Blackstepping-based robust-adaptive control of a nonlinear 2-DOF piezoactuator.

International audienceThis paper deals with the control of a two degrees of freedom (2-DOF) piezoelectric actuator for precise positioning and which exhibits strong hysteresis nonlinearity and strong cross-couplings. To tackle the non linearity and the cross-couplings, we propose two decoupled models in which they are considered as (fictive) external disturbances which require proper characterization. Then, a backstepping technique is proposed to construct a robust controller that merges sliding-mode and adaptive schemes. Extensive experimental tests are finally carried out to prove the efficiency of the modeling and control technique proposed

H∞-based Position Control of a 2DOF Piezocantilever Using Magnetic Sensors.

International audienceThe article addresses the position control problem of a 2 degrees of freedom (DOF) piezoelectric cantilever by means of an embedded magnetic-based position sensor. The active part of the piezocantilever used in the experimental setup is similar to cantilevers previously developed and already used for low-frequency micro-actuators in microrobotics devices. The contribution relies on the estimation of the biaxial displacement of the piezocantilever via conventional Hall-effect (HE) sensors, reducing the mechanical complexity and cost aspects.The actual sensing approach is validated via the implementation of a real-time position control based on the H1 scheme. In comparison with high resolution sensors, as laser or confocal chromatic (high-cost) or capacitive displacement (bulky), the actual sensor-control system is provides a satisfactory performance to cope with traditional micro-positioning tasks requiring a micrometer resolution. The performanceof the embedded magnetic-based position sensor is evaluated, in open- and closed-loop, with respect the measurements provided by a Keyence laser sensors

A pedagogical approach to data fusion and kalman filter

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Autonomous Decisional High-level Planning for UAVs-based Forest-fire Localization

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Application of Sensory Body Schemas to Path Planning for Micro Air Vehicles (MAVs)

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Design, modeling and control of a convertible UAV

COMPIEGNE-BU (601592101) / SudocSudocFranceF

Backstepping-based robust-adaptive control of a nonlinear 2-DOF piezoactuator

International audienc

Control of a Piezoelectric Actuator Using a Bounded-Adaptive Backstepping Scheme and Sliding-Mode Observer

International audienceThe present paper deals with the motion control of a piezoelectric cantilevered actuator. The motion behavior of the piezoelectric cantilever is affected by two main parasitic effects, the hysteresis and creep effects, which are considered as a generalized disturbance.Additionally, cantilevers's position is the only available measured state. The latter operational scenario reveals that the trajectory-tracking or regulation problem is a non-obvious task. Prior to the controller design, we have focused on the estimation of the velocity using a sliding-mode observer (SMO) in order to avoid the numerical derivative. On the other hand, the control design takes into account the limited response of the control input (bounded states) and it is obtained using the Backstepping framework assuring the stability of origin. Numerical simulations are _rst presented to verify the efficiency of the observer and controller algorithm, showing that the control objective is fulfilled. The experimental tests were carried out to validate and evaluated the control strategy

Image Schema Based Landing and Navigation for Rotorcraft MAV-s

International audienceTo date, most autonomous micro air vehicles (MAV-s) operate in a controlled environment, where the location of and attitude of the aircraft are measured with an infrared (IR) tracking systems. If MAV-s are to ever exit the lab, their flight control needs to become autonomous and based on on-board image and attitude sensors. To address this need, several groups are developing monocular and binocular image based navigation systems. One of the challenges of these systems is the need for exact calibration in order to determine the vehicle's position and attitude through the solution of an inverse problem. Body schemas are a biologically-inspired approach, emulating the plasticity of the animal brain, which allows it to learn non-linear mappings between the body configurations, i.e. its generalized coordinates and the resulting sensory outputs. The advantages of body schemas has long been recognized in the cognitive robotic literature and resulting studies on human-robot interactions based on artificial neural networks, however little effort has been made so far to develop avian-inspired flight control strategies utilizing body and image schemas. This paper presents a numerical experiment of controlling the trajectory of a miniature rotorcraft during landing maneuvers suing the notion of body and image schemas. More specifically, we demonstrate how trajectory planning can be executed in the image space using gradient-based maximum seeking algorithm of a pseudo-potential. It is demonstrated that a neural-gas type artificial neural network (ANN), trained through Hebbian-type learning algorithm, can be effective in learning a mapping between the rotorcraft's position/attitude and the output of its vision sensors. Numerical simulation of the landing performance, including resulting landing errors are presented using an experimentally validated rotorcraft model. Copyright © 2015 by ASME

Robust micro-positionnig control of a 2DOF piezocantilever based on an extended-state LKF

International audience"This paper presents a control scheme regarding to improve the performances of a piezoelectric actuator (PEA) for precise positioning tasks. The piezoelectric actuator exhibits strong nonlinear disturbances for 1- and 2-DOF motion, i.e. input-dependent hysteresis, creep and cross-couplings. These unwanted phenomena undeniably compromise the final precision of the targeted tasks (micromanipulation) and therefore it should be conveniently considered during the controller synthesis. In this regard, the dynamic equation is also split into a nominal modeland a uncertain model including parametric uncertainties. We propose to use simultaneously a the discrete linear extended-state linear Kalman filter (ES-LKF), to estimate the aforementioned disturbances and the velocity, and Lyapunov-based controller to guarantee asymptotic stability while meeting the actuator limits. The proposed strategy permits to perform accurate positioning, for regulation and trajectory-tracking tasks, without a prior knowledge of parametric and unmodeled uncertainties. Real-time experiments were carried out with circulartrajectories to demonstrate the efficiency of the proposed approach.