Bandwidth Management for Distributed Control of Highly Articulated Robots

Velasco, Manel; Martí, Pau; Frigola, Manel

doi:10.1109/robot.2005.1570130

Cited by 17 publications

(21 citation statements)

References 16 publications

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“…Bandwidth manager embedded in controller adjusts timely the next sampling period (h i ) of each control loop according to desired global bandwidth utilization factor (B a ) and the state-variables fed back from network. The relation between bandwidth and sampling period for each control loop is given by (2), where b i is assignable bandwidth to the i th control loop, and c i is the time spent on the messages required to perform each closed loop operation (which may include communication data exchange from sensor to controller and from controller to actuator, as well as the time spent on execute the controller) [12,13].…”

Section: System Architecture and Problem Descriptionmentioning

confidence: 99%

“…Its matrix coefficient K is [0.5916 1.2382]. The upper and lower bound of the sampling period are 0.5s and 0.2857s inferred by (12), (13), and (14), respectively.…”

Section: Simulation Setupmentioning

confidence: 99%

“…Another reason may be sharing resources optimization so as to adjust dynamically sampling period. As a consequence, those novel methodologies can sufficiently utilize the sharing network resources or CPU resources, and effectively improve the performance of control system, see for example [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the approaches in [9,10,13], our proposed approach is to adjust sampling period based on fuzzy logic methodology, namely fuzzy bandwidth management (FBM) relative to FBA strategy mentioned above. It is worthwhile here to evaluate the ideas presented in [11] and in [12].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Managing quality-of-control and requirement-of-bandwidth in networked control systems via fuzzy bandwidth scheduling

Wang

Jiang

2009

Int. J. Control Autom. Syst.

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There is an unavoidable tradeoff between the control performance and the quality of service in networked control systems with resource constraints. To address the impact of network resources availability on requirement of bandwidth (RoB) and quality of control (QoC), an intelligent control approach to dynamic bandwidth management, namely fuzzy bandwidth management, is proposed based on fuzzy logic control technique. In order to guarantee the system's stability, the lower and upper bound of the assignable bandwidth are evaluated in terms of linear matrix inequalities and the resource constraints, respectively. In addition, the normalizable criterions of QoC and RoB are also defined, which can estimate the performance of the whole networked control systems. Preliminary simulations are carried out to highlight the merits of the proposed approach. It is argued that the proposed approach can save significant bandwidth and simultaneously improve overall control performance in comparison with the fixed bandwidth allocation and optimal bandwidth allocation.Keywords: Fuzzy bandwidth management, networked control systems, quality of control, requirement of bandwidth.

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Section: System Architecture and Problem Descriptionmentioning

confidence: 99%

“…Its matrix coefficient K is [0.5916 1.2382]. The upper and lower bound of the sampling period are 0.5s and 0.2857s inferred by (12), (13), and (14), respectively.…”

Section: Simulation Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Managing quality-of-control and requirement-of-bandwidth in networked control systems via fuzzy bandwidth scheduling

Wang

Jiang

2009

Int. J. Control Autom. Syst.

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“…In our networked control system model, each control loop operation takes place within its period (h 0 ). However, this period may change at run time due to dynamic bandwidth allocation and scheduling decisions [2], or by adaptive bandwidth management techniques such as [12]. In practical engineering, the actual sampling period often varies due to the use of the non-real time operating system and this variation can degrade the control performance and even make the system unstable.…”

Section: Introductionmentioning

confidence: 99%

Stability analysis of networked control systems with time-varying sampling periods

Liu

Yao

et al. 2008

J. Control Theory Appl.

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In this paper, we present an interval model of networked control systems with time-varying sampling periods and time-varying network-induced delays and discuss the problem of stability of networked control systems using Lyapunov stability theory. A sufficient stability condition is obtained by solving a set of linear matrix inequalities. In the end, the illustrative example demonstrates the correctness and effectiveness of the proposed approach.

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Optimal Bandwidth Scheduling for Resource-constrained Networks

Wang

Cheng

2009

Acta Automatica Sinica

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Bandwidth Management for Distributed Control of Highly Articulated Robots

Cited by 17 publications

References 16 publications

Managing quality-of-control and requirement-of-bandwidth in networked control systems via fuzzy bandwidth scheduling

Managing quality-of-control and requirement-of-bandwidth in networked control systems via fuzzy bandwidth scheduling

Stability analysis of networked control systems with time-varying sampling periods

Optimal Bandwidth Scheduling for Resource-constrained Networks

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