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.

Imaging Sensors for State Estimation

Abstract: This chapter aims at the implementation of a vision system onboard the UAV, with the purpose of estimating the sates of the platform during flights Theoretical background concerning computer vision is given in first instance The pinhole camera model as well as the camera calibration procedure is shown Stereo imaging, together with a method for stereo calibration and rectification are presented also The concept of optical flow and a method for its computation are detailed Relevant issues that must be considered when implementing an imaging system onboard a quad-rotor UAV are discussed The development of both a monocular and a stereo imaging system are presented, as well as the software architecture conceived for estimating the data required for performing vision-based tasks

Modeling and control of a vertical take-off airplane

Abstract: This paper presents the mathematical model of a fixed wing UAV (Unmanned Aerial Vehicle). The UAV is not a tail-sitter configuration and takes-off with the fuselage at the horizontal position. An experimental prototype driven by four brushless motors has been built including the onboard avionics. The applied control law is based on separated saturation functions. Numerical simulations have shown the satisfactory performance of the proposed control law. The prototype has also been tested experimentally in hover only and the control strategy has achieved that orientation angles have been regulated close to the origin.

Stability of interconnected passive systems

Abstract: In this paper, we present a brief review of the various definitions on passivity of nonlinear systems and positive real transfer functions. The relationships between the definitions are clarified and illustrated with examples. Furthermore we review the stability of the feedback interconnection of passive systems to relax the assumptions on the plant to be controlled. In particular we present a version of the passivity theorem that requires weaker conditions on the interconnected systems.

The reaction wheel pendulum

Abstract: The reaction wheel pendulum is one of the simplest non-linear underactuated systems. It is a pendulum with a rotating wheel at the end, which is free to spin about an axis parallel to the axis of rotation of the pendulum (see Figure 7.1). The wheel is actuated by a DC-motor, while the pendulum is unactuated. The coupling torque generated by the angular acceleration of the disk can be used to actively control the system. This mechanical system was introduced and studied in [108], where a partial feedback linearization control law was 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: