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.

Adaptive altitude control for a small helicopter in a vertical flying stand

Abstract: In this paper, we focus on the design and implementation of a controller for a two degree-of-freedom system. This system is composed of a small-scale helicopter which is mounted on a vertical platform. The model is based on Lagrangian formulation and the controller is obtained by classical pole-placement techniques for the yaw dynamics and adaptive pole-placement for the altitude dynamics. Experimental results show the performance of such a controller.

Robustness with respect to delay uncertainties of a predictor-observer based discrete-time controller

Abstract: This paper focuses on the delay-dependent stability problem of a discrete-time prediction scheme to stabilize possible unstable continuous-time systems. The delay-dependent stability condition is expressed in terms of LMIs. The separation principle between the proposed predictor and a state observer is also proved. The closed-loop system is shown to be robust with respect to uncertainties in the knowledge on the plant parameters, the delay and the sampling period. The proposed scheme has been tested in a real-time application to control the roll angle in a prototype of a quad-rotor mini-helicopter.

Robust Trajectory Tracking for Unmanned Aircraft Systems using a Nonsingular Terminal Modified Super-Twisting Sliding Mode Controller

Abstract: Precision trajectory tracking problem for Unmanned Aerial Systems (UAS) is addressed in this work. A novel algorithm that combines a Nonsingular Modified Super-Twisting Controller with a High Order Sliding Mode Observer to enable an aerial vehicle tracking a desired trajectory under the assumption that i) its translational velocities are not available and ii) there are unmodeled dynamics and external disturbances. The proposed Sliding Mode Controller is based on a nonlinear sliding mode surface that ensures that the position and velocity tracking errors of all system’s state variables converge to zero in finite time. Moreover, the proposed controller generates a continuous control signal eliminating the chattering phenomenon. Finally, simulation results and an extensive set of experiments are presented in order to illustrate the robustness and effectiveness of the proposed control strategy.

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: