On task schedulability in real-time control systems

Seto, Danbing; Lehoczky, John P.; Sha, Lui; Shin, Kang G.

doi:10.1109/real.1996.563693

Cited by 342 publications

(332 citation statements)

References 6 publications

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“…The quality of control (QoC) of each loop is measured by the following performance index function [5]:…”

Section: Control Loop Modelmentioning

confidence: 99%

“…Note that, when the controller is implemented, the system performance will deviate from the ideal value of the performance measure attained using continuous-time control, and the deviation will depend on the sampling rate. As mentioned in Equation 1 like [5], we quantify this deviation by defining the control cost for every control loop F i by a monotonic and convex function…”

Section: Formulation Of the Rate Selection Problemmentioning

confidence: 99%

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Near Optimal Rate Selection for Wireless Control Systems

Saifullah

Tiwari

et al. 2012

2012 IEEE 18th Real Time and Embedded Technology and Applications Symposium

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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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“…The quality of control (QoC) of each loop is measured by the following performance index function [5]:…”

Section: Control Loop Modelmentioning

confidence: 99%

Section: Formulation Of the Rate Selection Problemmentioning

confidence: 99%

Near Optimal Rate Selection for Wireless Control Systems

Saifullah

Tiwari

et al. 2012

2012 IEEE 18th Real Time and Embedded Technology and Applications Symposium

View full text Add to dashboard Cite

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“…Increasing the task activation period leads to a performance degradation, which is typically measured through a Performance Index J(T ) [10], [28]. Often, instead of using the performance index, many algorithms use the difference ∆J(T ) between the index and the value of the performance index J * of the optimal control.…”

Section: B Quality Of Control Indexmentioning

confidence: 99%

Adaptive TDMA bus allocation and elastic scheduling: A unified approach for enhancing robustness in multi-core RT systems

Burgio

Ruggiero

Esposito

et al. 2010

2010 IEEE International Conference on Computer Design

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Abstract-Next-generation real-time systems will be increasingly based on heterogeneous MPSoC design paradigms, where predictability and performance will be key issues to deal with. Such issues can be tackled both at the hardware level, by embedding technologies such as TDMA busses, and at the OS level, where suitable scheduling techniques can improve performance and reduce energy consumption. Among these, elastic scheduling has been proved to provide satisfactory results by dynamically reducing task periods at run-time to ensure the highest utilization possible of the processors. On the other hand, elastic scheduling lowers the degree of predictability and increases the complexity of the analysis at the system level. This reduces the benefits given by the TDMA bus, which relies on the high level task analysis for a robust and efficient slot allocation. Starting from this consideration, we propose a system where the elastic scheduling and the TDMA bus work synergistically. We introduce a QoS-aware adaptive bus service which takes the best of both techniques, mitigating their drawbacks at the same time. We show how the overhead introduced by coordination action is small, and it is however dominated by the benefits of the overall strategy in terms of performance and predictability guarantees.

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“…Many variations of basic scheduling model have been explored, but the emphasis is not on quantifying the errors introduced during mapping control model to the task model. Perhaps the most related of these efforts is control-aware scheduling [17] and control-scheduling co-design [3].…”

Section: Introductionmentioning

confidence: 99%

Time-Triggered Implementations of Dynamic Controllers

Nghiem

Pappas

Alur

et al. 2012

ACM Trans. Embed. Comput. Syst.

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Bridging the gap between model-based design and platformbased implementation is one of the critical challenges for embedded software systems. In the context of embedded control systems that interact with an environment, a variety of errors due to quantization, delays, and scheduling policies may generate executable code that does not faithfully implement the model-based design. In this paper, we show that the performance gap between the model-level semantics of proportional-integral-derivative (PID) controllers and their implementation-level semantics can be rigorously quantified if the controller implementation is executed on a predictable time-triggered architecture. Our technical approach uses lifting techniques for periodic, timevarying linear systems in order to compute the exact error between the model semantics and the execution semantics. Explicitly computing the impact of the implementation on overall system performance allows us to compare and partially order different implementations with various scheduling or timing characteristics. Disciplines Computer SciencesThis conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/500 ABSTRACTBridging the gap between model-based design and platformbased implementation is one of the critical challenges for embedded software systems. In the context of embedded control systems that interact with an environment, a variety of errors due to quantization, delays, and scheduling policies may generate executable code that does not faithfully implement the model-based design. In this paper, we show that the performance gap between the model-level semantics of proportional-integral-derivative (PID) controllers and their implementation-level semantics can be rigorously quantified if the controller implementation is executed on a predictable time-triggered architecture. Our technical approach uses lifting techniques for periodic, time-varying linear systems in order to compute the exact error between the model semantics and the execution semantics. Explicitly computing the impact of the implementation on overall system performance allows us to compare and partially order different implementations with various scheduling or timing characteristics.

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On task schedulability in real-time control systems

Cited by 342 publications

References 6 publications

Near Optimal Rate Selection for Wireless Control Systems

Near Optimal Rate Selection for Wireless Control Systems

Adaptive TDMA bus allocation and elastic scheduling: A unified approach for enhancing robustness in multi-core RT systems

Time-Triggered Implementations of Dynamic Controllers

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