Improved Transients in Multiple Frequencies Estimation via Dynamic Regressor Extension and Mixing

A problem of performance enhancement for multiple frequencies estimation is studied. First, we consider a basic gradient-based estimation approach with global exponential convergence. Next, we apply dynamic regressor extension and mixing technique to improve transient performance of the basic approach and ensure non-strict monotonicity of estimation errors. Simulation results illustrate benefits of the proposed solution.Comment: This paper is submitted for the ALCOSP 2016 conferenc

Modeling Pointing Tasks in Mouse-Based Human-Computer Interactions

International audiencePointing is a basic gesture performed by any user during human-computer interaction. It consists in covering a distance to select a target via the cursor in a graphical user interface (e.g. a computer mouse movement to select a menu element). In this work, a dynamic model is proposed to describe the cursor motion during the pointing task. The model design is based on experimental data for pointing with a mouse. The obtained model has switched dynamics, which corresponds well to the state of the art accepted in the human-computer interaction community. The conditions of the model stability are established. The presented model can be further used for the improvement of user performance during pointing tasks

A Globally Convergent Adaptive Indirect Field-Oriented Torque Controller for Induction Motors

International audienceOne of the most challenging problems in AC drives applications is the design of a simple plug-in adaptation scheme to estimate the unknown rotor resistance and load torque for the industry-standard indirect field oriented control. In this paper we give the first globally convergent solution to this problem for torque control of current-fed induction motors that does not rely on any excitation assumption. Some results on speed regulation are also presented

Next-Point Prediction for Direct Touch Using Finite-Time Derivative Estimation

International audienceEnd-to-end latency in interactive systems is detrimental to performance and usability, and comes from a combination of hardware and software delays. While these delays are steadily addressed by hardware and software improvements, it is at a decelerating pace. In parallel, short-term input prediction has shown promising results in recent years, in both research and industry, as an addition to these efforts. We describe a new prediction algorithm for direct touch devices based on (i) a state-of-the-art finite-time derivative estimator, (ii) a smoothing mechanism based on input speed, and (iii) a post-filtering of the prediction in two steps. Using both a pre-existing dataset of touch input as benchmark, and subjective data from a new user study, we show that this new predictor outperforms the predictors currently available in the literature and industry, based on metrics that model user-defined negative side-effects caused by input prediction. In particular, we show that our predictor can predict up to 2 or 3 times further than existing techniques with minimal negative side-effects

Iterative Learning Control Strategy for a Furuta Pendulum System with Variable-Order Linearization

International audienceWe consider Iterative Learning Control for the Furuta Pendulum nonlinear mechanical system, where the goal is to learn the input torque such that the pendulum angle follows a reference. We show that the linearization of the considered system is of variable trajectorydependent order and thus some existing solutions do not apply. We propose a novel method based on the observability matrix inversion allowing to deal with the variable-order minimum realization. The applicability of the proposed method is illustrated with simulations

On DREM regularization and unexcited linear regression estimation

International audienceThe problem of estimation of unknown constant parameters in the linear regression with measurement noise is considered. Analysing different levels of excitation of the regressor, two notions of partial and feeble excitation are introduced. The former implies the absence of the persistent or interval excitation, while the latter property says that the excitation is just insufficient for an efficient estimation in a noisy setting. The dynamic extension and mixing method (DREM) is used for the problem solution, and in order to improve its estimation performance, regularization is proposed and the resulting improvement is investigated analytically. The theoretical findings are illustrated in the simulations

On DREM regularization and unexcited linear regression estimation

The problem of estimation of unknown constant parameters in the linear regression with measurement noise is considered. Analysing different levels of excitation of the regressor, two notions of partial and feeble excitation are introduced. The former implies the absence of the persistent or interval excitation, while the latter property says that the excitation is just insufficient for an efficient estimation in a noisy setting. The dynamic extension and mixing method (DREM) is used for the problem solution, and in order to improve its estimation performance, regularization is proposed and the resulting improvement is investigated analytically. The theoretical findings are illustrated in the simulations