Closing the loop: Architectures and algorithms for real-time control over wireless networks.

摘要

Wireless Cyber-Physical Systems (CPS) are fundamentally constrained by the tight coupling and closed-loop control of physical processes. To address actuation in these systems there is a strong need to re-think the system architecture, including communication architectures and protocols for reliability, coordination and control. The objective of this work is to propose algorithms and architectures for control over wireless networks that ensure the desired closed-loop behavior, while maintaining ease of implementation in real-world networks. We focus on two domains, Industrial and Medical CPS.;Unlike standard control approaches that statically map a set of tasks to a specific physical node at design time, to deal with the inherit unreliability of wireless nodes and links we proposed two programming abstractions where control functionalities are assigned to a group of wireless nodes as a single component. Furthermore, by providing composable architectures, we have been able to harness the benefits of the use of wireless networks in Industrial CPS, and to design modular, 'plug-n-play' automation systems. Along these lines, in this work we introduce two orthogonal, composable approaches for in-network control: Embedded Virtual Machine (EVM) and Wireless Control Networks (WCN).;EVM provides software support for centralized in-network control, where control functionality can be migrated from one node to another to adapt to changes in network conditions. In the context of process and discrete control, an EVM is the distributed runtime system that dynamically selects primary-backup sets of controllers given spatial and temporal constraints of the underlying wireless network. The EVM architecture allows for runtime extension of the system functionalities along with the development of an automated design flow: from Simulink to platform-independent domain specific languages, and subsequently, to platform-dependent code generation. WCN is a distributed architecture used for control over multi-hop networks where each node acts as a local dynamical compensator, causing the network itself to act as a controller. The proposed scheme has several benefits, including low overhead, easy scheduling, and compositionality. We present methods for WCN synthesis that can guarantee system stability, robustness to link and node failures, and optimality. Furthermore, we provide conditions on the network topology for which such WCN configurations exist. To demonstrate effectiveness of the EVM and WCN, we present their use on practical industrial case studies in discrete and process control.;In the Medical CPS domain, we introduce a methodology for the analysis of safety properties of closed-loop medical device systems, and illustrate its use on a system of clinical importance. Our method combines simulation-based analysis of a detailed model of the system that contains continuous patient dynamics with uncertain parameters, with model checking of a more abstract timed automata model. We show that the relationship between the two models preserves the crucial aspect of the timing behavior that ensures the conservativeness of the safety analysis. To guarantee system applicability when wireless networks are used for control, we extended the initial system design to provide open-loop safety under network failure. Finally, to address the need for rigorous model-driven design tools to generate verified code from verified software models, we have developed the UPP2SF model-translation tool, which facilitates automatic conversion of verified timed-automata models (in UPPAAL) to models that may be simulated and tested (in Simulink/Stateflow). We demonstrate how UPP2SF is used in the model-driven design of medical device software whose model is (a) designed and verified in UPPAAL, (b) automatically translated to Stateflow for simulation-based testing, and then (c) automatically generated into modular code for hardware-level integration testing of timing-related errors. In addition, we show how UPP2SF may be used for worst-case execution time estimation early in the design stage.