106 research outputs found

    A survey on compositional algorithms for verification and synthesis in supervisory control

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    This survey gives an overview of the current research on compositional algorithms for verification and synthesis of modular systems modelled as interacting finite-state machines. Compositional algorithms operate by repeatedly simplifying individual components of a large system, replacing them by smaller so-called abstractions, while preserving critical properties. In this way, the exponential growth of the state space can be limited, making it possible to analyse much bigger state spaces than possible by standard state space exploration. This paper gives an introduction to the principles underlying compositional methods, followed by a survey of algorithmic solutions from the recent literature that use compositional methods to analyse systems automatically. The focus is on applications in supervisory control of discrete event systems, particularly on methods that verify critical properties or synthesise controllable and nonblocking supervisors

    Lower Bound for the Duration of Event Sequences of Given Length in Timed Discrete Event Systems

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    The Supervisory Control Theory (SCT) of Discrete Event Systems (DES) provides a framework for synthesizing a DES supervisor to ensure a DES plant satisfies its design specification. In SCT, supervisor synthesis is performed offline before the functioning of the plant. Generally, the size of the plant and the specifications models are large resulting in supervisors that need huge computer memory for storage -- usually unavailable in embedded systems. A solution to this problem proposed in the literature is Limited Lookahead Policy (LLP). In LLP, the supervisory control commands are calculated online during the plant operation. After the occurrence of each event, the next control command is calculated based on the plant behaviour over a limited number of events into the future. In practice such frequent LLP computation would not be feasible as multiple events can occur consecutively over a short duration, not leaving enough time for LLP computation between them. To tackle this issue, a method is proposed called LLP with Buffering where the supervisory control commands are calculated online and buffered in advance for a predefined window of events in future. Determining the correct size of the buffer is crucial in order to achieve a trade-off between the on-board memory requirement and the computational resources and also ensuring that new supervisor commands are computed before the buffer runs out empty. The size of the buffer primarily depends on (1) the execution time of the code for supervisor calculation and (2) the (fastest) rate of event generation in the plant. This thesis focuses on the second factor. Previously, the minimum execution duration of event sequences has been calculated experimentally. The experimental approach is not exhaustive and thus results in an overestimate in the value of the minimum execution duration of event sequences. In this thesis, a model-based approach to the computation of the minimum duration is proposed which begins by transforming the untimed model of the plant under supervision into a timed automaton (TA) by incorporating timing information of the events. Next, an exhaustive symbolic matrix-based search algorithm is proposed where all the event sequences from every mode of the TA model are traversed to determine the minimum execution duration of the event sequences. The proposed method avoids the reachability analysis of TA needed to determine the reachable clock regions for each mode. The number of these regions is exponential in the number of events. Instead, the method uses reachability on the graph of the untimed model (polynomial in the number of events). This algorithm runs faster but provides an underestimate for the minimum execution duration of event sequences. Next, a two-degree-of-freedom solar tracker system is used as a plant to analyse the timing behaviour of the events and the implementation of LLP with buffering. In this study, the model-based and experimental methods have been used together to choose a suitable buffer size. The resulting LLP supervisor with buffering has been successfully implemented

    Efficient Supervisor Synthesis for Feature Models

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    Model-based supervisory control synthesis of cyber-physical systems

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    Towards predictive runtime modelling of Kubernetes microservices

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    Kubernetes is one of the major container management platforms utilised by Cloud Service Providers offering to host applications and services. As cloud based services become more prevalent, platform providers are faced with an increasingly complex problem of trying to meet contracted performance levels. Providers must strike a balance between management of resource allocations and contractual obligations to ensure that their service is profitable, while offering competitive pricing rates for contracts. This research explores performance modelling of microservice application tenants within the Kubernetes container management platform. We present a self-adaptive architecture to achieve modelling at runtime. We establish the potential for automated classification of cloud systems, and utilise a hybridised modelling approach to verify system properties and evaluate performance. We achieve this through the modelling of components as Extended Finite State Machines in WATERS, from which we automate the generating of performance models using the PEPA syntax

    Model Properties for Efficient Synthesis of Nonblocking Modular Supervisors

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    Supervisory control theory provides means to synthesize supervisors for systems with discrete-event behavior from models of the uncontrolled plant and of the control requirements. The applicability of supervisory control theory often fails due to a lack of scalability of the algorithms. We propose a format for the requirements and a method to ensure that the crucial properties of controllability and nonblockingness directly hold, thus avoiding the most computationally expensive parts of synthesis. The method consists of creating a control problem dependency graph and verifying whether it is acyclic. Vertices of the graph are modular plant components, and edges are derived from the requirements. In case of a cyclic graph, potential blocking issues can be localized, so that the original control problem can be reduced to only synthesizing supervisors for smaller partial control problems. The strength of the method is illustrated on two case studies: a production line and a roadway tunnel.Comment: Submitted to Journal of Control Engineering Practice, revision

    On Supervisor Synthesis via Active Automata Learning

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    Our society\u27s reliance on computer-controlled systems is rapidly growing. Such systems are found in various devices, ranging from simple light switches to safety-critical systems like autonomous vehicles. In the context of safety-critical systems, safety and correctness are of utmost importance. Faults and errors could have catastrophic consequences. Thus, there is a need for rigorous methodologies that help provide guarantees of safety and correctness. Supervisor synthesis, the concept of being able to mathematically synthesize a supervisor that ensures that the closed-loop system behaves in accordance with known requirements, can indeed help.This thesis introduces supervisor learning, an approach to help automate the learning of supervisors in the absence of plant models. Traditionally, supervisor synthesis makes use of plant models and specification models to obtain a supervisor. Industrial adoption of this method is limited due to, among other things, the difficulty in obtaining usable plant models. Manually creating these plant models is an error-prone and time-consuming process. Thus, supervisor learning intends to improve the industrial adoption of supervisory control by automating the process of generating supervisors in the absence of plant models.The idea here is to learn a supervisor for the system under learning (SUL) by active interaction and experimentation. To this end, we present two algorithms, SupL*, and MSL, that directly learn supervisors when provided with a simulator of the SUL and its corresponding specifications. SupL* is a language-based learner that learns one supervisor for the entire system. MSL, on the other hand, learns a modular supervisor, that is, several smaller supervisors, one for each specification. Additionally, a third algorithm, MPL, is introduced for learning a modular plant model.The approach is realized in the tool MIDES and has been used to learn supervisors in a virtual manufacturing setting for the Machine Buffer Machine example, as well as learning a model of the Lateral State Manager, a sub-component of a self-driving car. These case studies show the feasibility and applicability of the proposed approach, in addition to helping identify future directions for research

    Modular Learning and Optimization for Planning of Discrete Event Systems

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    Optimization of industrial processes, such as manufacturing cells, can have great impact on their performance. Finding optimal solutions to these large-scale systems is, however, a complex problem. They typically include multiple subsystems, and the search space generally grows exponentially with each subsystem. This is usually referred to as the state explosion problem and is a well-known problem within the control and optimization of automation systems. This thesis proposes two main contributions to improve and to simplify the optimization of these systems. The first is a new method of solving these optimization problems using a compositional optimization approach. This integrates optimization with techniques from compositional supervisory control using modular formal models, dividing the optimization of subsystems into separate subproblems. The second is a modular learning approach that alleviates the need for prior knowledge of the systems and system experts when applying compositional optimization. The key to both techniques is the division of the large system into smaller subsystems and the identification of local behavior in these subsystems, i.e. behavior that is independent of all other subsystems. It is proven in this thesis that this local behavior can be partially optimized individually without affecting the global optimal solution. This is used to reduce the state space in each subsystem, and to construct the global optimal solution compositionally.The thesis also shows that the proposed techniques can be integrated to compute global optimal solutions to large-scale optimization problems, too big to solve based on traditional monolithic models
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