Showing papers by "Rogelio Lozano published in 2011"

Stabilization and Trajectory Tracking of a Quad-Rotor Using Vision

Abstract: We propose a vision-based position control method, with the purpose of providing some level of autonomy to a quad-rotor unmanned aerial vehicle. Our approach estimates the helicopter X-Y-Z position with respect to a landing pad on the ground. This technique allows us to measure the position variables that are difficult to compute when using conventional navigation systems, for example inertial sensors or Global Positioning Systems in urban environment or indoor. We also present a method to measure translational speed in a local frame. The control strategy implemented is based on a full state feedback controller. Experimental results validate the effectiveness of our method.

Hovering Flight Improvement of a Quad-rotor Mini UAV Using Brushless DC Motors

Abstract: This paper present the design of a novel embedded control system for improving attitude stabilization of a quad-rotor mini UAV. The control strategy uses low cost components and includes an extra control loop based on motor armature current feedback. This additional control loop significantly improves the performance of the quad-rotor attitude stability. This technique results in a controller that is robust with respect to external disturbances as has been observed experimentally.

Introduction to Adaptive Control

Abstract: Adaptive Control covers a set of techniques which provide a systematic approach for automatic adjustment of the controllers in real time, in order to achieve or to maintain a desired level of performance of the control system when the parameters of the plant dynamic model are unknown and/or change in time.

Attitude Stabilization with Real-time Experiments of a Tail-sitter Aircraft in Horizontal Flight

Abstract: This paper focusses on the attitude stabilization of a mini tail-sitter aircraft, considering aerodynamic effects. The main characteristic of this vehicle is that it operates in either the hover mode for launch and recovery, or the horizontal mode during cruise. The dynamic model is obtained using the Euler–Lagrange formulation, and aerodynamic effects are obtained by studying the propeller effects. A nonlinear saturated Proportional-Integral-Derivative (SPID) control with compensation of aerodynamic moments is proposed in order to achieve the asymptotic stabilization of the vehicle in horizontal mode. In addition, a homemade inertial measurement unit (HIMU) is built for operating the complete operational range of the vehicle (including vertical and horizontal modes). Finally, simulation results are presented for validating the control law, and practical results are obtained in real-time during the flight.

Attitude control of a quad-rotor using speed sensing in brushless DC motors

Abstract: Recently, the research in the area of autonomous miniature flying robots is growing thanks to the development of new configurations and prototypes of Unmanned Aerial Vehicles (UAV) In this paper, we introduce the problem of attitude and altitude control of a mini-quadrotor UAV in hovering-mode to indoor and outdoor applications Therefore, we have implemented a control strategy based on speed sensing in each brushless motors As result, the miniature aerial vehicle has a good and robust stabilization on the horizontal plane This paper also describes a control strategy to stabilize the quad-rotor using a control algorithm based on PD controller as well as the speed sensing scheme Finally, the experimental results of speed sensing control for stabilizing the quad-rotor at hover are presented

UAV real-time location using a Wireless Sensor Network

Abstract: A real-time localization algorithm is presented in this paper. The algorithm uses an Extended Kalman Filter and is based on time difference of arrivals (TDOA) measurements of radio signal. The position and velocity of an Unmanned Aerial Vehicle (UAV) are successfully estimated in closed-loop in realtime in both hover and path tracking flights. Relatively small position errors obtained from the experiments, proves a good performance of the proposed algorithm.

Robust Parameter Estimation

Abstract: In the previous chapters, we assumed that: 1. The true plant model and the estimated plant model have the same structure (the true plant model is described by a discrete time model with known upper bounds for the degrees n A , n B + d). 2. The disturbances are zero mean and of stochastic nature (with various assumptions). 3. For parameter estimation in closed loop operation, the controller a. has constant parameters and stabilizes the closed loop. b. contains the internal model of the deterministic disturbance for which perfect state disturbance rejection is assured. 4. The parameters are constant or piece-wise constant. 5. The domain of possible parameters values is in general not constrained (exception: recursive maximum likelihood and adaptive filtered closed loop output error).

Adaptive Regulation—Rejection of Unknown Disturbances

Abstract: This chapter addresses the problem of attenuation (rejection) of unknown disturbances without measuring them by using a feedback approach In this context, the disturbance model is unknown and time varying while the model of the plant is known (obtained by system identification) and almost invariant This requires an adaptive approach The term “adaptive regulation” has been coined to characterize this control paradigm Direct and indirect adaptive regulation strategies using the internal model principle and the Youla-Kucera parameterization will be presented The evaluation of the methodology is done in real time on an active vibration control system using an inertial actuator

Vision based stabilization of a quadrotor using nested saturation control approach

Abstract: The problem of estimating position and orientation (pose) of an object in real time constitutes an important issue for vision-based control of robots. In this paper, we present a vision based controller design and its implementation on a quadrotor. The dynamic model is obtained using the well known Euler-Lagrange approach. Experiment results show good performance of the proposed controller using “real-time” optical flow and image processing.

Stabilization of a helicopter using optical flow

Abstract: Estimating position and orientation (pose) of an object in real time constitutes an important issue for vision-based control of robots. In this paper, we present a nonlinear controller design based on vision and its application in a quadrotor. Experiment results show good performance of the proposed controller using “real-time” optical flow and image processing.

Parameter Adaptation Algorithms—Deterministic Environment

Abstract: On-line estimation of the parameters of a plant model or of a controller is one of the key steps in building an adaptive control system. As shown in Chapter 1 the direct estimation of the controller parameters (when possible) can also be interpreted as a plant model estimation in a re-parameterized form. Therefore the problem of on-line estimation of plant model parameters is a generic problem in adaptive control. Such systems will feature a parametric adaptation algorithm which will up-date the estimated parameters at each sampling instant.

Recursive Plant Model Identification in Open Loop

Abstract: Iterative combination of identification in closed loop and robust control redesign leads to a two time scale adaptive control system very appealing in practice. The chapter is dedicated to the presentation of recursive algorithms for plant identification in closed-loop operation and their application. Two classes of algorithms will be presented, analyzed and evaluated experimentally: closed-loop output error algorithms and filtered open-loop recursive identification algorithms. Specific techniques for model validation in the context of identification in closed loop will also be presented. The performance of the various algorithms will be illustrated by simulation and by their application to the identification in closed loop and controller re-design of a flexible transmission control system.

Adaptive Feedforward Compensation of Disturbances

Abstract: Adaptive feedforward broadband vibration (or noise) compensation is currently used when an image (a correlated measurement) of the disturbance is available. However, in most of the systems there is a “positive” feedback coupling, between the compensator system and the measurement of the image of the disturbances, which cannot be ignored. The feedforward filter should compensate for the effect of the disturbance while assuring the stability of the internal “positive” feedback loop. Algorithms for adaptive feedforward compensation in the context of this internal positive feedback will be presented and analyzed. The algorithms are evaluated in real time on an active vibration control (AVC) system using an inertial actuator.

Conception et réalisation d'un démonstrateur pour un système hybride projectile/drone miniature - état des travaux après 8 mois

Abstract: Dans le cadre de la protection du citoyen et des infrastructures vitales et des reseaux, l'Institut franco-allemand de recherches de Saint-Louis (ISL) propose un concept innovant de drone miniature : il s'agit de lancer a partir d'un tube portable dedie, un projectile subsonique qui se transforme en drone miniature (MAV) une fois arrive au-dessus du site a observer. Un tel systeme hybride, appele GLMAV pour Gun Launch Micro Air Vehicle, est dedie a toutes formes de surveillance et de controle de personnes et d'infrastructures par la voie aerienne puisqu'il sera dote d'un systeme de vision embarque avec transmission des images en temps reel. L'ISL s'est associe au Centre de Recherche en Automatique de Nancy (CRAN - Nancy-Universites), a l'unite mixte de recherche Heuristique et Diagnostic des Systemes Complexes (HEUDIASYC - Universite de Technologie de Compiegne) et a la tres petite entreprise SBG Systems SAS. Le consortium etudie le projet " DEMONSTRATEUR_GLMAV " dans le cadre de l'ANR CSOSG qui aboutira vers la fin 2012 a la realisation d'un demonstrateur. Le Groupe d'Intervention de la Gendarmerie Nationale (GIGN) et la Direction Generale de l'Armement (DGA) en tant qu'experts operationnels et MBDA-Systems en France en tant qu'expert industriel sont associes au comite de pilotage du projet. L'etat d'avancement des travaux relatifs a ce projet apres huit mois d'etudes est presente dans cette publication. Certains points durs non soupconnes au debut de l'etude se sont reveles et ils devront etre leves afin de conduire, a l'issue du projet, a la realisation d'une demonstration de mise en œuvre du systeme GLMAV

Parameter Adaptation Algorithms—Stochastic Environment

Abstract: Consider the estimation of a plant model disturbed by a stochastic process which can be represented by: $$\begin{array}{*{20}{c}} {y(t + 1) = - {{A}^{*}}({{q}^{{ - 1}}})y(t) + {{B}^{*}}({{q}^{{ - 1}}})u(t) + w(t + 1)} \\ { = {{\theta }^{T}}\phi (t) + w(t + 1)} \\ \end{array}$$ (4.1.1) where u is the input, y is the output, w is a the zero mean stationary stochastic disturbance with finite moments and: $$ {\theta ^T} = \left[ {{a_1} \ldots {a_{{n_A},}}{b_1} \ldots {b_{{n_B}}}} \right] $$ (4.1.2) $$ {\phi ^T}\left( t \right) = \left[ { - y\left( t \right) \ldots - y\left( {t - {n_A} + 1} \right),u\left( t \right) \ldots u\left( {t - {n_B} + 1} \right)} \right] $$ (4.1.3) Let’s consider an equation error type adjustable predictor (like in RLS).

Direct Adaptive Control

Abstract: Direct adaptive control covers schemes where the parameters of the controller are directly updated from a signal error (adaption error) reflecting the performance error. The chapter presents strategies for direct adaptive control in a deterministic and in a stochastic environment and the corresponding analysis. This includes adaptive tracking and regulation with independent objectives, adaptive minimum variance control and their extensions. Robustification of direct adaptive control schemes is also discussed.

Attitude robust stabilization of a mini-rotorcraft UAV: Simulation and experimental results

Abstract: In this paper the modeling and the application of robust control design techniques to the attitude control of a mini-rotorcraft are discussed. More specifically, for controlling attitude, a controller is proposed by applying the robust control technique which is based on an inverse dynamics control law that cancels the mathematical model of the system. The main objective of this paper is to discuss the use of robust control techniques applied to the mini-rotorcraft UAV, when considering uncertainty in the mathematical model. The results are supported by simulation and experimental tests.

Multimodel Adaptive Control with Switching

Abstract: The principles of adaptive control with switching are presented. This method insures high control performance in the presence of large and abrupt parameter variations. The stability of this type of adaptive control is studied and shown to be guaranteed with a minimum dwell-time between switchings. An application of adaptive control with switching and tuning to a flexible transmission system is presented. The advantages of this scheme with respect to classical adaptive control and fixed robust control are illustrated via some experimental results. The use of CLOE adaptation in the adaptive control with switching will also improve the performance of the system in the tuning phase.

Indirect Adaptive Control

Abstract: Indirect adaptive control is a widely applicable adaptive control strategy. In real-time, it combines plant model parameter estimation in closed loop with the redesign of the controller. Adaptive pole placement and its robustified version, together with adaptive generalized predictive control constitute the core of the chapter. Adaptive linear quadratic control is also presented. Application of various strategies for the indirect adaptive control of a flexible transmission illustrates the methodology presented in this chapter.

Discrete-Time System Models for Control

Abstract: We will consider single-input single-output time invariant systems described by input-output discrete-time models of the form: $$ y(t) = - \sum\limits_{i = 1}^{{n_A}} {{a_i}y} (t - i) + \sum\limits_{i = 1}^{{n_B}} {{b_i}u} (t - d - i) $$ (2.1.1) where t denotes the normalized sampling time (i.e., \( t = \frac{t}{{{T_S}}} \), TS sampling period) , u(t) is the input, y(t) is the output, d is the integer number of sampling periods contained in the time delay of the systems, ai and bi are the parameters (coefficients) of the models.

Robust Digital Control Design

Abstract: One of the basic questions in control design is how to account for the discrepancies between the model of the plant used for design and the true model. A number of factors may be responsible for modelling errors. A generic term for this modelling error is model uncertainty which can itself be represented mathematically in different ways. Two major classes of uncertainty are: structured uncertainty (or more commonly termed parametric uncertainty) and unstructured uncertainty (usually specified in the frequency domain). The use of unstructured uncertainty description was one of the starting points for the impressive development of robust control design in the 80’s (Zames 1981).

Digital Control Strategies

Abstract: Building an adaptive control system supposes that in the case in which the plant parameters are known, a controller achieving the desired performances can be designed. Therefore this chapter reviews a number of digital control strategies used for the design of the underlying controller whose parameters will be adapted. Pole placement, tracking and regulation with independent objectives, minimum variance control, generalized predictive control and linear quadratic control are presented in detail.