157 research outputs found
A survey on compositional algorithms for verification and synthesis in supervisory control
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
Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing
Preparation and control of intelligent automation systems
In the automation systems of tomorrow, it is likely that the devices included have various degrees of autonomy, and include advanced algorithms for perception and control. Human operators will be expected to work together with collaborative robots as well as with roaming robots for material handling.The volatile nature of the environment of such intelligent automation systems lead to an enormous amount of possible situations that can arise and which need to be suitably handled. This complexity makes development of control systems for intelligent automation systems difficult using traditional methods.As an alternative, this thesis presents a model-based control framework, which uses a combination of formal specification and automated planning. The proposed framework allows for defining the intentions of the automation system on a high level, which enables decisions that influence when things should occur to be modeled using logical constraints, rather than programming. To achieve a modular framework, low level, reusable, resource models are composed by 1) formal specification to ensure safety and 2) applying an abstraction called an operation, which couples the reusable resources to the intentions of the system. By planning also the resources\u27 detailed actions, the operations can, when possible, be completed regardless of the resources\u27 current state. This eases error-recovery, as resources do not have to be reset when an error occurs.Additionally, the thesis proposes an iterative and interactive workflow for integrating the proposed model-based control framework into a virtual preparation process, using computer-based simulation as a tool for validating formal specifications. The control framework allows for adding new constraints to a running system, enabling an efficient and interactive preparation process.The framework has been applied to a use case from final assembly, which features human-robot collaboration. Experimental results on the ability to handle unforeseen errors and planning performance are presented
Computer Aided Verification
This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications
Programming Languages and Systems
This open access book constitutes the proceedings of the 29th European Symposium on Programming, ESOP 2020, which was planned to take place in Dublin, Ireland, in April 2020, as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The actual ETAPS 2020 meeting was postponed due to the Corona pandemic. The papers deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems
Model Properties for Efficient Synthesis of Nonblocking Modular Supervisors
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
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
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