Performance bounds on state-feedback controllers with network delay

Bhave, Ajinkya; Krogh, Bruce H.

doi:10.1109/cdc.2008.4739330

Cited by 21 publications

(11 citation statements)

References 9 publications

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“…For the data-driven implementation approach, the control input u(t) is held (by a zero-order hold) until its next update. Using the model presented in [17], we have…”

Section: Control Designmentioning

confidence: 99%

Optimising Quality-of-Control for Data-Intensive Multiprocessor Image-Based Control Systems Considering Workload Variations

Mohamed¹,

Zhu²,

Goswami³

et al. 2018

2018 21st Euromicro Conference on Digital System Design (DSD)

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Image-Based Control (IBC) systems have a long sample period. Sensing in these systems consists of computeintensive image processing algorithms whose response times are dependent on image workload. IBC systems are typically designed for the worst-case workload that results in a long sample period and hence suboptimal quality-of-control (QoC). This worst-case based design is further considered for mapping of controller tasks and allocating platform resources, resulting in significant resource over-provisioning. Our design philosophy is to sample as fast as possible to optimise QoC for a given platform allocation, and for this, we present a structured design flow. Workload variations determine how fast we can sample and we model this dynamic behaviour using the concept of workload scenarios. Our choice of scenario-aware dataflow as the formal model for our application enables us to: i) model dynamic behaviour, analyse timing, and optimally map application tasks to the platform for maximising the effective utilisation of allocated resources, ii) relate throughput of the dataflow graph to the sample period, and thus combine dataflow analysis and mapping with control design parameters and QoC to identify system scenarios, and iii) to efficiently implement a run-time mechanism that manages necessary dynamic reconfiguration between system scenarios. Our results show that our design approach outperforms the worst-case based design with respect to optimising QoC and maximising effective resource utilisation.

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“…For the data-driven implementation approach, the control input u(t) is held (by a zero-order hold) until its next update. Using the model presented in [17], we have…”

Section: Control Designmentioning

confidence: 99%

Optimising Quality-of-Control for Data-Intensive Multiprocessor Image-Based Control Systems Considering Workload Variations

Mohamed¹,

Zhu²,

Goswami³

et al. 2018

2018 21st Euromicro Conference on Digital System Design (DSD)

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“…We can choose to obtain the smallest possible upper bound on , by solving the convex optimization problem (19) The variable here is . For more on convex problems, see [23].…”

Section: Upper Boundmentioning

confidence: 99%

“…The problem (19) can be expressed as a (semi-infinite) SDP by introducing a new scalar variable : (20) Here the variables are and .…”

Section: Upper Boundmentioning

confidence: 99%

“…Discretization: We first describe a simple method for approximately solving (19) by discretizing (18). This approach is certainly adequate for any practical problem; in any case, we describe below a conservative discretization.…”

Section: Upper Boundmentioning

confidence: 99%

“…The issue of control in the presence of jitter has also been of interest in the field of scheduling: see [17] on the topic of jitter compensation in scheduling tasks and [18] on the design of real-time controllers under scheduling and timing constraints. The methodology we descibe in this technical note was subsequently used by Bhave and Krogh [19] in the context of state-feedback controllers with network delay.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis and Synthesis of State-Feedback Controllers With Timing Jitter

Skaf¹,

Boyd²

2009

IEEE Trans. Automat. Contr.

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Abstract-We consider a continuous-time linear system with sampled constant linear state-feedback control and a convex quadratic performance measure. The sample times, however, are subject to variation within some known interval. We use linear matrix inequality (LMI) methods to derive a Lyapunov function that establishes an upper bound on performance degradation due to the timing jitter. The same Lyapunov function can be used in a heuristic for finding a bad timing jitter sequence, which gives a lower bound on the possible performance degradation. Numerical experiments show that these two bounds are often close, which means that our bound is tight. We show how LMI methods can be used to synthesize a constant statefeedback controller that minimizes the performance bound, for a given level of timing jitter.

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