Eckhard, D, Campestrini L, Bergel ME, Bazanella AS.
2009.
Data-Based Control Design for a Process Class with Guaranteed Convergence to the Globally Optimum Controller. European Control Conference 2009. :993–998., Budapest: IEEE
AbstractThis work addresses data-based (DB) control design; the properties and limitations inherent to DB design are discussed under a common theoretical framework and illustrated through experimental results. Theoretical results concerning the convergence and precision are discussed and specified for a particular class of processes. Two DB methods, representative of this design approach, are used to illustrate the general properties of DB design: the Virtual Reference Feedback Tuning (VRFT) and the Iterative Feedback Tuning (IFT).
Garcia, G, Tarbouriech S, Gomes da Silva Jr. JM, Eckhard D.
2009.
Finite {L2} gain and internal stabilisation of linear systems subject to actuator and sensor saturations. IET Control Theory Applications. 3:799–812., Number 7
AbstractThis study addresses the control of linear systems subject to both sensor and actuator saturations and additive L2-bounded disturbances. Supposing that only the output of the linear plant is measurable, the synthesis of stabilising output feedback dynamic controllers, allowing to ensure the internal closed-loop stability and the finite L2-gain stabilisation, is considered. In this case, it is shown that the closed-loop system presents a nested saturation term. Therefore, based on the use of some modified sector conditions and appropriate variable changes, synthesis conditions in a quasi-linear matrix inequality (LMI) form are stated in both regional (local) as well as global stability contexts. Different LMI-based optimisation problems for computing a controller in order to maximise the disturbance tolerance, the disturbance rejection or the region of stability of the closed-loop system are proposed.
Concei, AGS, Carvalho C, Rohr ER, Porath D, Eckhard D, Pereira LFA.
2009.
A Neural Network Strategy Applied in Autonomous Mobile Localization. European Control Conference 2009. :4439–4444., Budapest: IEEE
AbstractIn this article, a new approach to the problem of indoor navigation based on ultrasonic sensors is presented, where artificial neural networks (ANN) are used to estimate the position and orientation of a mobile robot. This approach proposes the use of three Radial Basis Function (RBF) Networks, where environment maps from an ultrasonic sensor and maps synthetically generated are used to estimate the robot localization. The mobile robot is mainly characterized by its real time operation based on the Matlab/Simulink environment, where the whole necessary tasks for an autonomous navigation are done in a hierarchical and easy reprogramming way. Finally, practical results of real time navigation related to robot localization in a known indoor environment are shown.
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