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.

Correction to "Adaptive control of robot manipulators with flexible joint"

Abstract: The above paper by the authors (Lozano and Brogliato, ibid., vol.37, p.174-81, 1992) contains two flaws that were brought to their attention. The first mistake concerns the definition of a signal which then effects the initial conditions on the state and on the desired trajectory. The second mistake is in the regressor matrix. Consequently the stability analysis given in one of the appendices is not complete. The authors provide corrections to the above mistakes.

A new AUV configuration with four tilting thrusters: Navigation and hover tasks

Abstract: This paper presents the study, development and implementation of an attitude controller for an AUV(Autonomous Underwater Vehicle). The goal is the hover stabilization of the prototype taking in account the dynamic modeling of translation and orientation. Missions were performed at slow speed to evaluated real time autonomous navigation. The control is based on a saturated PD control law. Results are presented from real time experiments.

Altitude control improvement for a Quadrotor UAV using integral action in a sliding-mode controller

Abstract: In this paper, a robust altitude control scheme is proposed for a mini-Quadrotor UAV system based on sliding mode controller with an integral action to eliminate the steady-state error induced by the boundary layer in order to achieve asymptotic convergence to the desired altitude with continuous control input. The proposed integral sliding mode controller is chosen to ensure the stability and robustness of overall dynamics during the altitude control at a desired height reference on the z-axis. Furthermore, we propose a Control Lyapunov Function (CLF) via Lyapunov theory in order to construct the robust stabilizing controller and demonstrate the stability of the z-dynamics of our system. A suitable sliding manifold is designed to achieve the control objective. At last, the theoretical results are supported by different simulation tests to verify the robustness and effectiveness of proposed robust control scheme in presence of bounded external disturbances.

Modeling and analysis of a tricopter/flying-wing convertible UAV with tilt-rotors

Abstract: The aim of this paper is to provide a mathematical model for a convertible unmanned aerial vehicle that combines the capabilities of a flying wing and tricopter with tilt rotors. This article presents the mathematical model for airplane and tricopter modes as well as the way they are related during the transition phase. Also is presented a control strategy to hover flying. Finally, it is presented simulation results of tricopter mathematical model under controls developed.

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: