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.

Prediction of unstable behavior in enzymatic diffusion-reaction Processes

Abstract: The prediction of the behavior of biological mechanisms represents an important challenge in the comprehension of some disorders in living organisms. In this sense a better understanding of the system behavior is mandatory. In this paper we use mathematical tools and well established concepts of nonlinear dynamics and control for modeling and prediction of the biochemical behavior of an enzyme immobilized into an artificial membrane. The results are helpful to determine the causes of alterations in the enzymatic kinetic properties as well as in the prediction of complex behavior (oscillations) resulting from the catalytic activity. We first study the hydrolysis of Acetilcholine to show that it can be interpreted in terms of a coupling between the enzyme reaction and the diffusion process. The model is then extrapolated to a distributed system in order to analyze its behavior as a function of both time and space.

Super-twisting control in a Solar Unmanned Aerial Vehicle: Application to Solar Tracking

Abstract: Renewable energies have been used more commonly over time, mainly used as source of energy for cities or like a main or auxiliary power sources for cars. Moreover the technology needed to produced and used this kind of energy, has reached enough efficiency and weight to be incorporated into the Unmanned Aerial Vehicle(UAV), especially the solar energy. However, it is necessary to solve some challenges to integrate the technology needed to used the solar energy, with the final objective of increasing the autonomy in this vehicle. Most of the problems has been solve, with some special constructions techniques or electronic boards designed for this purpose, in order to add the less weight possible but giving the UAV more energy and consequently more autonomy. Nevertheless, it can still do some research to increase the total energy produced along a day, one of them is taking into account the solar tracking procedure used in the solar panel fixed to the ground. The present paper show the design of a robust control over the roll angle, used to imitate the solar tracking procedure mentioned above, with a solar panel placed on the wing of a Solar Unmanned Aerial Vehicle(SUAV), with the main objective of generated the largest amount of solar energy, to continue with the effort to make more efficient systems. The control will set an appropriate roll angle to ensure the highest energy production depending on the position of the sun, using a control technique based on super-twisting, to ensures the convergence in finite time to a desired position under bounded external perturbations such as wind gusts. Only the design of the control and the results of the simulation are shown to evaluate the effectiveness of the proposed control algorithm.

Recursive Plant Model Identification in Open Loop

Abstract: Iterative combination of identification in closed loop and robust control redesign leads to a two time scale adaptive control system very appealing in practice. The chapter is dedicated to the presentation of recursive algorithms for plant identification in closed-loop operation and their application. Two classes of algorithms will be presented, analyzed and evaluated experimentally: closed-loop output error algorithms and filtered open-loop recursive identification algorithms. Specific techniques for model validation in the context of identification in closed loop will also be presented. The performance of the various algorithms will be illustrated by simulation and by their application to the identification in closed loop and controller re-design of a flexible transmission control system.

Autonomous Ground Vehicle Navigation Using a Novel Visual Positioning System

Abstract: In this paper a novel visual positioning system for slip correction of vehicles in GPS-denied environments based on Kalman filter is presented. Kalman filter fuses 2 sources of data; the first source of data is obtained from odometry, while a computer vision algorithm is used to obtain the second source of data. Using a plane-based pose estimation algorithm that employs four reference points and the Consensus-based Tracking and Matching algorithm (CMT) we were able to obtain pose estimation with centimeter accuracy. The four reference points correspond to the pixel coordinates of the corners of a rectangle closing a region in the roof where the camera should be pointing. Since the employed camera is mounted into the vehicle and it has eye fish lens, an image distortion correction method is used. As the CMT algorithm is robust to scale and rotations, position and orientation (POSE) estimation using computer vision is obtained even if the roof is not completely visible. Autonomous navigation of a ground wheeled vehicle was achieved using the proposed algorithm performing different courses on long period of time and no big slip was observed.

Fixed-wing MAV adaptive PD control based on a modified MIT rule with sliding-mode control

Abstract: This paper presents an adaptive PD control law by using a modified MIT rule. The adjustment mechanism of the MIT rule has been implemented with three types of sliding-mode control, i.e., classical sliding-mode control, second order sliding-mode (2-SM), and high order sliding-mode control (HOSM). The proposed controllers have been designed for the directional and lateral dynamics of a fixed-wing mini aerial autonomous vehicle (MAV). Several simulations have been carried out in order to analyze the modified MIT rule.

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: