CN110727249A - Method for controlling maximum permitted behavior information of automatic manufacturing system based on unobservable events - Google Patents
Method for controlling maximum permitted behavior information of automatic manufacturing system based on unobservable events Download PDFInfo
- Publication number
- CN110727249A CN110727249A CN201910788214.2A CN201910788214A CN110727249A CN 110727249 A CN110727249 A CN 110727249A CN 201910788214 A CN201910788214 A CN 201910788214A CN 110727249 A CN110727249 A CN 110727249A
- Authority
- CN
- China
- Prior art keywords
- state
- manufacturing system
- states
- automatic manufacturing
- deadlock
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 132
- 238000000034 method Methods 0.000 title claims abstract description 46
- 230000007704 transition Effects 0.000 claims abstract description 124
- 230000008569 process Effects 0.000 claims description 21
- 230000001338 necrotic effect Effects 0.000 claims description 15
- 230000017074 necrotic cell death Effects 0.000 claims description 10
- 238000009795 derivation Methods 0.000 claims description 4
- 125000004122 cyclic group Chemical group 0.000 claims description 2
- 230000002441 reversible effect Effects 0.000 claims description 2
- 239000002699 waste material Substances 0.000 abstract description 4
- 230000006399 behavior Effects 0.000 description 43
- 230000008859 change Effects 0.000 description 5
- 238000013461 design Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 2
- 230000002829 reductive effect Effects 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000036961 partial effect Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000013468 resource allocation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32339—Object oriented modeling, design, analysis, implementation, simulation language
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- General Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Debugging And Monitoring (AREA)
Abstract
The invention belongs to the technical field of automatic manufacturing systems, and discloses a method for controlling maximum permitted behavior information of an automatic manufacturing system based on an unobservable event, which is used for obtaining a global reachable graph under consideration of full-observable transition; considering the unobservable transition, classifying the states before and after the unobservable transition is transmitted into a state class, and constructing a new reachable graph; then, the front and the back are deduced; obtaining a maximum allowable behavior of the automated manufacturing system; the resulting good states, as long as controlling the occurrence (emission) of events (transitions) of the automated manufacturing system between these states to critical states, enables the automated manufacturing system to remain in these states, and the system must not go to deadlock even if an unexpected event occurs in the automated manufacturing system in an unpredictable manner. The invention can realize the maximum permitted behavior of the automatic manufacturing system, fully utilize resources, reduce unnecessary waste and continuously improve the production efficiency of the automatic manufacturing system.
Description
Technical Field
The invention belongs to the technical field of automatic manufacturing systems, and particularly relates to a maximum permitted behavior information control method of an automatic manufacturing system based on an unobservable event.
Background
Currently, the closest prior art: an Automated Manufacturing System (AMS) is a typical resource allocation System, which is an unmanned modern Manufacturing technology integrating mechanical, electronic, automation, and computer technologies. The Petri net is a mathematical tool based on graphs, is suitable for describing, simulating and analyzing system behaviors, and is widely applied to the research of automatic manufacturing systems due to the unique advantages of the Petri net in the design of the automatic manufacturing systems. Behavioral permissivity is one of the important indicators for active Petri network controller design, and thus how to design a maximum permissive or optimal Petri network controller has been a question of interest to many scholars.
How much behavior the controlled system permits is an important indicator for evaluating an active Petri network controller. In general, the more licensing activities a controlled system possesses means, in large part, that automated manufacturing systems have more flexibility and higher productivity. Therefore, the research on the design method of the Petri network controller with the maximum allowable behavior has not only theoretical significance, but also practical application value. In the prior art, numerous scholars research the maximum permissibility of an automatic manufacturing system, and may not consider the influence of an unobservable event on the automatic manufacturing system based on the unobservable occurrence of the unobservable event in the automatic manufacturing system and the algorithm complexity, and such consideration is obviously incomplete for the actual system, and is specifically shown in the following steps: the existence of more or less invisible events in an actual automatic manufacturing system causes ambiguity and uncertainty in the automatic manufacturing system, and if not taken into account, the absence of sensors in the corresponding locations cannot detect and return the result, for example in a nuclear reactor, the temporary absence of adequate sensors, i.e. an invisible event is generated. And processed, the system will inevitably enter a deadlock situation with the end result of a stoppage of the automated manufacturing system. Since there are some unobservable events in practice, it is necessary in the Petri Net model to consider the maximum allowable behavior control of the automated manufacturing system for the unobservable events.
In summary, the problems of the prior art are as follows: currently, in the research of maximum allowable behavior control based on the Petri net model, the fact that an unobservable event is contained is not considered, the unobservable event is ubiquitous in an automatic manufacturing system, and the unobservable event cannot be observed in an actual system, so that the system is likely to move to deadlock, and finally, the production is greatly lost.
The difficulty of solving the technical problems is as follows: the method comprises the steps that the launching of transitions in a Petri network model is random, a generated reachable graph is complicated and intricate, firstly, the state conditions before and after the launching of each uncontrollable transition are analyzed, then all states are divided, secondly, whether the unobserved events occur or not is unknown, the influence of the unobserved events on the automatic manufacturing system needs to be obtained, the complexity of the proposed algorithm is a challenge, and then, the complexity is higher after the unobserved transitions are considered for the Petri network model with the larger number of states.
The significance of solving the technical problems is as follows: in real life, an automatic manufacturing system inevitably has an unobservable event, and the occurrence of the unobservable event cannot be judged in a working path of the automatic manufacturing system, so that a current progress state of a processing process may be unknown, that is, the current state may enter a deadlock state. It is then necessary to control the boundary values by knowing the boundary values at which the system outputs good conditions, i.e. by knowing the maximum allowable behaviour taking into account the non-observable events.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method for controlling the maximum allowable behavior information of an automatic manufacturing system based on an unobserved event.
The invention is realized in such a way that the control method of the maximum allowed behavior information of the automatic manufacturing system based on the unobservable event comprises the following steps:
firstly, generating a global reachability graph of the whole Petri network by Petri network initial identification and input and output transitions based on an automatic manufacturing system of a full observable event;
step two, considering the unobservable events of the automatic manufacturing system, and generating a new reachability graph by the Petri network initial identification and the input and output transitions;
step three, considering the unobservable events of the automatic manufacturing system, and reversely deducing the deadlock state to obtain the bad state of the automatic manufacturing system considering the unobservable events;
step four, considering the unobservable events of the automatic manufacturing system, and reversely deducing from the necrosis state to obtain the critical state of the automatic manufacturing system considering the unobservable events;
and step five, removing deadlock states and bad states from all the states, wherein the rest states are good states, and considering the maximum allowable behavior of the automatic manufacturing system of the unobserved events.
Further, the step one is performed in an automatic manufacturing system of the full observable event, and the generating of the global reachability graph of the whole Petri net by the Petri net initial identification and the input and output transitions specifically comprises the following steps:
(1) inputting an initial identification M of a Petri net0Input, output transitions;
(2) initializing current state M ═ M0And the flag is set as a flag and initialized to false;
(3) if the system has the state which is not searched, the following process is continuously executed, otherwise, the process is terminated;
(4) selecting a flag M with a state of "false", i.e. not accessed;
1) if M is searched, marking M as true, and starting to search other false identifications;
2) if the enabled transition does not exist under M, marking M as deadlock, and adding the deadlock into a deadlock state set deadlock states;
(5) for all transitions t meeting the enabling condition under M, executing the following operations;
1) exciting t to obtain a new mark M';
2) adding an arc t from M to M';
(6) marking the state of M as "true" and returning to (2);
(7) and finally obtaining the global reachability graph R (G) of the whole Petri net.
Further, the step two of generating a new reachability graph from the Petri net initial identification and the input and output transitions by considering the unobservable event of the automatic manufacturing system specifically includes:
(1) input and output transitions and set of unobservable transitions T of input Petri netsuoStep one, obtaining a global reachable graph R (G) and a deadlock state set deadlockstates;
(2) initializing current state M ═ M0And it is marked as a flag, initialized to false, where M0Is in an initial state;
(3) for an unobservable transition set TuoIf one state reaches the other state through one invisible transition emission, classifying the two states before and after the invisible transition emission into one class, namely the state class;
(4) considering the state class as a node state, from an initial state M0Starting to construct a new reachable graph R' (G), the following operations are performed;
1) if the system has the state or the node state which is not searched, the following process is continuously executed, otherwise, the process is terminated;
2) selecting a state or node state as false, and a state or node state which is not accessed;
① if it has already been searched, it marks it as true and starts to search other false or node states;
② if there is no transition that can be enabled in the state or node state, then mark the state or node state as deadlock and add it to the set of deadlock states;
3) for all transitions t in this state or node state that satisfy the enable condition, the following operations are performed:
① arouses t to get a new state or node state;
② adding an arc t from the current state or node state to a new state or node state;
4) return to 2 after marking the state or node state as true);
5) and obtaining a new reachability graph R' (G) of the whole Petri net.
Further, the third step considers an unobservable event of the automatic manufacturing system, and the inversely deriving from the deadlock state a bad state of the automatic manufacturing system considering the unobservable event specifically includes:
(1) inputting all transitions of the Petri network to obtain a deadlock state set;
(2) initializing current state M ═ MdeadlockstateWherein M isdeadlockstateIs a deadlock state;
(3) traversing all non-deadlock states, and entering a deadlock state M if one state or node state is transmitted through one or more transition tdeadlockstateIf the state or node state is a bad state MbadstateAdding the bad state set baddstates into the bad state set baddstates;
(4) performing upward cyclic search layer by layer to traverse all non-deadlock states, wherein one state or node state is transmitted through one or more transition t, and inevitably enters a deadlock state MdeadlockstateOr bad state MbadstateIf the state or node state is a bad state MbadstateThe bad state set badstates is updated and added into the bad state set badstates to obtain an updated bad state set badstates'.
Further, considering the unobservable events of the automatic manufacturing system, the inversely deriving the critical state of the automatic manufacturing system considering the unobservable events from the necrotic state specifically includes:
(1) inputting all transitions of the Petri network, and obtaining a deadlock state set deadlockstates and an updated bad state set badstates' obtained in the third step;
(2) combining the obtained deadlock state set deadlocks and the updated bad state set badstates' obtained in the step three into a set, wherein the necrosis state set badOrdeadlocks;
(3) initializing current state M ═ MbadOrdeadlockstateWherein M isbadOrdeadlockstateA necrotic state;
(4) traversing all non-necrotic states, and if one state or node state is transmitted through two or more transition t; if present, isTransition t-emission to necrotic state MbadOrdeadlockstateBut not all reach the necrotic state, the state or node state is the critical state McriticalstateIt is added to the critical state sets criticalstates.
Further, the removing all states from deadlock and bad states, the remaining states being good states, the maximum allowable behavior of the automated manufacturing system considering the unobservable events specifically including:
(1) inputting the obtained set of all the states, obtaining a deadlock state set deadlocks, obtaining an updated bad state set badstates', obtaining a necrosis state set badOrdedockstates and a critical state set criticalitates;
(2) subtracting the necrotic state set badOrdedockstates from the set of all states to obtain a good state set goodstates, and considering the maximum allowable behavior of the automatic manufacturing system of the unobservable events;
(3) good, critical, bad, and deadlock conditions of the automated manufacturing system are output that take into account the unobservable events.
Another object of the present invention is to provide an unobserved event based maximum permitted behavior information control system of an automated manufacturing system based on an unobserved event based maximum permitted behavior information control method, the unobserved event based maximum permitted behavior information control system comprising:
the global reachability graph generation module is used for generating a global reachability graph of the whole Petri network by the Petri network initial identification, input and output transition based on the automatic manufacturing system of the full observable event;
the new reachability graph generation module is used for generating a new reachability graph by the Petri network initial identification, input and output transition in consideration of the invisible events of the automatic manufacturing system;
the bad state obtaining module is used for considering the unobservable events of the automatic manufacturing system and obtaining the bad state of the automatic manufacturing system considering the unobservable events through reverse derivation of the deadlock state;
a critical state obtaining module for considering the unobservable events of the automatic manufacturing system, and obtaining the critical state of the automatic manufacturing system considering the unobservable events by inverse derivation of the necrosis state;
and the maximum permission behavior module is used for removing the deadlock state and the bad state from all the states, and the rest states are good states and take the maximum permission behavior of the automatic manufacturing system of the unobserved events into consideration.
Another object of the present invention is to provide an automatic manufacturing system applying the maximum allowable behavior information control method for an automatic manufacturing system based on an unobservable event.
In summary, the advantages and positive effects of the invention are: the invention considers the maximum allowable behavior control method of the automatic manufacturing system of the unobservable event, reconstructs the whole Petri network reachable graph obtained by the original full observable transition based on the unobservable transition to obtain a new reachable graph, and then reversely deduces from the deadlock state; after obtaining the good state of the automatic manufacturing system considering the unobservable event, namely the maximum allowable behavior of the automatic manufacturing system considering the unobservable event, all the next step trends of the boundary state, namely the critical state, positioned in the maximum allowable behavior are controlled, so that the system never reaches the deadlock state.
Compared with the prior art, the invention has the following advantages:
(1) the invention can realize the maximum permission of the automatic manufacturing system, fully utilize resources and reduce unnecessary waste. Once the system enters a deadlock state, the system processing process is stopped, and finally output products cannot be used normally, so that resource waste is caused.
(2) The present invention takes into account the impact of an unobservable event on an automated manufacturing system, resulting in the maximum permitted behavior of the system. The non-observable events are ubiquitous in automated manufacturing systems and therefore their effect must be adequately considered. The controller can make a proper control decision to decide which transition should not be transmitted in the current state, so that the phenomenon that the production line is stopped and huge loss is caused due to deadlock in the processing process is avoided.
(3) The maximum allowed behavior of the automatic manufacturing system obtained in the prior art is the maximum allowed behavior based on the full observable events, and has certain limitation when being applied to the automatic manufacturing system in real life, so that deadlock caused by occurrence of the unobservable events cannot be observed, and further adverse effects such as delay of the processing process of the automatic manufacturing system and the like are further caused.
Drawings
FIG. 1 is a flowchart of a method for controlling maximum allowable behavior information of an AMHS based on an unobserved event according to an embodiment of the present invention.
FIG. 2 is a diagram of S provided in an embodiment of the present invention4And (4) a schematic diagram of the R model.
FIG. 3 is a diagram of S provided by an embodiment of the present invention4Partial state diagram of the R model.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems in the prior art, the present invention provides a method for controlling maximum allowable behavior information of an automatic manufacturing system based on an unobserved event, and the present invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the method for controlling maximum allowable behavior information of an automatic manufacturing system based on an unobserved event according to an embodiment of the present invention includes the following steps:
s101: based on an automatic manufacturing system of the full observable event, generating a global reachability graph of the whole Petri network by the Petri network initial identification and input and output transitions;
s102: generating a new reachability graph by the Petri net initial identification and input and output transitions by considering the invisible events of the automatic manufacturing system;
s103: considering the unobservable events of the automatic manufacturing system, and reversely deducing a bad state of the automatic manufacturing system considering the unobservable events from the deadlock state;
s104: considering the unobservable events of the automatic manufacturing system, and reversely deducing a critical state of the automatic manufacturing system considering the unobservable events from the necrosis state;
s105: all states are removed from deadlock and bad states, and the remaining states are good states, i.e., the maximum allowable behavior of the automated manufacturing system that takes into account the unobservable events.
The technical solution of the present invention is further described below with reference to the accompanying drawings.
Definition 1: a Petri Net (structure) PN is a quadruple (P, T, F, W),
wherein:
(1) p and T are called the set of libraries and transitions, respectively, and are non-empty, finite and disjoint, i.e.:
(2)
a set called flow relations or directed arcs;
(3)
is a mapping that assigns a weight to each arc, i.e., w (f) > 0.
In the graph theory, the Petri network is a two-branch directed graph, and the places and the transitions are called nodes of the Petri network. When the Petri net is represented graphically, the libraries are represented by circles and the transitions are represented by rectangles or bars. The library places and the transitions are connected by directed arcs, and nodes of the same type cannot be connected by directed arcs.
Definition 2(N, M)0)＝(P,T,F,W,M0) Is an identification net of the Petri net (P, T, F, W).
Wherein: m0Is an initial identification, representing the initial state of the system.
Definition 3: observability and observable and unobservable transitions
(1) Observability: which describes the possibility of the system to determine and observe the state of the dynamic behaviour inside the system by means of input and output values obtained by direct measurement.
(2) Observable and unobservable transitions: according to whether the event occurrence can be observed or not, dividing the behavior event into an observable event and an unobservable event, and correspondingly dividing the behavior event into an observable transition and an unobservable transition in the Petri network according to whether transition excitation energy is observed or not.
The method for controlling the maximum permitted behavior of the automatic manufacturing system based on the unobserved events, provided by the embodiment of the invention, specifically comprises the following steps:
the method comprises the following steps: based on an automatic manufacturing system of the full observable event, generating a global reachability graph of the whole Petri network by the Petri network initial identification and input and output transitions; the specific implementation process is as follows:
(1) inputting an initial identification M of a Petri net0Input, output transitions;
(2) initializing current state M ═ M0And the flag is set as a flag and initialized to false;
(3) if the system has the state which is not searched, the following process is continuously executed, otherwise, the process is terminated;
(4) selecting a flag M with a state of "false", i.e. not accessed;
1) if M has been searched, marking M as "true" and starting to search other "false" identifications;
2) if there is no transition that can be enabled under M, marking M as 'deadlock' and adding it to deadlockstates;
(5) for all transitions t under M that satisfy the enable condition, the following operations are performed:
1) exciting t to obtain a new mark M';
2) adding an arc t from M to M';
(6) marking the state of M as "true" and returning to (2);
(7) and finally obtaining the global reachability graph R (G) of the whole Petri net.
Step two: generating a new reachability graph by the Petri net initial identification and input and output transitions by considering the invisible events of the automatic manufacturing system; the specific implementation process is as follows:
(1) input and output transitions and set of unobservable transitions T of input Petri netsuoThe global reachable graph R (G) and deadlockstates obtained in the step one;
(2) initializing current state M ═ M0And it is marked as a flag, initialized to false, where M0Is in an initial state;
(3) for an unobservable transition set TuoIf one state reaches the other state through one invisible transition emission, the two states before and after the invisible transition emission are classified into one type, which is called as a state type;
(4) considering the state class as a node state, from an initial state M0Starting to construct a new reachable graph R' (G), the following operations are performed:
1) if the system has the state or the node state which is not searched, the following process is continuously executed, otherwise, the process is terminated;
2) selecting a state or node state as 'false', i.e. a state or node state that has not been accessed;
① marks it as "true" if it has been searched, and starts to search for other "false" states or node states;
② if there is no transition that can be enabled in the state or node state, then mark the state or node state as "deadlock" and add it to deadlockstates;
3) for all transitions t in this state or node state that satisfy the enable condition, the following operations are performed:
① arouses t to get a new state or node state;
② adding an arc t from the current state or node state to a new state or node state;
4) return to 2 after marking the state or node state as "true");
5) and obtaining a new reachability graph R' (G) of the whole Petri net.
Step three: considering the unobservable events of the automatic manufacturing system, and reversely deducing a bad state of the automatic manufacturing system considering the unobservable events from the deadlock state; the specific implementation process is as follows:
(1) inputting all transitions of the Petri network, and deadlockstates obtained in the step two;
(2) initializing current state M ═ MdeadlockstateWherein M isdeadlockstateIs a deadlock state;
(3) traversing all the non-deadlockstates, if one state or node state is transmitted through one or more transition t, inevitably entering MdeadlockstateThen the state or node state is MbadstateIt is added to baddstates;
(4) searching upwards in a circulating mode layer by layer, traversing all non-deadlockstates, transmitting one state or node state through one or more transition t, and inevitably entering MdeadlockstateOr MbadstateThen the state or node state is MbadstateUpdating baddstates, adding baddstates to obtain updated baddstates'.
Step four: considering the unobservable events of the automatic manufacturing system, and reversely deducing a critical state of the automatic manufacturing system considering the unobservable events from the necrosis state; the specific implementation process is as follows:
(1) inputting all transitions of the Petri network, deadlockstates obtained in the step two and updated baddstates' obtained in the step three;
(2) merging the deadlocks obtained in the step two and the updated badstates' obtained in the step three into a set, namely badOrdeadlocks;
(3) initializing current state M ═ MbadOrdeadlockstateWherein M isbadOrdeadlockstateA necrotic state;
(4) traversing all non-baddstate, if one state or node state is transmitted by two or more transition t, if the transition t is transmitted to MbadOrdeadlockstateBut not all arrive at MbadOrdeadlockstateThen the state or node state is McriticalstateIt is added to criticalstates.
Step five: removing deadlock states and bad states from all states, wherein the rest states are good states, namely the maximum allowable behavior of the automatic manufacturing system considering the unobservable events; the specific implementation process is as follows:
(1) inputting the set of all the states obtained in the step one, the deadlocks obtained in the step two, the updated baddstates' obtained in the step three, and the badOrdeadlocks and criticalstates obtained in the step four;
(2) subtracting badOrdeduplockstates from the set of all states to obtain goodstates, i.e., the maximum allowable behavior of the automatic manufacturing system considering the unobserved events;
(3) the goodstate, criticalstate, baddstate, and deadlockstate of the automated manufacturing system taking into account the unobservable events are output.
As shown in fig. 2, according to S4The above steps are explained in detail by the R-net model and the partial states of the model: an algorithm for automated manufacturing system maximum allowable behavior control that takes into account unobservable events.
As shown in FIG. 3, assume Tuo＝{t6I.e. transition t6The changes are invisible, and the other changes are observable. According to the first step, the reachable graph of the Petri net can be obtained, which results in a total of 87 states, and the results respectively output goodstates {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 19, 21, 26, 28, 29, 30, 36, 37, 42, 43, 45, 51, 53, 54, 59, 61, 62, 65, 67, 69, 70, 73, 74, 75, 77, 79, 80, 81, 83, 84, 85, 86, 87}, a total of 48 goodstates, critialstates {12, 14, 18, 20, 22, 23, 25, 27, 31, 33, 34, 38, 41, 44, 46, 49, 55, 60}, a total of 18 critialstates, baddstates {24, 32, 35, 39, 40, 47, 48, 56, 52, 63, 57, 56, 68, 58, 76, 78, 72, 76, and a total of baddstates }. According to step two, the unobservable transitions t are taken into account6Influence of (2) moving the unobservable transition t6The two states before and after the emission are classified into one class, which is called as a state class by the invention, then M2Through transition t6Transmission arrival M4Therefore, M will be2And M4Classified as a state class, M5Through transition t6Transmission arrival M10，M10Through transition t6Transmission arrival M18Therefore, M will be5、M10And M18Classified as a state class, M6Through transition t6Transmission arrival M11Therefore, M will be6And M11Classified as a state class, M12Through transition t6Transmission arrival M20Therefore, M will be12And M20Classified as a state class, M13Through transition t6Transmission arrival M21Therefore, M will be13And M21Classified as a state class, M14Through transition t6Transmission arrival M22Therefore, M will be14And M22Classified as a state class, M17Through transition t6Transmission arrival M29Therefore, M will be17And M29Classified as a state class, M19Through transition t6Transmission arrival M31Therefore, M will be19And M31Classified as a state class, M20Through transition t6Transmission arrival M32Therefore, M will be20And M32Classified as a state class, M23Through transition t6Transmission arrival M34Therefore, M will be23And M34Classified as a state class, M24Through transition t6Transmission arrival M35，M35Through transition t6Transmission arrival M48Therefore, M will be24、M35And M48Classified as a state class, M25Through transition t6Transmission arrival M38Therefore, M will be25And M38Classified as a state class, M30Through transition t6Transmission arrival M45，M45Through transition t6Transmission arrival M61Therefore, M will be30、M45And M61Fall into one stateClass, M33Through transition t6Transmission arrival M47Therefore, M will be33And M47Classified as a state class, M39Through transition t6Transmission arrival M52Therefore, M will be39And M52Classified as a state class, M41Through transition t6Transmission arrival M55Therefore, M will be41And M55Classified as a state class, M50Through transition t6Transmission arrival M64Therefore, M will be50And M64Classified as a state class, M51Through transition t6Transmission arrival M67Therefore, M will be51And M67Classified as a state class, M57Through transition t6Transmission arrival M68Therefore, M will be57And M68Classified as a state class, M62Through transition t6Transmission arrival M75Therefore, M will be62And M75Classified as a state class, M65Through transition t6Transmission arrival M77Therefore, M will be65And M77And (4) grouping the states into a state class, regarding all the states as a state node, and reconstructing the reachable graph, so that the deadlock state in the new reachable graph can be obtained without changing, and the deadlockstates are {76, 78, 82} 3 deadlockstates in total. According to step three, baddates {24, 32, 35, 39, 40, 47, 48, 50, 52, 56, 57, 58, 63, 64, 66, 68, 71, 72, 20, 33, 12} can be obtained, for a total of 21 baddates. According to the fourth step, criticalstates can be obtained as {14, 18, 22, 23, 25, 27, 31, 34, 38, 41, 44, 46, 49, 55, 60, 10, 11, 19, 5, 6}, and 20 criticalstates in total. According to the fifth step, there can be obtained goodstates {1, 2, 3, 4, 7, 8, 9, 13, 15, 16, 17, 21, 26, 28, 29, 30, 36, 37, 42, 43, 45, 51, 53, 54, 59, 61, 62, 65, 67, 69, 70, 73, 74, 75, 77, 79, 80, 81, 83, 84, 85, 86, 87}, a total of 43 goodstates, criticalstates {14, 18, 22, 23, 25, 27, 31, 34, 38, 41, 44, 46, 49, 55, 60, 10, 11, 19, 5, 6}, a total of 20 criticalstates, baddstates { (24, 32, 35, 39, 40, 47, 48),50, 52, 56, 57, 58, 63, 64, 66, 68, 71, 72, 20, 33, 12}, a total of 21 baddates, and a total of 3 deadlocks {76, 78, 82 }. From the final output result, considering the automatic manufacturing system of the unobserved events, the deadlock state is not changed, and the bad state is increased on the original basis; the critical state will change, and the change is: a part of the data can be changed into a bad state, and in addition, some critical states are added; good states will be reduced on the original basis, and the change situation is: one part may become a bad state and one part may become a critical state.
According to the maximum permissible behavior control algorithm of the automatic manufacturing system considering the unobservable events, firstly, a global reachable graph considering the fully observable transitions is obtained, secondly, the unobservable transitions are considered, states before and after the unobservable transitions are transmitted are classified into state classes, a new reachable graph is constructed, then, the new reachable graph is deduced from the back to the front, namely, the state is moved one step by one step from the deadlock state, and finally, the maximum permissible behavior of the automatic manufacturing system can be obtained, namely, goodstates finally obtained by the algorithm can be obtained, as long as the automatic manufacturing system is controlled to occur the events (transitions) between the states and the critical state, the automatic manufacturing system can be always in the states, and even if the unobservable events exist in the automatic manufacturing system, the automatic manufacturing system cannot move to deadlock.
In the invention, the symbols are as follows: PN represents the Petri network, M0Representing an initial state, M representing a current state, TuoRepresenting a set of unobservable transitions, t representing a transition, MgoodstateIndicating a good state, McriticalstateRepresents a critical state, MbadstateIndicating a bad state, MdeadlockstateIndicating a deadlock condition, MbadOrdeadstateIndicating a necrotic state, goodstates indicating a set of good states, criticalstates indicating a set of critical states, baddstates indicating a set of bad states, badOrdedockstates indicating a set of necrotic states, deadoldstates indicating a set of deadlock states,
means non-0 natureAnd (4) counting.
The technical solution of the present invention is further described with reference to the following specific examples.
The input Petri Net structure is shown below:
the output result is shown in the following graph:
when all the materials are in observable transition:
Total states count：29
Critical States count：7
5 6 7 12 18 23 26
Deadlock States count：2
11 28
Bad States count：4
14 19 24 29
when no appreciable transitions are considered:
The number of states containing unobservable transitions is asfollows：
Total number of states：29
Good states：13
The good states are：1 4 8 9 10 13 15 16 17 20 22 25 27
Critical States：9
The critical Statesare：6 7 12 18 23 26 2 3 21
Bad States：5
The bad Statesare：14 19 24 29 5
Deadlock States：2
The deadlock Statesare：11 28
from the output results, the unobservable transition t is taken into account2The deadlock state of the system is not changed, and the bad state is added with a state 5 on the original basis; the critical state changes, and the change condition is as follows: first, the good state 2, 3, 21 becomes the critical state, and the state 5 becomes the bad state; the good state is reduced on the original basis, and the change condition is as follows: the good state 2, 3, 21 becomes the critical state.
The technical effects of the present invention will be described in detail with reference to experiments.
(1) When the unobserved events are not considered, the output of the algorithm is as follows:
Total states count：87
Critical States count：18
12 14 18 20 22 23 25 27 31 33 34 38 41 44 46 49 55 60
Deadlock States count：3
76 78 82
Bad States count：18
24 32 35 39 40 47 48 50 52 56 57 58 63 64 66 68 71 72
(2) when considering an unobserved event, the algorithm outputs the following:
The number of states containing unobservable transitions is asfollows：
Tota lnumber of states：87
Good states：43
The good states are：1 2 3 4 7 8 9 13 15 16 17 21 26 28 29 30 36 37 4243 45 51 53 54 59 61 62 65 67 69 70 73 74 75 77 79 80 81 83 84 85 86 87
Critical States：20
The critical States are：14 18 22 23 25 27 31 34 38 41 44 46 49 55 6010 11 19 56
Bad States：21
The bad States are：24 32 35 39 40 47 48 50 52 56 57 58 63 64 66 68 7172 20 33 12
Deadlock States：3
The deadlock States are：76 78 82
from the analysis of experimental results, it can be known that the maximum allowable behavior of the automatic manufacturing system considering the unobservable events is essentially to perform clipping on the maximum allowable behavior of the automatic manufacturing system considering the unobservable events, and to eliminate some original good states from bad states due to the occurrence of the unobservable events, thereby reducing the maximum allowable behavior of the system.
The invention firstly obtains a global reachable graph considering the fully observable transition, secondly considers the unobservable transition, classifies the states before and after the unobservable transition is transmitted into the state classes, constructs a new reachable graph, and deduces from the back to the front, namely, the maximum allowable behavior of the automatic manufacturing system can be finally obtained by stepping from a deadlock state to the front one step, namely, the invention provides a good state obtained by an algorithm finally, as long as the occurrence of an event (transition) of the automatic manufacturing system between the states and a critical state is controlled, the automatic manufacturing system can be always in the states, and the system can not move to deadlock even if the unobservable event occurs in the automatic manufacturing system under the unpredictable condition. The invention can realize the maximum permitted behavior of the automatic manufacturing system, fully utilize resources, reduce unnecessary waste and continuously improve the production efficiency of the automatic manufacturing system.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (8)
1. A control method for maximum permitted behavior information of an automatic manufacturing system based on an unobservable event is characterized by comprising the following steps:
firstly, generating a global reachability graph of the whole Petri network by Petri network initial identification and input and output transitions based on an automatic manufacturing system of a full observable event;
step two, considering the unobservable events of the automatic manufacturing system, and generating a new reachability graph by the Petri network initial identification and the input and output transitions;
step three, considering the unobservable events of the automatic manufacturing system, and reversely deducing the deadlock state to obtain the bad state of the automatic manufacturing system considering the unobservable events;
step four, considering the unobservable events of the automatic manufacturing system, and reversely deducing from the necrosis state to obtain the critical state of the automatic manufacturing system considering the unobservable events;
and step five, removing deadlock states and bad states from all the states, wherein the rest states are good states, and considering the maximum allowable behavior of the automatic manufacturing system of the unobserved events.
2. The method according to claim 1, wherein the step one is for an automatic manufacturing system of full observable events, and the step of generating the global reachability graph of the whole Petri net from the initial identification of the Petri net and the input and output transitions specifically comprises:
(1) inputting an initial identification M of a Petri net0Input, output transitions;
(2) initializing current state M ═ M0And the flag is set as a flag and initialized to false;
(3) if the system has the state which is not searched, the following process is continuously executed, otherwise, the process is terminated;
(4) selecting a flag M with a state of "false", i.e. not accessed;
1) if M is searched, marking M as true, and starting to search other false identifications;
2) if the enabled transition does not exist under M, marking M as deadlock, and adding the deadlock into a deadlock state set deadlock states;
(5) for all transitions t meeting the enabling condition under M, executing the following operations;
1) exciting t to obtain a new mark M';
2) adding an arc t from M to M';
(6) marking the state of M as "true" and returning to (2);
(7) and finally obtaining the global reachability graph R (G) of the whole Petri net.
3. The method according to claim 1, wherein the step two considers the unobservable events of the automatic manufacturing system, and the step of generating a new reachability graph from the Petri net initial identification and the input and output transitions specifically comprises:
(1) input and output transitions and set of unobservable transitions T of input Petri netsuoStep one, obtaining a global reachable graph R (G) and a deadlock state set deadlockstates;
(2) initializing current state M ═ M0And it is marked as a flag, initialized to false, where M0Is in an initial state;
(3) for an unobservable transition set TuoIf one state reaches the other state through one invisible transition emission, classifying the two states before and after the invisible transition emission into one class, namely the state class;
(4) considering the state class as a node state, from an initial state M0Starting to construct a new reachable graph R' (G), the following operations are performed;
1) if the system has the state or the node state which is not searched, the following process is continuously executed, otherwise, the process is terminated;
2) selecting a state or node state as false, and a state or node state which is not accessed;
① if it has already been searched, it marks it as true and starts to search other false or node states;
② if there is no transition that can be enabled in the state or node state, then mark the state or node state as deadlock and add it to the set of deadlock states;
3) for all transitions t in this state or node state that satisfy the enable condition, the following operations are performed:
① arouses t to get a new state or node state;
② adding an arc t from the current state or node state to a new state or node state;
4) return to 2 after marking the state or node state as true);
5) and obtaining a new reachability graph R' (G) of the whole Petri net.
4. The method according to claim 1, wherein the third step considers the unobservable events of the automatic manufacturing system, and the inversely deriving the bad status of the automatic manufacturing system considering the unobservable events from the deadlock status comprises:
(1) inputting all transitions of the Petri network to obtain a deadlock state set;
(2) initializing current state M ═ MdeadlockstateWherein M isdeadlockstateIs a deadlock state;
(3) traversing all non-deadlock states, and entering a deadlock state M if one state or node state is transmitted through one or more transition tdeadlockstateIf the state or node state is a bad state MbadstateAdding the bad state set baddstates into the bad state set baddstates;
(4) performing upward cyclic search layer by layer to traverse all non-deadlock states, wherein one state or node state is transmitted through one or more transition t, and inevitably enters a deadlock state MdeadlockstateOr bad state MbadstateIf the state or node state is a bad state MbadstateThe bad state set badstates is updated and added into the bad state set badstates to obtain an updated bad state set badstates'.
5. The method as claimed in claim 1, wherein the step of considering the unobservable events of the automated manufacturing system and the step of inversely deriving the critical states of the automated manufacturing system considering the unobservable events from the necrotic state comprises:
(1) inputting all transitions of the Petri network, and obtaining a deadlock state set deadlockstates and an updated bad state set badstates' obtained in the third step;
(2) combining the obtained deadlock state set deadlocks and the updated bad state set badstates' obtained in the step three into a set, wherein the necrosis state set badOrdeadlocks;
(3) initializing current state M ═ MbadOrdeadlockstateWhich isMiddle MbadOrdeadlockstateA necrotic state;
(4) traversing all non-necrotic states, and if one state or node state is transmitted through two or more transition t; arriving at necrotic state M if there is a transition t emissionbadOrdeadlockstateBut not all reach the necrotic state, the state or node state is the critical state McriticalstateIt is added to the critical state sets criticalstates.
6. The method according to claim 1, wherein the removing all states from deadlock and bad states and the remaining states being good states, the method for controlling maximum allowable behavior of an automatic manufacturing system considering an unobservable event comprises:
(1) inputting the obtained set of all the states, obtaining a deadlock state set deadlocks, obtaining an updated bad state set badstates', obtaining a necrosis state set badOrdedockstates and a critical state set criticalitates;
(2) subtracting the necrotic state set badOrdedockstates from the set of all states to obtain a good state set goodstates, and considering the maximum allowable behavior of the automatic manufacturing system of the unobservable events;
(3) good, critical, bad, and deadlock conditions of the automated manufacturing system are output that take into account the unobservable events.
7. The system for controlling maximum permitted behavior information of the automatic manufacturing system based on the unobserved events, based on the method for controlling maximum permitted behavior information of the automatic manufacturing system based on the unobserved events as claimed in any one of claims 1 to 6, comprising:
the global reachability graph generation module is used for generating a global reachability graph of the whole Petri network by the Petri network initial identification, input and output transition based on the automatic manufacturing system of the full observable event;
the new reachability graph generation module is used for generating a new reachability graph by the Petri network initial identification, input and output transition in consideration of the invisible events of the automatic manufacturing system;
the bad state obtaining module is used for considering the unobservable events of the automatic manufacturing system and obtaining the bad state of the automatic manufacturing system considering the unobservable events through reverse derivation of the deadlock state;
a critical state obtaining module for considering the unobservable events of the automatic manufacturing system, and obtaining the critical state of the automatic manufacturing system considering the unobservable events by inverse derivation of the necrosis state;
and the maximum permission behavior module is used for removing the deadlock state and the bad state from all the states, and the rest states are good states and take the maximum permission behavior of the automatic manufacturing system of the unobserved events into consideration.
8. An automatic manufacturing system applying the control method of maximum allowable behavior information of the automatic manufacturing system based on the unobserved events as claimed in any claim 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910788214.2A CN110727249B (en) | 2019-08-26 | 2019-08-26 | Method for controlling maximum permitted behavior information of automatic manufacturing system based on unobservable events |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910788214.2A CN110727249B (en) | 2019-08-26 | 2019-08-26 | Method for controlling maximum permitted behavior information of automatic manufacturing system based on unobservable events |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110727249A true CN110727249A (en) | 2020-01-24 |
| CN110727249B CN110727249B (en) | 2022-10-04 |
Family
ID=69217120
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910788214.2A Active CN110727249B (en) | 2019-08-26 | 2019-08-26 | Method for controlling maximum permitted behavior information of automatic manufacturing system based on unobservable events |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110727249B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113359650A (en) * | 2021-07-02 | 2021-09-07 | 河北大学 | Method for controlling automatic manufacturing system with uncontrollable event |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5949676A (en) * | 1997-07-30 | 1999-09-07 | Allen-Bradley Company Llc | Method and system for diagnosing the behavior of a machine controlled by a discrete event control system |
| CN106227163A (en) * | 2016-07-15 | 2016-12-14 | 中国电子科技集团公司第二十八研究所 | Equipment manufacturing system no-dead-time control method based on Petri network and simulated annealing |
| CN108762221A (en) * | 2018-07-09 | 2018-11-06 | 西安电子科技大学 | The deadlock freedom control method of automated manufacturing system containing uncontrollable incident |
| CN109739196A (en) * | 2019-01-11 | 2019-05-10 | 西安电子科技大学 | Contain inconsiderable and uncontrollable incident automated manufacturing system deadlock freedom control method |
| CN109857083A (en) * | 2019-01-11 | 2019-06-07 | 西安电子科技大学 | A kind of deadlock freedom control method containing inconsiderable event based on Petri network |
-
2019
- 2019-08-26 CN CN201910788214.2A patent/CN110727249B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5949676A (en) * | 1997-07-30 | 1999-09-07 | Allen-Bradley Company Llc | Method and system for diagnosing the behavior of a machine controlled by a discrete event control system |
| CN106227163A (en) * | 2016-07-15 | 2016-12-14 | 中国电子科技集团公司第二十八研究所 | Equipment manufacturing system no-dead-time control method based on Petri network and simulated annealing |
| CN108762221A (en) * | 2018-07-09 | 2018-11-06 | 西安电子科技大学 | The deadlock freedom control method of automated manufacturing system containing uncontrollable incident |
| CN109739196A (en) * | 2019-01-11 | 2019-05-10 | 西安电子科技大学 | Contain inconsiderable and uncontrollable incident automated manufacturing system deadlock freedom control method |
| CN109857083A (en) * | 2019-01-11 | 2019-06-07 | 西安电子科技大学 | A kind of deadlock freedom control method containing inconsiderable event based on Petri network |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113359650A (en) * | 2021-07-02 | 2021-09-07 | 河北大学 | Method for controlling automatic manufacturing system with uncontrollable event |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110727249B (en) | 2022-10-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhou et al. | Petri net synthesis for discrete event control of manufacturing systems | |
| Estrada-Vargas et al. | A black-box identification method for automated discrete-event systems | |
| Gu et al. | Improved multi-step look-ahead control policies for automated manufacturing systems | |
| Shih et al. | Sequence of Control in S3 PMR | |
| Rintanen | Symmetry Reduction for SAT Representations of Transition Systems. | |
| CN110110402B (en) | Binary decision diagram-based method for quickly solving reachable state of resource distribution system | |
| Tsinarakis et al. | Modular Petri Net based modeling, analysis, synthesis and performance evaluation of random topology dedicated production systems | |
| Liu et al. | General mixed integer programming-based liveness test for system of sequential systems with shared resources nets | |
| Wang et al. | Macro liveness graph and liveness of ω-independent unbounded nets | |
| Cao et al. | Modeling and decision-making methods for a class of cyber–physical systems based on modified hybrid stochastic timed Petri net | |
| Li et al. | Data-driven analysis of downtime impacts in parallel production systems | |
| Szpyrka et al. | Formal description of Alvis language with α 0 system layer | |
| CN110727249B (en) | Method for controlling maximum permitted behavior information of automatic manufacturing system based on unobservable events | |
| Yang et al. | Implementation of generalized mutual exclusion constraints using critical places and marking estimation | |
| Li et al. | Iterative widen heuristic beam search algorithm for scheduling problem of flexible assembly systems | |
| CN110568826B (en) | Method for controlling maximum allowable behavior of automatic manufacturing system based on uncontrollable event | |
| Balbo et al. | First passage time computation in tagged GSPNs with queue places | |
| Barros | A modular representation of fluid stochastic petri nets. | |
| CN113835712B (en) | Fast data packet routing method for judging according to given field value | |
| Yang et al. | Robustness analysis of automated manufacturing systems with uncontrollable events using Petri nets | |
| Huang et al. | Design, analysis and implementation of a real-world manufacturing cell controller based on Petri nets | |
| Jia et al. | Decomposition and aggregation-based real-time analysis of assembly systems with geometric machines and small batch-based production tasks | |
| Abdul-Hussin | Flexible manufacturing system design and optimization using Petri net-based elementary siphons | |
| Liu et al. | Robust deadlock-free scheduling for FMS with liveness-enforcing supervisor combined with beam search controller | |
| Abdul-Hussin | Simulation and Control of Siphon Petri Nets for Manufacturing Systems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |