A hierarchical Varying Sampling H∞ Control of an AUV

Roche, Emilie; Sename, Olivier; Simon, Daniel; Varrier, Sébastien

doi:10.3182/20110828-6-it-1002.01580

Cited by 11 publications

(15 citation statements)

References 12 publications

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“…In [4] stability analysis of a controlled system under non uniform sampling is realized with a predictor/observer structure. Furthermore the authors have proposed in [5], [6] a LPV method for polytopic systems, under the assumption that the system representation is affine w.r.t the sampling period.…”

Section: Introductionmentioning

confidence: 99%

An LFR approach to varying sampling control of LPV systems: Application to AUVs

Roche

Sename

Simon

2012

2012 1st International Conference on Systems and Computer Science (ICSCS)

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Section: Introductionmentioning

confidence: 99%

An LFR approach to varying sampling control of LPV systems: Application to AUVs

Roche

Sename

Simon

2012

2012 1st International Conference on Systems and Computer Science (ICSCS)

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“…Many industrial applications are concerned with similar requirements of real-time control such as robotics, automotive and aerospace industries. Several such applications for aircraft control ), mechanical devices (Robert et al (2010)) and underwater robotics (Roche et al (2011)) are referenced in the paper. Anyway research efforts are still needed to efficiently integrate the constraints arising from the control and computing domains in a system wide Quality of Service viewpoint.…”

Section: Summary and Perspectivesmentioning

confidence: 99%

“…It can be also predefined, as in the case of statically scheduled systems. For example, predefined Time Division Multiple Access (TDMA) scheduling of ultrasonic sensors is used in underwater applications (Roche, Sename, Simon, and Varrier (2011)). Selective data dropping under control of a (m,k)-firm scheduler allows for network overload management as in (Felicioni, Jia, SimonotLion, and Song (2010)).…”

Section: Control Under a Data Dropping (Mk)-firm Policymentioning

confidence: 99%

Real-time control systems: feedback, scheduling and robustness

Simon

Seuret

Sename

2017

International Journal of Systems Science

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The efficient control of real-time distributed systems, where continuous components are governed through digital devices and communication networks, needs a careful examination of the constraints arising from the different involved domains inside co-design approaches. Thanks to the robustness of feedback control, both new control methodologies and slackened real-time scheduling schemes are proposed beyond the frontiers between these traditionally separated fields. A methodology to design robust aperiodic controllers is provided, where the sampling interval is considered as a control variable of the system. Promising experimental results are provided to show the feasibility and robustness of the approach.

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“…In the litterature, different control schemes were proposed in the litterature to solve the arising challenges encoutered in autonomous control of underwater robots. We find among them robust H ∞ control (Roche et al [2011]), predictive control (Steenson et al [2012]) and sliding mode control (Pisano and Usai [2004]). Intelligent control methods using reinforcement learning or artifical intelligence have also been proposed such as Chang et al [2003] and Carreras et al [2002].…”

Section: Introductionmentioning

confidence: 99%

A new extension of the łrc; adaptive controller to drastically reduce the tracking time lags

Maalouf

Chemori

Creuze

2013

IFAC Proceedings Volumes

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Abstract:The L 1 adaptive control scheme has proven its effectiveness and robustness in various fields thanks to its particular architecture where robustness and adaptation are decoupled. It was though noted that whenever the trajectory is varying, an inherent lag is present compared to other adaptive schemes due to the presence of a filter in the control architecture. To achieve a better tracking, we propose extending the architecture of the L 1 controller by augmenting it with a control input that could take the form of a nonlinear proportional or a proportional integral term. The extended scheme is validated through simulations via an illustrative example as well as experimental results performed on an underwater vehicle. INTRODUCTIONControlling nonlinear dynamic systems is a challenging task. Indeed, not being able to determnine the behavior of a system especially in presence of varying parameters, makes difficult the design of a suitable controller. For this reason, the idea of online estimating the uncertain or varying parameters from measurements has emerged. Based on these concepts, adaptive control was therefore born. The recently developped L 1 adaptive controller (Hovakimyan and Cao [2010]) stands out among all other developped adaptive methods in its particular architecture where robustness and adaptation are decoupled. The low pass filter introduced in its structure separates the estimation loop from the control loop. This guarantees a fast adaptation and compensation of the unmodeled dynamics while still preserving the stability of the closed-loop system. With such a novel method, various previously noted failures in adaptive control were revisited (Kharisov and Hovakimyan [2010] and Xargay et al. [2009]). Besides, an extensive study has been made in Rohrs et al. [1982] showing that restrictive assumptions were formulated upon the use of a wide range of adaptive controllers. These schemes were seen to exhibit undesirable frequency characteristics. An enough parameter excitation might be needed to ensure parameter convergence. This excitation phase allowing the parameters to adapt, will refect into a bad transient behavior on the system. Consequently, in order to avoid such a behavior, the adaptation gain is usually chosen small which would slow down the system's response. The advantage that the L 1 adaptive controller brings in this regard lies in the fact that the performance of the closed-loop system can be improved by increasing the adaptation gain without degrading the robustness. A zero steady-state tracking error is guaranteed for constant reference inputs. However, similarly to Model Reference Adaptive Control (MRAC), the error is only guaranteed bounded for time varying reference trajectories. A time lag can be noticed with the L 1 controller due to the presence of a filter in the control loop. A very careful filter design should then be done to compromise between this time lag and the desired performance bounds. In this paper, we propose a nonlinear proportional and a proportional integr...

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A hierarchical Varying Sampling H∞ Control of an AUV

Cited by 11 publications

References 12 publications

An LFR approach to varying sampling control of LPV systems: Application to AUVs

An LFR approach to varying sampling control of LPV systems: Application to AUVs

Real-time control systems: feedback, scheduling and robustness

A new extension of the łrc; adaptive controller to drastically reduce the tracking time lags

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