Rogelio Lozano

Bio: Rogelio Lozano is an academic researcher from University of Technology of Compiègne. The author has contributed to research in topics: Control theory & Adaptive control. The author has an hindex of 58, co-authored 496 publications receiving 14570 citations. Previous affiliations of Rogelio Lozano include University of Illinois at Urbana–Champaign & Instituto Politécnico Nacional.

GPS/INS/optic flow data fusion for position and Velocity estimation

Abstract: This paper presents a simple and easy to implement sensor data fusion algorithm, using a Kalman filter (KF) in a loosely coupled scheme, for estimation of the velocity and position of an object evolving in a three dimensional space. A global positioning system (GPS) provides the position measurement while the velocity measurement is taken from the optical flow sensor, finally, the inertial navigation system (INS) gives the acceleration, which is considered as the input of the system. Real time experimental results are shown to validate the proposed algorithm.

A new UAV configuration having eight rotors: Dynamical model and real-time control

Abstract: In this paper we present an original configuration of a small aerial vehicle having eight rotors, four of them are devoted to stabilize the helicopter and the rest are used to drive the lateral displacements. The dynamical model is obtained using Euler-Lagrange approach, the attitude dynamics (roll, pitch and yaw) are practically independent of the translational dynamics corresponding to the lateral displacements (x and y), except for a small compensation on the angles roll and pitch. This compensation is directly related to the velocity of corresponding lateral motors of each axis. With this particular configuration many task could be simplified, reducing the complexity to develop an autonomous flight. Moreover, we are able to apply an easier control strategy for the flying machine.

Real-time localization of an UAV using Kalman filter and a Wireless Sensor Network

Abstract: A real-time localization algorithm is presented in this paper. The algorithm presented here 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 real-time in both hover and path following flights. Relatively small position errors obtained from the experiments, proves a good performance of the proposed algorithm.

Control strategy using vision for the stabilization of an experimental PVTOL aircraft setup

Abstract: In this letter, we stabilize the planar vertical takeoff and landing (PVTOL) aircraft using a camera. The camera is used for measuring the position and the orientation of the PVTOL moving on an inclined plane. We have used a simple control strategy to stabilize the system in order to facilitate the real experiments. The proposed control law ensures convergence of the state to the origin.

Resetting process-model control for unstable systems with delay

Abstract: A modification of process-model control schemes is introduced. This modification consists in a periodic resetting of the initial condition of the predictor. It allows us to extend the use of these control laws to unstable linear systems with delay. The robustness of the scheme with respect to parameter uncertainty, delay uncertainty and external disturbances is shown. An illustrative example is presented.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Higher-order sliding modes, differentiation and output-feedback control

Abstract: Being a motion on a discontinuity set of a dynamic system, sliding mode is used to keep accurately a given constraint and features theoretically-infinite-frequency switching. Standard sliding modes provide for finite-time convergence, precise keeping of the constraint and robustness with respect to internal and external disturbances. Yet the relative degree of the constraint has to be 1 and a dangerous chattering effect is possible. Higher-order sliding modes preserve or generalize the main properties of the standard sliding mode and remove the above restrictions. r-Sliding mode realization provides for up to the rth order of sliding precision with respect to the sampling interval compared with the first order of the standard sliding mode. Such controllers require higher-order real-time derivatives of the outputs to be available. The lacking information is achieved by means of proposed arbitrary-order robust exact differentiators with finite-time convergence. These differentiators feature optimal asymptot...

Linear systems

Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge: