Fault Estimation for a Quad-Rotor MAV Using a Polynomial Observer

International audienceThis work addresses the problem of fault detection and diagnosis (FDD) for a quad-rotor mini air vehicle (MAV). Actuator faults are considered on this paper. The basic idea behind the proposed method is to estimate the faults signals using the extended state observers theory. To estimate the faults, a polynomial observer (Aguilar et al. 2011; Mata-Machuca et al., Commun Nonlinear Sci Numer Simul 15(12):4114-4130, 2010, BioSystems 100(1):65-69, 2010) is presented by using the available measurements and know inputs of the system. In order to investigate the diagnosability properties of the system, a differential algebra approach is proposed (Cruz-Victoria et al., J Frankl Inst 345(2):102-118, 2008; and Martinez-Guerra and Diop, IEE P-Contr Theor Ap 151(1):130-135, 2004). The effectiveness of the methodology is illustrated by means of numerical simulations

Fault Estimation and Control for a Quad-Rotor MAV Using a Polynomial Observer. Part I: Fault Detection

International audienceThis work addresses the problem of fault detection and diagnosis (FDD) for a quad-rotor mini aerial vehicle (MAV). Actuator faults are considered on this paper. The basic idea behind the proposed method is to estimate the faults signals using the extended state observers theory. To estimate the faults, a polynomial observer is presented by using the available measurements and know inputs of the system. In order to investigate the observability and diagnosability properties of the system, a differential algebra approach is proposed. Furthermore, an evaluation function depending on the system states is developed, in order to be used in a controller, which will compensate the failures. The effectiveness of the methodology is illustrated by means of numerical simulations and some experimental tests

Modelling and Nonlinear Robust Control of Delta-Like Parallel Kinematic Manipulators

International audienceThis book deals with modelling and control of parallel robots. Its content will help student, researchers and engineers in the field of robotics with a simplified methodology to obtain the dynamic model of parallel robots with a delta-type architecture. This methodology is compatible with real-time implementation of model-based and robust control schemes. Besides, the proposed robust control solutions can easily extended to other robotic architectures

Design and control of hybrid actuation lower limb exoskeleton

In this article, two types of actuators are applied for a lower limb exoskeleton. They are DC motors with the harmonic drive and the pneumatic artificial muscles. This combination takes advantages of both the harmonic drive and the pneumatic artificial muscle. It provides both high accuracy position control and high ratio of strength and weight. The shortcomings of the two actuators are overcome by the hybrid actuation, for example, low control accuracy and modeling difficult of pneumatic artificial muscle, compactness, and structural flexibility of DC motors. The design and modeling processes are discussed to show the proposed exoskeleton can increase the strength of human lower limbs. Experiments and analysis of the exoskeleton are given to evaluate the effectiveness of the design and modeling

Adaptive control for passive kinesiotherapy ELLTIO

International audienceExoskeletons are mechatronic devices used to increase human muscle strength and resistance. In the last decade these devices have become a very useful tool to assist active kinesiotherapy. This paper presents the design of exoskeleton focused on the rehabilitation of ankle and knee for the right leg. The construction of prototype like Exoskeleton for Lower Limb Training with Instrumented Orthoses (ELLTIO) using Series Elastic Actuator (SEA) to reduce the effort in the human joints, and a control law to perform a rehabilitation routine using an adaptive control scheme were first implemented in simulation to verify the control strategy and make a real rehabilitation test. The adaptive control law is proposed with the intention that the exoskeleton can adapt to user parameters at the time when performing the exercise. The results show the parameters estimation and tracking trajectory for the exoskeleton were proposed, and this trajectory could be a routine rehabilitation proposed by the therapist

Fault Estimation For a Quadrotor MAV Using a Polynomial Observer

International audienceThis work addresses the problem of fault detection and diagnosis (FDD) for a quad-rotor mini air vehicle (MAV). Actuator faults are considered on this paper. The basic idea behind the proposed method is to estimate the faults signals using the extended state observers theory. To estimate the faults, a polynomial observer is presented by using the available measurements and know inputs of the system. In order to investigate the observability and diagnosability properties of the system, a differential algebra approach is proposed. The effectiveness of the methodology is illustrated by means of numerical simulations

An Intelligent Compensation Through B-Spline Neural Network for a Delta Parallel Robot

International audienceIn this paper a PD controller with intelligent compensation is used to solve the problem of tracking trajectories for a Delta Parallel Robot with three degrees of freedom. This controller uses an artificial B-Spline neural network as a feedforward compensation term. To evaluate the proposed controller performance some numerical simulations under two different scenarios have been carried out in order to know its effectiveness respect to a simple PD controller

An Intelligent Compensation Through B-Spline Neural Network for a Delta Parallel Robot

International audienceIn this paper a PD controller with intelligent compensation is used to solve the problem of tracking trajectories for a Delta Parallel Robot with three degrees of freedom. This controller uses an artificial B-Spline neural network as a feedforward compensation term. To evaluate the proposed controller performance some numerical simulations under two different scenarios have been carried out in order to know its effectiveness respect to a simple PD controller

A New Adaptive RISE Feedforward Approach based on Associative Memory Neural Networks for the Control of PKMs

International audienceIn this paper, a RISE (Robust Integral of the Sign Error) controller with adaptive feedforward compensation terms based on Associative Memory Neu-ral Network (AMNN) type B-Spline is proposed to regulate the positioning of a Delta Parallel Robot (DPR) with three degrees of freedom. Parallel Kinematic Manipulators (PKMs) are highly nonlinear systems, so the design of a suitable control scheme represents a significant challenge given that these kinds of systems are continually dealing with parametric and non-parametric uncertainties and external disturbances. The main contribution of this work is the design of an adaptive feedforward compensation term using B-Spline Neural Networks (BSNNs). They make an on-line approximation of the DPR dynamics and integrates it into the control loop. The BSNNs' functions are bounded according to the extreme values of the desired joint space trajectories that are the BSNNs' inputs, and their weights are on-line adjusted by gradient descend rules. In order to evaluate the effectiveness of the proposed control scheme with respect to the standard RISE controller, numerical simulations for different case studies under different scenarios were performed