LPV Approaches for Varying Sampling Control Design: Application to Autonomous Underwater Vehicles

Roche, Emilie; Sename, Olivier; Simon, Daniel

doi:10.1007/978-3-642-36110-4_15

Cited by 4 publications

(13 citation statements)

References 22 publications

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“…The full (12 state variables) non-linear model (detailed in [9]) is used for the simulations, combined with the global control structure of Figure 4. The mission consists in bottom following at a constant altitude with a constant forward velocity (constant speed is needed for most payload, e.g., for map making using a sonar).…”

Section: B Simulation Resultsmentioning

confidence: 99%

“…Here the LFR approach proposed in [9] will be extended to set-up a LFR model that accounts for system and sampling parameters. The steps below describe the proposed methodology.…”

Section: A Lfr Model Depending On System Parametersmentioning

confidence: 99%

“…Then the LFR model in figure 1 can be discretized, assuming that the uncertain input/outputs (w ∆ and z ∆ ) are sampled and hold at the sampling frequency. Then, in that context, the methodology proposed in [9] can be extended to get a sampling dependent LFR discrete-time model of an LPV system. From the LFR model (1), where non-linearities are considered as varying parameters, a new LFR is computed by adding the sampling interval to the existing set of varying parameters.…”

Section: B Parametrized Discretizationmentioning

confidence: 99%

“…The Taylor series expansion associated with the uncertain model leads to an LFR representation depending on the variation of the sampling interval (δ) and the plant's uncertain parameters (∆) (see [9] for the details) as:…”

Section: B Parametrized Discretizationmentioning

confidence: 99%

“…This method has already been studied in [9] concerning the synthesis of discrete-time gain-scheduled controller, depending only on the sampling period. This paper extends this previous work and deals with the design of a gain scheduled LFR controller w.r.t the sampling interval and w.r.t system's parameters, given a discrete-time Linear Fractional Representation (LFR) of the LPV varying sampling model.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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: B Simulation Resultsmentioning

confidence: 99%

“…Here the LFR approach proposed in [9] will be extended to set-up a LFR model that accounts for system and sampling parameters. The steps below describe the proposed methodology.…”

Section: A Lfr Model Depending On System Parametersmentioning

confidence: 99%

Section: B Parametrized Discretizationmentioning

confidence: 99%

Section: B Parametrized Discretizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Robust heading control and its application to Ciscrea Underwater Vehicle

Yang

Clément

Mansour

et al. 2015

OCEANS 2015 - Genova

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Deep inside the ocean, the earth magnetic signal is one of the merely existing information that tells the heading of robots with very good cost efficiency. Therefore, this paper focuses on the AUV (Autonomous Underwater Vehicle) heading control problem using only one magnetic compass as feedback sensor. In this application, we address AUV modeling and control issues simultaneously. Because of quadratic damping factor, underwater vehicle hydrodynamic model is nonlinear. In addition, unmodeled dynamics, parameter variations and environmental disturbances create significant uncertainties between the nominal AUV model and the reality. Finally, sensor noise, signal delay as well as unmeasured states also affect the stability and control performance of AUV motions. In order to handle these issues with improved AUV observation quality and navigation ability, we propose a CFD (Computational Fluid Dynamics) model based H ∞ robust control scheme.Without loss of generality, the robust heading controller was implemented and validated in the sea on low-mass and complex-shaped Ciscrea AUV. Simulation and sea experimental results of both PID (Proportional Integral Derivative) and robust heading controller are analysed.

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LPV methods for fault-tolerant vehicle dynamic control

Sename

Tudón-Martínez

Fergani

2013

2013 Conference on Control and Fault-Tolerant Systems (SysTol)

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This paper aims at presenting the interest of the Linear Parameter Varying (LPV) methods for vehicle dynamics control, in particular when some actuators may be in failure. The cases of the semi-active suspension control problem and the yaw control using braking, steering and suspension actuators will be presented. In the first part, we will consider the semi-active suspension control problem, where some sensors or actuator (damper leakage) faults are considered. From a quarter-car vehicle model including a non linear semi-active damper model, an LPV model will be described, accounting for some actuator fault represented as some varying parameters. A single LPV fault-tolerant control approach is then developed to manage the system performances and constraints. In the second part the synthesis of a robust gain-scheduled H∞ vehicle dynamic stability controller, involving front steering, rear braking, and four active suspension actuators, is proposed to improve the yaw stability and lateral performances. An original LPV method for actuator coordination is proposed, when the actuator limitations and eventually failures, are taken into account. Some simulations on a complex full vehicle model (which has been validated on a real car), subject to critical driving situations (in particular a loss of some actuator), show the efficiency and robustness of the proposed solution.

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LPV Approaches for Varying Sampling Control Design: Application to Autonomous Underwater Vehicles

Cited by 4 publications

References 22 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

Robust heading control and its application to Ciscrea Underwater Vehicle

LPV methods for fault-tolerant vehicle dynamic control

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