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 control of a simple nonlinear system without a priori information on the plant parameters

Abstract: The authors present an adaptive control scheme for nonlinear systems of the form x=c*/sup T/f(x)+b*u, where f(x) is Lipschitz, c* is a constant vector, and b* is a constant scalar. The control scheme achieves asymptotical model matching without a priori knowledge of the sign of the b* gain. The adaptive scheme is free from singularities in the sense that the estimate of b*, entering in the denominator of the control law, is bounded away from zero. The singularity has been overcome through a suitable modification of the parameter estimates which is based on standard least squares covariance matrix properties. >

Combining Stereo Vision and Inertial Navigation System for a Quad-Rotor UAV

Abstract: This paper presents the development of a quad-rotor robotic platform equipped with a visual and inertial motion estimation system. Our objective consists of developing a UAV capable of autonomously perform take-off, positioning, navigation and landing in unknown environments. In order to provide accurate estimates of the UAV position and velocity, stereo visual odometry and inertial measurements are fused using a Kalman Filter. Real-time experiments consisting on motion detection and autonomous positioning demonstrate the performance of the robotic platform.

Tracking a Ground Moving Target with a Quadrotor Using Switching Control

Abstract: An unmanned aerial vehicle (UAV) stabilization strategy based on computer vision and switching controllers is proposed. The main goal of this system is to perform tracking of a moving target on ground. The architecture implemented consists of a quadrotor equipped with an embedded camera which provides real-time video to a computer vision algorithm where images are processed. A vision-based estimator is proposed, which makes use of 2-dimensional images to compute the relative 3-dimensional position and translational velocity of the UAV with respect to the target. The proposed estimator provides the required states measurements to a micro-controller for stabilizing the vehicle during flight. The control strategy consists of switching controllers, which allows making decisions when the target is lost temporarily or when it is out of the camera's field of view. Real time experiments are presented to demonstrate the performance of the target-tracking system proposed.

Stabilization of the Furuta pendulum around its homoclinic orbit

Abstract: We present an energy based control approach to control the Furuta pendulum. A controller is proposed for swinging the pendulum and raise it to its uppermost unstable equilibrium position. The passivity properties of the system are used as a guideline in the control strategy. The stability analysis is carried out by using a Lyapunov technique.

Stabilization and Trajectory Tracking of a Quad-Rotor Using Vision

Abstract: We propose a vision-based position control method, with the purpose of providing some level of autonomy to a quad-rotor unmanned aerial vehicle. Our approach estimates the helicopter X-Y-Z position with respect to a landing pad on the ground. This technique allows us to measure the position variables that are difficult to compute when using conventional navigation systems, for example inertial sensors or Global Positioning Systems in urban environment or indoor. We also present a method to measure translational speed in a local frame. The control strategy implemented is based on a full state feedback controller. Experimental results validate the effectiveness of our method.

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: