Towards a concurrency theory for supervisory control

In this paper we propose a process-theoretic concurrency model to express supervisory control properties. In light of the present importance of reliable control software, the current work ow of direct conversion from informal specication documents to control software implementations can be improved. A separate modeling step in terms of controllable and uncontrollable behavior of the device under control is desired. We consider the control loop as a feedback model for supervisory control, in terms of the three distinct components of plant, requirements and supervisor. With respect to the control ow, we consider event-based models as well as state-based ones. We study the process theory TCP as a convenient modeling formalism that includes parallelism, iteration, communication features and non-determinism. Via structural operational semantics, we relate the terms in TCP to labeled transition systems. We consider the partial bisimulation preorder to express controllability that is better suited to handle non-determinism, compared to bisimulation-based models. It is shown how precongruence of partial bisimulation can be derived from the format of the deduction rules. The theory of TCP is studied under nite axiomatization for which soundness and ground-completeness (modulo iteration) is proved with respect to partial bisimulation. Language-based controllability, as the neccesary condition for event-based supervisory control is expressed in terms of partial bisimulation and we discuss several drawbacks of the strict event-based approach. Statebased control is considered under partial bisimulation as a dependable solution to address non-determinism. An appropriate renaming operator is introduced to address an issue in parallel communication. A case for automated guided vehicles (AGV) is modeled using the theory TCP. The latter theory is henceforth extended to include state-based valuations for which partial bisimulation and an axiomatization are dened. We consider an extended case on industrial printers to show the modeling abilities of this extended theory. In our concluding remarks, we sketch a future research path in terms of a new formal language for concurrent control modeling

Efficient Symbolic Supervisory Synthesis and Guard Generation: Evaluating partitioning techniques for the state-space exploration

The supervisory control theory (SCT) is a model-based framework, which automatically synthesizes a supervisor that restricts a plant to be controlled based on specifications to be fulfilled. Two main problems, typically encountered in industrial applications, prevent SCT from having a major breakthrough. First, the supervisor which is synthesized automatically from the given plant and specification models might be incomprehensible to the users. To tackle this problem, an approach was recently presented to extract compact propositional formulae (guards) from the supervisor, represented symbolically by binary decision diagrams (BDD). These guards are then attached to the original models, which results in a modular and comprehensible representation of the supervisor. However, this approach, which computes the supervisor symbolically in the conjunctive way, might lead to another problem: the state-space explosion, because of the large number of intermediate BDD nodes during computation. To alleviate this problem, we introduce in this paper an alternative approach that is based on the disjunctive partitioning technique, including a set of selection heuristics. Then this approach is adapted to the guard generation procedure. Finally, the efficiency of the presented approach is demonstrated on a set of benchmark examples

Energy and Route Optimization of Moving Devices

This thesis highlights our efforts in energy and route optimization of moving devices. We have focused on three categories of such devices; industrial robots in a multi-robot environment, generic vehicles in a vehicle routing problem (VRP) context, automatedguided vehicles (AGVs) in a large-scale flexible manufacturing system (FMS). In the first category, the aim is to develop a non-intrusive energy optimization technique, based on a given set of paths and sequences of operations, such that the original cycle time is not exceeded. We develop an optimization procedure based on a mathematical programming model that aims to minimize the energy consumption and peak power. Our technique has several advantages. It is non-intrusive, i.e. it requires limited changes in the robot program and can be implemented easily. Moreover,it is model-free, in the sense that no particular, and perhaps secret, parameter or dynamic model is required. Furthermore, the optimization can be done offline, within seconds using a generic solver. Through careful experiments, we have shown that it is possible to reduce energy and peak-power up to about 30% and 50% respectively. The second category of moving devices comprises of generic vehicles in a VRP context. We have developed a hybrid optimization approach that integrates a distributed algorithm based on a gossip protocol with a column generation (CG) algorithm, which manages to solve the tested problems faster than the CG algorithm alone. The algorithm is developed for a VRP variation including time windows (VRPTW), which is meant to model the task of scheduling and routing of caregivers in the context of home healthcare routing and scheduling problems (HHRSPs). Moreover,the developed algorithm can easily be parallelized to further increase its efficiency. The last category deals with AGVs. The choice of AGVs was not arbitrary; by design, we decided to transfer our knowledge of energy optimization and routing algorithms to a class of moving devices in which both techniques are of interest. Initially, we improve an existing method of conflict-free AGV scheduling and routing, such that the new algorithm can manage larger problems. A heuristic version of the algorithm manages to solve the problem instances in a reasonable amount of time. Later, we develop strategies to reduce the energy consumption. The study is carried out using an AGV system installed at Volvo Cars. The results are promising; (1)the algorithm reduces performance measures such as makespan up to 50%, while reducing the total travelled distance of the vehicles about 14%, leading to an energy saving of roughly 14%, compared to the results obtained from the original traffic controller. (2) It is possible to reduce the cruise velocities such that more energy is saved, up to 20%, while the new makespan remains better than the original one

Structuring Multilevel Discrete-Event Systems With Dependence Structure Matrices

Despite the correct-by-construction property, one of the major drawbacks of supervisory control synthesis is state-space explosion. Several approaches have been proposed to overcome this computational difficulty, such as modular, hierarchical, decentralized, and multilevel supervisory control synthesis. Unfortunately, the modeler needs to provide additional information about the system's structure or controller's structure as input for most of these nonmonolithic synthesis procedures. Multilevel synthesis assumes that the system is provided in a tree-structured format, which may resemble a system decomposition. In this paper, we present a systematic approach to transform a set of plant models and a set of requirement models provided as extended finite automata into a tree-structured multilevel discrete-event system to which multilevel supervisory control synthesis can be applied. By analyzing the dependencies between the plants and the requirements using dependence structure matrix techniques, a multilevel clustering can be calculated. With the modeling framework of extended finite automata, plant models and requirements depend on each other when they share events or variables. We report on experimental results of applying the algorithm's implementation on several models available in the literature to assess the applicability of the proposed method. The benefit of multilevel synthesis based on the calculated clustering is significant for most large-scale systems

Towards an infrastructure for preparation and control of intelligent automation systems

In an attempt to handle some of the challenges of modern production, intelligent automation systems offer solutions that are flexible, adaptive, and collaborative. Contrary to traditional solutions, intelligent automation systems emerged just recently and thus lack the supporting tools and infrastructure that traditional systems nowadays take for granted. To support efficient development, commissioning, and control of such systems, this thesis summarizes various lessons learned during years of implementation. Based on what was learned, this thesis investigates key features of infrastructure for modern and flexible intelligent automation systems, as well as a number of important design solutions. For example, an important question is raised whether to decentralize the global state or to give complete access to the main controller.Moreover, in order to develop such systems, a framework for virtual preparation and commissioning is presented, with the main goal to offer support for engineers. As traditional virtual commissioning solutions are not intended for preparing highly flexible, collaborative, and dynamic systems, this framework aims to provide some of the groundwork and point to a direction for fast and integrated preparation and virtual commissioning of such systems.Finally, this thesis summarizes some of the investigations made on planning as satisfiability, in order to evaluate how different methods improve planning performance. Throughout the thesis, an industrial material kitting use case exemplifies presented perspectives, lessons learned, and frameworks

Supremica-An Efficient Tool for Large-Scale Discrete Event Systems

Supremica is a tool for the modelling and analysis of discrete-event control functions based on state machine models of the uncontrolled plant and specification of the desired closed-loop behaviour. The modelling framework in Supremica is based on finite-state machines extended with variables, guard conditions, and action functions. In order to handle large-scale problems of industrially interesting size, Supremica uses advanced model checking techniques such as symbolic representations and compositional abstraction. Supremica has been used in several industrial research projects to verify and synthesise control functions for embedded controllers, industrial robots, and flexible manufacturing systems, and to verify program code for autonomous vehicles. This paper gives an overview of the modelling features of Supremica, shows the verification and synthesis facilities and their performance for large problems, and presents some of the industrial applications where Supremica has been used