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.

Backstepping and Robust Control for a Quadrotor in Outdoors Environments: An Experimental Approach

Abstract: This paper addresses the tracking control of quadrotors flying outdoors. Two control laws are combined and tested in real-time experiments. The aircraft attitude and the translational displacement are controlled using the backstepping approach, while the altitude is controlled using the sliding mode control strategy. In both cases, new modifications are introduced with respect to the existing classical algorithms. Concerning the backstepping algorithm, we introduce the dynamical model of the quadrotor in the controller design and this guarantees that the virtual input is bounded. On the other hand, the proposed sliding mode control assures that the vehicle's altitude converges in finite time to the desired reference, even when uncertainties are considered in the system. The proposed controller is tested in an outdoor environment and the experiments highlighted the controllers' reliability. Additionally, the performance of the closed-loop plant with the proposed controllers is compared with the performance given by a proportional-derivative controller.

Introduction to Adaptive Control

Abstract: Adaptive Control covers a set of techniques which provide a systematic approach for automatic adjustment of the controllers in real time, in order to achieve or to maintain a desired level of performance of the control system when the parameters of the plant dynamic model are unknown and/or change in time.

Real-Time Attitude Stabilization of a Mini-UAV Quad-rotor Using Motor Speed Feedback

Abstract: A real-time attitude stabilization control scheme is proposed for the efficient performance of a mini-UAV Quad-Rotor. Brushless DC (BLDC) motor speed sensing is performed by reflective sensors in order to obtain a robust stabilization of the vehicle in hovering mode both indoor and outdoor. The speed measurement has the advantage of introducing this state information directly in the closed loop control which should be very useful for achieving robust stabilization of the mini-UAV. Furthermore a stabilizing control strategy based on Control Lyapunov Function (CLF) is proposed. The control scheme contains two control loops. The inner loop is devoted to control the motors speed while the outer loop is devoted to control the attitude stabilization of a mini-UAV. Assuming that the motors can be considered as a disturbance of the system, then by the standard singular perturbation theory, we may conclude that the system is asymptotically stable. Finally, to verify the satisfactory performance of proposed embedded controller, simulations and experimental results of speed sensing feedback in BLDC motors of the Quad-rotor aircraft in the presence of disturbances are 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: