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.

The PVTOL aircraft

Abstract: Flight control is an essential control problem that appears in many applications such as spacecraft, aircraft and helicopters. The complete dynamics of an aircraft, taking into account aeroelastic effects, flexibility of the wings, internal dynamics of the engine and the multitude of changing variables, are quite complex and somewhat unmanageable for the purposes of control. It is also particularly interesting to consider a simplified aircraft, which has a minimum number of states and inputs but retains the main features that must be considered when designing control laws for a real aircraft. Therefore, as considered by Hauser et al. [35], we focus our study on the planar vertical take-off and landing (PVTOL) aircraft, which is a highly manoeuvrable jet aircraft.

Least Airspeed Reduction Strategy & Flight Recuperation of a Fixed-Wing Drone

Abstract: An energy reduction strategy for a fixed-wing drone with classic configuration is presented in this paper. The strategy is developed using the longitudinal non-linear motion equations of the aircraft, considering the stall effects in the determination of lift and drag forces. Our strategy imposes the aircraft to track a special trajectory allowing the vehicle to perform a curve in ascend, acting against the gravity force. As a consequence of this tracking, the angle of attack of the vehicle is increased arriving to critical values that need to be compensated for avoiding the crash of the vehicle. Therefore, a strategy to avoid the stall stage and recovering the flight stability in the vehicle is proposed. The strategy is evaluated in simulations for validating the performance of the whole system.

Dynamic collaboration of far-infrared and visible spectrum for human detection

Abstract: This paper is about the collaborative use of a far-infrared spectrum human detector and a visible spectrum human detector; the idea is to make collaborate these two detectors of different nature to automatically adapt the human detection whatever the luminosity changes and whatever the infrared emission changes of the scene. Our collaborative approach of detection handles: 1) gradual luminosity changes due, for instance, to the passage from night to day (and vice-versa), 2) sudden luminosity changes due, for instance, to navigation in a forest (when going through a glade in a forest), 3) infrared emission saturation when the global temperature of the scene is very high and does not permit to distinguish human people in infrared. Our approach of detection permits to detect people 24 hours a day and regardless the weather conditions. Furthermore, the proposed approach is relatively fast: it is practically as fast as using one detector alone whereas two are used in the same time.

Control of the PVTOL aircraft using the forwarding technique and a Lyapunov approach

Abstract: A smooth control Lyapunov function for the planar vertical takeoff and landing (PVTOL) aircraft is proposed. Its construction relies on the nonlinear stabilization technique called forwarding. The PVTOL aircraft model represents a particular example which illustrates well "Lyapunov forwarding" technique.

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: