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.

Stabilization of a two-link robot using an energy approach

Abstract: A design method for the control of a two-link robotic mechanism with a spring between the links is presented in this paper. This system is underactuated because only the first link can be controlled while the second link is not directly actuated. Our control strategy will be based on an energy approach and the passivity properties of the system.

Vision-based Position Control of a Two-rotor VTOL miniUAV

Abstract: In this paper we address the stabilization of the attitude and position of a birotor miniUAV to perform autonomous flight. For this purpose, we have implemented a Kalman-based sensor fusion between inertial sensors (gyros-accelerometers) and the optical flow (OF) provided by the vehicle. This fusion algorithm extracts the translational-OF (TOF) component and discriminates the rotational OF (ROF). The aircraft's position is obtained through an object detection algorithm (centroid tracking). Newton-Euler motion equations were used to deduce the mathematical model of the vehicle. In terms of control we have employed a saturated-based control to stabilize the state of the aircraft around the origin. Experimental autonomous flight was successfully achieved, which validates the sensing strategy as well as the embedded control law.

Real-time Stabilization of a Quadrotor UAV: Nonlinear Optimal and Suboptimal Control

Abstract: In this paper a nonlinear suboptimal stabilizing control strategy based on Control Lyapunov Functions (CLF) is synthesized and applied to a quadrotor helicopter. Sufficient conditions are obtained for this control law to ensure the asymptotic stability of the closed loop system. Furthermore, a particular methodology to find a CLF candidate for nonlinear affine system is also presented, which is highly relevant because the dynamical model representing the VTOL aerial vehicles have this affine structure. Using this CLF candidate, we are able to synthesize a nonlinear stabilizing optimal control law which allows energy saving. Numerical simulations were developed for both control strategies and real time experiments have been performed using the nonlinear stabilizing control algorithm. The numerical simulations have shown a successful performance of the autonomous aerial vehicle.

New relationships between Lyapunov functions and the passivity theorem

Abstract: In this paper we study new relationships between a class of Lyapunov functions and the passivity theorem. It is proved that under some (sufficient) conditions a Lyapunov-stable system can be analysed as the feedback connection of two (strictly) passive subsystems. It is also shown that very recent adaptive schemes for linear plants of any relative degree can in a certain sense be unified through a passivity point of view.

Optimized Discrete Control Law for Quadrotor Stabilization: Experimental Results

Abstract: This paper deals with the real-time stabilization of a Quadrotor mini helicopter applying a discrete optimal control. A discrete control strategy is better adapted to be executed in a micro-controller, therefore better results are expected with respect to continuous case. Furthermore, the optimal control law allows to helicopter to save energy and then increase its time of flight. The discrete optimal control law is synthesized considering an infinite horizon together with an exact linearization of the nonlinear dynamics of flying vehicle. At the end of this procedure the optimal control law obtained through an exact linearization is simple and easier to tune compared to the optimal strategy where the exact linearization is not performed. Taking into account the obtained experimental results, the proposed control technique produces an appropriate stabilization of the mini UAV.

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: