Feedback Refinement Relations for the Synthesis of Symbolic Controllers
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This work builds on a general notion of system with set-valued dynamics and possibly non-deterministic quantizers to permit the synthesis of controllers that robustly, and provably, enforce the specification in the presence of various types of uncertainties and disturbances.Abstract:
We present an abstraction and refinement methodology for the automated controller synthesis to enforce general predefined specifications. The designed controllers require quantized (or symbolic) state information only and can be interfaced with the system via a static quantizer. Both features are particularly important with regard to any practical implementation of the designed controllers and, as we prove, are characterized by the existence of a feedback refinement relation between plant and abstraction. Feedback refinement relations are a novel concept introduced in this paper. Our work builds on a general notion of system with set-valued dynamics and possibly non-deterministic quantizers to permit the synthesis of controllers that robustly, and provably, enforce the specification in the presence of various types of uncertainties and disturbances. We identify a class of abstractions that is canonical in a well-defined sense, and provide a method to efficiently compute canonical abstractions. We demonstrate the practicality of our approach on two examples.read more
Citations
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Journal ArticleDOI
Optimized State Space Grids for Abstractions
TL;DR: In this article, the authors propose a method for computing abstractions whose state space is a cover of the state space of the plant by congruent hyper-intervals, and derive a functional to predict the number of transitions in dependence of the aspect ratio.
Proceedings ArticleDOI
Arbitrarily precise abstractions for optimal controller synthesis
Matthias Rungger,Gunther Reissig +1 more
TL;DR: This work proposes an algorithm to compute arbitrarily precise abstractions of discrete-time plants that represent the sampled behavior of continuous-time, perturbed, nonlinear control systems and establishes the convergence rate of the precision in dependence of the discretization parameters of the algorithm.
Posted Content
Symbolic Abstractions of Networked Control Systems
TL;DR: A general synthesis framework that can be flexibly adapted to a number of NCS setups is provided that is employed to synthesize hybrid controllers enforcing rich logical specifications over the concrete NCS models.
Proceedings ArticleDOI
Hybrid Path Planning of A Quadrotor UAV Based on Q-Learning Algorithm
TL;DR: The problem of path planning of a quadrotor unmanned aerial vehicle (UAV) is investigated in the framework of hybrid methodology by utilizing the Q-learning algorithm, which is one of the reinforcement method.
Book ChapterDOI
AMYTISS: Parallelized Automated Controller Synthesis for Large-Scale Stochastic Systems
TL;DR: In this paper, the authors propose a software tool called AMYTISS, implemented in C++/OpenCL, for designing correct-by-construction controllers for large-scale discrete-time stochastic systems.
References
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Differential Equations with Discontinuous Righthand Sides
TL;DR: The kind and level of sophistication of mathematics applied in various sciences has changed drastically in recent years: measure theory is used (non-trivially) in regional and theoretical economics, algebraic geometry interacts with physics, and such new emerging subdisciplines as "experimental mathematics", "CFD", "completely integrable systems", "chaos, synergetics and large-scale order", which are almost impossible to fit into the existing classification schemes.
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Principles of Model Checking
TL;DR: Principles of Model Checking offers a comprehensive introduction to model checking that is not only a text suitable for classroom use but also a valuable reference for researchers and practitioners in the field.
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Feedback Systems: An Introduction for Scientists and Engineers
TL;DR: Feedback Systems develops transfer functions through the exponential response of a system, and is accessible across a range of disciplines that utilize feedback in physical, biological, information, and economic systems.