mo>(</mo>
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<msub>
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<mrow>
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<mi>&Phi;</mi>
<mi>j</mi>
</msub>
<mrow>
<mo>(</mo>
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<mi>k</mi>
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</mrow>
</msup>
<mo>,</mo>
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<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
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</mrow>
</msup>
<mo>(</mo>
<mi>t</mi>
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<mo>,</mo>
<msub>
<mi>t</mi>
<mi>f</mi>
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</mrow>
</mrow>
<mrow>
<mo>&part;</mo>
<msup>
<mi>u</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>N</mi>
<mi>E</mi>
</mrow>
</munderover>
<msubsup>
<mo>&Integral;</mo>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
</msubsup>
<mo>{</mo>
<mfrac>
<mrow>
<mo>&part;</mo>
<msub>
<mi>L</mi>
<mi>j</mi>
</msub>
<mrow>
<mo>(</mo>
<msup>
<mi>u</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>,</mo>
<msup>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
<mo>,</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mo>&part;</mo>
<msup>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<msup>
<mi>&Gamma;</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>f</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mfrac>
<mrow>
<mo>&part;</mo>
<msub>
<mi>L</mi>
<mi>j</mi>
</msub>
<mrow>
<mo>(</mo>
<msup>
<mi>u</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>,</mo>
<msup>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
<mo>,</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mo>&part;</mo>
<msup>
<mi>u</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
</mrow>
</mfrac>
<mo>}</mo>
<mi>d</mi>
<mi>t</mi>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msup>
<mi>g</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msup>
<msub>
<mi>g</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
</mrow>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<mrow>
<msup>
<msub>
<mi>g</mi>
<mi>j</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</msup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>,</mo>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mi>m</mi>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, Φj(u(k),x(k)(t),tf) represent j-th of constraints function end conswtraint item, Lj(u(k),x(k)(t),t)
The integral term of j-th of constraints function is represented, m represents the number of constraints.
Described NLP problem solver modules, are realized using following steps：
Step 1)：If the target function value J of current iteration(k)With the target function value J of last iteration(k-1)Absolute value it
Difference is less than precision tol, then judges convergence sexual satisfaction, and the strategy of speed control of current iteration is converted to the control of traction electric machine
Instruction output；If convergence is unsatisfactory for, step 2) is continued executing with；
Step 2)：Use u(k),J(k),dJ(k),g(k)Value covering before an iteration u(k-1),J(k-1),dJ(k-1),g(k-1)Value, and will
Iterations k increases by 1；
Step 3)：By motor speed control strategy u(k-1)As some point in vector space, P is denoted as1, P1Corresponding target letter
Numerical value is exactly J(k-1)；
Step 4)：From point P1Set out, according to the NLP algorithms of selection and point P1The target function gradient information dJ at place(k-1)With constraint bar
Part gradient information g(k-1), construct a search direction d in vector space(k-1)With step-length α(k-1)；
Step 5)：Pass through formula u(k)=u(k-1)+α(k-1)d(k-1)U is corresponded in construction vector space(k)Another point P2；
Step 6)：Correct to obtain using optimizing and u is corresponded in vector space(k)Another point P3So that P3Corresponding target letter
Numerical value J(k)Compare J(k-1)It is more excellent.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10258987A (en) * | 1997-03-17 | 1998-09-29 | Mitsubishi Heavy Ind Ltd | Anti-swinging device for slung load |
| JPH11255474A (en) * | 1998-03-13 | 1999-09-21 | Yaskawa Electric Corp | Swing prevention control device for crane |
| JP2007161393A (en) * | 2005-12-13 | 2007-06-28 | Ishikawajima Harima Heavy Ind Co Ltd | Swing prevention control method for crane |
| EP2927177A1 (en) * | 2014-04-02 | 2015-10-07 | Patentic Oy Ab | Method and arrangement for controlling a crane |
| CN106629400A (en) * | 2016-11-16 | 2017-05-10 | 北华大学 | Intelligent electric anti-swinging control system for container crane |
| CN107150952A (en) * | 2017-05-22 | 2017-09-12 | 河南卫华机械工程研究院有限公司 | A kind of container crane and its stabilization method |
-
2017
- 2017-11-13 CN CN201711115228.5A patent/CN107857196B/en not_active Expired - Fee Related
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10258987A (en) * | 1997-03-17 | 1998-09-29 | Mitsubishi Heavy Ind Ltd | Anti-swinging device for slung load |
| JPH11255474A (en) * | 1998-03-13 | 1999-09-21 | Yaskawa Electric Corp | Swing prevention control device for crane |
| JP2007161393A (en) * | 2005-12-13 | 2007-06-28 | Ishikawajima Harima Heavy Ind Co Ltd | Swing prevention control method for crane |
| EP2927177A1 (en) * | 2014-04-02 | 2015-10-07 | Patentic Oy Ab | Method and arrangement for controlling a crane |
| CN106629400A (en) * | 2016-11-16 | 2017-05-10 | 北华大学 | Intelligent electric anti-swinging control system for container crane |
| CN107150952A (en) * | 2017-05-22 | 2017-09-12 | 河南卫华机械工程研究院有限公司 | A kind of container crane and its stabilization method |
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