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.

Hybrid autogyro: Airborne wind gust energy conversion using autorrotation

Abstract: An hybrid autogyro aircraft with wind energy conversion using autorotation is proposed. The fixed wing addition to classic autogyro assists the aircraft lifting reducing speed minimum wind for the flight. A PID control strategy is used to attitude aircraft stabilization. In order to verify the generation of energy, the relationship between energy and wind speed are recorded during experimental flights.

Micro Helicopter-airplane system: Trajectory tracking and attitude control

Abstract: This paper focuses on the regulation and trajectory tracking for a Micro Coaxial Rocket Helicopter (MCR UAV) and a mini aircraft The former has the characteristic of performing hover and forward flight while the latter is considered as an external air transporter for the MCR UAV For control purposes, the helicopter stabilization is based on the Backsteeping procedure, and a PD controller is implemented in the aircraft in order to execute semi-autonomous flight The avionics for these aerial vehicles consists of an embedded computer, low-cost sensors and homemade modules (Signal Conditioning Circuits, Analog Filters) and the flight control algorithm is implemented in the embedded system Simulation and experimental results show an effective performance of the developed system during the flight

Direct least squares estimation for linear time varying systems

Abstract: In this paper we propose a Least Squares based non-recursive identification algorithm capable of dealing with TV parameters. The minimized criterion is an L 2 norm of the identification error with forgetting factor. The estimates and their change ratio are shown to be bounded. We study the case when there is no persistent excitation and show how the estimates can be modified without losing their properties. The proposed estimation algorithm does not require explicit knowledge on the noise bound or the region where the true parameters lie.

Adaptive regulation for overmodeled linear systems

Abstract: This paper presents an adaptive regulator for general discrete-time linear systems knowing only an upper bound on its order. The scheme is based on a "lifted" representation of the plant and possible singularities are avoided by appropriately modifying the least squares parameter estimates. The stability analysis does not reply on any arguments about excitation of the plant signals. >

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: