Online control optimization using load driven scheduling

Sha, Lui; Liu, X.; Caccamo, Marco; Buttazzo, Giorgio

doi:10.1109/cdc.2001.914703

Cited by 31 publications

(9 citation statements)

References 12 publications

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“…Some notable works [5]- [7] among them address rate selection under schedulability constraints. However, these works do not apply for networked control systems since network induced delays have significant effects on control performance, and the schedulability analysis through the network is usually more complicated than CPU scheduling.…”

Section: Related Workmentioning

confidence: 99%

Near optimal rate selection for wireless control systems

Saifullah

Tiwari

et al. 2014

ACM Trans. Embed. Comput. Syst.

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Abstract-With the advent of industrial standards such as WirelessHART, process industries are now gravitating towards wireless control systems. Due to limited bandwidth in a wireless network shared by multiple control loops, it is critical to optimize the overall control performance. In this paper, we address the scheduling-control co-design problem of determining the optimal sampling rates of feedback control loops sharing a WirelessHART network. The objective is to minimize the overall control cost while ensuring that all data flows meet their end-toend deadlines. The resulting constrained optimization based on existing delay bounds for WirelessHART networks is challenging since it is non-differentiable, non-linear, and not in closed-form. We propose four methods to solve this problem. First, we present a subgradient method for rate selection. Second, we propose a greedy heuristic that usually achieves low control cost while significantly reducing the execution time. Third, we propose a global constrained optimization algorithm using a simulated annealing (SA) based penalty method. Finally, we formulate rate selection as a differentiable convex optimization problem that provides a closed-form solution through a gradient descent method. This is based on a new delay bound that is convex and differentiable, and hence simplifies the optimization problem. We evaluate all methods through simulations based on topologies of a 74-node wireless sensor network testbed. Surprisingly, the subgradient method is disposed to incur the longest execution time as well as the highest control cost among all methods. SA and the greedy heuristic represent the opposite ends of the tradeoff between control cost and execution time, while the gradient descent method hits the balance between the two.

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Section: Related Workmentioning

confidence: 99%

Near optimal rate selection for wireless control systems

Saifullah

Tiwari

et al. 2014

ACM Trans. Embed. Comput. Syst.

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“…2(a). The IC-WLAN includes two connections: connection 1 and 2, each controls an Inverted Pendulum (IP) [32], denoted as IP 1 and 2 in Fig. 2(a).…”

Section: Fine-grained Physical Layer Simulationmentioning

confidence: 99%

Building Robust Wireless LAN for Industrial Control with the DSSS-CDMA Cell Phone Network Paradigm

Wang¹,

Chen

et al. 2007

IEEE Trans. on Mobile Comput.

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“…The performance of the control system depends on several factors, including the sampling time . Following [17] and [18], in this work we assume that the control performance depends exponentially on the sampling time and, specifically (3) where is the map describing the performance of the control system, and , are appropriate constants. A numerical example validating our model of performance loss (3) is in Section III.…”

Section: Introductionmentioning

confidence: 99%

Design and Operation of Secure Cyber-Physical Systems

Pasqualetti

Zhu

2015

IEEE Embedded Syst. Lett.

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Abstract-This letter proposes a holistic framework for the design and operation of secure and reliable resource-constrained cyber-physical systems. The proposed framework combines control-theoretic methods, information security notions and computational models to characterize tradeoffs among different design and operation objectives. We quantify the intricate relation among control performance, system security and platform schedulability through a minimal set of interface variables. We argue that security mechanisms and control algorithms need to be codesigned and comanaged with the embedded platform, so as to avoid the design of algorithms that are too expensive to implement on the embedded platform, or significantly impede design objectives such as performance and timing robustness.

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Online control optimization using load driven scheduling

Cited by 31 publications

References 12 publications

Near optimal rate selection for wireless control systems

Near optimal rate selection for wireless control systems

Building Robust Wireless LAN for Industrial Control with the DSSS-CDMA Cell Phone Network Paradigm

Design and Operation of Secure Cyber-Physical Systems

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