Sajed Miremadi

A BDD-Based Approach for Modeling Plant and Supervisor by Extended Finite Automata

In this paper, we settle some problems that are encountered when modeling and synthesizing complex industrial systems by the supervisory control theory. First, modeling such huge systems with explicit state-transition models typically results in an intractable model. An alternative modeling approach is to use extended finite automata (EFAs), which is an augmentation of ordinary automata with variables. The main advantage of utilizing EFAs for modeling is that more compact models are obtained. The second problem concerns the ease to understand and implement the supervisor. To handle this problem, we represent the supervisor in a modular manner by extending the original EFAs by compact conditional expressions. This will provide a framework for the users where they can both model their system and obtain the supervisor in form of EFAs. In order to be able to handle complex systems efficiently, the models are symbolically represented by binary decision diagrams (BDDs). All computations that are performed in this framework are based on BDD operations. The framework has been implemented in a supervisory control tool and applied to industrially relevant benchmark problems.

Symbolic Computation of Reduced Guards in Supervisory Control

In the supervisory control theory, a supervisor is generated based on given plant and specification models. The supervisor restricts the plant in order to fulfill the specifications. A problem that is typically encountered in industrial applications is that the resulting supervisor is not easily comprehensible for the users. To tackle this problem, we introduce an efficient method to characterize a supervisor by tractable logic conditions, referred to as guards, generated from the models. The guards express under which conditions an event is allowed to occur to fulfill the specifications. To obtain tractable guard expressions, we reduce them by exploiting the structure of the given models. In order to be able to handle complex systems efficiently, the models are symbolically represented by binary decision diagrams and all computations are performed on these data structures. The algorithms have been implemented in a supervisory control tool and applied to an industrially relevant example.

Extraction and representation of a supervisor using guards in extended finite automata

In supervisory control theory, an issue that often arises in real industrial applications is the huge number of states for the supervisor, which requires a lot of memory. Another problem that is typically encountered for the users of supervisory synthesis tools is lack of information and unreadability of the supervisor. In this paper, we introduce a method to characterize a controllable and non-blocking supervisor directly on the modular automata (sub-plants and sub-specifications), by extracting some guard conditions from the synthesized supervisor and the synchronized automaton. Thus, the presented approach may potentially model a complex supervisor using a compact representation whilst not infringe the original modular structure. Furthermore, the guard conditions, which are generated from a set of states, may give the user of the synthesis procedure a better understanding of which states that were removed during the synthesis. In order to obtain more compact guard expressions, we include some unnecessary states (unreachable and extended forbidden states) in the set of states that will be used for guard generation. By exploiting this extra information, it is possible to reduce the logical expressions to more compact guard conditions.

Sequence Planning Using Multiple and Coordinated Sequences of Operations

The sequential behavior of a manufacturing system results from several constraints introduced during the product, manufacturing, and control logic development. This paper proposes methods and algorithms for automatically representing and visualizing this behavior from various perspectives throughout the development process. A new sequence planning approach is introduced that uses self-contained operations to model the activities and execution constraints. These operations can be represented and visualized from multiple perspectives using a graphical and formal language called Sequences of Operations (SOPs). The operations in a manufacturing system are related to each other in various ways, due to execution constraints expressed by operation pre- and post-conditions. These operation relations include parallel, sequence, arbitrary order, alternative, and hierarchy relations. Based on the SOP language, these relations are identified and visualized in various SOPs and sequences. A software tool, Sequence Planner, has been developed, for organizing the operations into SOPs that visualize only relevant operations and relations.

Solving two supervisory control benchmark problems using Supremica

Two supervisory control benchmark problems for WODESpsila08 are solved using the tool Supremica. Supremica is a tool for formal synthesis of discrete-event control functions based on discrete event models of the uncontrolled plant and specifications of the desired closed-loop behavior. By using formal synthesis of control functions the need for formal verification is reduced since the control functions are computed to automatically fulfill the given specifications, that is, they are ldquocorrect by constructionrdquo. The modeling framework in Supremica is based on finite automata. Supremica implements several techniques for being able to solve large scale problems. In this paper it is evaluated how the algorithms implemented in Supremica that are based on binary decision diagrams performs on the two benchmark problems. The two benchmark problems are generalization of two classical problems; cat and mouse, and the dining philosopherspsila problem. The benchmark problems are parameterized such that it is possible to create problem instances with huge state-spaces. The benchmark shows that supremica can efficiently solve rather large problem instances.

Automatic generation of controllers for collision-free flexible manufacturing systems

A method for automatic generation of non-blocking controllers that generate collision-free flexible manufacturing cells is presented in this paper. Today, industry demands on flexible production sometimes require significant changes in location, orientation and configuration of industrial robots and other moving devices, when new products are introduced. All these changes pose a threat to the devices to collide while sharing workspace. Although the use of simulation software to facilitate these changes is gaining popularity, the coordination of collision-free flexible manufacturing systems is still at best a semi-manual trial-and-error procedure. To avoid this, a formal model of the operations in a manufacturing system is generated, and for each operation state a corresponding 3D simulation shape is created. A collision-free system is then achieved by considering pairs of colliding shapes as forbidden states. The automatic generation also includes a synthesis procedure, where a non-blocking and controllable supervisor is generated based on guard generation. The guards are computed by binary decision diagrams, which means that complex systems can be handled, still generating comprehensible restrictions that are easily included in PLC-code.

Modeling sequential resource allocation systems using Extended Finite Automata

Deadlock avoidance for resource allocation systems (RAS) is a well-established problem in the Discrete Event System (DES) literature. This paper is mainly concerned with modeling the class of Conjunctive / Disjunctive sequential resource allocation systems (C/D RAS) as finite automata extended with variables. The proposed modeling approach allows for modeling multiple instance execution, routing flexibility and failure handling. With an appropriate model of the system, a symbolic approach is then used to synthesize the optimal supervisor, in the least restrictive sense. Furthermore, a set of compact logical formulae can be extracted and attached to the original model, which results in a modular and comprehensible representation of the supervisor

A BDD-Based Approach for Designing Maximally Permissive Deadlock Avoidance Policies for Complex Resource Allocation Systems

In order to develop a computationally efficient implementation of the maximally permissive deadlock avoidance policy (DAP) for complex resource allocation systems (RAS), a recent approach focuses on the identification of a set of critical states of the underlying RAS state-space, referred to as minimal boundary unsafe states. The availability of this information enables an expedient one-step-lookahead scheme that prevents the RAS from reaching outside its safe region. The work presented in this paper seeks to develop a symbolic approach, based on binary decision diagrams (BDDs), for efficiently retrieving the (minimal) boundary unsafe states from the underlying RAS state-space. The presented results clearly demonstrate that symbolic computation enables the deployment of the maximally permissive DAP for complex RAS with very large structure and state-spaces with limited time and memory requirements. Furthermore, the involved computational costs are substantially reduced through the pertinent exploitation of the special structure that exists in the considered problem. Note to Practitioners-A key component of the real-time control of many flexibly automated operations is the management of the allocation of a finite set of reusable resources among a set of concurrently executing processes so that this allocation remains deadlock-free. The corresponding problem is known as deadlock avoidance, and its resolution in a way that retains the sought operational flexibilities has been a challenging problem due to: (i) the inability to easily foresee the longer-term implications of an imminent allocation and (ii) the very large sizes of the relevant state spaces that prevent an online assessment of these implications through exhaustive enumeration. A recent methodology has sought to address these complications through the offline identification and storage of a set of critical states in the underlying state space that renders efficient the safety assessment of any given resource allocation. The results presented in this paper further extend and strengthen this methodology by complementing it with techniques borrowed from the area of symbolic computation; these techniques enable a more compressed representation of the underlying state spaces and of the various subsets and operations that are involved in the pursued computation.

Supervisory Control for State-Vector Transition Models—A Unified Approach

A generic state-vector transition (SVT) model is suggested, including a flexible synchronous composition involving both shared variables and events. This model is analyzed, focusing on properties that are important for supervisor synthesis. A synthesis procedure is then developed for the SVT model, where supervisor guards are generated that guarantee a controllable, nonblocking and maximally permissive supervisor. Novel conditions are introduced, such that more flexible specifications can be applied than earlier suggested for related models. Since the SVT model includes automata and (colored) Petri nets, optionally extended with variables, guards and actions, as special cases, the suggested synthesis approach unifies supervisor synthesis for the main discrete event model classes. Finally, the SVT model is naturally represented and efficiently computed based on binary decision diagrams, and the resulting supervisor guards are easily implemented in industrial control systems.

BDD-based supervisory control on extended finite automata

In this paper, we settle some problems that are encountered when modeling and synthesizing complex industrial systems by the supervisory control theory. First, modeling such huge systems with explicit state-transition models typically results in an intractable model. An alternative modeling approach is to use extended finite automata (EFAs), which is an augmentation of ordinary automata with variables. The main advantage of utilizing EFAs for modeling is that more compact models are obtained. The second problem concerns the ease to understand and implement the supervisor. To handle this problem, we represent the supervisor in a modular manner by extending the original EFAs by compact conditional expressions generated from the monolithic supervisor. In order to, potentially, be able to handle complex systems efficiently, the models are symbolically represented by binary decision diagrams (BDDs). All computations that are performed in this framework are based on BDD operations. The framework has been implemented in a supervisory control tool and applied to industrially relevant benchmark problems.