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.

Hovering Flight Improvement

Abstract: This chapter introduces an embedded control system for improving the attitude stabilization of a quad-rotor. The proposed control strategy uses low cost components and includes an extra control loop based on motor armature current feedback. The control strategy presented here is a controller that is robust with respect to external disturbances. Experimental results for indoor tests are presented to show how this additional control loop improves the performance of the quad-rotor attitude stability.

Local adaptive stabilization of a first order nonlinear system without lipschitz assumptions

Abstract: This paper presents an adaptive control scheme for nonlinear systems of the form x˙= c*Tf(x) + b*u where c* is a constant vector and b* a constant scalar. The control scheme achieves local asymptotic stability without a-priori knowledge of the b* coefficient. The singularity in the control law has been overcome through a suitable modification of the parameter estimates which is based on the inverse of the least squares covariance matrix.

Lower Limb Exoskeleton for Rehabilitation with Flexible Joints and Movement Routines Commanded by Electromyography and Baropodometry Sensors

Abstract: This paper presents the development of an instrumented exoskeleton with baropodometry, electromyography, and torque sensors. The six degrees of freedom (Dof) exoskeleton has a human intention detection system based on a classifier of electromyographic signals coming from four sensors placed in the muscles of the lower extremity together with baropodometric signals from four resistive load sensors placed at the front and rear parts of both feet. In addition, the exoskeleton is instrumented with four flexible actuators coupled with torque sensors. The main objective of the paper was the development of a lower limb therapy exoskeleton, articulated at hip and knees to allow the performance of three types of motion depending on the detected user’s intention: sitting to standing, standing to sitting, and standing to walking. In addition, the paper presents the development of a dynamical model and the implementation of a feedback control in the exoskeleton.

Technical Notes and Correspondence Indirect Adaptive Periodic Control

Abstract: In this paper, a new indirect adaptive control method is presented. This method is based on a lifted representation of the plant that can be stabilized using a simple performant periodic control scheme. The controller parameters computation involves the inverse of the controllability/observability matrix. Potential singularities of this matrix are avoided by means of an appropriate estimates modification. This estimates transformation is linked to the covariance matrix prop- erties, and hence, it preserves the convergence properties of the original estimates. This modification involves the singular value decomposition of the controllability/observability matrix's estimate. As compared with previous studies in the subject, the controller proposed here does not require the frequent introduction of periodic n-length sequences of zero inputs. Therefore, the new controller is such that the system is almost always operating in closed loop, which should lead to better performance characteristics.

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: